5W Research with Everything-PR
The Trade Press AI Index 2026
Which publications LLMs actually cite - a cross-industry study of citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews.
Trade press citation share in AI answers is highly concentrated. This report finds that the top 15 domains capture 68% of consolidated AI citations across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews, and that specialist trade publications beat general news inside their own verticals. The index covers nine industries and marks which findings are measured and which are inferred.
A research report from 5W, a PR, creative and GEO agency. In partnership with Everything-PR - the 12-publication intelligence network.
## What does the Trade Press AI Index 2026 find? The trade press is being quietly reranked, not by Google, audience, or ad spend, but by five Large Language Models that now mediate how brands, buyers, regulators, and journalists discover information. This study analyzes the citation behavior of ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews across 9 industries and identifies which trade publications LLMs reward, which they ignore, and where the structural white space sits for any publisher or brand prepared to claim it. ### The seven findings that matter - Citation Share is the new market share. Across all industries, the top 15 domains capture 68% of all consolidated AI citation share, a concentration far more extreme than Google PageRank ever produced. Most trade publications are not in that 68%. - Forbes alone owns 6.93% of ChatGPT citations. Wikipedia accounts for 47.9% of ChatGPT's top-10 cited sources. Reddit is the #1 source across every major AI engine at about 40% frequency. Trade publications fight for the remaining single-digit share. - Each engine cites differently. Meta AI draws 21.5% of citations from media. Gemini draws 5.7%. Perplexity rewards primary sources and named B2B trade authority. Claude leans on long-form journalism: The New York Times, The Atlantic, The New Yorker, The Economist. ChatGPT concentrates on Wikipedia, Reddit, Forbes, and Business Insider. - Specialist trade publications outperform general news on LLM citations within their vertical. PCMag, TechRadar, and CIO.com appear in the top 10 citations on 6-7 platforms despite a wide variation in overall platform-level media citation share. Inside a vertical, the specialist wins. - Some established trade titles appear to be losing ground. 5W observes that several legacy industry publications show declining visibility in LLM answers and have not adapted their content architecture for LLM retrieval. - AI engines already route traffic to publishers who structure for them. Everything-PR shows 61.68% of referral traffic originating from AI Chatbots and Tools, including Claude.ai, which indicates that LLM citation behavior produces measurable downstream clicks for publishers who structure for retrieval. - The white space is large. In every industry analyzed, LLM citation share is concentrated in 3-5 publications, and the 6th, 7th, and 8th positions are unowned. 5W estimates that a brand or publisher willing to publish entity-rich, prompt-shaped, schema-clean content into a vertical can claim them within 9-12 months. ## Why was this study created? In 2014, the dominant question was: Which publication ranks on Google for our category? In 2026, the dominant question is: Which publication does ChatGPT cite when a buyer asks who matters in our category? The first question still has answers, and they still matter. The second question has answers very few brands or publishers have measured. This study addresses it across 9 industries and five engines, built on published data and clearly marked inference. The thesis: the trade press is being rewired by the retrieval layer. Publications that adapt, with entity-rich, prompt-shaped, schema-clean, recursively cited content, are likely to hold the most B2B influence over the next decade. Publications that do not may lose citation share to those that do. This is a current problem, not a future one. ## How was the study built? This study synthesizes six major published LLM citation studies released between August 2024 and May 2026, covering more than 680 million individual citations across ChatGPT, Google AI Overviews, Perplexity, Gemini, Claude, Meta AI, and Microsoft Copilot. Sources include 5W's own AI Platform Citation Source Index 2026, Profound's AI Platform Citation Patterns, Britopian's tech media citation analysis, Averi's B2B SaaS Citation Benchmarks, The Digital Bloom (2025), Semrush (Nov 2025), SparkToro (Jan 2026), and SUSO Digital (Feb 2026). For each of the 9 industries, we identified the top trade publications by historical authority, documented citation share where the published studies provide it, parametric authority patterns where direct citation data is not yet published, and the white space: adjacent topical clusters and prompt categories where no publication currently dominates. Industries were selected based on the verticals 5W and Everything-PR work in, giving the study insider visibility into how trade press in each category is indexed, not just observed from the outside. Where direct measurement data is published, we cite it. Where the pattern is inferred from parametric authority signals, we mark it clearly. The 2026 Q3 update will add proprietary primary-source LLM query data covering 4,500 prompts (9 industries × 100 prompts × 5 engines), turning the framework into a quarterly series. ## How do LLMs cite trade press in 2026? ### Four macro facts shape the entire landscape: 1. Concentration is extreme. - Wikipedia: 47.9% of ChatGPT top-10 citations; about 22% of major LLM training data - Reddit: 46.7% of Perplexity's top citations; #2 in ChatGPT after Wikipedia - Forbes: 6.93% of ChatGPT citations - LinkedIn: 15% of AI Mode citations; 5%+ on ChatGPT - PR Newswire: 4.72% of ChatGPT citations (editorial-style content only) The top 15 domains capture 68% of all consolidated AI citation share. The trade press collectively fights for fractions of single-digit percentage points. 2. Engines differ, so strategy must differ. EngineMedia Citation ShareWhat It Rewards Meta AI21.5%Security-focused tech journalism, breaking news Microsoft Copilot~17%Balanced media + technical sources ChatGPT15.4%Wikipedia + Forbes + Business Insider + Reddit Perplexity<10%Reddit (46.7%), YouTube, primary sources, named B2B trade authority Google AI Overviews<10%Distributed across many sources; Reddit (2.2%) Gemini5.7%Most diversified; least media-dependent Claude(data limited)NYT, Atlantic, New Yorker, Economist; 36% recent / 64% historical A single-engine strategy is structurally a losing strategy. Visibility must be built per engine, then measured per engine. - Recency is weighted differently per engine. Perplexity rewards content updated within the past 12 months, with 3.2x more citations for fresh content. ChatGPT: 56% of journalism citations come from the past 12 months. Claude: only 36%, so Claude has a longer historical citation tail than any other engine. - Specialist beats generalist within a vertical. Inside tech, PCMag, TechRadar, and CIO.com each command 0.3%-2.1% citation share across multiple platforms, and they appear in the top 10 on 6-7 of 7 measured engines. Generalist news outlets like Reuters and the BBC accumulate higher single-engine shares but lower cross-platform reach. For a brand or publisher inside a vertical, the specialist trade publication is the more efficient citation asset. ## Chapter 1: Which technology publications do LLMs cite? Established trade publications: TechCrunch, The Verge, Wired, The Information, Ars Technica, PCMag, TechRadar, CIO.com, Engadget ### LLM citation leaders (measured) - PCMag: 0.8%-1.6% share across Perplexity, Google AI Mode, Google AI Overviews, Gemini, Copilot. Structured product evaluations drive consistent citations. - TechRadar: 0.3%-1.9% across six platforms. Comprehensive buying guides with comparable data. - CIO.com: 0.6%-2.1% across ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Copilot. Executive decision-maker framing. The pattern: structured, comparison-oriented, decision-grade content earns LLM citations. Editorial opinion does not. White space: AI infrastructure, vertical SaaS, security ops, dev tools, agentic systems. Most current top-citation tech publications are still optimizing for consumer tech. Implication for brands: technology PR programs should prioritize PCMag, TechRadar, and CIO.com placements over TechCrunch features for LLM visibility, which runs counter to current industry practice. ## Chapter 2: Which beauty and fashion publications do LLMs cite? Established trade publications: WWD, Vogue Business, Beauty Inc, Glossy, Allure, Cosmetic Business, Beauty Independent, BoF (Business of Fashion) ### Citation pattern (parametric authority signals) - WWD has the strongest parametric footprint, quoted across NYT, Forbes, Bloomberg, and Glossy in LLM training data - Glossy is rising fastest on Perplexity due to fresh, vertical-specific reporting - Vogue Business dominates the luxury cluster - Beauty Inc appears to be fading in LLM relevance, with declining web authority and fewer cross-citations White space: clean beauty regulation, AI in beauty product development, Asian beauty (K-beauty, J-beauty, C-beauty), male grooming, longevity skincare. Implication for brands: beauty PR should prioritize Glossy, WWD, and Vogue Business as the LLM-citation triad. Many beauty brands still over-index on traditional magazine placements that LLMs rarely cite. ## Chapter 3: Which consumer and retail publications do LLMs cite? Established trade publications: Ad Age, Adweek, Retail Dive, Modern Retail, Marketing Brew, Retail Brew, Chain Store Age ### Citation pattern (mixed measured + parametric) - Ad Age and Adweek dominate parametric recall, quoted across Forbes, Bloomberg, and NYT consistently - Retail Dive is winning Perplexity citations for retail-operations queries - Modern Retail and Marketing Brew are climbing fast on freshness-weighted engines White space: retail AI, store-of-the-future, supply chain resilience, retail media networks, social commerce conversion. Implication for brands: Adweek and Ad Age remain important for industry parametric authority. Modern Retail and Retail Dive are the citation arbitrage: easier to earn and increasingly rewarded. ## Chapter 4: Which food and beverage publications do LLMs cite? Established trade publications: Food & Wine, Eater, Modern Restaurant Management, FoodDive, Beverage Industry, Bon Appétit, The Spoon ### Citation pattern (parametric authority signals) - FoodDive leads in B2B food industry citations on Perplexity and Google AI Overviews - Eater dominates restaurant culture queries on ChatGPT - The Spoon is rising in food-tech and alt-protein clusters - Bon Appétit has a long parametric tail, but recency-weighted engines penalize it White space: alternative protein, food-tech investing, restaurant labor and operations, ghost kitchens, regenerative agriculture, GLP-1 impact on consumer food behavior. Implication for brands: the LLM citation opportunity in F&B sits inside trade B2B publications (FoodDive, The Spoon), not in lifestyle magazines. Many F&B PR strategies invert this. ## Chapter 5: Which healthcare publications do LLMs cite? Established trade publications: STAT, Endpoints News, Fierce Biotech, MedCity News, Healthcare Dive, Modern Healthcare, BioPharma Dive ### Citation pattern (parametric + Perplexity-rewarded primary sourcing) - STAT has the strongest LLM parametric recall in healthcare, cross-cited across NYT, WSJ, Bloomberg, and NEJM context - Endpoints News dominates biotech and pharma deal coverage citations - Fierce Biotech holds clinical-trial and drug-development citation queries - Perplexity rewards NIH and PubMed primary sources more than any trade publication, so healthcare PR programs need a parallel primary-source strategy White space: GLP-1s and metabolic health, AI in clinical workflow, payor consolidation, healthcare cybersecurity, mental health infrastructure, longevity medicine. Implication for brands: healthcare and pharma PR should build a two-track LLM strategy: trade press (STAT, Endpoints) for industry citation, and primary sources (peer-reviewed journals, NIH-indexed studies) for Perplexity-grade authority. ## Chapter 6: Which travel and hospitality publications do LLMs cite? Established trade publications: Skift, Travel Weekly, Hospitality Net, The Points Guy, Hotel News Now, Eater Travel, Condé Nast Traveler ### Citation pattern (parametric + measured) - Skift is the dominant LLM citation in B2B travel, winning Perplexity, ChatGPT, and Claude consistently for industry queries - The Points Guy dominates consumer travel-deal and loyalty queries on ChatGPT - Hospitality Net holds the operations and hotel-management citation tier White space: AI in travel booking, climate-aware travel, longer-stay corporate travel, alternative accommodations beyond Airbnb, group and event travel rebuild. Implication for brands: Skift is the leading trade press source in travel for AI citation. A travel brand not regularly cited in Skift is likely to be missing from LLM answers to B2B travel queries. ## Chapter 7: Which entertainment publications do LLMs cite? Established trade publications: Variety, The Hollywood Reporter, Deadline, Puck, Vulture, IndieWire ### Citation pattern (parametric authority signals) - Variety and THR dominate parametric recall, since studio deals, executive moves, and box office numbers run through them - Deadline holds breaking-news citation share on ChatGPT - Puck is the fastest-rising entertainment-business citation on Perplexity and Claude: newsletter-led, executive-grade, retrieval-friendly - Vulture and IndieWire hold criticism and indie clusters White space: streaming economics post-bundle, AI in content production, sports rights as the new prestige asset, creator economy as a legitimate Hollywood player. Implication for brands: entertainment publicity programs that focus on consumer-press placements can miss the LLM citation opportunity. The trade triangle of Variety, THR, and Puck is the LLM authority lane. ## Chapter 8: Which political and policy publications do LLMs cite? Established trade publications: Politico, Axios, The Hill, Punchbowl News, Roll Call, RealClearPolitics ### Citation pattern (measured + parametric) - Politico has the strongest parametric authority, cited across the mainstream news sources LLMs train on - Axios is winning fresh-content citations on ChatGPT and Perplexity due to its short, structured, AI-extractable format - Punchbowl News is rising fast in DC-policy-insider clusters on Claude and Perplexity - The Hill and Roll Call hold the legislative-procedural citation tier White space: state-level policy aggregation, regulatory affairs (FTC, FCC, SEC enforcement), AI policy and governance, election infrastructure. Implication for brands and lobbying firms: Axios's "smart brevity" format maps directly onto how LLMs extract information. Public affairs PR programs that do not study the Axios format may be leaving citation share on the table. ## Chapter 9: Which finance publications do LLMs cite? Established trade publications: Bloomberg, WSJ Pro, American Banker, Institutional Investor, Pensions & Investments, The Information (financial coverage) ### Citation pattern (measured + parametric) - Bloomberg has near-universal parametric authority and is the leading LLM citation source in finance - WSJ Pro vertical publications (WSJ Pro Bankruptcy, WSJ Pro Private Equity) are rising in B2B finance clusters - American Banker dominates banking-regulatory citations - Institutional Investor holds asset management citation share - CoinDesk is cited alongside Bloomberg for crypto-finance queries White space: private credit, RIA consolidation, fintech infrastructure, asset tokenization, regulatory state-of-play. Implication for brands: in 5W's assessment, Bloomberg coverage is among the most valuable placements for LLM citation. Financial services PR strategies should be Bloomberg-first, then specialist trade. ## What patterns hold across all nine industries? Four patterns hold across all 9 industries: - The specialist trade publication beats the generalist news outlet inside a vertical. In tech, healthcare, finance, retail, and travel, the specialist publication that owns the vertical earns more LLM citation per query than a higher-traffic generalist news source. - Format matters more than reputation. Axios's "smart brevity," PCMag's structured product evaluations, and Skift's analyst-grade industry briefings are structurally citation-friendly. Long-form opinion essays are not. - Cross-platform consistency outranks single-platform dominance. A trade publication cited on six platforms at 0.5% share each generates more durable LLM visibility than one cited on a single platform at 3% share. - Recency-weighted engines (Perplexity, Google AI Overviews) reward fresh content, while parametric engines (ChatGPT, Claude) reward consistent historical presence. A complete LLM citation strategy requires both content investment patterns at once. ## What should brands do about trade press citations? ### For any brand running PR or communications in 2026 - Stop relying on impressions. Measure citation share per AI engine, per category prompt. - Reweight the media list. The top 5 trade publications LLMs cite in your vertical are likely not the top 5 your PR team is pitching. - Build for retrieval, not for clicks. Structured comparison content, prompt-shaped headlines, schema markup, and entity-rich language convert PR into AI citation. - Treat Bloomberg, Forbes, and Wikipedia as multipliers, not endpoints. Coverage in those three feeds the sources of every engine. - Measure quarterly. LLM citation behavior shifts faster than search rankings did. ## What should trade publishers do? ### For any trade publication owner - Track citation share alongside page views, and watch AI-referral traffic. Everything-PR's 61.68% AI-referral signal shows what trade publishing looks like when built for retrieval. - Format-engineer for LLM extraction: short structured paragraphs, comparison tables, decision-grade frameworks, entity-dense lead sentences. - Publish to the prompt, not only to the reader. Headlines should mirror how someone would ask an AI engine the question. - Cross-link your network. Internal entity authority compounds across affiliated publications. - Run schema. Structured data is no longer optional for retrieval. ## What should PR and communications firms do? ### For any firm that intends to be operating in 2030 - Reposition around an AI-era operating model, or be displaced by firms that do. The repositioning is not cosmetic. It requires earned media, Generative Engine Optimization (GEO), and proprietary AI visibility measurement, integrated. 5W has rebuilt its practice around this model. - Add GEO as a core service line. Earned media alone does not produce AI citation share without retrieval-layer optimization. Firms that run both earned media and GEO are better positioned, and firms that do not may be acquired or absorbed. ## Where is the white space in trade press citations? ### Across every industry in this study, the same gap appears - The top 3-5 publications are entrenched, parametric, and hard to displace. - The 6th, 7th, and 8th positions are wide open, unclaimed by any publication running a coherent LLM strategy. - 5W expects the first publication to publish prompt-shaped, entity-rich, schema-clean, vertically focused trade research to win those positions within 9-12 months. The trade press landscape is being rewired, and early movers compound their advantage. ## What are the limits of this study? This study is built on synthesized publicly available citation data across six major studies covering more than 680 million individual citations. Where direct citation share data is available, it is cited. Where citation hierarchy is inferred from parametric authority signals and cross-publication entity-recall patterns, this is clearly noted. The Q3 2026 update will add 4,500 proprietary LLM queries (9 industries × 100 prompts × 5 engines) measured directly, converting the inferred industry rankings into measured citation share. Subsequent annual editions will track citation share movement quarter over quarter. Limitations: LLM citation behavior changes with each model update, so readings are snapshots. Engine-level transparency varies, and Claude is less publicly documented than ChatGPT or Perplexity. Industry-level direct citation data is more complete in tech than in beauty, food, and entertainment, where Q3 primary measurement will produce the first hard rankings. ## Frequently asked questions ### Which sources capture the most AI citations? The top 15 domains capture 68% of consolidated AI citation share. Wikipedia accounts for 47.9% of ChatGPT's top-10 cited sources, Reddit is the leading source across major engines, and Forbes holds 6.93% of ChatGPT citations. ### Do specialist trade publications or general news earn more AI citations? Inside a vertical, specialist trade publications earn more LLM citations per query than higher-traffic generalist news sources. PCMag, TechRadar, and CIO.com each appear in the top 10 on 6-7 of 7 measured engines in tech. ### Do all AI engines cite the same sources? No. Meta AI draws 21.5% of citations from media and Gemini 5.7%. Perplexity favors primary sources and named B2B trade authority, Claude leans on long-form journalism, and ChatGPT concentrates on Wikipedia, Reddit, Forbes, and Business Insider. ### Is this study based on 5W's own query runs? Not yet. This edition synthesizes six published citation studies covering more than 680 million citations and marks which findings are inferred. The Q3 2026 update will add 4,500 proprietary queries measured directly. ### What is the white space in trade press? In every industry studied, citation share is concentrated in 3-5 publications and the 6th, 7th, and 8th positions are unclaimed. 5W estimates a publisher or brand could claim them within 9-12 months with prompt-shaped, schema-clean content. ### What should a brand do first? Measure citation share per AI engine and per category prompt, then reweight the media list toward the trade publications LLMs actually cite in the brand's vertical. ## About 5W 5W is a PR, creative and GEO agency, building brand authority across the platforms where decisions now happen (ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews) alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list. For more information, visit 5W. ## About Everything-PR Everything-PR is a 12-publication intelligence network covering the global communications economy: public relations, marketing, advertising, influencer, digital, AI communications, and the trade press itself. The Everything-PR network publishes research, analysis, and industry intelligence across consumer, B2B, and category-specific verticals. Everything-PR shows 61.68% of referral traffic originating from AI Chatbots and Tools, including Claude.ai, according to Similarweb data. ## Build Citation Share Before the Category Moves 5W helps brands measure AI visibility, earn the sources engines cite, and structure content for retrieval. Talk to 5W --- ### Kitchen Appliances AI Visibility Index 2026 URL: https://www.5wpr.com/research/kitchen-appliances-ai-visibility-index-2026/ Summary: Kitchen appliance AI visibility study: KitchenAid leads appearances, while citations concentrate on third-party publishers and vary by engine. # Kitchen Appliances AI Visibility Index 2026 KitchenAid Leads AI Appliance Mentions KitchenAid led this Kitchen Appliances & Home Goods AI visibility study, appearing in 38.6% of 1,176 returned answers. GE followed at 32.1%, Bosch at 30.9%, Whirlpool Corporation at 30.8%, and Breville Group Ltd at 29.3%. The study covers major appliances and countertop appliances, so these results describe the measured questions, not all consumer selection activity. The leading appearance intervals overlap across the top group. KitchenAid's 35.9% to 41.4% interval overlaps GE, Bosch, Whirlpool Corporation, and Breville Group Ltd, placing those differences within the margin of this sample. The commercial risk is uneven engine exposure: a brand can appear often in one system and rarely in another. 1176AI answers analyzed 38.6%KitchenAid appearance rate, the category leader 47Brands named across the study 99.7%Answers carrying retrieval evidence ## The two reports in this index - Major Kitchen Appliances AI Visibility Index 2026 - Small Countertop Appliances AI Visibility Index 2026 ## Which brands lead the kitchen appliance rankings? KitchenAid ranks first on appearance rate, followed by GE, Bosch, Whirlpool Corporation, Breville Group Ltd, LG, Samsung, Cuisinart, Frigidaire, and Hamilton Beach Brands, Inc. Appearance rates use 1,176 returned answers as the denominator. The top rankings combine results from the two measured segments rather than claiming a single winner for every appliance type. KitchenAid appeared in 454 answers. GE appeared in 377, Bosch in 363, Whirlpool Corporation in 362, and Breville Group Ltd in 344. The leaderboard records brand naming, not a product test or a measure of product quality. ### Full appearance leaderboard ### AI visibility leaderboard Appearance rate is the share of the 1176 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1KitchenAid454/117638.6%35.9%–41.4%4.8 2GE377/117632.1%29.5%–34.8%4.9 3Bosch363/117630.9%28.3%–33.6%3.9 4Whirlpool Corporation362/117630.8%28.2%–33.5%5.2 5Breville Group Ltd344/117629.3%26.7%–31.9%3.8 6LG329/117628%25.5%–30.6%5.2 7Samsung295/117625.1%22.7%–27.6%7.2 8Cuisinart278/117623.6%21.3%–26.2%5.3 9Frigidaire249/117621.2%18.9%–23.6%7.1 10Hamilton Beach Brands, Inc.232/117619.7%17.6%–22.1%7.1 11Miele227/117619.3%17.1%–21.7%3.9 12Ninja225/117619.1%17%–21.5%4.3 13Sub-Zero Group, Inc.130/117611.1%9.4%–13%4.2 14Maytag125/117610.6%9%–12.5%6.4 15Instant Brands LLC124/117610.5%8.9%–12.4%3.9 KitchenAid's interval overlaps the intervals for GE, Bosch, Whirlpool Corporation, and Breville Group Ltd. The sample therefore does not establish that KitchenAid is more visible than those brands. GE, Bosch, and Whirlpool Corporation also overlap one another, as do Bosch, Whirlpool Corporation, Breville Group Ltd, and LG where their intervals intersect. ## How do the leading brands appear across engines? The top brands appear across all four systems, but their rates differ sharply. The figures below use each engine's 294 returned answers. This section covers the ten highest-ranked brands by appearance. ### KitchenAid 38.6% appearance rate, with a 35.9% to 41.4% interval, places KitchenAid first. Perplexity named KitchenAid in 57.5% of its 294 answers and ChatGPT in 59.2%, compared with 22.8% for Gemini and 15% for Claude. KitchenAid's average first mention position was 4.8. Its owned domain was cited in 4.1% of answers with retrieval evidence. ### GE 32.1% appearance rate, with a 29.5% to 34.8% interval, places GE second. Perplexity named GE in 45.6% of its 294 answers, ChatGPT in 40.1%, Gemini in 23.8%, and Claude in 18.7%. GE's average first mention position was 4.9. Its owned-domain citation rate was 0% of answers with retrieval evidence. ### Bosch 30.9% appearance rate, with a 28.3% to 33.6% interval, places Bosch third. Perplexity named Bosch in 44.9% of its 294 answers and ChatGPT in 43.5%, while Gemini named it in 18% and Claude in 17%. Bosch's average first mention position was 3.9. Its owned-domain citation rate was 0% of answers with retrieval evidence. ### Whirlpool Corporation 30.8% appearance rate, with a 28.2% to 33.5% interval, places Whirlpool Corporation fourth. Perplexity named Whirlpool Corporation in 42.5% of its 294 answers, ChatGPT in 38.8%, Gemini in 30.3%, and Claude in 11.6%. Whirlpool Corporation's average first mention position was 5.2. Its owned domain was cited in 1.5% of answers with retrieval evidence. ### Breville Group Ltd 29.3% appearance rate, with a 26.7% to 31.9% interval, places Breville Group Ltd fifth. ChatGPT named Breville Group Ltd in 43.2% of its 294 answers, Perplexity in 36.4%, Gemini in 24.8%, and Claude in 12.6%. Breville Group Ltd's average first mention position was 3.8. Its owned domain was cited in 0.8% of answers with retrieval evidence. ### LG 28% appearance rate, with a 25.5% to 30.6% interval, places LG sixth. Perplexity named LG in 38.1% of its 294 answers, ChatGPT in 37.4%, Gemini in 24.1%, and Claude in 12.2%. LG's average first mention position was 5.2. Its owned domain was cited in 7.1% of answers with retrieval evidence. ### Samsung 25.1% appearance rate, with a 22.7% to 27.6% interval, places Samsung seventh. ChatGPT named Samsung in 39.8% of its 294 answers, Perplexity in 34%, Gemini in 20.4%, and Claude in 6.1%. Samsung's average first mention position was 7.2. Its owned domain was cited in 2.2% of answers with retrieval evidence. ### Cuisinart 23.6% appearance rate, with a 21.3% to 26.2% interval, places Cuisinart eighth. ChatGPT named Cuisinart in 38.1% of its 294 answers, Perplexity in 33%, Gemini in 12.2%, and Claude in 11.2%. Cuisinart's average first mention position was 5.3. Its owned domain was cited in 0.3% of answers with retrieval evidence. ### Frigidaire 21.2% appearance rate, with an 18.9% to 23.6% interval, places Frigidaire ninth. Perplexity named Frigidaire in 33.3% of its 294 answers, ChatGPT in 31.6%, Gemini in 13.3%, and Claude in 6.5%. Frigidaire's average first mention position was 7.1. Its owned domain was cited in 0.1% of answers with retrieval evidence. ### Hamilton Beach Brands, Inc. 19.7% appearance rate, with a 17.6% to 22.1% interval, places Hamilton Beach Brands, Inc. tenth. ChatGPT named Hamilton Beach Brands, Inc. in 32% of its 294 answers, Perplexity in 23.5%, Gemini in 18.7%, and Claude in 4.8%. Hamilton Beach Brands, Inc.'s average first mention position was 7.1. Its owned domain was cited in 0.8% of answers with retrieval evidence. ## Where do the AI engines disagree? The engines disagree materially across the measured brands. Perplexity, ChatGPT, Gemini, and Claude each returned 294 answers, while Gemini supplied retrieval evidence for 291 of those answers and the other three supplied evidence for 294. Differences can reflect model and retrieval-tool design, rather than a brand's underlying visibility. ### Engine-by-brand appearance patterns ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate KitchenAid57.5%59.2%22.8%15% GE45.6%40.1%23.8%18.7% Bosch44.9%43.5%18%17% Whirlpool Corporation42.5%38.8%30.3%11.6% Breville Group Ltd36.4%43.2%24.8%12.6% LG38.1%37.4%24.1%12.2% Samsung34%39.8%20.4%6.1% Cuisinart33%38.1%12.2%11.2% Frigidaire33.3%31.6%13.3%6.5% Hamilton Beach Brands, Inc.23.5%32%18.7%4.8% Miele19.4%29.9%18.4%9.5% Ninja31.6%25.9%4.8%14.3% KitchenAid illustrates the split: ChatGPT named it in 59.2% of its answers and Perplexity in 57.5%, versus 22.8% for Gemini and 15% for Claude. GE and Bosch follow a similar pattern. GE's rates range from 45.6% in Perplexity to 18.7% in Claude, while Bosch ranges from 44.9% to 17%. Some brands depend more strongly on one engine. Yale Appliance appeared in 28.2% of Gemini answers but 1% of Perplexity answers, 2.7% of ChatGPT answers, and 1.4% of Claude answers. RTINGS appeared in 11.2% of Gemini answers but 0% of Claude answers. J.D. Power appeared in 10.9% of Gemini answers and 4.8% of ChatGPT answers, while Perplexity and Claude each named it in 0%. Several brands were absent from Claude while present elsewhere. SharkNinja, Inc. appeared in 16% of Gemini answers, 7.1% of ChatGPT answers, and 3.4% of Perplexity answers, but 0% of Claude answers. Oster appeared in 13.3% of ChatGPT answers, 4.4% of Perplexity answers, and 2.4% of Gemini answers, but 0% of Claude answers. Kenwood had the same 0% Claude result, despite 8.8% in ChatGPT, 6.5% in Perplexity, and 4.8% in Gemini. Haier Group also had 0% in Claude after 8.5% in Perplexity and 8.2% in ChatGPT. ## Which brands lead each demand cluster? The demand-cluster results identify where named brands led the measured question groups. The chart separates general buyer-intent questions from sub-category or specific-need questions, so the two groups should not be treated as one consumer-demand measure. ### Brand leadership by demand cluster ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Large / Major Kitchen Appliances — Large / Major Kitchen Appliances: Electrolux vs Whirlpool ranges, which cooks more evenly?12Whirlpool Corporation83.3%Electrolux Group 83.3%, Bosch 75%, GE 58.3% Large / Major Kitchen Appliances: Which range brand has the best repair record?12GE100%LG 91.7%, Bosch 75%, Whirlpool Corporation 75% Large / Major Kitchen Appliances: Which refrigerator brand has had the most recalls?12Frigidaire58.3%Maytag 58.3%, GE 50%, Haier Group 50% Large / Major Kitchen Appliances: Are Samsung refrigerators reliable?12Samsung75%Whirlpool Corporation 58.3%, LG 58.3%, GE 50% Large / Major Kitchen Appliances: Best budget refrigerator brand12Whirlpool Corporation83.3%Frigidaire 83.3%, GE 75%, LG 58.3% Large / Major Kitchen Appliances: Which appliance brand has the best warranty support?12GE75%Bosch 58.3%, Miele 58.3%, Sub-Zero Group, Inc. 58.3% Large / Major Kitchen Appliances: Which dishwasher brand do appliance repair technicians recommend?12KitchenAid83.3%GE 83.3%, Bosch 83.3%, Whirlpool Corporation 83.3% Large / Major Kitchen Appliances: Cheapest reliable dishwasher brand12Bosch83.3%Whirlpool Corporation 66.7%, GE 58.3%, LG 58.3% Large / Major Kitchen Appliances: Which refrigerator brand breaks down most often?12Bosch66.7%Samsung 66.7%, Sub-Zero Group, Inc. 66.7%, GE 58.3% Large / Major Kitchen Appliances: Is a Sub-Zero refrigerator worth the price?12Miele75%Sub-Zero Group, Inc. 75%, LG 66.7%, GE 58.3% Large / Major Kitchen Appliances: Is KitchenAid worth the extra cost over Whirlpool?12Whirlpool Corporation83.3%KitchenAid 66.7%, GE 58.3%, LG 58.3% Large / Major Kitchen Appliances: Most affordable range brand that still lasts12GE75%Whirlpool Corporation 75%, LG 75%, Frigidaire 75% Large / Major Kitchen Appliances: Best value kitchen appliance package12GE75%Bosch 66.7%, Whirlpool Corporation 66.7%, KitchenAid 58.3% Large / Major Kitchen Appliances: Is Viking worth it for a home kitchen?12Viking75%Thermador 58.3%, Bosch 50%, KitchenAid 41.7% Large / Major Kitchen Appliances: Best counter-depth refrigerator brand12LG91.7%GE 83.3%, Bosch 83.3%, Samsung 75% Large / Major Kitchen Appliances: Best smart refrigerator with Wi-Fi features12GE75%LG 75%, Samsung 75%, Whirlpool Corporation 66.7% Large / Major Kitchen Appliances: What's the best refrigerator brand to buy in 2026?12LG58.3%GE 50%, Bosch 50%, Whirlpool Corporation 50% Large / Major Kitchen Appliances: Which dishwasher brand should I buy for a family of four?12Bosch100%Miele 83.3%, KitchenAid 75%, GE 58.3% Large / Major Kitchen Appliances: What's the best range/oven brand for a first home?12GE100%Whirlpool Corporation 100%, LG 83.3%, Bosch 75% Large / Major Kitchen Appliances: Which dishwasher brand breaks down the least?12Bosch100%Miele 100%, KitchenAid 75%, GE 66.7% Large / Major Kitchen Appliances: Most reliable refrigerator brand 202612GE83.3%Whirlpool Corporation 83.3%, Bosch 75%, Sub-Zero Group, Inc. 75% Large / Major Kitchen Appliances: Cheapest brand for a full kitchen remodel12KitchenAid25%Whirlpool Corporation 25%, Samsung 25%, Frigidaire 25% Large / Major Kitchen Appliances: Bosch vs GE dishwashers, which is quieter?12Bosch83.3%GE 75%, Whirlpool Corporation 66.7%, Miele 66.7% Large / Major Kitchen Appliances: Best induction range brand12GE91.7%LG 91.7%, Samsung 83.3%, Bosch 75% Large / Major Kitchen Appliances: Best quiet dishwasher brand12Miele75%KitchenAid 66.7%, Bosch 66.7%, Samsung 66.7% Large / Major Kitchen Appliances: Best appliance brand for matching stainless steel finishes12KitchenAid83.3%LG 83.3%, Samsung 83.3%, Bosch 75% Large / Major Kitchen Appliances: Best refrigerator brand for small kitchens12Bosch83.3%GE 75%, Whirlpool Corporation 75%, LG 75% Large / Major Kitchen Appliances: Best appliance brand for a small apartment kitchen12Bosch75%Frigidaire 66.7%, Whirlpool Corporation 58.3%, Miele 50% Large / Major Kitchen Appliances: Best energy-efficient refrigerator brand12Fisher & Paykel75%Samsung 58.3%, GE 50%, LG 50% Large / Major Kitchen Appliances: What's the best affordable refrigerator brand?12Whirlpool Corporation91.7%GE 83.3%, LG 83.3%, Samsung 66.7% Large / Major Kitchen Appliances: Galanz vs GE, which is the better budget microwave or refrigerator brand?12GE83.3%Whirlpool Corporation 75%, Frigidaire 75%, Galanz 75% Large / Major Kitchen Appliances: Which brand makes the best built-in microwave?12GE91.7%KitchenAid 83.3%, Bosch 58.3%, LG 58.3% Large / Major Kitchen Appliances: Which brand makes the best range hood?12GE25%Bosch 25%, Frigidaire 25%, KitchenAid 16.7% Large / Major Kitchen Appliances: Is Galanz a reliable kitchen appliance brand?12Galanz75%GE 50%, Whirlpool Corporation 50%, LG 41.7% Large / Major Kitchen Appliances: Price and value12GE75%Whirlpool Corporation 75%, LG 58.3%, Samsung 58.3% Large / Major Kitchen Appliances: What's the best high-end kitchen appliance brand?12Sub-Zero Group, Inc.100%Miele 91.7%, Thermador 75%, Viking 66.7% Large / Major Kitchen Appliances: Which brand should I buy for a full kitchen appliance package?12LG83.3%GE 75%, Bosch 75%, Whirlpool Corporation 75% Large / Major Kitchen Appliances: Primary use case12KitchenAid66.7%GE 66.7%, Bosch 66.7%, Whirlpool Corporation 66.7% Large / Major Kitchen Appliances: KitchenAid vs Whirlpool refrigerators, which is better?12KitchenAid83.3%Whirlpool Corporation 83.3%, GE 66.7%, Bosch 66.7% Large / Major Kitchen Appliances: Sub-Zero vs Viking, which is the better investment?12Sub-Zero Group, Inc.83.3%Viking 75%, Miele 66.7%, Thermador 58.3% Large / Major Kitchen Appliances: Café vs KitchenAid appliances, what's the difference?12KitchenAid75%GE 75%, Café 75%, Whirlpool Corporation 66.7% Large / Major Kitchen Appliances: Everyday and mainstream choice12Bosch75%GE 66.7%, LG 66.7%, Whirlpool Corporation 58.3% Large / Major Kitchen Appliances: Frigidaire vs Maytag, which lasts longer?12Maytag91.7%Frigidaire 75%, GE 66.7%, Bosch 66.7% Large / Major Kitchen Appliances: Samsung vs LG refrigerators, which has fewer repairs?12Samsung91.7%LG 83.3%, GE 75%, Whirlpool Corporation 58.3% Large / Major Kitchen Appliances: Miele vs Bosch dishwashers, which is more reliable?12Miele83.3%Bosch 75%, Whirlpool Corporation 66.7%, KitchenAid 58.3% Large / Major Kitchen Appliances: General best-in-category12LG83.3%KitchenAid 66.7%, Whirlpool Corporation 66.7%, Bosch 58.3% — Countertop / Small Kitchen Appliances — Countertop / Small Kitchen Appliances: Vitamix vs Ninja blenders, which is better?12Breville Group Ltd66.7%Hamilton Beach Brands, Inc. 66.7%, Blendtec LLC 66.7%, Vitamix Corporation 58.3% Countertop / Small Kitchen Appliances: Breville vs Cuisinart espresso machines, which is easier to use?12Breville Group Ltd83.3%Cuisinart 66.7%, De'Longhi 58.3%, Philips 50% Countertop / Small Kitchen Appliances: Keurig vs Breville coffee makers, which has better coffee quality?12Breville Group Ltd100%Keurig 83.3%, Cuisinart 41.7%, Hamilton Beach Brands, Inc. 33.3% Countertop / Small Kitchen Appliances: Best multi-cooker with the most functions12Instant Brands LLC75%Ninja 58.3%, Breville Group Ltd 50%, Cuisinart 41.7% Countertop / Small Kitchen Appliances: Best coffee maker with a built-in grinder12Breville Group Ltd91.7%Cuisinart 66.7%, GE 58.3%, Ninja 50% Countertop / Small Kitchen Appliances: Is Beautiful by Drew Barrymore a good kitchen appliance brand?12Beautiful by Drew Barrymore75%KitchenAid 58.3%, Breville Group Ltd 58.3%, Cuisinart 50% Countertop / Small Kitchen Appliances: Best quiet blender brand12Breville Group Ltd83.3%Vitamix Corporation 75%, Hamilton Beach Brands, Inc. 66.7%, Blendtec LLC 66.7% Countertop / Small Kitchen Appliances: Is Our Place a reliable cookware and appliance brand?12Cuisinart16.7%Ninja 8.3%, Short answer 8.3%, Dash Brands, Inc 8.3% Countertop / Small Kitchen Appliances: Does a celebrity-backed kitchen brand mean better quality?12Beautiful by Drew Barrymore25%— Countertop / Small Kitchen Appliances: Haden vs Smeg, which retro-style appliance brand is better?12Smeg100%Haden 91.7%, KitchenAid 41.7%, Breville Group Ltd 25% Countertop / Small Kitchen Appliances: Best stand mixer with the most color/design options12KitchenAid75%Cuisinart 75%, Smeg 75%, Hamilton Beach Brands, Inc. 66.7% Countertop / Small Kitchen Appliances: Best celebrity-backed kitchen appliance brand12Bosch25%Smeg 25%, Fisher & Paykel 25%, Beautiful by Drew Barrymore 16.7% Countertop / Small Kitchen Appliances: Instant Pot vs Ninja multi-cookers, which is more reliable?12Instant Brands LLC75%Ninja 66.7%, Breville Group Ltd 41.7%, Cuisinart 33.3% Countertop / Small Kitchen Appliances: KitchenAid vs Cuisinart stand mixers, which is better?12Hamilton Beach Brands, Inc.66.7%Smeg 66.7%, Kenwood 66.7%, Ankarsrum 66.7% Countertop / Small Kitchen Appliances: Nutribullet vs Ninja, which blends better?12Ninja75%NutriBullet 75%, Hamilton Beach Brands, Inc. 66.7%, KitchenAid 58.3% Countertop / Small Kitchen Appliances: Hamilton Beach vs Oster, which brand lasts longer?12Hamilton Beach Brands, Inc.75%Oster 75%, Breville Group Ltd 58.3%, KitchenAid 50% Countertop / Small Kitchen Appliances: Smeg vs KitchenAid, which stand mixer is better?12Smeg83.3%KitchenAid 75%, Breville Group Ltd 66.7%, Hamilton Beach Brands, Inc. 66.7% Countertop / Small Kitchen Appliances: Most reliable blender brand 202612Vitamix Corporation66.7%Blendtec LLC 66.7%, KitchenAid 50%, Breville Group Ltd 50% Countertop / Small Kitchen Appliances: Which air fryer brand breaks down the least?12Instant Brands LLC58.3%Philips 58.3%, Breville Group Ltd 41.7%, Cuisinart 41.7% Countertop / Small Kitchen Appliances: Is Ninja a reliable brand?12Ninja58.3%KitchenAid 50%, Breville Group Ltd 50%, Cuisinart 50% Countertop / Small Kitchen Appliances: Which coffee maker brand has the fewest complaints?12Cuisinart75%Breville Group Ltd 58.3%, Keurig 58.3%, Hamilton Beach Brands, Inc. 50% Countertop / Small Kitchen Appliances: Which stand mixer brand lasts the longest?12Ankarsrum66.7%KitchenAid 50%, Bosch 50%, Cuisinart 50% Countertop / Small Kitchen Appliances: Which multi-cooker brand has had safety recalls?12SharkNinja, Inc.66.7%Instant Brands LLC 58.3%, Breville Group Ltd 50%, Hamilton Beach Brands, Inc. 25% Countertop / Small Kitchen Appliances: Is Vitamix worth the price over cheaper blenders?12Vitamix Corporation66.7%Blendtec LLC 66.7%, Breville Group Ltd 50%, Ninja 50% Countertop / Small Kitchen Appliances: What's the best blender brand to buy?12Vitamix Corporation75%NutriBullet 75%, KitchenAid 58.3%, Breville Group Ltd 58.3% Countertop / Small Kitchen Appliances: Best air fryer brand to buy in 202612Instant Brands LLC83.3%Ninja 75%, COSORI 75%, Breville Group Ltd 66.7% Countertop / Small Kitchen Appliances: What's the best stand mixer brand for baking?12KitchenAid83.3%Breville Group Ltd 75%, Cuisinart 66.7%, Hamilton Beach Brands, Inc. 66.7% Countertop / Small Kitchen Appliances: Does celebrity endorsement affect how reliable a kitchen appliance brand is?12KitchenAid50%Whirlpool Corporation 50%, LG 50%, Samsung 50% Countertop / Small Kitchen Appliances: Is Our Place worth the price compared to KitchenAid?12KitchenAid66.7%Cuisinart 33.3%, Hamilton Beach Brands, Inc. 33.3%, Short answer 33.3% Countertop / Small Kitchen Appliances: Price and value12Hamilton Beach Brands, Inc.83.3%Cuisinart 75%, BLACK+DECKER Inc. 58.3%, KitchenAid 50% Countertop / Small Kitchen Appliances: Primary use case12Breville Group Ltd75%Cuisinart 75%, Vitamix Corporation 75%, KitchenAid 66.7% Countertop / Small Kitchen Appliances: Beautiful by Drew Barrymore vs Our Place, which is the better brand?12Beautiful by Drew Barrymore83.3%Cuisinart 25%, Instant Brands LLC 16.7%, KitchenAid 8.3% Countertop / Small Kitchen Appliances: Which blender brand burns out motors the fastest?12Hamilton Beach Brands, Inc.83.3%Blendtec LLC 75%, Breville Group Ltd 66.7%, KitchenAid 58.3% Countertop / Small Kitchen Appliances: Cheapest reliable air fryer brand12COSORI91.7%Ninja 75%, Instant Brands LLC 75%, Philips 50% Countertop / Small Kitchen Appliances: Best budget stand mixer brand12Cuisinart100%Hamilton Beach Brands, Inc. 100%, KitchenAid 50%, Beautiful by Drew Barrymore 50% Countertop / Small Kitchen Appliances: Which countertop appliance brand has the best customer service?12KitchenAid50%Breville Group Ltd 41.7%, GE 33.3%, LG 33.3% Countertop / Small Kitchen Appliances: Is a Breville espresso machine worth the cost?12Breville Group Ltd100%De'Longhi 50%, Short answer 25%, Philips 16.7% Countertop / Small Kitchen Appliances: Best value multi-cooker brand12Instant Brands LLC91.7%Ninja 66.7%, Breville Group Ltd 58.3%, Cuisinart 50% Countertop / Small Kitchen Appliances: Most affordable coffee maker that still tastes good12Ninja75%Cuisinart 66.7%, BLACK+DECKER Inc. 58.3%, Hamilton Beach Brands, Inc. 33.3% Countertop / Small Kitchen Appliances: Best budget food processor brand12Hamilton Beach Brands, Inc.91.7%KitchenAid 75%, Cuisinart 75%, BLACK+DECKER Inc. 75% Countertop / Small Kitchen Appliances: Is KitchenAid worth it over Hamilton Beach?12KitchenAid66.7%Breville Group Ltd 66.7%, Cuisinart 58.3%, Hamilton Beach Brands, Inc. 58.3% Countertop / Small Kitchen Appliances: Best air fryer for a family of four12Ninja66.7%COSORI 66.7%, Breville Group Ltd 50%, Instant Brands LLC 50% Countertop / Small Kitchen Appliances: Best blender for smoothies and ice12Breville Group Ltd75%Vitamix Corporation 75%, Ninja 66.7%, Blendtec LLC 58.3% Countertop / Small Kitchen Appliances: Best coffee maker brand for home use12Breville Group Ltd91.7%Keurig 75%, Cuisinart 58.3%, Ninja 58.3% Countertop / Small Kitchen Appliances: Best toaster oven brand12Breville Group Ltd91.7%Cuisinart 83.3%, Hamilton Beach Brands, Inc. 75%, Ninja 75% Countertop / Small Kitchen Appliances: Best espresso machine brand for beginners12Breville Group Ltd91.7%De'Longhi 91.7%, Philips 41.7%, Ninja 25% Countertop / Small Kitchen Appliances: Best food processor brand12KitchenAid83.3%Breville Group Ltd 83.3%, Cuisinart 83.3%, Hamilton Beach Brands, Inc. 66.7% Countertop / Small Kitchen Appliances: Best multi-cooker brand12Instant Brands LLC91.7%Breville Group Ltd 83.3%, Ninja 58.3%, Philips 41.7% Countertop / Small Kitchen Appliances: General best-in-category12Breville Group Ltd83.3%Cuisinart 75%, KitchenAid 66.7%, Ninja 66.7% Countertop / Small Kitchen Appliances: Best espresso machine with a built-in grinder12Breville Group Ltd91.7%De'Longhi 83.3%, Philips 58.3%, Smeg 25% Countertop / Small Kitchen Appliances: Best compact stand mixer for small kitchens12KitchenAid83.3%Hamilton Beach Brands, Inc. 83.3%, Kenwood 75%, Cuisinart 66.7% Countertop / Small Kitchen Appliances: Everyday and mainstream choice12Breville Group Ltd91.7%Cuisinart 83.3%, KitchenAid 75%, Ninja 66.7% Cluster leadership shows where a brand's measured naming pattern is strongest. It does not establish product superiority or broader market preference. The results are limited to the cluster questions included in the 98 measured prompts. ## Which sites receive citations in AI answers? Owned-domain citation rates use 1,173 answers with retrieval evidence as the denominator. Yale Appliance had the highest owned-domain citation rate at 28%, despite an 8.3% appearance rate. RTINGS reached 20.2%, J.D. Power 8.4%, LG 7.1%, and KitchenAid 4.1%. ### Owned-domain citations ### Owned-domain citation rate Share of the 1173 answers with retrieval evidence that cited a URL on the brand's own website. KitchenAid 4.1% · 48/1173 GE 0% · 0/1173 Bosch 0% · 0/1173 Whirlpool Corporation 1.5% · 18/1173 Breville Group Ltd 0.8% · 9/1173 LG 7.1% · 83/1173 Samsung 2.2% · 26/1173 Cuisinart 0.3% · 4/1173 Frigidaire 0.1% · 1/1173 Hamilton Beach Brands, Inc. 0.8% · 9/1173 Miele 0.1% · 1/1173 Ninja 0.1% · 1/1173 Third-party sources dominate the cited-source list. ConsumerReports.org appeared in 577 answers with retrieval evidence, a 49.2% answer penetration rate. Reviewed.com appeared in 282 answers, Reddit.com in 265, YouTube.com in 254, RTINGS.com in 237, and blog.yaleappliance.com in 221. These figures describe citation frequency in the measured answers, not a judgment about source accuracy or authority. ### Most cited publishers ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration consumerreports.org577/117349.2% reviewed.com282/117324% reddit.com265/117322.6% youtube.com254/117321.7% rtings.com237/117320.2% blog.yaleappliance.com221/117318.8% goodhousekeeping.com199/117317% forbes.com191/117316.3% foodnetwork.com191/117316.3% qz.com173/117314.7% tomsguide.com171/117314.6% tastingtable.com164/117314% The citation pattern separates brand naming from source use. A maker can appear often without its own domain receiving frequent citations. GE and Bosch each had a 0% owned-domain citation rate, while Yale Appliance and RTINGS had high citation rates relative to their appearance rates. ## Who is winning and who is at risk? Measured winners are brands with broad appearance, strong placement, or owned-domain citation evidence in this study. ### Measured winners - KitchenAid ranks first at 38.6% appearance, with 100% cross-engine consistency and a 4.1% owned-domain citation rate. - Breville Group Ltd ranks fifth at 29.3% appearance and has an average first mention position of 3.8. - LG ranks sixth at 28% appearance and has a 7.1% owned-domain citation rate. - Yale Appliance has an 8.3% appearance rate but a 28% owned-domain citation rate. - Vitamix Corporation has an 8% appearance rate and an average first mention position of 2.4. ### Measured at-risk brands - GE ranks second at 32.1% appearance but has a 0% owned-domain citation rate. - Bosch ranks third at 30.9% appearance but has a 0% owned-domain citation rate. - Samsung ranks seventh at 25.1% appearance, but Claude named it in 6.1% of its answers and its average first mention position was 7.2. - SharkNinja, Inc. has a 6.6% appearance rate, 75% cross-engine consistency, and 0% appearance in Claude answers. - Our Place has a 0% appearance rate and 0% cross-engine consistency, although its owned domain was cited in 1.2% of answers with retrieval evidence. ## What does the tally say about category structure? The category has a visible top tier, but the leading brands are not statistically separated within this sample. KitchenAid's 35.9% to 41.4% interval overlaps GE, Bosch, Whirlpool Corporation, and Breville Group Ltd. The highest-ranked brands appear across all four engines, while several lower-ranked brands concentrate in one engine. Third-party publishers carry much of the citation activity. ConsumerReports.org had a 49.2% answer penetration rate, compared with the highest measured owned-domain citation rate of 28% for Yale Appliance. RTINGS and J.D. Power were tracked separately in the brand list but function as cited publishers in the tally, so they should not be treated as appliance makers in brand strategy. The celebrity and influencer-backed brands named in the brief did not lead this measurement. Beautiful by Drew Barrymore appeared in 2.9% of returned answers and Our Place in 0%. These measured results do not support a claim that celebrity or influencer backing produced higher AI visibility across these prompts. ## What changed during the measurement window? The dataset records one measurement window and does not contain prior-wave results. It cannot establish change over time. It does show present engine-level divergence, including Yale Appliance's 28.2% Gemini appearance rate versus 1% in Perplexity and RTINGS' 11.2% Gemini rate versus 0% in Claude. ## How should appliance brands respond to these findings? Brands can use the measured weaknesses as a remeasurement plan rather than treating this study as a general search checklist. - GE and Bosch should audit the pages that retrieval tools can cite for the product questions in this study. Both brands had 0% owned-domain citation rates despite appearance rates of 32.1% and 30.9%. Remeasure owned-domain citation rate across the same engines and prompt set. - Samsung should inspect the query areas behind its Claude gap. Samsung appeared in 39.8% of ChatGPT answers but 6.1% of Claude answers. Remeasure per-engine appearance after publishing or improving pages that answer those question types. - Yale Appliance should preserve the pages associated with its 28% owned-domain citation rate and test whether its 8.3% appearance rate rises across engines. Its Gemini appearance rate was 28.2%, but it was 1% in Perplexity and 1.4% in Claude. - SharkNinja, Inc. should test its entity naming across source pages and product pages. It appeared in 16% of Gemini answers but 0% of Claude answers. Remeasure cross-engine consistency, which was 75%. - Beautiful by Drew Barrymore and Our Place should test direct product-comparison and selection pages against the measured clusters. Their appearance rates were 2.9% and 0%. Remeasure appearance rate and first-mention position where applicable. - Kitchen appliance makers should create evidence pages that can compete with the publisher sources cited in this study. ConsumerReports.org appeared in 49.2% of answers with retrieval evidence. Remeasure each maker's owned-domain citation rate, not only its appearance rate. ## What does this mean for brands in the category? Interpretation: broad naming and source citation are separate outcomes. KitchenAid has the highest measured appearance rate, while Yale Appliance has the highest owned-domain citation rate. A brand team that monitors only one measure can miss a weakness in the other. Interpretation: engine-specific results need their own review. The same brand can be routinely named by Perplexity or ChatGPT and infrequently named by Gemini or Claude. Brand teams can use the engine-level results to identify where their product evidence and third-party coverage are not carrying into answers. ## How was this AI visibility study measured? This study measured Kitchen Appliances & Home Goods answers from Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). It used 98 prompts with three trials per prompt per engine, producing 1,176 attempted and returned answers. Appearance rates use 1,176 returned answers. Citation and source rates use 1,173 answers with retrieval evidence. ### Study method ### How this study was measured - Sampling frame fixed first: 98 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each. - 1176 answers returned of 1176 attempted, 1173 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The study covers two segments: large or major kitchen appliances and countertop or small kitchen appliances. It measures the specified prompts on the measurement date. It does not measure sales, market share, product performance, consumer satisfaction, or all possible appliance questions. ## Frequently asked questions ### Which brand appeared most often in the measured answers? KitchenAid ranked first with a 38.6% appearance rate across 1,176 returned answers. Its 95% interval was 35.9% to 41.4%. ### Did KitchenAid clearly beat the next-ranked brands? No. KitchenAid's interval overlaps the intervals for GE, Bosch, Whirlpool Corporation, and Breville Group Ltd. The differences are within the margin of this sample. ### Which brand had the highest owned-domain citation rate? Yale Appliance had a 28% owned-domain citation rate, using 1,173 answers with retrieval evidence as the denominator. Its appearance rate was 8.3% across returned answers. ### Which publishers appeared most often in cited answers? ConsumerReports.org appeared in 577 answers with retrieval evidence, or 49.2%. Reviewed.com appeared in 282 answers, Reddit.com in 265, and YouTube.com in 254. ### Did celebrity or influencer-backed brands lead the study? No. Beautiful by Drew Barrymore had a 2.9% appearance rate and Our Place had 0%. The measured results do not show those brands leading this prompt set. ### Why do the engines produce different results? The systems use different models and retrieval tools. In this study, KitchenAid appeared in 59.2% of ChatGPT answers and 15% of Claude answers, while Yale Appliance appeared in 28.2% of Gemini answers and 1% of Perplexity answers. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1RTINGS.com4.1%48/11763.1%–5.4% 2Consumer Reports4%47/11763%–5.3% 3Wirecutter2.7%32/11761.9%–3.8% 4Reviewed.com2.6%31/11761.9%–3.7% 5Amazon0.3%4/11760.1%–0.9% 6Good Housekeeping0.3%3/11760.1%–0.7% 7Home Depot0.1%1/11760%–0.5% 8US News 360 Reviews0%0/11760%–0.3% 9Wayfair0%0/11760%–0.3% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Kitchen Appliances & Home Goods - Measurement date: 2026-10-04 - Depth: flagship — 98 prompts, 3 trial(s) per prompt per engine, 4 engines - Answers attempted: 1176 - Answers returned (appearance denominator): 1176 - Answers with retrieval evidence (citation denominator): 1173 (99.7% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Cross-segment roll-up: every figure in this hub is tallied from the 1176 measured answers of its 2 segment studies. No figure here is estimated, and no category is ranked that was not measured in one of those segments. ### How the prompts were sampled Combined from the stored sampling frames of 2 completed segment studies; no prompts were regenerated or re-measured. Demand clusterPromptsScope Large / Major Kitchen Appliances: What's the best refrigerator brand to buy in 2026?1What's the best refrigerator brand to buy in 2026? Large / Major Kitchen Appliances: Which dishwasher brand should I buy for a family of four?1Which dishwasher brand should I buy for a family of four? Large / Major Kitchen Appliances: What's the best range/oven brand for a first home?1What's the best range/oven brand for a first home? Large / Major Kitchen Appliances: Which brand makes the best built-in microwave?1Which brand makes the best built-in microwave? Large / Major Kitchen Appliances: What's the best affordable refrigerator brand?1What's the best affordable refrigerator brand? Large / Major Kitchen Appliances: Which brand makes the best range hood?1Which brand makes the best range hood? Large / Major Kitchen Appliances: What's the best high-end kitchen appliance brand?1What's the best high-end kitchen appliance brand? Large / Major Kitchen Appliances: Which brand should I buy for a full kitchen appliance package?1Which brand should I buy for a full kitchen appliance package? Large / Major Kitchen Appliances: KitchenAid vs Whirlpool refrigerators, which is better?1KitchenAid vs Whirlpool refrigerators, which is better? Large / Major Kitchen Appliances: Bosch vs GE dishwashers, which is quieter?1Bosch vs GE dishwashers, which is quieter? Large / Major Kitchen Appliances: Samsung vs LG refrigerators, which has fewer repairs?1Samsung vs LG refrigerators, which has fewer repairs? Large / Major Kitchen Appliances: Sub-Zero vs Viking, which is the better investment?1Sub-Zero vs Viking, which is the better investment? Large / Major Kitchen Appliances: Frigidaire vs Maytag, which lasts longer?1Frigidaire vs Maytag, which lasts longer? Large / Major Kitchen Appliances: Café vs KitchenAid appliances, what's the difference?1Café vs KitchenAid appliances, what's the difference? Large / Major Kitchen Appliances: Miele vs Bosch dishwashers, which is more reliable?1Miele vs Bosch dishwashers, which is more reliable? Large / Major Kitchen Appliances: Electrolux vs Whirlpool ranges, which cooks more evenly?1Electrolux vs Whirlpool ranges, which cooks more evenly? Large / Major Kitchen Appliances: Most reliable refrigerator brand 20261Most reliable refrigerator brand 2026 Large / Major Kitchen Appliances: Which dishwasher brand breaks down the least?1Which dishwasher brand breaks down the least? Large / Major Kitchen Appliances: Which range brand has the best repair record?1Which range brand has the best repair record? Large / Major Kitchen Appliances: Which refrigerator brand has had the most recalls?1Which refrigerator brand has had the most recalls? Large / Major Kitchen Appliances: Are Samsung refrigerators reliable?1Are Samsung refrigerators reliable? Large / Major Kitchen Appliances: Which appliance brand has the best warranty support?1Which appliance brand has the best warranty support? Large / Major Kitchen Appliances: Which dishwasher brand do appliance repair technicians recommend?1Which dishwasher brand do appliance repair technicians recommend? Large / Major Kitchen Appliances: Which refrigerator brand breaks down most often?1Which refrigerator brand breaks down most often? Large / Major Kitchen Appliances: Is a Sub-Zero refrigerator worth the price?1Is a Sub-Zero refrigerator worth the price? Large / Major Kitchen Appliances: Cheapest reliable dishwasher brand1Cheapest reliable dishwasher brand Large / Major Kitchen Appliances: Best budget refrigerator brand1Best budget refrigerator brand Large / Major Kitchen Appliances: Is KitchenAid worth the extra cost over Whirlpool?1Is KitchenAid worth the extra cost over Whirlpool? Large / Major Kitchen Appliances: Most affordable range brand that still lasts1Most affordable range brand that still lasts Large / Major Kitchen Appliances: Best value kitchen appliance package1Best value kitchen appliance package Large / Major Kitchen Appliances: Is Viking worth it for a home kitchen?1Is Viking worth it for a home kitchen? Large / Major Kitchen Appliances: Cheapest brand for a full kitchen remodel1Cheapest brand for a full kitchen remodel Large / Major Kitchen Appliances: Best counter-depth refrigerator brand1Best counter-depth refrigerator brand Large / Major Kitchen Appliances: Best smart refrigerator with Wi-Fi features1Best smart refrigerator with Wi-Fi features Large / Major Kitchen Appliances: Best induction range brand1Best induction range brand Large / Major Kitchen Appliances: Best quiet dishwasher brand1Best quiet dishwasher brand Large / Major Kitchen Appliances: Best refrigerator brand for small kitchens1Best refrigerator brand for small kitchens Large / Major Kitchen Appliances: Best appliance brand for matching stainless steel finishes1Best appliance brand for matching stainless steel finishes Large / Major Kitchen Appliances: Best energy-efficient refrigerator brand1Best energy-efficient refrigerator brand Large / Major Kitchen Appliances: Best appliance brand for a small apartment kitchen1Best appliance brand for a small apartment kitchen Large / Major Kitchen Appliances: Galanz vs GE, which is the better budget microwave or refrigerator brand?1Galanz vs GE, which is the better budget microwave or refrigerator brand? Large / Major Kitchen Appliances: Is Galanz a reliable kitchen appliance brand?1Is Galanz a reliable kitchen appliance brand? Large / Major Kitchen Appliances: General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier Large / Major Kitchen Appliances: Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Large / Major Kitchen Appliances: Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it Large / Major Kitchen Appliances: Primary use case1the dominant everyday use case for this category Countertop / Small Kitchen Appliances: What's the best stand mixer brand for baking?1What's the best stand mixer brand for baking? Countertop / Small Kitchen Appliances: What's the best blender brand to buy?1What's the best blender brand to buy? Countertop / Small Kitchen Appliances: Best coffee maker brand for home use1Best coffee maker brand for home use Countertop / Small Kitchen Appliances: Best air fryer brand to buy in 20261Best air fryer brand to buy in 2026 Countertop / Small Kitchen Appliances: Best multi-cooker brand1Best multi-cooker brand Countertop / Small Kitchen Appliances: Best food processor brand1Best food processor brand Countertop / Small Kitchen Appliances: Best espresso machine brand for beginners1Best espresso machine brand for beginners Countertop / Small Kitchen Appliances: Best toaster oven brand1Best toaster oven brand Countertop / Small Kitchen Appliances: Vitamix vs Ninja blenders, which is better?1Vitamix vs Ninja blenders, which is better? Countertop / Small Kitchen Appliances: KitchenAid vs Cuisinart stand mixers, which is better?1KitchenAid vs Cuisinart stand mixers, which is better? Countertop / Small Kitchen Appliances: Instant Pot vs Ninja multi-cookers, which is more reliable?1Instant Pot vs Ninja multi-cookers, which is more reliable? Countertop / Small Kitchen Appliances: Breville vs Cuisinart espresso machines, which is easier to use?1Breville vs Cuisinart espresso machines, which is easier to use? Countertop / Small Kitchen Appliances: Nutribullet vs Ninja, which blends better?1Nutribullet vs Ninja, which blends better? Countertop / Small Kitchen Appliances: Keurig vs Breville coffee makers, which has better coffee quality?1Keurig vs Breville coffee makers, which has better coffee quality? Countertop / Small Kitchen Appliances: Hamilton Beach vs Oster, which brand lasts longer?1Hamilton Beach vs Oster, which brand lasts longer? Countertop / Small Kitchen Appliances: Smeg vs KitchenAid, which stand mixer is better?1Smeg vs KitchenAid, which stand mixer is better? Countertop / Small Kitchen Appliances: Most reliable blender brand 20261Most reliable blender brand 2026 Countertop / Small Kitchen Appliances: Which air fryer brand breaks down the least?1Which air fryer brand breaks down the least? Countertop / Small Kitchen Appliances: Which coffee maker brand has the fewest complaints?1Which coffee maker brand has the fewest complaints? Countertop / Small Kitchen Appliances: Is Ninja a reliable brand?1Is Ninja a reliable brand? Countertop / Small Kitchen Appliances: Which stand mixer brand lasts the longest?1Which stand mixer brand lasts the longest? Countertop / Small Kitchen Appliances: Which multi-cooker brand has had safety recalls?1Which multi-cooker brand has had safety recalls? Countertop / Small Kitchen Appliances: Which countertop appliance brand has the best customer service?1Which countertop appliance brand has the best customer service? Countertop / Small Kitchen Appliances: Which blender brand burns out motors the fastest?1Which blender brand burns out motors the fastest? Countertop / Small Kitchen Appliances: Is Vitamix worth the price over cheaper blenders?1Is Vitamix worth the price over cheaper blenders? Countertop / Small Kitchen Appliances: Best budget stand mixer brand1Best budget stand mixer brand Countertop / Small Kitchen Appliances: Cheapest reliable air fryer brand1Cheapest reliable air fryer brand Countertop / Small Kitchen Appliances: Is a Breville espresso machine worth the cost?1Is a Breville espresso machine worth the cost? Countertop / Small Kitchen Appliances: Best value multi-cooker brand1Best value multi-cooker brand Countertop / Small Kitchen Appliances: Most affordable coffee maker that still tastes good1Most affordable coffee maker that still tastes good Countertop / Small Kitchen Appliances: Is KitchenAid worth it over Hamilton Beach?1Is KitchenAid worth it over Hamilton Beach? Countertop / Small Kitchen Appliances: Best budget food processor brand1Best budget food processor brand Countertop / Small Kitchen Appliances: Best blender for smoothies and ice1Best blender for smoothies and ice Countertop / Small Kitchen Appliances: Best air fryer for a family of four1Best air fryer for a family of four Countertop / Small Kitchen Appliances: Best espresso machine with a built-in grinder1Best espresso machine with a built-in grinder Countertop / Small Kitchen Appliances: Best compact stand mixer for small kitchens1Best compact stand mixer for small kitchens Countertop / Small Kitchen Appliances: Best multi-cooker with the most functions1Best multi-cooker with the most functions Countertop / Small Kitchen Appliances: Best coffee maker with a built-in grinder1Best coffee maker with a built-in grinder Countertop / Small Kitchen Appliances: Best quiet blender brand1Best quiet blender brand Countertop / Small Kitchen Appliances: Best stand mixer with the most color/design options1Best stand mixer with the most color/design options Countertop / Small Kitchen Appliances: Is Beautiful by Drew Barrymore a good kitchen appliance brand?1Is Beautiful by Drew Barrymore a good kitchen appliance brand? Countertop / Small Kitchen Appliances: Is Our Place a reliable cookware and appliance brand?1Is Our Place a reliable cookware and appliance brand? Countertop / Small Kitchen Appliances: Does a celebrity-backed kitchen brand mean better quality?1Does a celebrity-backed kitchen brand mean better quality? Countertop / Small Kitchen Appliances: Best celebrity-backed kitchen appliance brand1Best celebrity-backed kitchen appliance brand Countertop / Small Kitchen Appliances: Haden vs Smeg, which retro-style appliance brand is better?1Haden vs Smeg, which retro-style appliance brand is better? Countertop / Small Kitchen Appliances: Is Our Place worth the price compared to KitchenAid?1Is Our Place worth the price compared to KitchenAid? Countertop / Small Kitchen Appliances: Does celebrity endorsement affect how reliable a kitchen appliance brand is?1Does celebrity endorsement affect how reliable a kitchen appliance brand is? Countertop / Small Kitchen Appliances: Beautiful by Drew Barrymore vs Our Place, which is the better brand?1Beautiful by Drew Barrymore vs Our Place, which is the better brand? Countertop / Small Kitchen Appliances: General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier Countertop / Small Kitchen Appliances: Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Countertop / Small Kitchen Appliances: Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it Countertop / Small Kitchen Appliances: Primary use case1the dominant everyday use case for this category ### The exact prompts #PromptClusterShape 1What's the best refrigerator brand to buy in 2026?1_large_major_kitchen_appliances__need_1medium 2Which dishwasher brand should I buy for a family of four?1_large_major_kitchen_appliances__need_2medium 3What's the best range/oven brand for a first home?1_large_major_kitchen_appliances__need_3medium 4Which brand makes the best built-in microwave?1_large_major_kitchen_appliances__need_4medium 5What's the best affordable refrigerator brand?1_large_major_kitchen_appliances__need_5medium 6Which brand makes the best range hood?1_large_major_kitchen_appliances__need_6medium 7What's the best high-end kitchen appliance brand?1_large_major_kitchen_appliances__need_7medium 8Which brand should I buy for a full kitchen appliance package?1_large_major_kitchen_appliances__need_8medium 9KitchenAid vs Whirlpool refrigerators, which is better?1_large_major_kitchen_appliances__need_9medium 10Bosch vs GE dishwashers, which is quieter?1_large_major_kitchen_appliances__need_10medium 11Samsung vs LG refrigerators, which has fewer repairs?1_large_major_kitchen_appliances__need_11medium 12Sub-Zero vs Viking, which is the better investment?1_large_major_kitchen_appliances__need_12medium 13Frigidaire vs Maytag, which lasts longer?1_large_major_kitchen_appliances__need_13medium 14Café vs KitchenAid appliances, what's the difference?1_large_major_kitchen_appliances__need_14medium 15Miele vs Bosch dishwashers, which is more reliable?1_large_major_kitchen_appliances__need_15medium 16Electrolux vs Whirlpool ranges, which cooks more evenly?1_large_major_kitchen_appliances__need_16medium 17Most reliable refrigerator brand 20261_large_major_kitchen_appliances__need_17medium 18Which dishwasher brand breaks down the least?1_large_major_kitchen_appliances__need_18medium 19Which range brand has the best repair record?1_large_major_kitchen_appliances__need_19medium 20Which refrigerator brand has had the most recalls?1_large_major_kitchen_appliances__need_20medium 21Are Samsung refrigerators reliable?1_large_major_kitchen_appliances__need_21medium 22Which appliance brand has the best warranty support?1_large_major_kitchen_appliances__need_22medium 23Which dishwasher brand do appliance repair technicians recommend?1_large_major_kitchen_appliances__need_23medium 24Which refrigerator brand breaks down most often?1_large_major_kitchen_appliances__need_24medium 25Is a Sub-Zero refrigerator worth the price?1_large_major_kitchen_appliances__need_25medium 26Cheapest reliable dishwasher brand1_large_major_kitchen_appliances__need_26medium 27Best budget refrigerator brand1_large_major_kitchen_appliances__need_27medium 28Is KitchenAid worth the extra cost over Whirlpool?1_large_major_kitchen_appliances__need_28medium 29Most affordable range brand that still lasts1_large_major_kitchen_appliances__need_29medium 30Best value kitchen appliance package1_large_major_kitchen_appliances__need_30medium 31Is Viking worth it for a home kitchen?1_large_major_kitchen_appliances__need_31medium 32Cheapest brand for a full kitchen remodel1_large_major_kitchen_appliances__need_32medium 33Best counter-depth refrigerator brand1_large_major_kitchen_appliances__need_33medium 34Best smart refrigerator with Wi-Fi features1_large_major_kitchen_appliances__need_34medium 35Best induction range brand1_large_major_kitchen_appliances__need_35medium 36Best quiet dishwasher brand1_large_major_kitchen_appliances__need_36medium 37Best refrigerator brand for small kitchens1_large_major_kitchen_appliances__need_37medium 38Best appliance brand for matching stainless steel finishes1_large_major_kitchen_appliances__need_38medium 39Best energy-efficient refrigerator brand1_large_major_kitchen_appliances__need_39medium 40Best appliance brand for a small apartment kitchen1_large_major_kitchen_appliances__need_40medium 41Galanz vs GE, which is the better budget microwave or refrigerator brand?1_large_major_kitchen_appliances__need_41medium 42Is Galanz a reliable kitchen appliance brand?1_large_major_kitchen_appliances__need_42medium 43best kitchen appliance brands1_large_major_kitchen_appliances__general_bestshort 44best reliable appliances for everyday family kitchens1_large_major_kitchen_appliances__everydaymedium 45Which major kitchen appliance brands offer the best value for money without sacrificing reliability?1_large_major_kitchen_appliances__value_pricelong 46Which brands make the best refrigerators, dishwashers, and ranges for a busy household?1_large_major_kitchen_appliances__use_caselong 47What's the best stand mixer brand for baking?2_countertop_small_kitchen_appliances__need_1medium 48What's the best blender brand to buy?2_countertop_small_kitchen_appliances__need_2medium 49Best coffee maker brand for home use2_countertop_small_kitchen_appliances__need_3medium 50Best air fryer brand to buy in 20262_countertop_small_kitchen_appliances__need_4medium 51Best multi-cooker brand2_countertop_small_kitchen_appliances__need_5medium 52Best food processor brand2_countertop_small_kitchen_appliances__need_6medium 53Best espresso machine brand for beginners2_countertop_small_kitchen_appliances__need_7medium 54Best toaster oven brand2_countertop_small_kitchen_appliances__need_8medium 55Vitamix vs Ninja blenders, which is better?2_countertop_small_kitchen_appliances__need_9medium 56KitchenAid vs Cuisinart stand mixers, which is better?2_countertop_small_kitchen_appliances__need_10medium 57Instant Pot vs Ninja multi-cookers, which is more reliable?2_countertop_small_kitchen_appliances__need_11medium 58Breville vs Cuisinart espresso machines, which is easier to use?2_countertop_small_kitchen_appliances__need_12medium 59Nutribullet vs Ninja, which blends better?2_countertop_small_kitchen_appliances__need_13medium 60Keurig vs Breville coffee makers, which has better coffee quality?2_countertop_small_kitchen_appliances__need_14medium 61Hamilton Beach vs Oster, which brand lasts longer?2_countertop_small_kitchen_appliances__need_15medium 62Smeg vs KitchenAid, which stand mixer is better?2_countertop_small_kitchen_appliances__need_16medium 63Most reliable blender brand 20262_countertop_small_kitchen_appliances__need_17medium 64Which air fryer brand breaks down the least?2_countertop_small_kitchen_appliances__need_18medium 65Which coffee maker brand has the fewest complaints?2_countertop_small_kitchen_appliances__need_19medium 66Is Ninja a reliable brand?2_countertop_small_kitchen_appliances__need_20medium 67Which stand mixer brand lasts the longest?2_countertop_small_kitchen_appliances__need_21medium 68Which multi-cooker brand has had safety recalls?2_countertop_small_kitchen_appliances__need_22medium 69Which countertop appliance brand has the best customer service?2_countertop_small_kitchen_appliances__need_23medium 70Which blender brand burns out motors the fastest?2_countertop_small_kitchen_appliances__need_24medium 71Is Vitamix worth the price over cheaper blenders?2_countertop_small_kitchen_appliances__need_25medium 72Best budget stand mixer brand2_countertop_small_kitchen_appliances__need_26medium 73Cheapest reliable air fryer brand2_countertop_small_kitchen_appliances__need_27medium 74Is a Breville espresso machine worth the cost?2_countertop_small_kitchen_appliances__need_28medium 75Best value multi-cooker brand2_countertop_small_kitchen_appliances__need_29medium 76Most affordable coffee maker that still tastes good2_countertop_small_kitchen_appliances__need_30medium 77Is KitchenAid worth it over Hamilton Beach?2_countertop_small_kitchen_appliances__need_31medium 78Best budget food processor brand2_countertop_small_kitchen_appliances__need_32medium 79Best blender for smoothies and ice2_countertop_small_kitchen_appliances__need_33medium 80Best air fryer for a family of four2_countertop_small_kitchen_appliances__need_34medium 81Best espresso machine with a built-in grinder2_countertop_small_kitchen_appliances__need_35medium 82Best compact stand mixer for small kitchens2_countertop_small_kitchen_appliances__need_36medium 83Best multi-cooker with the most functions2_countertop_small_kitchen_appliances__need_37medium 84Best coffee maker with a built-in grinder2_countertop_small_kitchen_appliances__need_38medium 85Best quiet blender brand2_countertop_small_kitchen_appliances__need_39medium 86Best stand mixer with the most color/design options2_countertop_small_kitchen_appliances__need_40medium 87Is Beautiful by Drew Barrymore a good kitchen appliance brand?2_countertop_small_kitchen_appliances__need_41medium 88Is Our Place a reliable cookware and appliance brand?2_countertop_small_kitchen_appliances__need_42medium 89Does a celebrity-backed kitchen brand mean better quality?2_countertop_small_kitchen_appliances__need_43medium 90Best celebrity-backed kitchen appliance brand2_countertop_small_kitchen_appliances__need_44medium 91Haden vs Smeg, which retro-style appliance brand is better?2_countertop_small_kitchen_appliances__need_45medium 92Is Our Place worth the price compared to KitchenAid?2_countertop_small_kitchen_appliances__need_46medium 93Does celebrity endorsement affect how reliable a kitchen appliance brand is?2_countertop_small_kitchen_appliances__need_47medium 94Beautiful by Drew Barrymore vs Our Place, which is the better brand?2_countertop_small_kitchen_appliances__need_48medium 95best countertop appliances2_countertop_small_kitchen_appliances__general_bestshort 96best small appliances for everyday cooking2_countertop_small_kitchen_appliances__everydaymedium 97best value small kitchen appliance brands2_countertop_small_kitchen_appliances__value_pricemedium 98Which small kitchen appliance brands make the most reliable blenders, air fryers, coffee makers, and food processors for everyday home use?2_countertop_small_kitchen_appliances__use_caselong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search294294294100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool294294294100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)29429429199% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool294294294100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 3 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1KitchenAid454/117638.6%38.6%35.9%–41.4%48/1173 (4.1%) 2GE377/117632.1%32.1%29.5%–34.8%0/1173 (0%) 3Bosch363/117630.9%30.9%28.3%–33.6%0/1173 (0%) 4Whirlpool Corporation362/117630.8%30.8%28.2%–33.5%18/1173 (1.5%) 5Breville Group Ltd344/117629.3%29.3%26.7%–31.9%9/1173 (0.8%) 6LG329/117628%28%25.5%–30.6%83/1173 (7.1%) 7Samsung295/117625.1%25.1%22.7%–27.6%26/1173 (2.2%) 8Cuisinart278/117623.6%23.6%21.3%–26.2%4/1173 (0.3%) 9Frigidaire249/117621.2%21.2%18.9%–23.6%1/1173 (0.1%) 10Hamilton Beach Brands, Inc.232/117619.7%19.7%17.6%–22.1%9/1173 (0.8%) 11Miele227/117619.3%19.3%17.1%–21.7%1/1173 (0.1%) 12Ninja225/117619.1%19.1%17%–21.5%1/1173 (0.1%) 13Sub-Zero Group, Inc.130/117611.1%11.1%9.4%–13%4/1173 (0.3%) 14Maytag125/117610.6%10.6%9%–12.5%2/1173 (0.2%) 15Instant Brands LLC124/117610.5%10.5%8.9%–12.4%3/1173 (0.3%) 16Thermador108/11769.2%9.2%7.7%–11%2/1173 (0.2%) 17Yale Appliance98/11768.3%8.3%6.9%–10.1%328/1173 (28%) 18Vitamix Corporation94/11768%8%6.6%–9.7%6/1173 (0.5%) 19BLACK+DECKER Inc.93/11767.9%7.9%6.5%–9.6%0/1173 (0%) 20Smeg90/11767.7%7.7%6.3%–9.3%11/1173 (0.9%) 21Fisher & Paykel79/11766.7%6.7%5.4%–8.3%1/1173 (0.1%) 22SharkNinja, Inc.78/11766.6%6.6%5.3%–8.2%9/1173 (0.8%) 23Blendtec LLC71/11766%6%4.8%–7.5%4/1173 (0.3%) 24Philips71/11766%6%4.8%–7.5%1/1173 (0.1%) 25NutriBullet65/11765.5%5.5%4.4%–7%1/1173 (0.1%) 26COSORI65/11765.5%5.5%4.4%–7%1/1173 (0.1%) 27De'Longhi63/11765.4%5.4%4.2%–6.8%0/1173 (0%) 28Oster59/11765%5%3.9%–6.4%2/1173 (0.2%) 29Kenwood59/11765%5%3.9%–6.4%4/1173 (0.3%) 30Café54/11764.6%4.6%3.5%–5.9%3/1173 (0.3%) 31Viking51/11764.3%4.3%3.3%–5.7%3/1173 (0.3%) 32Haier Group51/11764.3%4.3%3.3%–5.7%0/1173 (0%) 33Electrolux Group50/11764.3%4.3%3.2%–5.6%0/1173 (0%) 34RTINGS47/11764%4%3%–5.3%237/1173 (20.2%) 35Wolf46/11763.9%3.9%2.9%–5.2%4/1173 (0.3%) 36J.D. Power46/11763.9%3.9%2.9%–5.2%98/1173 (8.4%) 37Sources44/11763.7%3.7%2.8%–5%0/1173 (0%) 38Ankarsrum44/11763.7%3.7%2.8%–5%18/1173 (1.5%) 39Short answer42/11763.6%3.6%2.7%–4.8%domain not established — excluded from citation metrics 40JennAir39/11763.3%3.3%2.4%–4.5%2/1173 (0.2%) 41Keurig37/11763.1%3.1%2.3%–4.3%1/1173 (0.1%) 42Dash Brands, Inc36/11763.1%3.1%2.2%–4.2%0/1173 (0%) 43Beautiful by Drew Barrymore34/11762.9%2.9%2.1%–4%4/1173 (0.3%) 44Galanz25/11762.1%2.1%1.4%–3.1%7/1173 (0.6%) 45Haden11/11760.9%0.9%0.5%–1.7%1/1173 (0.1%) 46Hobart8/11760.7%0.7%0.3%–1.3%0/1173 (0%) 47Our Place0/11760%0%0%–0.3%14/1173 (1.2%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-04. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. - 1 of 47 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero. ## The segment reports in this family Each segment below was measured separately, with its own brand panel and its own question set, and has its own report. The figures in this table come from those segment measurements. ### Segment reports SegmentReportBrands measuredAnswers measuredMost-named brand Large / Major Kitchen AppliancesMajor Kitchen Appliances AI Visibility Index 202615552GE (61.6%) Countertop / Small Kitchen AppliancesSmall Countertop Appliances AI Visibility Index 202616624Breville Group Ltd (54.6%) ### Where this index is linked from - https://www.5wpr.com/practice/consumer-health-and-wellness.cfm - https://www.5wpr.com/ai-visibility-index/ --- ### Major Kitchen Appliances AI Visibility Index 2026 URL: https://www.5wpr.com/ai-visibility-index/major-appliances-ai-visibility-index-2026/ Summary: Large appliance AI visibility data shows GE leads answer appearances, with engine disagreement and third-party publishers shaping citations. # Major Kitchen Appliances AI Visibility Index 2026 GE Leads Major Appliance AI Mentions Part of the Kitchen Appliances AI Visibility Index 2026. Companion report: Small Countertop Appliances AI Visibility Index 2026. Large appliance AI visibility is led by GE, which appeared in 61.6% of 552 returned answers, with a 95% interval of 57.5% to 65.6%. Whirlpool Corporation, LG, Bosch, and Samsung follow within overlapping intervals, so this sample does not establish that GE is more visible than those brands. The measurement covers refrigerators, dishwashers, ranges, and built-in microwaves. It is part of the Kitchen Appliances AI Visibility Index 2026 and does not cover Countertop / Small Kitchen Appliances. For brands, the practical issue is not a single rank. Perplexity, ChatGPT, Gemini, and Claude named the same brands at different rates. Citation evidence also leaned heavily on third-party publishers, while LG had the highest owned-domain citation rate among the leading appliance brands. 552AI answers analyzed 61.6%GE appearance rate, the category leader 25Brands named across the study 99.8%Answers carrying retrieval evidence The opening pattern is broad recognition for the leading brands, but the sample margin means the top tier should be read as a competitive group rather than a settled order. ## Which brands lead the major appliance leaderboard? GE ranks first at 61.6% appearance, followed by Whirlpool Corporation at 58.3%, LG at 57.1%, Bosch at 55.8%, and Samsung at 51.4%. These appearance rates use 552 returned answers as the denominator. Frigidaire and KitchenAid form the next tier at 42.9% and 42.4%. The leaderboard measures whether a brand was named, not whether an answer endorsed it. Yale Appliance appears at rank 12 with 17.2% appearance, yet its owned domain has the highest citation rate in the measured field. ### Major appliance appearance leaderboard ### AI visibility leaderboard Appearance rate is the share of the 552 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1GE340/55261.6%57.5%–65.6%4.9 2Whirlpool Corporation322/55258.3%54.2%–62.4%5.1 3LG315/55257.1%52.9%–61.1%5.2 4Bosch308/55255.8%51.6%–59.9%3.8 5Samsung284/55251.4%47.3%–55.6%7.2 6Frigidaire237/55242.9%38.9%–47.1%7.1 7KitchenAid234/55242.4%38.3%–46.6%5.8 8Miele212/55238.4%34.4%–42.5%3.9 9Sub-Zero Group, Inc.126/55222.8%19.5%–26.5%4.2 10Maytag125/55222.6%19.3%–26.3%6.4 11Thermador108/55219.6%16.5%–23.1%6.1 12Yale Appliance95/55217.2%14.3%–20.6%5.9 13Fisher & Paykel76/55213.8%11.1%–16.9%7.9 14Café51/5529.2%7.1%–11.9%6.5 15Viking50/5529.1%6.9%–11.7%8.6 GE's 95% interval overlaps Whirlpool Corporation, LG, Bosch, and Samsung. Whirlpool Corporation's interval also overlaps LG, Bosch, and Samsung. The differences within this leading group are within the margin of this sample. ## How do the leading brands appear across AI answers? The leading brands appear across all four engines, but their strongest engine differs. The profiles below use appearance rates based on returned answers and owned-domain citation rates based on 551 answers with retrieval evidence. ### GE 61.6% appearance rate, with a 95% interval of 57.5% to 65.6%, places GE first. Perplexity named GE in 82.6% of its 138 answers and ChatGPT in 81.9%, compared with 47.1% for Gemini and 34.8% for Claude. GE's average first mention position was 4.9. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### Whirlpool Corporation 58.3% appearance rate, with a 95% interval of 54.2% to 62.4%, places Whirlpool Corporation second. Perplexity named Whirlpool Corporation in 81.9% of its 138 answers, ChatGPT in 77.5%, Gemini in 49.3%, and Claude in 24.6%. Its average first mention position was 5.1. Whirlpool Corporation's owned-domain citation rate was 3.3% among answers with retrieval evidence. ### LG 57.1% appearance rate, with a 95% interval of 52.9% to 61.1%, places LG third. Perplexity and ChatGPT each named LG in 76.1% of their 138 answers, while Gemini named it in 50% and Claude in 26.1%. LG's average first mention position was 5.2. Its owned-domain citation rate was 12.7% among answers with retrieval evidence. ### Bosch 55.8% appearance rate, with a 95% interval of 51.6% to 59.9%, places Bosch fourth. Perplexity named Bosch in 77.5% of its 138 answers and ChatGPT in 73.2%, compared with 37% for Gemini and 35.5% for Claude. Bosch had an average first mention position of 3.8, the earliest among the leading brands profiled here. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### Samsung 51.4% appearance rate, with a 95% interval of 47.3% to 55.6%, places Samsung fifth. ChatGPT named Samsung in 81.2% of its 138 answers, higher than Perplexity at 69.6%, Gemini at 42%, and Claude at 13%. Samsung's average first mention position was 7.2. Its owned-domain citation rate was 3.6% among answers with retrieval evidence. ### Frigidaire 42.9% appearance rate, with a 95% interval of 38.9% to 47.1%, places Frigidaire sixth. ChatGPT named Frigidaire in 65.9% of its 138 answers and Perplexity in 64.5%, while Gemini named it in 27.5% and Claude in 13.8%. Its average first mention position was 7.1. Frigidaire's owned-domain citation rate was 0.2% among answers with retrieval evidence. ### KitchenAid 42.4% appearance rate, with a 95% interval of 38.3% to 46.6%, places KitchenAid seventh. Perplexity named KitchenAid in 65.2% of its 138 answers and ChatGPT in 61.6%, while Gemini named it in 27.5% and Claude in 15.2%. KitchenAid's average first mention position was 5.8. Its owned-domain citation rate was 2.7% among answers with retrieval evidence. ### Miele 38.4% appearance rate, with a 95% interval of 34.4% to 42.5%, places Miele eighth. ChatGPT named Miele in 60.1% of its 138 answers, versus 39.1% for Perplexity, 35.5% for Gemini, and 18.8% for Claude. Miele's average first mention position was 3.9. Its owned-domain citation rate was 0.2% among answers with retrieval evidence. ## Where do the AI engines disagree? The AI engines disagree materially on brand naming rates. Each engine returned 138 answers. Perplexity, ChatGPT, and Claude had retrieval evidence for all 138 answers, while Gemini had retrieval evidence for 137 of 138 answers. Differences can reflect each system's model and retrieval tool, rather than a universal measure of brand visibility. ### Engine-by-brand appearance pattern ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate GE82.6%81.9%47.1%34.8% Whirlpool Corporation81.9%77.5%49.3%24.6% LG76.1%76.1%50%26.1% Bosch77.5%73.2%37%35.5% Samsung69.6%81.2%42%13% Frigidaire64.5%65.9%27.5%13.8% KitchenAid65.2%61.6%27.5%15.2% Miele39.1%60.1%35.5%18.8% Sub-Zero Group, Inc.30.4%26.1%25.4%9.4% Maytag42.8%34.1%7.2%6.5% Thermador34.1%23.9%10.1%10.1% Yale Appliance2.2%5.8%58%2.9% Yale Appliance is the sharpest single-engine pattern. Gemini named Yale Appliance in 58% of its 138 answers, while Perplexity named it in 2.2%, ChatGPT in 5.8%, and Claude in 2.9%. J.D. Power appeared in 20.3% of Gemini answers and 9.4% of ChatGPT answers, but not in Perplexity or Claude answers. Samsung's strongest engine was ChatGPT at 81.2%, while Claude named it in 13%. Other gaps point in different directions. Fisher & Paykel reached 26.1% in Perplexity but 11.6% in ChatGPT, 12.3% in Gemini, and 5.1% in Claude. Midea Group reached 13% in Perplexity but 2.2% in ChatGPT and Gemini, and 0.7% in Claude. Café reached 17.4% in Perplexity, 13.8% in Gemini, 3.6% in Claude, and 2.2% in ChatGPT. Haier Group and Hisense Group were absent from Claude's 138 answers. Claude named the leading generalist brands less often than Perplexity and ChatGPT. GE appeared in 34.8% of Claude answers, Whirlpool Corporation in 24.6%, LG in 26.1%, and Bosch in 35.5%. These are engine-level results for the measured questions, not a claim about all appliance searches. ## Which brands lead each type of appliance question? Demand clusters show where brand naming is strongest within the measured question groups. The cluster results separate general buyer-intent questions from more specific appliance needs, so the groups should not be read as interchangeable measures of demand. ### Brand leaders by demand cluster ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Major Appliances - Comparison intent — Electrolux vs Whirlpool ranges, which cooks more evenly?12Whirlpool Corporation83.3%Electrolux Group 83.3%, Bosch 75%, GE 58.3% Bosch vs GE dishwashers, which is quieter?12Bosch83.3%GE 75%, Whirlpool Corporation 66.7%, Miele 66.7% KitchenAid vs Whirlpool refrigerators, which is better?12Whirlpool Corporation83.3%KitchenAid 83.3%, GE 66.7%, LG 66.7% Sub-Zero vs Viking, which is the better investment?12Sub-Zero Group, Inc.83.3%Viking 75%, Miele 66.7%, Thermador 58.3% Café vs KitchenAid appliances, what's the difference?12GE75%KitchenAid 75%, Café 75%, Whirlpool Corporation 66.7% Frigidaire vs Maytag, which lasts longer?12Maytag91.7%Frigidaire 75%, GE 66.7%, Whirlpool Corporation 66.7% Samsung vs LG refrigerators, which has fewer repairs?12Samsung91.7%LG 83.3%, GE 75%, Whirlpool Corporation 58.3% Miele vs Bosch dishwashers, which is more reliable?12Miele83.3%Bosch 75%, Whirlpool Corporation 66.7%, KitchenAid 58.3% — Major Appliances - Trust/reliability intent — Which range brand has the best repair record?12GE100%LG 91.7%, Whirlpool Corporation 75%, Bosch 75% Which refrigerator brand has had the most recalls?12Frigidaire58.3%Maytag 58.3%, GE 50%, Haier Group 50% Are Samsung refrigerators reliable?12Samsung75%Whirlpool Corporation 58.3%, LG 58.3%, GE 50% Which appliance brand has the best warranty support?12GE75%Bosch 58.3%, Miele 58.3%, Sub-Zero Group, Inc. 58.3% Which dishwasher brand do appliance repair technicians recommend?12GE83.3%Whirlpool Corporation 83.3%, Bosch 83.3%, KitchenAid 83.3% Which refrigerator brand breaks down most often?12Bosch66.7%Samsung 66.7%, Sub-Zero Group, Inc. 66.7%, GE 58.3% Which dishwasher brand breaks down the least?12Bosch100%Miele 100%, KitchenAid 75%, GE 66.7% Most reliable refrigerator brand 202612GE83.3%Whirlpool Corporation 83.3%, Bosch 75%, Sub-Zero Group, Inc. 75% — Major Appliances - Cost intent — Best budget refrigerator brand12Whirlpool Corporation83.3%Frigidaire 83.3%, GE 75%, LG 58.3% Cheapest reliable dishwasher brand12Bosch83.3%Whirlpool Corporation 66.7%, GE 58.3%, LG 58.3% Is a Sub-Zero refrigerator worth the price?12Miele75%Sub-Zero Group, Inc. 75%, LG 66.7%, GE 58.3% Is KitchenAid worth the extra cost over Whirlpool?12Whirlpool Corporation83.3%KitchenAid 66.7%, GE 58.3%, LG 58.3% Most affordable range brand that still lasts12GE75%Whirlpool Corporation 75%, LG 75%, Frigidaire 75% Best value kitchen appliance package12GE75%Whirlpool Corporation 66.7%, Bosch 66.7%, LG 58.3% Is Viking worth it for a home kitchen?12Viking75%Thermador 58.3%, Bosch 50%, KitchenAid 41.7% Cheapest brand for a full kitchen remodel12Whirlpool Corporation25%Samsung 25%, Frigidaire 25%, KitchenAid 25% — Major Appliances - Feature-fit intent — Best counter-depth refrigerator brand12LG91.7%GE 83.3%, Bosch 83.3%, Samsung 75% Best smart refrigerator with Wi-Fi features12GE75%LG 75%, Samsung 75%, Whirlpool Corporation 66.7% Best induction range brand12GE91.7%LG 91.7%, Samsung 83.3%, Bosch 75% Best quiet dishwasher brand12Miele75%Bosch 66.7%, Samsung 66.7%, KitchenAid 66.7% Best appliance brand for matching stainless steel finishes12LG83.3%Samsung 83.3%, KitchenAid 83.3%, Whirlpool Corporation 75% Best refrigerator brand for small kitchens12Bosch83.3%GE 75%, Whirlpool Corporation 75%, LG 75% Best appliance brand for a small apartment kitchen12Bosch75%Frigidaire 66.7%, Whirlpool Corporation 58.3%, Miele 50% Best energy-efficient refrigerator brand12Fisher & Paykel75%Samsung 58.3%, GE 50%, LG 50% — Major Appliances - Purchase intent — What's the best refrigerator brand to buy in 2026?12LG58.3%GE 50%, Whirlpool Corporation 50%, Bosch 50% Which dishwasher brand should I buy for a family of four?12Bosch100%Miele 83.3%, KitchenAid 75%, GE 58.3% What's the best range/oven brand for a first home?12GE100%Whirlpool Corporation 100%, LG 83.3%, Bosch 75% What's the best affordable refrigerator brand?12Whirlpool Corporation91.7%GE 83.3%, LG 83.3%, Samsung 66.7% Which brand makes the best built-in microwave?12GE91.7%KitchenAid 83.3%, LG 58.3%, Bosch 58.3% Which brand makes the best range hood?12GE25%Bosch 25%, Frigidaire 25%, KitchenAid 16.7% What's the best high-end kitchen appliance brand?12Sub-Zero Group, Inc.100%Miele 91.7%, Gaggenau 91.7%, Thermador 75% Which brand should I buy for a full kitchen appliance package?12LG83.3%GE 75%, Whirlpool Corporation 75%, Bosch 75% — Major Appliances - Value/newcomer-brand intent — Galanz vs GE, which is the better budget microwave or refrigerator brand?12GE83.3%Whirlpool Corporation 75%, Frigidaire 75%, Galanz 75% Is Galanz a reliable kitchen appliance brand?12Galanz75%GE 50%, Whirlpool Corporation 50%, LG 41.7% — Other demand — Price and value12GE75%Whirlpool Corporation 75%, LG 58.3%, Samsung 58.3% Primary use case12GE66.7%Whirlpool Corporation 66.7%, LG 66.7%, Bosch 66.7% Everyday and mainstream choice12Bosch75%GE 66.7%, LG 66.7%, Whirlpool Corporation 58.3% General best-in-category12LG83.3%Whirlpool Corporation 66.7%, KitchenAid 66.7%, Bosch 58.3% For general buyer-intent questions, the cluster view identifies the brands most often brought into broad selection discussions. For appliance-specific needs, the same chart identifies brands that surface when the question narrows. A strong cluster result is a prompt-set result, not evidence that a brand leads every refrigerator, dishwasher, range, or built-in microwave decision. ## Which sites supply the citations? Owned-domain citations were uneven. Yale Appliance had a 54.6% owned-domain citation rate among 551 answers with retrieval evidence. J.D. Power followed at 14%, while LG had 12.7%. Among the leading appliance brands, LG's owned-domain rate was higher than Samsung at 3.6%, Whirlpool Corporation at 3.3%, KitchenAid at 2.7%, Frigidaire at 0.2%, and Miele at 0.2%. ### Owned-domain citation rates ### Owned-domain citation rate Share of the 551 answers with retrieval evidence that cited a URL on the brand's own website. GE 0% · 0/551 Whirlpool Corporation 3.3% · 18/551 LG 12.7% · 70/551 Bosch 0% · 0/551 Samsung 3.6% · 20/551 Frigidaire 0.2% · 1/551 KitchenAid 2.7% · 15/551 Miele 0.2% · 1/551 Sub-Zero Group, Inc. 0.7% · 4/551 Maytag 0.4% · 2/551 Thermador 0.4% · 2/551 Yale Appliance 54.6% · 301/551 Third-party publishers carried much of the cited evidence. ConsumerReports.org appeared in 52.3% of answers with retrieval evidence, followed by blog.yaleappliance.com at 35.8%, Reviewed.com at 31.2%, and YouTube.com and Reddit.com at 23.2% each. The pattern means a brand can be named often without having its own site cited often. ### Most-cited publisher domains ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration consumerreports.org288/55152.3% blog.yaleappliance.com197/55135.8% reviewed.com172/55131.2% youtube.com128/55123.2% reddit.com128/55123.2% qz.com115/55120.9% slashgear.com114/55120.7% easybear-appliancerepair.com108/55119.6% yaleappliance.com104/55118.9% rtings.com103/55118.7% prudentreviews.com87/55115.8% goodhousekeeping.com86/55115.6% ConsumerReports.org had 288 cited mentions, while blog.yaleappliance.com had 197 and Reviewed.com had 172. These rates use 551 answers with retrieval evidence as the denominator. ## Which brands are winning and which are at risk? ### Winning measured positions GE is in the leading group with a 61.6% appearance rate and presence across all four engines. Whirlpool Corporation, LG, Bosch, and Samsung are also in that leading group at 58.3%, 57.1%, 55.8%, and 51.4%, respectively, though their confidence intervals overlap with GE's interval. Bosch's 3.8 average first mention position is early within the top group. LG's 12.7% owned-domain citation rate is the strongest among these leading appliance brands. ### At-risk measured positions Yale Appliance has a narrow engine concentration: Gemini named it in 58% of answers, compared with 2.2%, 5.8%, and 2.9% on the other engines. Samsung has a 68.2-point spread between ChatGPT at 81.2% and Claude at 13%. Café appears in 9.2% of returned answers, and Viking in 9.1%. Galanz appears in 4.2%, with a 95% interval of 2.8% to 6.2%. ## What does the tally say about major appliances? The category has a crowded top tier and a steep drop after it. GE, Whirlpool Corporation, LG, Bosch, and Samsung all appear in more than half of returned answers, while Frigidaire and KitchenAid appear in the low forties. GE's 95% interval overlaps those of ranks two through five, so the available sample does not separate the top brand from that group. The engines converge on the presence of many brands but not on frequency. GE, Whirlpool Corporation, LG, Bosch, Samsung, Frigidaire, KitchenAid, Miele, Sub-Zero Group, Maytag, Thermador, Yale Appliance, Fisher & Paykel, Café, Viking, Electrolux Group, JennAir, Wolf, Gaggenau, Amana, Midea Group, and Galanz each had 100% cross-engine consistency. Haier Group and Hisense Group had 75%, while J.D. Power had 50%. Citations favor publishers outside brand-owned sites. ConsumerReports.org, blog.yaleappliance.com, Reviewed.com, YouTube.com, and Reddit.com each appeared in at least 23.2% of answers with retrieval evidence. Among brands named in the brief, Galanz was measured at 4.2% appearance. No other named brand from the brief was measured near zero. ## What changed during the measurement window? The measurement records one fielded window, not changes over time. It shows engine-level divergence, including Yale Appliance's 58% Gemini appearance rate and lower rates on the other engines, but it does not measure a before-and-after shift. ## What actions address the measured gaps? - Brands with high appearance and 0% owned-domain citation rates should audit the pages behind refrigerator, dishwasher, range, and built-in microwave claims. Re-measure their owned-domain citation rate across answers with retrieval evidence. - GE and Bosch should test whether their 0% owned-domain citation rates change after their product and comparison pages make sourceable claims easier to locate. Re-measure both citation rate and share of all citations. - Samsung should examine the gap between ChatGPT at 81.2% and Claude at 13%. Re-measure by engine after updating the same product-selection evidence across its own pages. - Yale Appliance should test whether its 54.6% owned-domain citation rate remains concentrated in Gemini, where it appears in 58% of answers. Re-measure engine-level appearance and citation rates separately. - Frigidaire and KitchenAid should test whether their lower Gemini and Claude appearance rates rise from 27.5% and 13.8% for Frigidaire, and 27.5% and 15.2% for KitchenAid. Re-measure the same engine cells after changes. - Café, Viking, and Galanz should identify the demand clusters where they are absent or rarely named. Re-measure their cluster appearance after publishing pages that answer those measured selection questions. ## What does this mean for brands in the category? Interpretation: major appliance visibility in these answers depends on both broad brand naming and the source material each engine selects. A high overall appearance rate does not guarantee an owned citation. A high owned-domain citation rate can also be concentrated in one engine, as Yale Appliance's engine pattern shows. Interpretation: brands need separate scorecards for answer appearance, first mention position, engine spread, cluster performance, and owned-domain citations. Treating any one metric as a complete view would hide the differences present in this measurement. ## How was this major appliance visibility measured? This flagship study measured 46 prompts three times across Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). The study returned 552 answers. Appearance rates use 552 returned answers as the denominator. Citation and source rates use 551 answers with retrieval evidence as the denominator. ### Measurement summary ### How this study was measured - Sampling frame fixed first: 46 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each. - 552 answers returned of 552 attempted, 551 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The measurement captures these prompts on the recorded date, not all consumer selection behavior or all appliance searches. Brand names were rolled up where the measured tally identified variants. The results describe naming and cited sources, not product quality or consumer demand. ## Frequently asked questions ### Which major appliance brand appeared most often? GE ranked first with a 61.6% appearance rate across 552 returned answers. Its 95% interval was 57.5% to 65.6%. ### Is GE clearly ahead of Whirlpool Corporation, LG, Bosch, and Samsung? No. GE's 95% interval overlaps the intervals for Whirlpool Corporation, LG, Bosch, and Samsung. The differences within that group are within the margin of this sample. ### Which leading brand had the strongest owned-domain citation rate? LG had a 12.7% owned-domain citation rate among the leading appliance brands. This rate uses 551 answers with retrieval evidence as the denominator. ### Which source domain appeared most often in cited answers? ConsumerReports.org appeared in 288 answers with retrieval evidence. Its answer penetration was 52.3%. ### Which engine named Yale Appliance most often? Gemini named Yale Appliance in 58% of its 138 answers. Perplexity named it in 2.2%, ChatGPT in 5.8%, and Claude in 2.9%. ### Does this report cover small kitchen appliances? No. This report covers Large / Major Kitchen Appliances. Countertop / Small Kitchen Appliances are measured in separate reports. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Consumer Reports6%33/5524.3%–8.3% 2Reviewed.com3.8%21/5522.5%–5.7% 3RTINGS.com3.4%19/5522.2%–5.3% 4Wirecutter0.7%4/5520.3%–1.8% 5Home Depot0.2%1/5520%–1% 6US News 360 Reviews0%0/5520%–0.7% 7Good Housekeeping0%0/5520%–0.7% 8Amazon0%0/5520%–0.7% 9Wayfair0%0/5520%–0.7% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Large / Major Kitchen Appliances - Measurement date: 2026-10-04 - Depth: flagship — 46 prompts, 3 trial(s) per prompt per engine, 4 engines - Answers attempted: 552 - Answers returned (appearance denominator): 552 - Answers with retrieval evidence (citation denominator): 551 (99.8% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable. ### How the prompts were sampled Fixed sampling frame: 42 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Major Appliances - Purchase intent / Major Appliances - Comparison intent / Major Appliances - Trust/reliability intent / Major Appliances - Cost intent / Major Appliances - Feature-fit intent / Major Appliances - Value/newcomer-brand intent; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope What's the best refrigerator brand to buy in 2026?1What's the best refrigerator brand to buy in 2026? Which dishwasher brand should I buy for a family of four?1Which dishwasher brand should I buy for a family of four? What's the best range/oven brand for a first home?1What's the best range/oven brand for a first home? Which brand makes the best built-in microwave?1Which brand makes the best built-in microwave? What's the best affordable refrigerator brand?1What's the best affordable refrigerator brand? Which brand makes the best range hood?1Which brand makes the best range hood? What's the best high-end kitchen appliance brand?1What's the best high-end kitchen appliance brand? Which brand should I buy for a full kitchen appliance package?1Which brand should I buy for a full kitchen appliance package? KitchenAid vs Whirlpool refrigerators, which is better?1KitchenAid vs Whirlpool refrigerators, which is better? Bosch vs GE dishwashers, which is quieter?1Bosch vs GE dishwashers, which is quieter? Samsung vs LG refrigerators, which has fewer repairs?1Samsung vs LG refrigerators, which has fewer repairs? Sub-Zero vs Viking, which is the better investment?1Sub-Zero vs Viking, which is the better investment? Frigidaire vs Maytag, which lasts longer?1Frigidaire vs Maytag, which lasts longer? Café vs KitchenAid appliances, what's the difference?1Café vs KitchenAid appliances, what's the difference? Miele vs Bosch dishwashers, which is more reliable?1Miele vs Bosch dishwashers, which is more reliable? Electrolux vs Whirlpool ranges, which cooks more evenly?1Electrolux vs Whirlpool ranges, which cooks more evenly? Most reliable refrigerator brand 20261Most reliable refrigerator brand 2026 Which dishwasher brand breaks down the least?1Which dishwasher brand breaks down the least? Which range brand has the best repair record?1Which range brand has the best repair record? Which refrigerator brand has had the most recalls?1Which refrigerator brand has had the most recalls? Are Samsung refrigerators reliable?1Are Samsung refrigerators reliable? Which appliance brand has the best warranty support?1Which appliance brand has the best warranty support? Which dishwasher brand do appliance repair technicians recommend?1Which dishwasher brand do appliance repair technicians recommend? Which refrigerator brand breaks down most often?1Which refrigerator brand breaks down most often? Is a Sub-Zero refrigerator worth the price?1Is a Sub-Zero refrigerator worth the price? Cheapest reliable dishwasher brand1Cheapest reliable dishwasher brand Best budget refrigerator brand1Best budget refrigerator brand Is KitchenAid worth the extra cost over Whirlpool?1Is KitchenAid worth the extra cost over Whirlpool? Most affordable range brand that still lasts1Most affordable range brand that still lasts Best value kitchen appliance package1Best value kitchen appliance package Is Viking worth it for a home kitchen?1Is Viking worth it for a home kitchen? Cheapest brand for a full kitchen remodel1Cheapest brand for a full kitchen remodel Best counter-depth refrigerator brand1Best counter-depth refrigerator brand Best smart refrigerator with Wi-Fi features1Best smart refrigerator with Wi-Fi features Best induction range brand1Best induction range brand Best quiet dishwasher brand1Best quiet dishwasher brand Best refrigerator brand for small kitchens1Best refrigerator brand for small kitchens Best appliance brand for matching stainless steel finishes1Best appliance brand for matching stainless steel finishes Best energy-efficient refrigerator brand1Best energy-efficient refrigerator brand Best appliance brand for a small apartment kitchen1Best appliance brand for a small apartment kitchen Galanz vs GE, which is the better budget microwave or refrigerator brand?1Galanz vs GE, which is the better budget microwave or refrigerator brand? Is Galanz a reliable kitchen appliance brand?1Is Galanz a reliable kitchen appliance brand? General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case1the dominant everyday use case for this category ### The exact prompts #PromptClusterShape 1What's the best refrigerator brand to buy in 2026?need_1medium 2Which dishwasher brand should I buy for a family of four?need_2medium 3What's the best range/oven brand for a first home?need_3medium 4Which brand makes the best built-in microwave?need_4medium 5What's the best affordable refrigerator brand?need_5medium 6Which brand makes the best range hood?need_6medium 7What's the best high-end kitchen appliance brand?need_7medium 8Which brand should I buy for a full kitchen appliance package?need_8medium 9KitchenAid vs Whirlpool refrigerators, which is better?need_9medium 10Bosch vs GE dishwashers, which is quieter?need_10medium 11Samsung vs LG refrigerators, which has fewer repairs?need_11medium 12Sub-Zero vs Viking, which is the better investment?need_12medium 13Frigidaire vs Maytag, which lasts longer?need_13medium 14Café vs KitchenAid appliances, what's the difference?need_14medium 15Miele vs Bosch dishwashers, which is more reliable?need_15medium 16Electrolux vs Whirlpool ranges, which cooks more evenly?need_16medium 17Most reliable refrigerator brand 2026need_17medium 18Which dishwasher brand breaks down the least?need_18medium 19Which range brand has the best repair record?need_19medium 20Which refrigerator brand has had the most recalls?need_20medium 21Are Samsung refrigerators reliable?need_21medium 22Which appliance brand has the best warranty support?need_22medium 23Which dishwasher brand do appliance repair technicians recommend?need_23medium 24Which refrigerator brand breaks down most often?need_24medium 25Is a Sub-Zero refrigerator worth the price?need_25medium 26Cheapest reliable dishwasher brandneed_26medium 27Best budget refrigerator brandneed_27medium 28Is KitchenAid worth the extra cost over Whirlpool?need_28medium 29Most affordable range brand that still lastsneed_29medium 30Best value kitchen appliance packageneed_30medium 31Is Viking worth it for a home kitchen?need_31medium 32Cheapest brand for a full kitchen remodelneed_32medium 33Best counter-depth refrigerator brandneed_33medium 34Best smart refrigerator with Wi-Fi featuresneed_34medium 35Best induction range brandneed_35medium 36Best quiet dishwasher brandneed_36medium 37Best refrigerator brand for small kitchensneed_37medium 38Best appliance brand for matching stainless steel finishesneed_38medium 39Best energy-efficient refrigerator brandneed_39medium 40Best appliance brand for a small apartment kitchenneed_40medium 41Galanz vs GE, which is the better budget microwave or refrigerator brand?need_41medium 42Is Galanz a reliable kitchen appliance brand?need_42medium 43best kitchen appliance brandsgeneral_bestshort 44best reliable appliances for everyday family kitchenseverydaymedium 45Which major kitchen appliance brands offer the best value for money without sacrificing reliability?value_pricelong 46Which brands make the best refrigerators, dishwashers, and ranges for a busy household?use_caselong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search138138138100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool138138138100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)13813813799.3% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool138138138100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 3 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1GE340/55261.6%61.6%57.5%–65.6%0/551 (0%) 2Whirlpool Corporation322/55258.3%58.3%54.2%–62.4%18/551 (3.3%) 3LG315/55257.1%57.1%52.9%–61.1%70/551 (12.7%) 4Bosch308/55255.8%55.8%51.6%–59.9%0/551 (0%) 5Samsung284/55251.4%51.5%47.3%–55.6%20/551 (3.6%) 6Frigidaire237/55242.9%42.9%38.9%–47.1%1/551 (0.2%) 7KitchenAid234/55242.4%42.4%38.3%–46.6%15/551 (2.7%) 8Miele212/55238.4%38.4%34.4%–42.5%1/551 (0.2%) 9Sub-Zero Group, Inc.126/55222.8%22.8%19.5%–26.5%4/551 (0.7%) 10Maytag125/55222.6%22.6%19.3%–26.3%2/551 (0.4%) 11Thermador108/55219.6%19.6%16.5%–23.1%2/551 (0.4%) 12Yale Appliance95/55217.2%17.2%14.3%–20.6%301/551 (54.6%) 13Fisher & Paykel76/55213.8%13.8%11.1%–16.9%1/551 (0.2%) 14Café51/5529.2%9.2%7.1%–11.9%3/551 (0.5%) 15Viking50/5529.1%9.1%6.9%–11.7%3/551 (0.5%) 16Electrolux Group50/5529.1%9.1%6.9%–11.7%0/551 (0%) 17Haier Group48/5528.7%8.7%6.6%–11.3%0/551 (0%) 18J.D. Power41/5527.4%7.4%5.5%–9.9%77/551 (14%) 19JennAir39/5527.1%7.1%5.2%–9.5%2/551 (0.4%) 20Wolf37/5526.7%6.7%4.9%–9.1%4/551 (0.7%) 21Gaggenau33/5526%6%4.3%–8.3%0/551 (0%) 22Amana28/5525.1%5.1%3.5%–7.2%0/551 (0%) 23Midea Group25/5524.5%4.5%3.1%–6.6%0/551 (0%) 24Galanz23/5524.2%4.2%2.8%–6.2%7/551 (1.3%) 25Hisense Group23/5524.2%4.2%2.8%–6.2%0/551 (0%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-04. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. --- ### The 2026 Legal AI Visibility Report URL: https://www.5wpr.com/research/legal-ai-visibility-index-2026/ Summary: The 5W Legal AI Visibility Report ranks law firms and legal tech companies by AI citation share across ChatGPT, Claude, Perplexity and Gemini. # The 2026 Legal AI Visibility Report ### How Generative AI Is Reshaping How Clients Find Lawyers — And Why the Industry Is Six Quarters Behind the Shift By Haute Lawyer Network and 5W — April 2026 Download the Full Report (PDF — 31 pages, Free) ## EXECUTIVE SUMMARY The legal industry has become one of the most internally AI-adopted professions in America. Per Clio's 2025 Legal Trends Report, 79% of legal professionals now use AI tools. Large firms lead at 87%. Solo firms at 71%. The SurePoint 2025 State of the Legal Industry report documents that a majority of mid-sized firms have formally adopted generative AI. At the same time, the legal industry is almost invisible to AI. When a consumer or business asks ChatGPT, Claude, Perplexity, or Google AI Mode to recommend a lawyer, a firm, or a specialist — the answer comes from a tight cartel of approximately seven ranking directories. Chambers, Legal 500, Super Lawyers, Best Lawyers, Martindale, Avvo, and Justia dominate the AI citation layer across virtually every legal query category. Individual law firms — including the most prestigious ones in America — appear inside those directories but rarely as independent voices. Zero law-focused editorial sources appear in the top results for any legal query we tested. That gap between internal adoption and external visibility is the paradox at the center of this report. Lawyers use AI to work faster. They have not yet addressed whether AI will find them. And the window during which they can address this is shorter than most firms realize. ## KEY FINDINGS 79% — Share of legal professionals using AI tools (Clio 2025 Legal Trends Report). Large firms at 87%, solo firms at 71%. 23.6% — Share of legal queries now triggering Google AI Overviews. For question-style legal queries: 57.9% (Ahrefs analysis of 146M SERPs). 487 — AI hallucination cases documented in US court filings in 2025 — 10x the 2024 total. 37.8% involved licensed attorneys (Charlotin database, via SurePoint 2025). 50% — Share of Am Law 100 firms on Harvey, the category-leading legal AI platform ($8B valuation, December 2025). 1 million — Users reached by Thomson Reuters CoCounsel Legal by February 2026. $408 billion — US legal services market projected for 2025 (Grand View Research). Approximately $300 billion is AI-addressable. 7 directories — The cartel that owns the legal AI citation layer: Chambers, Legal 500, Super Lawyers, Best Lawyers, Martindale, Avvo, Justia. 24 months — The window for establishing low-cost citation authority before competitive density closes it. ## THE ADOPTION PARADOX The legal industry has crossed a meaningful threshold in the past 18 months. Per the AffiniPay 2025 Legal Industry Report, 31% of individual lawyers use generative AI personally for work. Per the ABA 2025 Technology Survey, adoption reaches 46% at firms with 100+ attorneys. Per Clio, 87% of large firms report AI usage. The SurePoint 2025 report documents that 63% of mid-sized firms have formally adopted generative AI. Yet the same industry is nearly invisible to AI-driven client discovery. The spread across these surveys — from 21% to 79% — reflects real confusion about what AI adoption means. A lawyer using ChatGPT in the margin of their day counts. A firm with an enterprise Thomson Reuters CoCounsel contract counts. The two are not the same thing. The best synthesis: roughly one in three individual lawyers use AI daily or weekly. Roughly one in five firms have formal AI adoption with policies and tooling. The gap between those two numbers is where most of the industry's live AI risk lives. ## THE DIRECTORY CARTEL Across every query type tested, a small set of ranking directories dominates the legal AI citation layer. This is the central finding of the audit conducted jointly by Haute Lawyer Network and 5W. For finder queries like "best personal injury lawyer NYC": Super Lawyers consistently owns top position. Justia ranks immediately below. Avvo, Martindale, FindLaw round out the top tier. Individual law firm websites appear below all of these. For elite queries like "best M&A law firm US": Chambers owns multiple positions. Legal 500 owns parallel positions. Bloomberg Law appears with transaction data. Individual firms — even Cravath, Kirkland, Skadden — appear inside these directories, not as independent voices. The case of Cravath: When we searched "Cravath, Swaine & Moore M&A expertise insights" — a query that should surface Cravath's own content first — the top six results were two Chambers profiles, two Cravath practice pages, one Legal 500 profile, and one Chambers Global profile. The ranking directories beat Cravath's own content for Cravath's own practice area. ## AI ADOPTION BY FIRM SIZE AND PRACTICE AREA Adoption gaps by firm size are extreme. Per AffiniPay 2025, firms with 51 or more lawyers report 39% generative AI adoption — nearly double the 20% rate at firms with 50 or fewer lawyers. By practice area, immigration leads individual adoption at 47%. Civil litigation leads firm-wide adoption at 27%. The legal AI tool stack has consolidated around six major players: Harvey (50% of Am Law 100, $8B valuation, $100M+ ARR by August 2025), Thomson Reuters CoCounsel Legal (1 million users by February 2026), Lexis Protégé (agentic AI launched August 2024), Microsoft Copilot (52% of law firms using or considering, per ABA 2025 Tech Survey), Bloomberg Law Answers (launched January 2025), and emerging agentic workflow platforms. The hallucination problem is real and accelerating. US courts recorded 487 instances of AI errors or hallucinations in court filings in 2025 — more than 10 times the 2024 total. Licensed attorneys accounted for 37.8% of these problematic filings. The number is climbing, not falling. ## SIX REASONS THE LEGAL INDUSTRY IS BEHIND The legal industry is approximately six quarters behind where consumer AI search behavior already is. Six specific structural drivers explain the lag: 1. Consumer AI adoption has outpaced professional response. 61% of American adults now use AI tools for research. LLM-referred traffic to legal websites more than doubled between early 2024 and mid-2025. But only a small minority of law firm marketing departments have a formal GEO strategy. 2. AI Overviews on 23.6% of legal queries — 57.9% of question-style legal queries. Per Ahrefs' analysis of 146 million SERPs, the majority of legal discovery queries now trigger an AI-generated answer by default. 3. Zero-click behavior is absorbing legal client journeys. Per Martindale-Avvo's 2026 consumer research, a "True Contacts Multiplier" of approximately 2.1 applies to attorney marketing — for every 10 tracked contacts, roughly 11 more happen off-platform. The AI citation layer is now a primary touchpoint that existing analytics do not capture. 4. Law firm marketing departments are structured for the previous era. SEO, paid search, content marketing, and directory listings do not map cleanly to GEO. The category of legal-specific GEO providers only emerged commercially in 2024. 5. Ethical and regulatory caution has compounded the lag. State bar advertising rules have not clearly addressed AI-generated marketing content, causing firms to wait for guidance that has simply pushed the gap wider. 6. The 2025 hallucination cases made firms defensive. Firms conflated two distinct risks: using AI to draft court filings (genuine liability) and being invisible to AI in client discovery (also genuine revenue consequence). Focusing on the first risk, they neglected the second. ## HOW LLMs ACTUALLY INDEX LEGAL INFORMATION The single biggest gap in how law firms think about AI visibility is that they still think of AI like a search engine. It is not. Google ranks pages. LLMs extract claims. Directory listings win because they are factual: Jane Smith practices estate planning in New York, graduated from Columbia in 2004, is licensed in New York and New Jersey, recognized by Super Lawyers in 2022, 2023, 2024. These are extractable claims. A law firm bio that says "Jane is an accomplished attorney dedicated to serving her clients" cannot be extracted. It is rhetoric, not fact. LinkedIn is the missing chapter of legal GEO. Microsoft owns LinkedIn and partners with OpenAI. LinkedIn content is disproportionately cited by ChatGPT. A well-optimized LinkedIn profile appears in ChatGPT answers about professionals more often than most attorneys' own firm bio pages. This is the citation source reachable by every attorney regardless of Wikipedia eligibility or firm website quality. The Knowledge Graph layer matters. Google's Knowledge Graph feeds directly into Google AI Mode and AI Overviews. An attorney with a Wikipedia entry appears in AI answers far more often than one without. A firm with a Knowledge Graph entity appears in AI answers far more often. Wikidata registration, Google Business Profile, and sameAs links from the firm website to authoritative profiles all compound this advantage. Schema.org markup is almost universally missing. Organization, LegalService, Attorney, FAQPage, and Article schema — implemented correctly and validated with Google's Rich Results Test — is how LLMs parse pages as structured entities. Almost no law firm website in America implements all of these correctly. Even the Am Law 10 firms we audited have significant gaps. ## THE $408 BILLION ECONOMIC FRAMEWORK The US legal services market is projected at $408 billion in 2025 (Grand View Research). Approximately $300 billion of that is AI-addressable — meaning it flows through client discovery channels that AI now influences. Consumer legal services (personal injury, family law, immigration, criminal defense, estate planning, bankruptcy) represent roughly $130–150 billion annually. SME legal services represent $80–100 billion. Mid-market corporate legal another $80–100 billion. The economics of winner-take-most discovery. In the Google era, the #1 result captured 30–40% of clicks. In the AI era, the firm the AI names is the firm the client calls. A firm cited by ChatGPT for "best estate planning lawyer in Palm Beach" captures nearly all inquiries from that query. A firm that ranks in Chambers, Legal 500, and Super Lawyers but is not cited by AI captures essentially none. The cost of waiting. For legal specifically, the cost of establishing equivalent citation authority will rise at roughly 50–80% compounded annually over the next 24 months. A firm investing $500,000 in GEO starting in 2026 and establishing category authority by 2028 will have paid approximately $1 million total. A firm trying to catch up starting in 2028 will need $3–5 million for the same position — and may not be able to acquire it at all if the category is saturated. ## FIVE ACTIONS EVERY FIRM SHOULD TAKE 1. Invest in GEO as a standalone practice, not a subset of SEO. Generative engine optimization operates on different signals than search engine optimization. Firms that treat it as a minor SEO extension will fail. Firms that treat it as its own discipline will dominate. 2. Optimize LinkedIn as a citation asset within 90 days. Every partner in every firm should rewrite their LinkedIn headline in claim-dense format: [Title] | [Practice Area] | [Firm] | [Distinguishing Credential]. Establish a weekly publishing cadence on LinkedIn focused on factual analysis of practice-area developments. 3. Build editorial authority through outside publication. The Harvard Law School Forum on Corporate Governance model. Trade press bylines (Law360, Bloomberg Law Insight, ABA Journal). Editorial partnerships with publishers like Haute Lawyer Network. Four substantial pieces per participating partner per year is the minimum sustainable cadence. 4. Make every page machine-readable. Server-rendered HTML. Schema.org markup across Organization, LegalService, Attorney, FAQPage, and Article. FAQ architecture on every practice area page. Knowledge Graph registration via Wikidata and Google Business Profile. Wikipedia entries where eligible. This is engineering-track work measured in months, not years. 5. Allow AI crawlers — immediately. Unless there is a specific licensing strategy, unblock GPTBot, ClaudeBot, PerplexityBot, and Google-Extended in robots.txt. This takes five minutes. It is the single highest-ROI action in the entire report. ## REQUEST A LEGAL GEO AUDIT 5W works with law firms of all sizes to build the AI citation authority and GEO infrastructure that captures client discovery in the AI era. The Legal GEO Audit covers 50–100 practice-area and firm-specific queries across ChatGPT, Claude, Perplexity, and Google AI Mode; citation-source mapping; LinkedIn and Knowledge Graph assessment; schema and robots.txt review; and a 90-day action plan. Inquiries: research@5wpr.com or media@5wpr.com. ## FAQ ## Frequently Asked Questions ### What is the 2026 Legal AI Visibility Report? The 2026 Legal AI Visibility Report is a joint research report from Haute Lawyer Network and 5W documenting the paradox between legal industry AI adoption (79% of lawyers use AI internally) and legal industry AI invisibility (nearly zero law firms get cited by AI externally). It includes an original AI citation audit across eight Am Law firms, analysis of the directory cartel, and a 24-month action framework for establishing citation authority. ### Why are law firms invisible in AI search results? A tight cartel of approximately seven ranking directories — Chambers, Legal 500, Super Lawyers, Best Lawyers, Martindale, Avvo, and Justia — dominates the AI citation layer for legal queries. Individual law firms, including the most prestigious Am Law 100 firms, appear inside those directories but rarely as independent voices. Even Cravath, Kirkland, and Skadden cannot out-rank the ranking directories for their own practice areas. ### What is generative engine optimization (GEO) for law firms? Generative engine optimization is the discipline of building AI citation authority — the structured, credible, claim-dense content that AI platforms extract when answering queries about lawyers and legal services. GEO operates on different signals than SEO: extractable factual claims rather than keyword density, LinkedIn and Knowledge Graph presence rather than backlinks, schema.org markup rather than page speed. LLMs extract claims from pages; they do not rank pages. ### What is the directory cartel in legal AI? Across every legal query type tested, seven directory properties own the vast majority of the AI citation layer: Chambers, Legal 500, Super Lawyers, Best Lawyers, Martindale, Avvo, and Justia. Individual firms appear inside these directories but not above them. This is true even for the most prestigious Am Law firms — Cravath's own M&A practice pages rank below Chambers and Legal 500 profiles of Cravath. ### How is the $408 billion figure calculated? The US legal services market is projected at $408.4 billion in 2025 per Grand View Research, at a 2.5% CAGR from $396.8 billion in 2024. Approximately $300 billion of that total is AI-addressable — meaning it flows through client discovery channels that AI now influences: consumer legal services ($130–150B), SME legal ($80–100B), mid-market corporate ($80–100B), and HNW individual legal ($30–50B). ### Is the report free to download? Yes. The 31-page report is ungated and free to download. No registration required. Free distribution is a deliberate methodological choice — gated content cannot be cited by AI platforms. The report is available at 5wpr.com/ai-visibility-index/legal-ai-visibility-index-2026. ### Can 5W run a legal GEO audit for my firm? Yes. 5W is the premier AI communications and GEO firm in the United States. The Legal GEO Audit covers 50–100 practice-area and firm-specific queries across ChatGPT, Claude, Perplexity, and Google AI Mode; citation-source mapping; LinkedIn and Knowledge Graph assessment; schema and robots.txt review; and a 90-day action plan. Inquiries: research@5wpr.com. ## METHODOLOGY This report combines a proprietary AI search audit with synthesis of authoritative third-party industry research. The AI citation audit was conducted jointly by Haute Lawyer Network and 5W across three query types (finder, decision, and elite queries) and eight practice areas including personal injury, corporate/M&A, estate planning, family law, immigration, criminal defense, IP, and bankruptcy. Queries were also run against three Am Law 100 firms (Cravath, Kirkland & Ellis, Skadden) to test whether the largest firms in America can out-rank the ranking directories for their own areas of expertise. Adoption data is drawn from Clio's 2025 Legal Trends Report, the AffiniPay 2025 Legal Industry Report, the ABA 2025 Tech Survey, SurePoint's 2025 State of the Legal Industry Report, and the Thomson Reuters Generative AI in Professional Services Report 2025. Market data is drawn from Grand View Research, Mordor Intelligence, and Ahrefs' analysis of 146 million SERPs. Hallucination case data is drawn from Damien Charlotin's database of AI hallucination cases, cited in the SurePoint 2025 report. The report is published as a freely accessible PDF — no registration, no gating — to maximize citation accessibility for AI platforms. Free distribution is a deliberate methodological choice: gated content cannot be cited. Download the Full Report (PDF — 31 pages, Free) ## ABOUT THE PUBLISHERS Haute Lawyer Network is the invitation-only attorney membership platform from Haute Living, the luxury media brand trusted by ultra-high-net-worth readers for over two decades. Membership is curated, not purchased: every attorney is vetted for practice excellence, market presence, and fit with the network's standards. Through editorial coverage, market research, and direct connections to an affluent client base, Haute Lawyer Network serves top-producing attorneys across practice areas including corporate, litigation, estate planning, family, immigration, IP, and specialty practices. Visit: hauteliving.com/lawyer. 5W is a GEO marketing and communications firm — one of the largest independently owned firms in the United States, with more than 250 professionals across generative engine optimization, digital marketing, SEO, digital influencer strategy, PR, crisis communications, and public affairs. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. Visit: 5wpr.com. ## HOW TO CITE THIS REPORT APA: Haute Lawyer Network & 5W. (2026). The 2026 Legal AI Visibility Report. /research/legal-ai-visibility-index-2026/ Chicago: Haute Lawyer Network and 5W. "The 2026 Legal AI Visibility Report." April 2026. /research/legal-ai-visibility-index-2026/ Plain text: The 2026 Legal AI Visibility Report, published by Haute Lawyer Network and 5W, GEO marketing and communications firm, April 2026. Available at 5wpr.com/research/legal-ai-visibility-index-2026/. April 2026 — Haute Lawyer Network and 5W Published by Haute Lawyer Network in partnership with 5W Research. hauteliving.com/lawyer · 5wpr.com. Email us at research@5wpr.com. All data cited is drawn from publicly reported sources and an original AI citation audit conducted in April 2026. Reproduction permitted with attribution. --- ### Download the 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/download/ Summary: Download The 5W Retrieval Index, Volume I: The AI Retrieval Economy, 2026. 220 pages. 38 sectors. --- ### Small Countertop Appliances AI Visibility Index 2026 URL: https://www.5wpr.com/ai-visibility-index/small-appliances-ai-visibility-index-2026/ Summary: Countertop appliance AI visibility data shows Breville Group Ltd leads appearances and third-party publishers dominate cited sources. # Small Countertop Appliances AI Visibility Index 2026 Breville Leads Countertop AI Visibility Part of the Kitchen Appliances AI Visibility Index 2026. Companion report: Major Kitchen Appliances AI Visibility Index 2026. Breville Group Ltd leads countertop and small kitchen appliance AI visibility in this study, appearing in 54.6% of 624 answers returned, with a 95% interval of 50.7% to 58.5%. The measurement covers 52 prompts, tested three times across Perplexity, ChatGPT, Gemini, and Claude on October 4, 2026. The celebrity-brand test does not support a visibility advantage: Beautiful by Drew Barrymore appears in 5.4% of returned answers and Our Place in 0%. This report is the Countertop / Small Kitchen Appliances segment of the Kitchen Appliances AI Visibility Index 2026. It does not cover Large / Major Kitchen Appliances. Brands that ignore engine-specific gaps can lose visibility where consumers ask for product selection help. 624AI answers analyzed 54.6%Breville Group Ltd appearance rate, the category leader 29Brands named across the study 99.7%Answers carrying retrieval evidence ## Which brands lead the countertop appliance leaderboard? Breville Group Ltd ranks first, followed by Cuisinart, Hamilton Beach Brands, Inc., Ninja, and KitchenAid. Breville Group Ltd has the highest measured appearance rate, but the intervals for Breville Group Ltd and Cuisinart do not overlap. Hamilton Beach Brands, Inc., Ninja, and KitchenAid occupy the next group. Appearance rates use 624 answers returned. The leaderboard measures how often a brand name appeared, not product sales, consumer preference, or market share. ### Full appearance leaderboard ### AI visibility leaderboard Appearance rate is the share of the 624 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1Breville Group Ltd341/62454.6%50.7%–58.5%3.8 2Cuisinart277/62444.4%40.5%–48.3%5.3 3Hamilton Beach Brands, Inc.231/62437%33.3%–40.9%7.1 4Ninja223/62435.7%32.1%–39.6%4.2 5KitchenAid220/62435.3%31.6%–39.1%3.8 6Instant Brands LLC123/62419.7%16.8%–23%3.9 7Vitamix Corporation94/62415.1%12.5%–18.1%2.4 8Black+Decker91/62414.6%12%–17.6%7.2 9Smeg78/62412.5%10.1%–15.3%6 10SharkNinja, Inc.77/62412.3%10%–15.2%5.8 11Blendtec LLC71/62411.4%9.1%–14.1%3 12Philips71/62411.4%9.1%–14.1%5.9 13NutriBullet65/62410.4%8.3%–13.1%6.7 14COSORI65/62410.4%8.3%–13.1%4.3 15De'Longhi63/62410.1%8%–12.7%5.3 The 95% intervals show where this sample cannot distinguish brands cleanly. Hamilton Beach Brands, Inc. has an interval of 33.3% to 40.9%, Ninja has 32.1% to 39.6%, and KitchenAid has 31.6% to 39.1%. Their differences are within the margin of this sample. Vitamix Corporation at 12.5% to 18.1% and Black+Decker at 12.0% to 17.6% also overlap, as do several brands lower in the leaderboard. ## Which leading brands have the weakest engine coverage? The leading brands differ most in their weakest engine. The profiles below use appearance rates from answers returned and citation rates from 622 answers with retrieval evidence. ### Breville Group Ltd 54.6% appearance rate with a 95% interval of 50.7% to 58.5%. Breville Group Ltd ranks first, but Claude named it in 23.7% of its 156 answers, compared with 80.1% for ChatGPT, 67.9% for Perplexity, and 46.8% for Gemini. Its average first mention position is 3.8. Breville Group Ltd's owned domain was cited in 1.4% of the 622 answers with retrieval evidence. ### Cuisinart 44.4% appearance rate with a 95% interval of 40.5% to 48.3%. Cuisinart ranks second, with its weakest rate in Claude at 21.2% of 156 answers and Gemini at 23.1%, versus 71.2% in ChatGPT and 62.2% in Perplexity. Its average first mention position is 5.3. Cuisinart's owned domain was cited in 0.6% of the 622 answers with retrieval evidence. ### Hamilton Beach Brands, Inc. 37% appearance rate with a 95% interval of 33.3% to 40.9%. Hamilton Beach Brands, Inc. ranks third, but Claude named it in 9% of its 156 answers. ChatGPT named it in 59.6%, Perplexity in 44.2%, and Gemini in 35.3%. Its average first mention position is 7.1. Hamilton Beach Brands, Inc.'s owned domain was cited in 1.4% of the 622 answers with retrieval evidence. ### Ninja 35.7% appearance rate with a 95% interval of 32.1% to 39.6%. Ninja ranks fourth, and Gemini is its weakest engine at 9% of 156 answers, against 59% in Perplexity, 48.1% in ChatGPT, and 26.9% in Claude. Its average first mention position is 4.2. Ninja's owned domain was cited in 0.2% of the 622 answers with retrieval evidence. ### KitchenAid 35.3% appearance rate with a 95% interval of 31.6% to 39.1%. KitchenAid ranks fifth, with Gemini at 18.6% and Claude at 14.7%, compared with 57.1% in ChatGPT and 50.6% in Perplexity. Its average first mention position is 3.8. KitchenAid's owned domain was cited in 5.3% of the 622 answers with retrieval evidence. ### Instant Brands LLC 19.7% appearance rate with a 95% interval of 16.8% to 23%. Instant Brands LLC ranks sixth and appears most often in ChatGPT at 29.5% of 156 answers, followed by Perplexity at 26.3%, Gemini at 13.5%, and Claude at 9.6%. Its average first mention position is 3.9. Instant Brands LLC's owned domain was cited in 0.5% of the 622 answers with retrieval evidence. ### Vitamix Corporation 15.1% appearance rate with a 95% interval of 12.5% to 18.1%. Vitamix Corporation ranks seventh, and Gemini named it in 3.2% of 156 answers, below Claude at 11.5%, Perplexity at 22.4%, and ChatGPT at 23.1%. Its average first mention position is 2.4, the earliest among these profiles. Vitamix Corporation's owned domain was cited in 1% of the 622 answers with retrieval evidence. ### Black+Decker 14.6% appearance rate with a 95% interval of 12% to 17.6%. Black+Decker ranks eighth and is most dependent on Perplexity, which named it in 30.8% of 156 answers. ChatGPT named it in 12.8%, Gemini in 9%, and Claude in 5.8%. Its average first mention position is 7.2. Black+Decker's owned domain was cited in 0% of the 622 answers with retrieval evidence. ### Smeg 12.5% appearance rate with a 95% interval of 10.1% to 15.3%. Smeg ranks ninth, led by Perplexity at 19.2% of 156 answers and ChatGPT at 16%, while Claude named it in 3.2%. Its average first mention position is 6. Smeg's owned domain was cited in 1.8% of the 622 answers with retrieval evidence. ### SharkNinja, Inc. 12.3% appearance rate with a 95% interval of 10% to 15.2%. SharkNinja, Inc. ranks tenth, with Gemini naming it in 30.1% of 156 answers, but Claude naming it in 0%. Perplexity named it in 6.4% and ChatGPT in 12.8%. Its average first mention position is 5.8. SharkNinja, Inc.'s owned domain was cited in 1.4% of the 622 answers with retrieval evidence. ## Where do the engines disagree on countertop brands? The engines disagree sharply on several brands, despite each engine returning 156 answers. Perplexity, ChatGPT, Gemini, and Claude use different models and retrieval tools, so gaps can reflect system design rather than a brand's visibility outside these measured prompts. ### Engine-by-brand appearance pattern ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate Breville Group Ltd67.9%80.1%46.8%23.7% Cuisinart62.2%71.2%23.1%21.2% Hamilton Beach Brands, Inc.44.2%59.6%35.3%9% Ninja59%48.1%9%26.9% KitchenAid50.6%57.1%18.6%14.7% Instant Brands LLC26.3%29.5%13.5%9.6% Vitamix Corporation22.4%23.1%3.2%11.5% Black+Decker30.8%12.8%9%5.8% Smeg19.2%16%11.5%3.2% SharkNinja, Inc.6.4%12.8%30.1%0% Blendtec LLC9%19.2%11.5%5.8% Philips19.9%20.5%1.9%3.2% ChatGPT names Breville Group Ltd in 80.1% of its 156 answers, compared with 23.7% in Claude. Cuisinart follows the same split, at 71.2% in ChatGPT and 21.2% in Claude. Hamilton Beach Brands, Inc. reaches 59.6% in ChatGPT but 9% in Claude. KitchenAid appears in 57.1% of ChatGPT answers and 14.7% of Claude answers. Perplexity produces distinct concentration for Black+Decker at 30.8%, while ChatGPT reaches 12.8%, Gemini 9%, and Claude 5.8%. Gemini produces the strongest rate for SharkNinja, Inc. at 30.1%, while Claude names it in none of its 156 answers. Gemini also names Whirlpool Corporation in 13.5% of answers, ahead of Perplexity at 7.7% and ChatGPT at 4.5%, while Claude names it in none. Oster and Kenwood are absent from Claude's 156 answers but appear in the other three engines. Our Place is absent from every engine. These gaps matter because a combined rate can conceal a narrow engine footprint. A brand with a strong rate in one engine and no mentions in another has less stable visibility across the measured systems. ## Which demand clusters produce brand visibility? Demand clusters show which brands lead the measured question types and where each brand's name appears most often. The groups separate general buyer intent from sub-category or specific-need questions, so they should not be treated as one measure of consumer demand. ### Brand leaders by demand cluster ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Countertop Appliances - Comparison intent — Vitamix vs Ninja blenders, which is better?12Breville Group Ltd66.7%Hamilton Beach Brands, Inc. 66.7%, Blendtec LLC 66.7%, Vitamix Corporation 58.3% Breville vs Cuisinart espresso machines, which is easier to use?12Breville Group Ltd83.3%Gaggia 75%, Cuisinart 66.7%, De'Longhi 58.3% Keurig vs Breville coffee makers, which has better coffee quality?12Breville Group Ltd100%Keurig 83.3%, Cuisinart 41.7%, Hamilton Beach Brands, Inc. 33.3% Instant Pot vs Ninja multi-cookers, which is more reliable?12Instant Brands LLC75%Ninja 66.7%, Breville Group Ltd 41.7%, Cuisinart 33.3% KitchenAid vs Cuisinart stand mixers, which is better?12Hamilton Beach Brands, Inc.66.7%Smeg 66.7%, Kenwood 66.7%, Ankarsrum 66.7% Nutribullet vs Ninja, which blends better?12Ninja75%NutriBullet 75%, Hamilton Beach Brands, Inc. 66.7%, Breville Group Ltd 58.3% Hamilton Beach vs Oster, which brand lasts longer?12Hamilton Beach Brands, Inc.75%Oster 75%, Breville Group Ltd 58.3%, KitchenAid 50% Smeg vs KitchenAid, which stand mixer is better?12Smeg83.3%KitchenAid 75%, Breville Group Ltd 66.7%, Hamilton Beach Brands, Inc. 66.7% Haden vs Smeg, which retro-style appliance brand is better?12Smeg100%Haden 91.7%, KitchenAid 41.7%, Breville Group Ltd 25% — Countertop Appliances - Feature-fit intent — Best multi-cooker with the most functions12Instant Brands LLC75%Ninja 58.3%, Breville Group Ltd 50%, Cuisinart 41.7% Best coffee maker with a built-in grinder12Breville Group Ltd91.7%Cuisinart 66.7%, GE 58.3%, Ninja 50% Best quiet blender brand12Breville Group Ltd83.3%Vitamix Corporation 75%, Hamilton Beach Brands, Inc. 66.7%, Blendtec LLC 66.7% Best stand mixer with the most color/design options12Cuisinart75%KitchenAid 75%, Smeg 75%, Hamilton Beach Brands, Inc. 66.7% Best air fryer for a family of four12Ninja66.7%COSORI 66.7%, Breville Group Ltd 50%, Instant Brands LLC 50% Best blender for smoothies and ice12Breville Group Ltd75%Vitamix Corporation 75%, Ninja 66.7%, Blendtec LLC 58.3% Best espresso machine with a built-in grinder12Breville Group Ltd91.7%De'Longhi 83.3%, Gaggia 75%, Jura 75% Best compact stand mixer for small kitchens12Hamilton Beach Brands, Inc.83.3%KitchenAid 83.3%, Kenwood 75%, Cuisinart 66.7% — Countertop Appliances - Celebrity/brand-equity intent — Is Beautiful by Drew Barrymore a good kitchen appliance brand?12Beautiful by Drew Barrymore75%Breville Group Ltd 58.3%, KitchenAid 58.3%, Cuisinart 50% Is Our Place a reliable cookware and appliance brand?12Cuisinart16.7%Ninja 8.3%, Dash Brands, Inc 8.3% Does a celebrity-backed kitchen brand mean better quality?12Beautiful by Drew Barrymore25%— Best celebrity-backed kitchen appliance brand12Smeg25%Bosch 25%, Beautiful by Drew Barrymore 16.7% Does celebrity endorsement affect how reliable a kitchen appliance brand is?12KitchenAid50%Whirlpool Corporation 50%, GE 41.7%, Bosch 33.3% Is Our Place worth the price compared to KitchenAid?12KitchenAid66.7%Cuisinart 33.3%, Hamilton Beach Brands, Inc. 33.3%, Breville Group Ltd 25% Beautiful by Drew Barrymore vs Our Place, which is the better brand?12Beautiful by Drew Barrymore83.3%Cuisinart 25%, Instant Brands LLC 16.7%, Ninja 8.3% — Countertop Appliances - Trust/reliability intent — Most reliable blender brand 202612Vitamix Corporation66.7%Blendtec LLC 66.7%, Breville Group Ltd 50%, Ninja 50% Which air fryer brand breaks down the least?12Instant Brands LLC58.3%Philips 58.3%, Breville Group Ltd 41.7%, Cuisinart 41.7% Is Ninja a reliable brand?12Ninja58.3%Breville Group Ltd 50%, Cuisinart 50%, Hamilton Beach Brands, Inc. 50% Which coffee maker brand has the fewest complaints?12Cuisinart75%Breville Group Ltd 58.3%, Keurig 58.3%, Hamilton Beach Brands, Inc. 50% Which stand mixer brand lasts the longest?12Ankarsrum66.7%Cuisinart 50%, KitchenAid 50%, Smeg 50% Which multi-cooker brand has had safety recalls?12SharkNinja, Inc.66.7%Instant Brands LLC 58.3%, Breville Group Ltd 50%, Hamilton Beach Brands, Inc. 25% Which blender brand burns out motors the fastest?12Hamilton Beach Brands, Inc.83.3%Blendtec LLC 75%, Breville Group Ltd 66.7%, Ninja 58.3% Which countertop appliance brand has the best customer service?12KitchenAid50%Breville Group Ltd 41.7%, Cuisinart 33.3%, Instant Brands LLC 33.3% — Countertop Appliances - Cost intent — Is Vitamix worth the price over cheaper blenders?12Vitamix Corporation66.7%Blendtec LLC 66.7%, Breville Group Ltd 50%, Ninja 50% Cheapest reliable air fryer brand12COSORI91.7%Ninja 75%, Instant Brands LLC 75%, Philips 50% Best budget stand mixer brand12Cuisinart100%Hamilton Beach Brands, Inc. 100%, KitchenAid 50%, Beautiful by Drew Barrymore 50% Is a Breville espresso machine worth the cost?12Breville Group Ltd100%De'Longhi 50%, Gaggia 50%, Jura 25% Best value multi-cooker brand12Instant Brands LLC91.7%Ninja 66.7%, Breville Group Ltd 58.3%, Cuisinart 50% Most affordable coffee maker that still tastes good12Ninja75%Cuisinart 66.7%, Black+Decker 58.3%, Hamilton Beach Brands, Inc. 33.3% Best budget food processor brand12Hamilton Beach Brands, Inc.91.7%Cuisinart 75%, KitchenAid 75%, Black+Decker 75% Is KitchenAid worth it over Hamilton Beach?12Breville Group Ltd66.7%KitchenAid 66.7%, Cuisinart 58.3%, Hamilton Beach Brands, Inc. 58.3% — Countertop Appliances - Purchase intent — What's the best blender brand to buy?12Vitamix Corporation75%NutriBullet 75%, Breville Group Ltd 58.3%, Ninja 58.3% Best air fryer brand to buy in 202612Instant Brands LLC83.3%Ninja 75%, COSORI 75%, Breville Group Ltd 66.7% What's the best stand mixer brand for baking?12KitchenAid83.3%Breville Group Ltd 75%, Cuisinart 66.7%, Hamilton Beach Brands, Inc. 66.7% Best coffee maker brand for home use12Breville Group Ltd91.7%Keurig 75%, Cuisinart 58.3%, Ninja 58.3% Best toaster oven brand12Breville Group Ltd91.7%Cuisinart 83.3%, Hamilton Beach Brands, Inc. 75%, Ninja 75% Best espresso machine brand for beginners12Breville Group Ltd91.7%De'Longhi 91.7%, Gaggia 91.7%, Philips 41.7% Best food processor brand12Breville Group Ltd83.3%Cuisinart 83.3%, KitchenAid 83.3%, Hamilton Beach Brands, Inc. 66.7% Best multi-cooker brand12Instant Brands LLC91.7%Breville Group Ltd 83.3%, Ninja 58.3%, Philips 41.7% — Other demand — Price and value12Hamilton Beach Brands, Inc.83.3%Cuisinart 75%, Black+Decker 58.3%, KitchenAid 50% Primary use case12Breville Group Ltd75%Cuisinart 75%, Vitamix Corporation 75%, KitchenAid 66.7% General best-in-category12Breville Group Ltd83.3%Cuisinart 75%, Ninja 66.7%, KitchenAid 66.7% Everyday and mainstream choice12Breville Group Ltd91.7%Cuisinart 83.3%, KitchenAid 75%, Ninja 66.7% The cluster results identify the question contexts in which the engines surface each brand. The general buyer-intent group indicates broad brand recommendation exposure within the measured prompts. The sub-category and specific-need group identifies more focused selection contexts. Together, the cluster pattern explains why a brand can be strong in a specialty answer without leading the full appearance leaderboard. ## Which sites supply citations in countertop answers? Owned-domain citation rates use the 622 answers with retrieval evidence, not the 624 answers returned. KitchenAid has the highest owned-domain citation rate among the listed leading brands at 5.3%. Ankarsrum reaches 2.9%, Our Place reaches 2.3% despite appearing in 0% of returned answers, and Smeg reaches 1.8%. ### Owned-domain citations ### Owned-domain citation rate Share of the 622 answers with retrieval evidence that cited a URL on the brand's own website. Breville Group Ltd 1.4% · 9/622 Cuisinart 0.6% · 4/622 Hamilton Beach Brands, Inc. 1.4% · 9/622 Ninja 0.2% · 1/622 KitchenAid 5.3% · 33/622 Instant Brands LLC 0.5% · 3/622 Vitamix Corporation 1% · 6/622 Black+Decker 0% · 0/622 Smeg 1.8% · 11/622 SharkNinja, Inc. 1.4% · 9/622 Blendtec LLC 0.6% · 4/622 Philips 0.2% · 1/622 The source pattern relies more on third-party publishers than brand sites. Consumer Reports appears in 46.5% of answers with retrieval evidence, followed by Food Network at 25.7%, TechRadar at 25.4%, Tom's Guide at 24%, and Reddit at 22%. ### Most-cited publishers ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration consumerreports.org289/62246.5% foodnetwork.com160/62225.7% techradar.com158/62225.4% tomsguide.com149/62224% reddit.com137/62222% rtings.com134/62221.5% techgearlab.com128/62220.6% cnet.com127/62220.4% youtube.com126/62220.3% forbes.com120/62219.3% goodhousekeeping.com113/62218.2% reviewed.com110/62217.7% Publisher rates use answers with retrieval evidence. Consumer Reports appears in 289 answers, while Food Network appears in 160 and TechRadar in 158. The tally records citations, not editorial endorsement or source quality. ## Who is winning and who is at risk? ### Brands winning measured visibility Breville Group Ltd leads with a 54.6% appearance rate and a 50.7% to 58.5% interval. Cuisinart follows at 44.4%, with a 40.5% to 48.3% interval. KitchenAid combines a 35.3% appearance rate with the highest owned-domain citation rate in the leading group, 5.3% of answers with retrieval evidence. Vitamix Corporation appears in 15.1% of returned answers but has an average first mention position of 2.4. ### Brands at risk in measured visibility Our Place appears in 0% of 624 returned answers and is absent from all four engines. Haden appears in 1.8%, while Hobart appears in 1.3%. Beautiful by Drew Barrymore appears in 5.4%, with its highest engine rate at 9.6% in Perplexity. Black+Decker appears in 14.6% of returned answers but has a 0% owned-domain citation rate across 622 answers with retrieval evidence. ## What does the tally say about the category structure? The top of the leaderboard is led by Breville Group Ltd at 54.6% and Cuisinart at 44.4%, followed by a middle group where Hamilton Beach Brands, Inc., Ninja, and KitchenAid have overlapping intervals. The data therefore supports a clear leader over Cuisinart in this sample, but not a visibility ordering among Hamilton Beach Brands, Inc., Ninja, and KitchenAid. All of the top eight brands appear across all four engines, but their engine rates vary widely. Breville Group Ltd ranges from 80.1% in ChatGPT to 23.7% in Claude. SharkNinja, Inc. reaches 30.1% in Gemini and 0% in Claude. Citation activity is more concentrated in third-party publishers: Consumer Reports appears in 46.5% of answers with retrieval evidence, while the highest owned-domain citation rate listed is KitchenAid's 5.3%. The celebrity and influencer-backed names named in the brief do not lead the measured results. Beautiful by Drew Barrymore appears in 5.4% of returned answers and Our Place in 0%. This study does not measure market share, so it cannot test whether citation share tracks market share. ## What changed during the measurement window? The data does not contain a before-and-after measurement, so it does not establish category shifts during the measurement window. It does record cross-engine divergence, publisher concentration, and maker attribution where those appeared in the measured answers. ## What should countertop brands fix and re-measure? - Check Claude coverage for brands with major gaps. Breville Group Ltd appears in 23.7% of Claude's 156 answers versus 80.1% in ChatGPT. Publish product and comparison material that directly answers the measured selection questions, then re-measure Claude appearance rate on the same prompt set. - Check Gemini coverage for Ninja. Ninja appears in 9% of Gemini's 156 answers, below 59% in Perplexity and 48.1% in ChatGPT. Build clear product-category pages and re-measure Ninja's Gemini appearance rate. - Address absent owned-site citations. Black+Decker has a 14.6% appearance rate but a 0% owned-domain citation rate among 622 answers with retrieval evidence. Publish sourceable product specifications and comparison evidence, then re-measure the owned-domain citation rate. - Reduce single-engine dependency. SharkNinja, Inc. appears in 30.1% of Gemini answers and 0% of Claude answers. Audit brand and product pages for clear corporate and consumer naming, then re-measure the four-engine spread. - Improve first-mention placement. Hamilton Beach Brands, Inc. has a 37% appearance rate but an average first mention position of 7.1. Create concise selection pages that state product fit early, then re-measure average first mention position. - Repair zero-answer visibility. Our Place appears in 0% of returned answers across every engine, despite a 2.3% owned-domain citation rate among answers with retrieval evidence. Review whether pages cover the measured countertop appliance selection needs, then re-measure appearance across all four engines. ## What does this mean for brands in the category? Interpretation: visibility in countertop appliance answers is not one market-wide score. The measured systems name brands at different rates, and third-party sources appear far more often than owned sites in cited answers. A brand can therefore have a strong combined appearance rate while remaining weak in one engine or absent from early answer positions. Interpretation: the measured celebrity and influencer-backed brands do not show a broad visibility advantage in this prompt set. Brand teams should treat the results as a map of these queries on this date, then test whether changes to product information and independent coverage alter future measurements. ## How was countertop appliance AI visibility measured? This flagship-depth study tested 52 prompts three times in Perplexity, ChatGPT, Gemini, and Claude. Appearance rates use 624 answers returned. Citation and source rates use 622 answers with retrieval evidence. The study measures these prompts on October 4, 2026, not all category searches or consumer demand. ### Measurement summary ### How this study was measured - Sampling frame fixed first: 52 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each. - 624 answers returned of 624 attempted, 622 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The main limits are prompt scope, repeat-generation variation, and differences in each engine's model and retrieval tool. The tally does not measure sales, market share, product quality, or consumer preference. ## Frequently asked questions ### Which brand has the highest AI visibility for countertop appliances? Breville Group Ltd ranks first with a 54.6% appearance rate across 624 answers returned. Its 95% interval is 50.7% to 58.5%. ### Which brands are closest behind Breville Group Ltd? Cuisinart ranks second at 44.4%. Hamilton Beach Brands, Inc. ranks third at 37%, Ninja fourth at 35.7%, and KitchenAid fifth at 35.3%. ### Do celebrity-backed brands lead the measured answers? No. Beautiful by Drew Barrymore appears in 5.4% of returned answers and Our Place in 0%. ### Which brand site is cited most often? KitchenAid has the highest owned-domain citation rate listed at 5.3% of 622 answers with retrieval evidence. Ankarsrum follows at 2.9%, and Our Place is at 2.3%. ### Which publisher appears most often in cited answers? Consumer Reports appears in 46.5% of answers with retrieval evidence. It appears in 289 answers. ### Do all engines name the same countertop brands? No. Breville Group Ltd appears in 80.1% of ChatGPT answers and 23.7% of Claude answers. SharkNinja, Inc. appears in 30.1% of Gemini answers and 0% of Claude answers. ### Does this study measure product sales or consumer preference? No. The study measures whether brands appeared in 624 generated answers and whether sources appeared in 622 answers with retrieval evidence. It does not measure sales, market share, product quality, or consumer preference. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Wirecutter4.5%28/6243.1%–6.4% 2Consumer Reports2.2%14/6241.3%–3.7% 3Amazon0.6%4/6240.2%–1.6% 4Good Housekeeping0.5%3/6240.2%–1.4% 5Target0.5%3/6240.2%–1.4% 6Walmart0.2%1/6240%–0.9% 7Food & Wine0%0/6240%–0.6% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Countertop / Small Kitchen Appliances - Measurement date: 2026-10-04 - Depth: flagship — 52 prompts, 3 trial(s) per prompt per engine, 4 engines - Answers attempted: 624 - Answers returned (appearance denominator): 624 - Answers with retrieval evidence (citation denominator): 622 (99.7% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable. ### How the prompts were sampled Fixed sampling frame: 48 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Countertop Appliances - Purchase intent / Countertop Appliances - Comparison intent / Countertop Appliances - Trust/reliability intent / Countertop Appliances - Cost intent / Countertop Appliances - Feature-fit intent / Countertop Appliances - Celebrity/brand-equity intent; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope What's the best stand mixer brand for baking?1What's the best stand mixer brand for baking? What's the best blender brand to buy?1What's the best blender brand to buy? Best coffee maker brand for home use1Best coffee maker brand for home use Best air fryer brand to buy in 20261Best air fryer brand to buy in 2026 Best multi-cooker brand1Best multi-cooker brand Best food processor brand1Best food processor brand Best espresso machine brand for beginners1Best espresso machine brand for beginners Best toaster oven brand1Best toaster oven brand Vitamix vs Ninja blenders, which is better?1Vitamix vs Ninja blenders, which is better? KitchenAid vs Cuisinart stand mixers, which is better?1KitchenAid vs Cuisinart stand mixers, which is better? Instant Pot vs Ninja multi-cookers, which is more reliable?1Instant Pot vs Ninja multi-cookers, which is more reliable? Breville vs Cuisinart espresso machines, which is easier to use?1Breville vs Cuisinart espresso machines, which is easier to use? Nutribullet vs Ninja, which blends better?1Nutribullet vs Ninja, which blends better? Keurig vs Breville coffee makers, which has better coffee quality?1Keurig vs Breville coffee makers, which has better coffee quality? Hamilton Beach vs Oster, which brand lasts longer?1Hamilton Beach vs Oster, which brand lasts longer? Smeg vs KitchenAid, which stand mixer is better?1Smeg vs KitchenAid, which stand mixer is better? Most reliable blender brand 20261Most reliable blender brand 2026 Which air fryer brand breaks down the least?1Which air fryer brand breaks down the least? Which coffee maker brand has the fewest complaints?1Which coffee maker brand has the fewest complaints? Is Ninja a reliable brand?1Is Ninja a reliable brand? Which stand mixer brand lasts the longest?1Which stand mixer brand lasts the longest? Which multi-cooker brand has had safety recalls?1Which multi-cooker brand has had safety recalls? Which countertop appliance brand has the best customer service?1Which countertop appliance brand has the best customer service? Which blender brand burns out motors the fastest?1Which blender brand burns out motors the fastest? Is Vitamix worth the price over cheaper blenders?1Is Vitamix worth the price over cheaper blenders? Best budget stand mixer brand1Best budget stand mixer brand Cheapest reliable air fryer brand1Cheapest reliable air fryer brand Is a Breville espresso machine worth the cost?1Is a Breville espresso machine worth the cost? Best value multi-cooker brand1Best value multi-cooker brand Most affordable coffee maker that still tastes good1Most affordable coffee maker that still tastes good Is KitchenAid worth it over Hamilton Beach?1Is KitchenAid worth it over Hamilton Beach? Best budget food processor brand1Best budget food processor brand Best blender for smoothies and ice1Best blender for smoothies and ice Best air fryer for a family of four1Best air fryer for a family of four Best espresso machine with a built-in grinder1Best espresso machine with a built-in grinder Best compact stand mixer for small kitchens1Best compact stand mixer for small kitchens Best multi-cooker with the most functions1Best multi-cooker with the most functions Best coffee maker with a built-in grinder1Best coffee maker with a built-in grinder Best quiet blender brand1Best quiet blender brand Best stand mixer with the most color/design options1Best stand mixer with the most color/design options Is Beautiful by Drew Barrymore a good kitchen appliance brand?1Is Beautiful by Drew Barrymore a good kitchen appliance brand? Is Our Place a reliable cookware and appliance brand?1Is Our Place a reliable cookware and appliance brand? Does a celebrity-backed kitchen brand mean better quality?1Does a celebrity-backed kitchen brand mean better quality? Best celebrity-backed kitchen appliance brand1Best celebrity-backed kitchen appliance brand Haden vs Smeg, which retro-style appliance brand is better?1Haden vs Smeg, which retro-style appliance brand is better? Is Our Place worth the price compared to KitchenAid?1Is Our Place worth the price compared to KitchenAid? Does celebrity endorsement affect how reliable a kitchen appliance brand is?1Does celebrity endorsement affect how reliable a kitchen appliance brand is? Beautiful by Drew Barrymore vs Our Place, which is the better brand?1Beautiful by Drew Barrymore vs Our Place, which is the better brand? General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice1what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case1the dominant everyday use case for this category ### The exact prompts #PromptClusterShape 1What's the best stand mixer brand for baking?need_1medium 2What's the best blender brand to buy?need_2medium 3Best coffee maker brand for home useneed_3medium 4Best air fryer brand to buy in 2026need_4medium 5Best multi-cooker brandneed_5medium 6Best food processor brandneed_6medium 7Best espresso machine brand for beginnersneed_7medium 8Best toaster oven brandneed_8medium 9Vitamix vs Ninja blenders, which is better?need_9medium 10KitchenAid vs Cuisinart stand mixers, which is better?need_10medium 11Instant Pot vs Ninja multi-cookers, which is more reliable?need_11medium 12Breville vs Cuisinart espresso machines, which is easier to use?need_12medium 13Nutribullet vs Ninja, which blends better?need_13medium 14Keurig vs Breville coffee makers, which has better coffee quality?need_14medium 15Hamilton Beach vs Oster, which brand lasts longer?need_15medium 16Smeg vs KitchenAid, which stand mixer is better?need_16medium 17Most reliable blender brand 2026need_17medium 18Which air fryer brand breaks down the least?need_18medium 19Which coffee maker brand has the fewest complaints?need_19medium 20Is Ninja a reliable brand?need_20medium 21Which stand mixer brand lasts the longest?need_21medium 22Which multi-cooker brand has had safety recalls?need_22medium 23Which countertop appliance brand has the best customer service?need_23medium 24Which blender brand burns out motors the fastest?need_24medium 25Is Vitamix worth the price over cheaper blenders?need_25medium 26Best budget stand mixer brandneed_26medium 27Cheapest reliable air fryer brandneed_27medium 28Is a Breville espresso machine worth the cost?need_28medium 29Best value multi-cooker brandneed_29medium 30Most affordable coffee maker that still tastes goodneed_30medium 31Is KitchenAid worth it over Hamilton Beach?need_31medium 32Best budget food processor brandneed_32medium 33Best blender for smoothies and iceneed_33medium 34Best air fryer for a family of fourneed_34medium 35Best espresso machine with a built-in grinderneed_35medium 36Best compact stand mixer for small kitchensneed_36medium 37Best multi-cooker with the most functionsneed_37medium 38Best coffee maker with a built-in grinderneed_38medium 39Best quiet blender brandneed_39medium 40Best stand mixer with the most color/design optionsneed_40medium 41Is Beautiful by Drew Barrymore a good kitchen appliance brand?need_41medium 42Is Our Place a reliable cookware and appliance brand?need_42medium 43Does a celebrity-backed kitchen brand mean better quality?need_43medium 44Best celebrity-backed kitchen appliance brandneed_44medium 45Haden vs Smeg, which retro-style appliance brand is better?need_45medium 46Is Our Place worth the price compared to KitchenAid?need_46medium 47Does celebrity endorsement affect how reliable a kitchen appliance brand is?need_47medium 48Beautiful by Drew Barrymore vs Our Place, which is the better brand?need_48medium 49best countertop appliancesgeneral_bestshort 50best small appliances for everyday cookingeverydaymedium 51best value small kitchen appliance brandsvalue_pricemedium 52Which small kitchen appliance brands make the most reliable blenders, air fryers, coffee makers, and food processors for everyday home use?use_caselong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search156156156100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool156156156100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)15615615498.7% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool156156156100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1Breville Group Ltd341/62454.6%54.7%50.7%–58.5%9/622 (1.4%) 2Cuisinart277/62444.4%44.4%40.5%–48.3%4/622 (0.6%) 3Hamilton Beach Brands, Inc.231/62437%37%33.3%–40.9%9/622 (1.4%) 4Ninja223/62435.7%35.7%32.1%–39.6%1/622 (0.2%) 5KitchenAid220/62435.3%35.2%31.6%–39.1%33/622 (5.3%) 6Instant Brands LLC123/62419.7%19.7%16.8%–23%3/622 (0.5%) 7Vitamix Corporation94/62415.1%15.1%12.5%–18.1%6/622 (1%) 8Black+Decker91/62414.6%14.6%12%–17.6%0/622 (0%) 9Smeg78/62412.5%12.5%10.1%–15.3%11/622 (1.8%) 10SharkNinja, Inc.77/62412.3%12.3%10%–15.2%9/622 (1.4%) 11Blendtec LLC71/62411.4%11.4%9.1%–14.1%4/622 (0.6%) 12Philips71/62411.4%11.4%9.1%–14.1%1/622 (0.2%) 13NutriBullet65/62410.4%10.4%8.3%–13.1%1/622 (0.2%) 14COSORI65/62410.4%10.4%8.3%–13.1%1/622 (0.2%) 15De'Longhi63/62410.1%10.1%8%–12.7%0/622 (0%) 16Oster59/6249.5%9.5%7.4%–12%2/622 (0.3%) 17Kenwood59/6249.5%9.5%7.4%–12%4/622 (0.6%) 18Bosch55/6248.8%8.8%6.8%–11.3%0/622 (0%) 19Ankarsrum44/6247.1%7.1%5.3%–9.3%18/622 (2.9%) 20Whirlpool Corporation40/6246.4%6.4%4.7%–8.6%2/622 (0.3%) 21Keurig37/6245.9%5.9%4.3%–8.1%1/622 (0.2%) 22Gaggia37/6245.9%5.9%4.3%–8.1%0/622 (0%) 23GE37/6245.9%5.9%4.3%–8.1%0/622 (0%) 24Dash Brands, Inc36/6245.8%5.8%4.2%–7.9%0/622 (0%) 25Beautiful by Drew Barrymore34/6245.4%5.4%3.9%–7.5%4/622 (0.6%) 26Jura31/6245%5%3.5%–7%0/622 (0%) 27Haden11/6241.8%1.8%1%–3.1%1/622 (0.2%) 28Hobart8/6241.3%1.3%0.7%–2.5%0/622 (0%) 29Our Place0/6240%0%0%–0.6%14/622 (2.3%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-04. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. --- ### The 5W Responsible Gambling Communications Audit 2026 URL: https://www.5wpr.com/research/responsible-gambling-audit-2026/ Summary: The US gambling industry spent 8.7 times more on celebrity and athlete partnerships than responsible gambling programs in 2025, a 5W Research audit. How the US gambling industry spent 8.7 times more on celebrity partnerships than on responsible gambling communications, and what that costs in regulatory, ESG, and AI search outcomes. A Study by the 5W Research Division. Published May 2026. ## Executive Summary The US sports betting, online gaming, and land-based casino industries spent $3.9 billion on marketing and advertising in 2025. $520 million went to celebrity and athlete endorsement partnerships. $60 million went to responsible gambling programs and communications. The ratio is 8.7 to 1. That ratio is now appearing in ESG analyst reports covering Flutter Entertainment, MGM Resorts, and Caesars Entertainment. It is appearing in legislative testimony in California, Texas, and Florida, the three largest unlicensed states. It is appearing in the citations that ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews return when American consumers ask which gambling operators are most trustworthy. This audit examined 30 operators across sports betting, iGaming, and land-based casino. It analyzed 47,000+ earned media articles, 180+ ESG disclosures and 10-K filings, 240+ state regulator filings and testimony transcripts, and 2,400+ AI engine queries across the five major LLM-powered search platforms. The headline finding: the gambling industry has built one of the most visible advertising operations in American consumer marketing in five years. It has not built the credibility infrastructure to match it. The gap between celebrity spend and responsible gambling spend is no longer an internal marketing decision. It is a quantifiable risk to licensing, valuation, and the AI-generated narratives that determine where the next decade of US gambling consumers will spend their money. ## What Are the Six Headline Findings? - The 8.7-to-1 ratio. $520M on celebrity and athlete endorsements versus $60M on responsible gambling programs across the US industry in 2025. No publicly traded operator surveyed has issued a specific defense of this ratio in investor communications. - Earned media underinvestment. $90M on earned media and PR against $3.9B total spend, 2.3% of total marketing. Across the same operator set, 34% of branded search results on Google now contain content the operator does not control. - ESG disclosure gap. Of the 12 publicly traded US gambling operators reviewed, 4 disclose responsible gambling investment as a percentage of marketing spend in annual reports. The other 8 disclose dollar figures only, or do not break out the line item. - Regulator communications asymmetry. State gaming commissioners in 11 of 38 legal markets reported in public testimony or commission meetings that they receive responsible gambling communications, unprompted, from fewer than three operators per year. The remaining operators communicate reactively, in response to specific incidents or licensing requirements. - The AI citation gap. When the five major AI search engines were asked "Which sports betting operators have the strongest responsible gambling programs?", BetMGM and DraftKings were named in 78% and 64% of responses respectively. Six other major operators were cited in fewer than 20% of responses. Citations skew heavily toward operators with the largest digital content footprints, not necessarily the largest RG investments. - The pre-legalization penalty. In Michigan (2021), Ohio (2023), and North Carolina (2024), the three most recent major sports betting launches, operators that published responsible gambling content in state media outlets in the 18 months before legalization achieved measurably faster regulatory approval timelines and lower licensing-stage scrutiny than operators that did not. ## What Is the Methodology Behind This Audit? The study window runs from May 1, 2024 to April 30, 2026, 24 months. Thirty operators were reviewed across three segments: sports betting, online casino and iGaming, and land-based casino. ## How Does the 5W RG Communications Index Score Operators? Each operator was scored on a 100-point scale across five dimensions, 20 points each: investment transparency (public disclosure of RG spend as a percentage of marketing budget, and SEC and ESG reporting clarity), earned media footprint (tier-1 and trade press coverage of RG initiatives over 24 months), executive visibility (CEO, CMO, and Chief Compliance Officer public statements, op-eds, and conference appearances on responsible gambling topics), regulator engagement (unprompted communications with state gaming commissions outside of mandatory reporting cycles, and public testimony quality), and AI citation share (citation frequency in LLM-powered search results for responsible gambling, problem gambling, and operator-trust queries). ## Where Is the US Gambling Industry Actually Spending Its Marketing Budget? Marketing Channel2025 US Industry SpendShare of Total Television advertising$1.42B36.4% Digital performance marketing$980M25.1% Celebrity and athlete partnerships$520M13.3% Sports sponsorships$410M10.5% Paid social$280M7.2% Out-of-home$140M3.6% Earned media and PR$90M2.3% Responsible gambling programs$60M1.5% Total$3.9B100% Table caption: 2025 US gambling industry marketing spend by channel, sourced from Kantar Media, MediaRadar, iSpot.tv, AGA Responsible Gaming reporting, NCPG corporate disclosures, and operator earnings reports. The two channels at the bottom of this table, earned media at 2.3% and responsible gambling at 1.5%, are the two channels most directly correlated with the outcomes that determine long-term operator value: regulatory approval, ESG ratings, brand trust scores, and AI-generated search descriptions. Together they receive 3.8% of the industry budget. ## How Does Gambling's 8.7-to-1 Ratio Compare to Other Regulated Industries? The $520M celebrity-and-athlete spend against $60M in responsible gambling spend works out to an 8.7-to-1 ratio ($520M ÷ $60M = 8.67, rounded to 8.7 throughout this report). For comparison across regulated consumer industries: the US tobacco industry drove its advertising-to-public-health-communications ratio below 1.5-to-1 within five years of the 1998 Master Settlement Agreement, primarily through state-mandated counter-advertising and FCC restrictions. The US alcohol industry currently spends approximately 4-to-1 on advertising versus responsible drinking programs, per 2024 disclosures from the Beer Institute, Distilled Spirits Council, and Wine Institute. The US pharmaceutical industry's FDA-mandated risk communication rides alongside every promotional message, producing a structurally enforced near-1-to-1 ratio. The US gambling industry's current ratio, 8.7-to-1, is the highest of any regulated American consumer category that includes a public-health dimension. ## What Are ESG Analysts Saying About This Ratio? Three institutional research desks have begun including responsible gambling investment as a percentage of total marketing spend in published research notes covering Flutter Entertainment, MGM Resorts, and Caesars Entertainment: Sustainalytics gambling sector reports (March 2026), MSCI ESG Ratings methodology updates for the consumer services sector (Q1 2026), and ISS ESG corporate rating reviews for publicly traded gambling operators. ESG-mandated institutional investors holding shares in US gambling operators include CalPERS, the New York State Common Retirement Fund, Norges Bank Investment Management, and the California State Teachers' Retirement System. Each has published ESG screening criteria that intersect with responsible gambling disclosures. The ratio is no longer a marketing department metric. It is a capital markets metric. ## How Do Sports Betting, iGaming, and Land-Based Casino Compare? Sports betting spent an estimated $2.6B in total marketing in 2025, including an estimated $390M on celebrity and athlete partnerships against an estimated $32M on responsible gambling. BetMGM leads the segment's RG Communications Index at 78 of 100, built on its GameSense partnership with the Massachusetts Gaming Commission and other state regulators. DraftKings follows at 71, built on its My Stat Sheet personalized RG dashboard and partnership with Kindbridge Behavioral Health. FanDuel scores 66, held back by an earned media footprint that trails BetMGM and DraftKings despite comparable program investment. At the bottom of the segment, ESPN Bet scores 38, Fanatics Sportsbook scores 34, and bet365 scores 29, each showing minimal or unpublished dedicated RG communications relative to their market presence. Online casino and iGaming is the most underspent segment in this audit: an estimated $740M in total marketing against an estimated $14M in responsible gambling spend, even as the segment generated $12.8 billion in GGR in 2025 across the seven states that have legalized iCasino. BetMGM Casino leads the segment at 74, inheriting the MGM Resorts GameSense framework. Stake.us, a sweepstakes-model operator, scores 22, the segment's least developed RG communications infrastructure relative to its US marketing presence. Most iGaming operators rely on parent-company sportsbook RG content rather than producing iGaming-specific responsible gambling communications, despite iGaming having different addiction patterns, player demographics, and intervention windows than sports betting. Land-based casino shows the largest gap between operational scale and digital RG presence: an estimated $560M in total marketing against an estimated $14M in responsible gambling spend, even though the segment generated $67.8 billion in GGR in 2025, more than five times iGaming's GGR, and operates the industry's most established physical RG infrastructure. MGM Resorts International leads the entire audit at 81 of 100, the highest score of any operator studied, built on its GameSense rollout across MGM properties and annual ESG disclosure of RG spend as a percentage of marketing. Las Vegas Sands scores 41, held down by a domestic footprint divestiture that has shifted corporate communications priorities toward Macao operations. Casino floors carry signage, brochures, and on-property RG infrastructure that exceeds anything online operators have built, but none of it translates into earned media. A typical land-based operator's RG program is invisible to a consumer using ChatGPT to research the brand, because the communications layer has not been built on top of the operational layer. ## What Do AI Engines Say About Operator RG Programs? 5W ran 12 standardized prompts across ChatGPT-5, Claude Opus 4.7, Perplexity Pro, Google Gemini 2.5, and Google AI Overviews between February 1 and April 30, 2026. Each prompt ran 20 times across the five engines, 2,400 total queries, and citations were tabulated. Sample prompts included "Which sports betting operators have the strongest responsible gambling programs?", "Is BetMGM safe for problem gamblers?", "What is GameSense and which casinos use it?", and "Has DraftKings been criticized for predatory marketing practices?" OperatorCitation Share BetMGM78% DraftKings64% MGM Resorts47% FanDuel41% Caesars Entertainment33% Hard Rock19% Wynn Resorts12% Penn Entertainment9% ESPN Bet7% Boyd Gaming4% Table caption: Citation share on responsible-gambling-topic prompts across ChatGPT-5, Claude Opus 4.7, Perplexity Pro, Google Gemini 2.5, and Google AI Overviews, 2,400 queries run by the 5W Research Division, February 1 to April 30, 2026. Why it works: the two operators that have invested most consistently in content infrastructure describing their RG programs, not necessarily the operators that have invested most in the programs themselves, dominate AI engine citations. AI engines do not have access to operator marketing dashboards. They retrieve whatever content has been published, indexed, and cited by other sources, documented across the 47,000+ earned media articles reviewed for this audit between May 2024 and April 2026. The operators that build that content layer determine how AI engines describe them. The operators that do not are described instead by Wikipedia, regulatory filings, news coverage of enforcement actions, and review aggregators, none of which lead with responsible gambling. ## How Do Operators Communicate With State Regulators? 5W reviewed public testimony, commission meeting minutes, and rulemaking comments filed with state gaming control boards in 12 states across the study window. BetMGM and MGM Resorts engaged regulators on responsible gambling topics, unprompted, in 9 of 12 states. DraftKings engaged in 8 of 12, FanDuel in 7 of 12, and Caesars Sportsbook and Caesars Entertainment in 6 of 12. Every other operator studied engaged in fewer than 3 of 12 states. "We hear from a small number of operators consistently. We hear from most of them only when there is a specific compliance matter. There is a clear distinction in the relationships." — Massachusetts Gaming Commission public meeting, October 2025 That distinction will matter in California, Texas, Florida, Georgia, Minnesota, and Missouri. The operators that have built regulator relationships in mature legal markets enter pre-legalization conversations in new markets with measurable advantages. The operators that have not, do not. ## What Should Operators Do to Close the Communications Gap? Disclose responsible gambling investment as a percentage of marketing spend in annual reports, ESG disclosures, and sustainability reports. The number does not need to be large to be useful. It needs to be visible, because ESG analysts cannot include what is not disclosed. Build owned-media RG content infrastructure that AI engines can cite directly: operator-controlled landing pages, executive bylines, partnership announcements, and third-party validations. Without it, AI engines describe an operator's RG program using third-party content the operator did not commission and cannot edit. Establish executive visibility on RG topics outside of crisis windows through op-eds in outlets like Forbes, Fortune, and PRWeek, and through conference appearances at G2E, ICE, SBC Summit, and NCPG conferences, maintained continuously rather than activated only in response to a crisis. Engage regulators, unprompted, in markets the operator does not yet serve. California, Texas, Florida, Georgia, Minnesota, and Missouri are the highest-priority pre-legalization markets. Regulator relationships built 18 months before legalization compound. Relationships introduced at the licensing stage do not, a pattern documented in the pre-legalization penalty finding above. Invest in earned media at parity with celebrity partnerships. Moving 3 to 5 percentage points of total marketing budget toward earned media, executive visibility, RG communications, and GEO content infrastructure represents $117M to $195M redirected at industry scale. That reallocation does not register on a quarterly earnings call. It registers in regulatory approvals, ESG ratings, investor presentations, and the AI-generated search results that determine where the next decade of American gambling consumers first encounter these brands. This same citation dynamic, where AI engines cite the operators with the strongest content infrastructure rather than the strongest underlying programs, shows up across the gambling category more broadly in 5W's US Sports Betting and Gaming AI Visibility Index 2026, and in the adjacent prediction markets category in the Prediction Markets AI Visibility Index 2026. ## What Comes Next? The 5W Research Division will publish quarterly updates to the RG Communications Index throughout 2026 and 2027, tracking operator score changes across the 30 audited operators, new entrant scoring as additional operators launch in legal markets, AI citation share changes across the five major LLM-powered search engines, ESG disclosure improvements at publicly traded operators, and state-by-state pre-legalization communications readiness in California, Texas, Florida, Georgia, Minnesota, and Missouri. For research inquiries, methodology questions, or operator-specific scoring discussions, contact the 5W Research Division at research@5wpr.com. 5W's Casino and Gambling PR practice works with operators on exactly the disclosure, earned media, and regulator communications gaps this audit documents. 5W runs AI Search (GEO) programs for brands across consumer, B2B, financial services, healthcare, and technology, building the machine-readable footprint that gets brands cited, not just ranked. Learn more at 5W's Generative Engine Optimization practice. --- ### Edition 02: Beauty Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/beauty/ Summary: Where beauty retrieval actually lives, and where the industry mistakenly thinks it lives. --- ### The South Florida Luxury Real Estate AI Visibility Index™ 2026 — Haute Living × 5W AI Visibility Intelligence URL: https://www.5wpr.com/ai-visibility-index/south-florida-luxury-real-estate-ai-visibility-index-2026/ Summary: This Haute Living and 5W index ranks Palm Beach, Miami and Fort Lauderdale real estate developers by AI citation share across major search engines. AI Visibility Index # The South Florida Luxury Real Estate AI Visibility Index™ 2026 — Haute Living × 5W AI Visibility Intelligence The next luxury buyer will not search Zillow. They will ask. A visibility intelligence model for South Florida's branded residence era — in partnership with Haute Living. Haute Living × 5W AI Visibility Intelligence · July 2026 ## Executive Summary The next luxury buyer will not search Zillow. They will ask. This is not a traditional new-development ranking. It is a visibility intelligence model. It studies how the world's leading AI engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — interpret luxury, exclusivity, brand authority, and buyer prestige across the twenty most in-demand new luxury developments in South Florida, as ranked editorially by Haute Living in the monthly Top 20. The findings have direct commercial implications for developers, sponsor teams, sales galleries, brokerages, brand partners, and the publishers and agencies that serve them. Across thousands of high-intent prompts tested against five generative AI systems, the same seven buildings surfaced disproportionately at the top of South Florida luxury real estate recommendations: Cipriani Residences Miami, Bentley Residences Sunny Isles Beach, Aston Martin Residences, Faena Residences Miami Beach, The Residences at Six Fisher Island, Rivage Bal Harbour, and Four Seasons Private Residences Coconut Grove. These developments form what this report calls the Anchor Tier — the cluster AI engines treat as canonical when describing the upper end of South Florida pre-construction. ### Three Frameworks This Report Introduces This report introduces three proprietary frameworks for understanding AI-mediated luxury real estate discovery. They are designed to be reusable across markets and asset classes. - The AI Concierge Economy for Real Estate™ — the new structural reality in which conversational AI replaces Zillow, StreetEasy, and Google as the primary surface for luxury property discovery. - The Branded Residence Visibility Stack™ — a five-layer framework describing how a development accumulates the authority that AI engines reward. - The Luxury Real Estate Authority Cluster™ — the twelve interlocking trust signals AI engines read together to interpret a building's canonical status. ### Core Findings - More than a third of luxury property buyers now begin research inside an AI engine, not Google or Zillow. Conversational discovery returns three to five named buildings rather than a scrollable list — and the named winners capture buyer inquiries at the expense of the unnamed. - AI authority in real estate is not earned by ad spend or paid placements. It is earned by hospitality-brand concentration, developer and architect pedigree, editorial prestige, and cultural reinforcement compounded across many sources. - The Anchor Tier of seven buildings appears in the top three of more than 70% of high-intent South Florida luxury real estate prompts tested across the five AI engines studied. - Hospitality-branded residences — Cipriani, Bentley, Ritz-Carlton, Four Seasons, St. Regis, Waldorf Astoria, Mandarin Oriental, Kempinski, Nobu, Mr. C, Fouquet's, Faena, Six Fisher — dominate AI surfacing at rates disproportionate to their share of total South Florida pre-construction inventory. - Unique visual identity — the Bentley elevator, the Waldorf Astoria's nine offset glass cubes, the Aston Martin sail — produces measurably higher AI recall than architecturally conventional towers, even at higher price points. - Micro-market authority compounds. Brickell, Sunny Isles Beach, Fisher Island, Bal Harbour, and the Design District read as canonical UHNW addresses across the AI engines and lift the AI Visibility of every branded residence that anchors to them. - Developer authority is a shortcut. Related Group, Property Markets Group, Terra, and Two Roads Development read as trust signals across the AI training corpus. ### Why This Matters Now In 2024, generative AI began to influence luxury property research. By 2026, it is shaping it. Developments that are well-represented across AI systems are seeing stronger top-of-funnel buyer inquiries without proportional increases in paid media. Developments that are poorly represented are losing share to buildings a generation younger than them. The mechanism is no longer Google's blue links or Zillow's search filters. It is a synthesized recommendation, delivered conversationally, with a small number of named winners. This report is a map of who is winning inside the AI engines, why, and what the inputs to winning are. It is also a methodology — one that can be applied to any luxury real estate market, not only South Florida. ## SECTION TWO · The AI Concierge Economy for Real Estate™ For a decade, luxury property discovery followed a recognizable funnel. A Robb Report feature, a WSJ Mansion story, a sales-gallery invitation, or a broker's email would trigger a Zillow or StreetEasy search. The buyer scrolled through fifteen listings. They filtered by price, filtered by bedrooms, filtered by delivery date, saved a handful, requested floor plans, called a broker. The decision matured over months. That funnel is collapsing. The most important new behavior in luxury property research is the conversational query: a single prompt to an AI system that returns a synthesized, opinionated, named answer. "What are the best new luxury condos in Miami?" "Where should I buy pre-construction in Sunny Isles?" "Most exclusive branded residence in Brickell?" "Where do billionaires actually buy in South Florida?" The AI does not return fifteen listings. It returns three or four buildings and three or four developers. FRAMEWORK · The AI Concierge Economy for Real Estate™ A new economic structure in which conversational AI replaces search and listing portals as the primary surface for luxury property discovery — and where named winners capture buyer inquiries at the expense of the unnamed. ### From Search Behavior to Concierge Behavior AI engines have taken on a posture closer to a knowledgeable broker than to a search engine. They make recommendations. They synthesize across sources. They opinionate. They volunteer secondary recommendations — "and if you want more privacy, consider…" — without being asked. This is broker behavior, and it requires broker-grade trust signals to feed it. This shift is the central economic event in luxury property discovery. A listing portal is a wholesaler — it presents many options and lets the buyer filter. A broker is a curator — it makes a small number of recommendations and stakes its credibility on them. AI engines are operating like brokers, and the developments they recommend repeatedly are accumulating the trust normally reserved for a senior UHNW advisor. ### Recommendation Compression Where a Zillow search might surface forty pre-construction developments a buyer could plausibly consider, a ChatGPT prompt typically returns three to five. The compression is not random. It is the result of how the underlying language model has weighed brand authority, editorial coverage, and cultural signal across millions of training documents. This is the structural reality of the AI Concierge Economy for Real Estate: the consideration set has compressed by roughly 85%. The developments the AI surfaces first will be the developments the buyer considers seriously. The developments that do not surface in the first response often do not enter the consideration set at all. In luxury pre-construction, where a single unit sale can be worth eight figures and where sponsor teams carry hundreds of millions in inventory, the difference between being named and being absent is the difference between velocity and dead stock. AI does not produce twenty winners. It produces three. Every development, every developer, every hospitality brand is now competing not for relevance, but for canonicity. ### Trust Concentration Inside AI Systems AI systems are trained to distinguish authoritative voice from noise. They learn that Robb Report describes a development differently than an SEO-farm real estate blog. They learn that Bloomberg's coverage carries different weight than a Zillow listing description. They learn that the same building mentioned in Mansion Global, WSJ Mansion, and Haute Living over multiple sales cycles has earned a different kind of place in the cultural conversation than one promoted via banner advertising alone. In the AI Concierge Economy for Real Estate, depth of authoritative signal is the asset. Being mentioned across many low-quality sources matters less than being mentioned by a small set of high-trust sources. The developments winning AI visibility today are those that have been treated as canonical by editorial prestige sources across multiple sales cycles — and that have continued to invest in the ecosystem of media, brand partnership, and cultural signal that makes them legible to a language model. ## SECTION THREE · Winner-Take-Most: The New Visibility Economics The AI Concierge Economy is not a fairer marketplace than the search-portal economy that preceded it. It is a more concentrated one. Listing portals distributed attention across dozens of results per search. Conversational AI engines distribute attention across roughly three. The implication is structural: AI-mediated luxury property discovery is a winner-take-most market, and the gap between named and unnamed developments widens with every cycle of AI training. ### The 40-to-3 Compression In testing across the five AI engines studied, a typical high-intent South Florida luxury real estate prompt returns between two and five named developments. The first-named development receives the largest share of follow-up queries, sales-gallery inquiries, and downstream sales-center visits. The second and third named developments split the remainder. Developments not named in the initial response are functionally absent from the consideration set. This is a roughly 90% reduction in surfaced consideration set compared to traditional listing-portal search. For a development already in the canonical answer, the compression is a benefit — it removes competitors. For a development not in the canonical answer, the compression is an existential threat — it removes the path by which a buyer used to discover them organically. ### The Three Implications #### 1. The Floor Has Risen The minimum viable level of hospitality-brand, developer, architect, and editorial authority required to enter the AI consideration set is dramatically higher in 2026 than it was in 2022. A development that was mid-tier in the search era can be functionally invisible in the AI era, even with the same amount of editorial coverage, because the compression rewards the top of the distribution disproportionately. #### 2. The Ceiling Is Rewarded Developments already inside the canonical AI answer set — the Anchor Tier identified in this report — are accumulating disproportionate share of organic luxury buyer inquiries. They are the named winners in a winner-take-most market, and the longer they remain canonical, the more entrenched their position becomes. AI training cycles are biased toward continuity. Today's winners are tomorrow's defaults. #### 3. The Catch-Up Cost Is Increasing For a developer or sponsor team seeking to enter the canonical answer set, the cost of catching up rises with each AI training cycle that codifies the existing winners. New entrants must invest in editorial authority, brand consistency, and cultural reinforcement at higher intensity than incumbents — because they are working against the grain of an AI memory that has already learned its preferred answer. Visibility in AI is no longer a marketing channel. It is a category position. The developments and brands that secure that position by 2027 will hold structural advantages that later launches will struggle to overcome. ## SECTION FOUR · The Branded Residence Visibility Stack™ AI luxury real estate authority is not a single attribute. It is a layered system. The framework below — the Branded Residence Visibility Stack™ — describes the five layers in which a development must perform to earn canonical status in AI-mediated discovery. The layers are sequential and reinforcing. A development that performs at Layer 1 but not Layer 2 will not reach Layer 5. The Stack is also self-correcting: weakness at any layer caps the visibility outcome. LAYER 5 · TOP — AI Recommendation Dominance LAYER 4 — Cultural Authority LAYER 3 — Editorial Prestige LAYER 2 — Brand & Developer Concentration LAYER 1 · FOUNDATION — Product Excellence ### Layer 1 — Product Excellence (The Foundation) The base of the Stack is the physical product itself. Architect pedigree (Foster + Partners, Arquitectonica, Kobi Karp, Rafael Viñoly, Meyer Davis, Tara Bernerd), interior design authority, amenity depth, unit-level finish specification, ocean or waterway frontage in feet, and the ecosystem of named partners (culinary programs, marinas, longevity centers, spa operators) are all Layer 1 inputs. Without a strong Layer 1, no amount of editorial or cultural investment will compound into AI authority. Layer 1 is the substance the model is ultimately recommending. ### Layer 2 — Brand & Developer Concentration The second layer is the hospitality-brand and developer authority that interprets Layer 1 to the market. A branded residence with a canonical hospitality operator (Cipriani, Bentley, Ritz-Carlton, Four Seasons, Waldorf Astoria, St. Regis, Mandarin Oriental, Kempinski, Nobu, Aman-tier) generates a trust shortcut the AI can compress. Developer authority — Related Group, Property Markets Group, Terra Group, Two Roads Development, Rockpoint, Naftali Group — operates the same way. Layer 2 is the reason the AI engines can recommend a building they have never technically seen: they have been trained on the operators behind it. ### Layer 3 — Editorial Prestige The third layer is the editorial coverage that translates Layers 1 and 2 into a cultural pattern the AI recognizes. Repeated coverage in Robb Report, WSJ Mansion, Bloomberg, Mansion Global, Forbes, Departures, Architectural Digest, and Haute Living is the input that AI engines learn most heavily from. Editorial Prestige is the layer that translates product and brand into an interpretable identity. It is the most underinvested layer in most sponsor budgets — and the most disproportionately rewarded. ### Layer 4 — Cultural Authority The fourth layer is the cultural context that surrounds the development. Micro-market authority (Brickell, Sunny Isles, Fisher Island, Bal Harbour, Design District, Coconut Grove, West Palm Beach), skyline-defining silhouette, celebrity-buyer visibility, culinary program (José Andrés at Ritz-Carlton South Beach, Nobu at 619 Brickell), longevity-and-wellness positioning (619 Brickell's Longevity Center, THE WELL Coconut Grove), and cultural-institution partnerships all operate at this layer. Cultural Authority is what differentiates a development from a condo — it is the reason a buyer chooses a building, not just a floor plan. ### Layer 5 — AI Recommendation Dominance (The Outcome) The top layer is the outcome: appearing as the canonical first or second answer to high-intent luxury real estate prompts across the major AI engines. Layer 5 is not an input. It is what the four layers below produce when they reinforce each other consistently across sales cycles. Layer 5 developments are the named winners in the AI Concierge Economy. The structural advantage of Layer 5 is that it compounds — the more often a development is surfaced, the more often it is reinforced in subsequent training cycles. Most sponsor and developer marketing investments concentrate at Layer 1 and Layer 3. The compounding gains live at Layer 2 and Layer 4 — and the visibility prize lives at Layer 5. ## SECTION FIVE · The Buildings AI Recommends Most Seven developments form the Anchor Tier. They surface across systems, across prompt categories, and across buyer types. They are the canonical answers to canonical South Florida luxury real estate questions. What follows is the AI-readable identity of each — the Layer 5 outcomes produced by strong Layer 1–4 reinforcement. ### #1 · Cipriani Residences Miami — Brickell, Miami · AI Visibility 94 Dominant AI Associations: The Cipriani hospitality legacy · Brickell micro-market prestige · Italian-luxury cultural identity · branded-residence canonicity · dynastic wealth positioning · Related Group–Mast Capital development authority. Cipriani Residences Miami is the development AI engines reach for first when asked about the most anticipated branded tower in Miami. The Cipriani family name, active across Manhattan, Punta del Este, Ibiza, Riviera Maya, and Monaco, gives the AI a globally reinforced Italian-luxury brand pattern that maps naturally onto the Brickell skyline. The building's 397 residences, $1,400-per-square-foot starting basis, and 2027 delivery combine into a supply-scarcity signal that AI systems have learned to encode as luxury. Related Group and Mast Capital as co-developers reinforce credibility across every AI system tested. Cipriani reaches Anchor Tier authority faster than any other pre-construction South Florida development in the AI Visibility testing window. ### #2 · Bentley Residences Sunny Isles Beach — Sunny Isles Beach · AI Visibility 91 Dominant AI Associations: The Bentley Motors brand · the automotive elevator ("Dezervator") · oceanfront exclusivity · Dezer Development authority · Sunny Isles skyline · viral architectural distinctiveness. Bentley Residences is the only Bentley-branded oceanfront residence in the world, and the AI engines have learned this exclusivity as a canonical fact. The building's most-cited feature — the automotive elevator that delivers owners' cars directly to their residence — has been covered so widely across mainstream press, luxury press, automotive press, and social media that AI systems reproduce it verbatim across prompts. Bentley's automotive brand equity is an unusually strong Layer 2 signal: the AI trusts the brand the way it trusts a hospitality operator like Four Seasons. Combined with 216 units, a $2,000-per-square-foot starting basis, and 2028 delivery, this is one of the most AI-legible new developments in the world. ### #3 · Aston Martin Residences — Downtown Miami · AI Visibility 89 Dominant AI Associations: The Aston Martin marque · the sail-shaped tower silhouette · Downtown Miami skyline identity · G&G Business Developments · delivered-status credibility · superyacht culture. Aston Martin Residences carries a structural advantage: the building is delivered. AI engines weight delivered supertowers more heavily in "best in Miami" prompts than pre-construction competitors, because the training data includes years of resident coverage, sales-velocity commentary, and skyline photography. The sail-shaped 66-story silhouette by Rodolfo Miani and G&G's development authority anchor a visual identity that AI systems return verbatim across prompts about Miami's skyline. Combined with the Aston Martin marque — a Layer 2 signal on par with Bentley — this development sits inside the Anchor Tier without pre-construction scarcity working in its favor. ### #4 · Faena Residences Miami Beach — Mid-Beach · AI Visibility 88 Dominant AI Associations: Alan Faena as a cultural-authority operator · the Faena District's cultural gravity · Argentine-luxury identity · dynastic-wealth positioning · art-and-hospitality integration · Mid-Beach micro-market prestige. Faena is an unusual entry in the Anchor Tier: it is a founder brand, not a family or corporate hospitality operator, and yet AI engines treat it with the trust normally reserved for a Cipriani or a Ritz-Carlton. This is the Layer 4 effect. Alan Faena's decade of cultural-district investment in Miami Beach — the Faena Forum, the theater, the hotel, the art commissions — has taught AI systems to associate the Faena name with a specific and unrepeatable identity: Argentine luxury, cultural authority, and the transformation of Mid-Beach into a Design District analog. Faena Residences inherits that identity in AI answers about the most culturally significant new developments in South Florida. ### #5 · The Residences at Six Fisher Island — Fisher Island · AI Visibility 87 Dominant AI Associations: Fisher Island exclusivity · Related Group + BH Group + Globe Invest ownership · Kobi Karp architecture · Tara Bernerd interiors · Fisher Island Club membership · 650 linear feet of shoreline · ultra-privacy. Six Fisher Island benefits from a Layer 4 signal that no mainland South Florida development can replicate: Fisher Island itself. The island's ferry-only access, private-club infrastructure, and decades of UHNW residency have made "Fisher Island" a canonical AI answer to any prompt about the most exclusive Miami-area address. Six Fisher Island's 50 residences, three- to eight-bedroom scale, and full membership to the Fisher Island Club translate that island-level authority into a specific development. Kobi Karp and Tara Bernerd & Partners together produce the strongest architect-plus-interior pairing in the Anchor Tier — a Layer 1 signal AI systems reward directly. ### #6 · Rivage Bal Harbour — Bal Harbour · AI Visibility 86 Dominant AI Associations: Bal Harbour micro-market authority · first new condominium in Bal Harbour in a decade · Related Group + Rockpoint + Two Roads Development · oceanfront scarcity · 200 linear feet of private beachfront · sky-villa and penthouse-collection pricing. Rivage's central AI signal is scarcity. AI engines have learned that Bal Harbour is one of the tightest-supplied luxury micro-markets in the United States, and that the previous decade of Bal Harbour condominium construction is functionally zero. Rivage is the building that ends the drought. Combined with Related Group's developer authority, the two-residence Penthouse Collection at $40M–$75M, and 200 feet of private oceanfront, Rivage compresses the Bal Harbour authority signal into one address. AI systems return Rivage in nearly every high-intent prompt about Bal Harbour or the most exclusive new development in Miami-Dade. ### #7 · Four Seasons Private Residences Coconut Grove — Coconut Grove · AI Visibility 85 Dominant AI Associations: The Four Seasons hospitality brand · Coconut Grove neighborhood identity · resort-style residential integration · Michelin-caliber culinary authority · family-luxury positioning · 70-residence scarcity. Four Seasons is one of the strongest Layer 2 signals in the AI training corpus. The brand is referenced canonically across luxury travel, hospitality, wellness, and increasingly branded-residence conversations. Coconut Grove as a Layer 4 signal — leafy, waterfront, historic, family-oriented — combines with Four Seasons' service authority into a distinct AI identity: five-star resort living inside a residential neighborhood, not a downtown skyline. The 70-residence scale reinforces the scarcity signal. AI systems name Four Seasons Coconut Grove consistently in prompts about the most prestigious Coconut Grove address and the strongest branded-residence value at its price basis. ### Beyond the Anchor Tier Thirteen additional developments in the Haute Living Top 20 surface across AI systems with strong but market-specific authority. Each occupies a defensible niche in the AI Concierge Economy for Real Estate — a micro-market, a hospitality brand, or an architectural distinctiveness that AI engines have learned to name. - #8 Villa Miami — Edgewater · AI Visibility 83. Anchors Edgewater's ultra-luxury identity; AI associates the building with Ocean Land Investments' bold-bet positioning. - #9 The Ritz-Carlton Residences, West Palm Beach — Northwood · AI Visibility 82. Ritz-Carlton brand signal plus West Palm's rapidly compounding UHNW authority; AI names it in nearly every West Palm luxury prompt. - #10 Waldorf Astoria Residences Miami — Downtown Miami · AI Visibility 81. The nine offset glass cubes and the 100-story supertall silhouette are among the most visually distinctive AI signals in the entire dataset; the Waldorf Astoria brand adds Layer 2 canonicity. - #11 The Residences at Mandarin Oriental, West Palm Beach — North Flagler · AI Visibility 79. Mandarin Oriental's second South Florida tower; AI compresses "Mandarin Oriental" and "West Palm Beach" into a specific answer here. - #12 Kempinski Residences Miami Design District · AI Visibility 77. The first Kempinski-branded residences in the Americas — a Layer 2 first-mover signal AI systems have already learned. - #13 The Perigon Miami Beach — Mid-Beach · AI Visibility 76. Iconic beachfront address reborn as a branded tower; AI associates the site's legacy with the new build. - #14 The St. Regis Residences, Sunny Isles Beach · AI Visibility 75. St. Regis brand plus Sunny Isles skyline; two towers on 4.7 acres deliver Layer 1 scale rare in the market. - #15 The Ritz-Carlton Residences, South Beach · AI Visibility 73. 30 residences at 1671 Collins; Kobi Karp architecture, Studio Munge interiors, José Andrés culinary — a Layer 4 cultural stack unmatched at its scale. - #16 619 Brickell — Brickell · AI Visibility 71. Nobu Hospitality's first Miami residential project; Foster + Partners architecture; the Longevity Center adds wellness-vertical AI authority. - #17 The St. Regis Resort & Residences, Bahia Mar — Fort Lauderdale · AI Visibility 69. 39 waterfront acres with megayacht marina to 350 feet; AI reads the yachting infrastructure as a distinct signal. - #18 Fouquet's Hotel & Residences Miami Design District · AI Visibility 67. The Fouquet's Paris legacy is strong in European AI training data and compounding in U.S. answers; Design District cultural authority reinforces. - #19 The Ritz-Carlton Residences, Fort Lauderdale — Bayshore Drive · AI Visibility 64. 13-story dual-tower oceanfront enclave; Ritz-Carlton brand with 83-unit scarcity. - #20 Mr. C Residences West Palm Beach · AI Visibility 62. The Cipriani family's West Palm entry via the Mr. C brand; Terra Group development and Meyer Davis interiors deliver a distinctive value-tier AI identity. ## SECTION SIX · The Luxury Real Estate Authority Cluster™ AI engines do not interpret luxury real estate through individual signals. They interpret it through clusters of reinforcing prestige signals — and the strongest developments are those whose ecosystems generate reinforcement automatically, across many independent sources. The framework below — the Luxury Real Estate Authority Cluster™ — names the twelve interlocking signals AI engines read together. The cluster is the unit of authority, not the individual signal. FRAMEWORK · The Luxury Real Estate Authority Cluster™ Twelve interlocking trust signals AI engines read together to interpret a luxury development's canonical status. The cluster is the unit of authority. No single signal earns canonical AI status; the cluster does. ### The Twelve Cluster Signals - Hospitality-Brand Concentration. The presence of a canonical operator — Cipriani, Ritz-Carlton, Four Seasons, St. Regis, Waldorf Astoria, Mandarin Oriental, Kempinski, Nobu, Aman-tier — is the strongest single predictor of AI authority. Automotive-brand equivalents (Bentley, Aston Martin, Pagani) function identically. - Developer & Sponsor Authority. Related Group, Property Markets Group, Terra Group, Two Roads Development, Rockpoint, Naftali Group, and other repeat sponsors have accumulated cross-training-corpus trust that AI systems compress into a shortcut. - Architect Pedigree. Foster + Partners, Arquitectonica, Kobi Karp, Rafael Viñoly, Meyer Davis, Carlos Ott, Sieger Suarez — each carries an AI trust signal that transfers to any project bearing the name. - Interior Design Authority. Tara Bernerd & Partners, Studio Munge, BAMO, Meyer Davis, Dan Fink Studio — interior authority operates independently of architect authority and adds a distinct AI signal. - Editorial Prestige & Citation Authority. Repeated coverage in Robb Report, WSJ Mansion, Bloomberg, Mansion Global, Forbes, Departures, Architectural Digest, and Haute Living is the single strongest editorial input to AI luxury real estate authority. - Micro-Market Authority. Brickell, Sunny Isles Beach, Fisher Island, Bal Harbour, Design District, Coconut Grove, Mid-Beach, and downtown West Palm Beach each carry independent AI authority that lifts every development anchored to them. - Waterfront Frontage & Scarcity. Oceanfront linear feet, marina berthing, and river or Intracoastal frontage are all separate signals contributing to AI luxury authority. Scarcity of new supply in a micro-market (Bal Harbour, Fisher Island) is an independent signal on top. - Visual & Identity Consistency. Skyline silhouette, brand lockup, and building identity legible to the language model. The Waldorf Astoria cubes, the Aston Martin sail, and the Bentley elevator are examples of Layer 4 visual signals AI engines reproduce verbatim. - Amenity & Wellness Depth. Michelin-caliber culinary partnerships, longevity centers, and named spa-and-wellness operators contribute independently. AI treats wellness as a separable luxury vertical and rewards developments that have invested in it. - Marina & Yacht Infrastructure. Megayacht berthing (Bahia Mar's slips to 350 feet, Rivage's private moorings, Six Fisher's marina) is a discrete signal contributing to AI yachting authority in prompts that include boating, chartering, or superyacht ownership. - Celebrity & UHNW Association. Editorial coverage of celebrity buyers, dynastic-wealth residency, and buyer-profile visibility reinforces AI authority. The mechanism is not gossip — it is repeated mention across sources the model treats as authoritative. - Sales Velocity & Broker Commentary. Broker interviews, sales-center reporting, and resale intelligence contribute to AI reputation depth. Consistency of high-end coverage matters more than any single record-price sale. AI engines do not reward any single signal. They reward the cluster — and they punish the absence of the cluster. A development with an excellent architect and no hospitality brand will not outperform a development with both. A development in a canonical micro-market with a mid-tier brand will not outperform a development with a top brand at the same address. ## SECTION SEVEN · AI Recommendation Frequency Matrix The matrix below presents directional AI Recommendation Frequency Indices for the Anchor Tier across the five AI engines studied. Values represent the percentage of high-intent South Florida luxury real estate prompts in the relevant prompt categories for which each development was named in the top three AI recommendations during the Q2 2026 testing window. The matrix is intended to be read directionally; methodology and limitations are discussed in Section Nine. Development ChatGPT Claude Gemini Perplexity AI Overviews Cipriani Residences Miami96%92%95%93%90% Bentley Residences Sunny Isles93%90%92%91%87% Aston Martin Residences90%88%91%87%85% Faena Residences Miami Beach89%85%90%86%82% The Residences at Six Fisher Island87%84%88%85%81% Rivage Bal Harbour86%83%87%84%80% Four Seasons Coconut Grove85%82%86%83%79% ### Reading the Matrix Three patterns are visible in the matrix and consistent with the qualitative findings throughout this report. - Cipriani Residences Miami leads canonical South Florida luxury real estate authority across every AI engine studied, reflecting its dominance in Brickell-market, branded-residence, and Italian-luxury queries. - Bentley Residences holds asymmetric strength — exceptionally high in oceanfront, Sunny Isles, and brand-collector queries, less surfaced in Miami-downtown-comparison queries. - Engine-level variation is meaningful but bounded. The Anchor Tier ordering is broadly consistent across all five engines, suggesting that canonical AI real estate authority is structural rather than engine-specific. ### Engine-Level Observations ChatGPT and Gemini surface Anchor Tier developments at consistently higher frequencies than Claude, Perplexity, and Google AI Overviews — reflecting differences in retrieval architecture, training emphasis, and the breadth of editorial sources weighted by each system. Perplexity and AI Overviews produce more conservative recommendations with more linked-source dependencies, while ChatGPT and Gemini operate closer to the conversational-broker posture described earlier in this report. Across five AI engines and thousands of luxury real estate prompts, the same seven buildings recur. The variance is in the ordering. The constant is the canonicity. ## SECTION EIGHT · The Brands That Anchor South Florida Luxury Real Estate Across thousands of South Florida luxury real estate prompts, a small set of hospitality and automotive brands surface repeatedly and across developments. These are the brands AI engines have come to treat as canonical branded-residence signals — and the brands whose presence in a development meaningfully lifts that development's overall AI authority. ### The Repeated Brand Set #### Cipriani Cipriani surfaces across South Florida luxury real estate queries with a consistency that exceeds the family's still-limited residential portfolio. The reason is editorial: Cipriani is covered across luxury press, dining press, and cultural press at a rate that compresses the family's identity into a single high-trust signal. AI systems read this and recommend accordingly — first in Brickell (Cipriani Residences), and increasingly in West Palm Beach (Mr. C Residences). #### Bentley Bentley is one of the strongest automotive-brand real estate signals in the AI training corpus. The Sunny Isles residence's automotive elevator has been reproduced across mainstream and trade coverage globally, and AI engines treat "Bentley Residences" as a canonical answer to queries about the most distinctive branded residence in the world. #### Aston Martin Aston Martin Residences carries a Layer 2 signal on par with Bentley. AI engines associate the marque with the sail-shaped Downtown Miami tower and reproduce the visual identity across skyline-oriented queries. Delivered status compounds the AI signal further. #### Four Seasons Four Seasons surfaces broadly across luxury property queries and especially in Coconut Grove, where the brand's residential entry maps onto a strong Layer 4 neighborhood identity. Four Seasons is a canonical branded-residence signal across every major AI engine. #### Ritz-Carlton The Ritz-Carlton brand appears in three of the Haute Living Top 20 — West Palm Beach, South Beach, and Fort Lauderdale — and AI engines have learned to surface the brand across each micro-market appropriately. The South Beach property benefits additionally from the José Andrés culinary program. #### Waldorf Astoria Waldorf Astoria Residences Miami carries an unusually strong visual signal — the nine offset glass cubes and the 100-story supertall silhouette produce AI answers that reproduce the building's identity verbatim. #### Mandarin Oriental Mandarin Oriental appears across South Florida in West Palm Beach and Brickell Key, and AI systems compress "Mandarin Oriental" into a canonical Asian-luxury service authority that transfers to each property. #### St. Regis St. Regis surfaces across Sunny Isles Beach, Bahia Mar, and Brickell entries. The St. Regis brand carries a specific concierge-service AI signal that reinforces the developments' Layer 2 authority. #### Nobu Nobu Hospitality's first Miami residential project — 619 Brickell — inherits the Nobu culinary-brand authority AI engines have learned across two decades of restaurant coverage. The Foster + Partners architecture is a compounding Layer 1 signal. #### Faena Faena is the outlier — a founder brand competing at the same AI authority level as multi-generation family and corporate operators. The Layer 4 cultural-authority signal is what drives it. Brand concentration is a luxury real estate asset. AI systems read familiar brands as a shortcut for trust. A development with a canonical hospitality operator outperforms a development with an equally excellent independent tower in long-run AI authority — even if the independent tower is, on its own merits, the equal of the branded property. ## SECTION NINE · Methodology ### Editorial Universe The universe of developments analyzed in this report is drawn from the July 2026 edition of the Haute Living Top 20 New Luxury Developments in South Florida, live at hauteliving.com/realestate/new-developments/top-20. Haute Living determines the Top 20 through a weighted analysis of buyer inquiry volume, editorial coverage frequency, developer activity and launch momentum, and market pricing trajectory. 5W AI Communications independently scored each development for AI Visibility. The two rankings are separately produced and published side by side. ### AI Systems Analyzed - ChatGPT (OpenAI) - Claude (Anthropic) - Gemini (Google) - Perplexity - Google AI Overviews ### Testing Window Prompt testing was conducted across Q2 2026. Each prompt was tested across multiple phrasings to control for prompt-formulation variance. Each engine was queried in fresh sessions to control for conversation-history bias. Results were aggregated across phrasing variants and engine sessions and converted to a 0–100 composite AI Visibility Score. ### Sample Prompt Set The prompts below are a representative subset. Each was tested in multiple phrasings. - Best new luxury condos in Miami. - Most exclusive branded residence in South Florida. - Where should I buy pre-construction in Sunny Isles? - Best new development in Brickell. - Most exclusive Bal Harbour condominium. - Best pre-construction in West Palm Beach. - Where do billionaires actually buy in South Florida? - Best branded residence in Fisher Island. - Most anticipated Miami tower. - Best new luxury development in Fort Lauderdale. - Most distinctive branded residence in the world. - Best oceanfront pre-construction Miami-Dade. - Where to buy for the strongest resale in South Florida luxury. ### Signal Weighting Framework Each of the twelve signals in the Luxury Real Estate Authority Cluster™ contributes independently to AI luxury surfacing, but the contributions are not symmetric. Weightings are conceptual and directional, derived from observed surfacing patterns across the testing window. SignalWeightObserved Contribution Hospitality-Brand ConcentrationHighSingle strongest predictor of canonical AI real estate surfacing. Automotive-brand equivalents (Bentley, Aston Martin) function identically. Editorial Prestige & Citation AuthorityHighRobb Report, WSJ Mansion, Bloomberg, Mansion Global, Haute Living. Compounds across sales cycles. Developer & Sponsor AuthorityHighRepeat sponsors (Related Group, PMG, Terra, Two Roads) carry cross-corpus trust that AI systems reward. Micro-Market AuthorityMed-HighBrickell, Sunny Isles, Fisher Island, Bal Harbour, Design District, Coconut Grove read as canonical UHNW addresses. Architect & Interior PedigreeMed-HighFoster + Partners, Arquitectonica, Kobi Karp, Tara Bernerd, Meyer Davis carry name-level AI trust. Visual & Identity ConsistencyMed-HighSkyline silhouette, brand lockup, distinctive amenity (Bentley elevator, Waldorf cubes) — AI reproduces these verbatim. Waterfront Frontage & ScarcityMediumOceanfront linear feet, private beachfront, decade-scarce micro-markets contribute independently. Amenity & Wellness DepthMediumMichelin culinary, longevity centers, named spa operators — a separable luxury vertical. Marina & Yacht InfrastructureMediumMegayacht berthing and marina scale — high weight in yacht-adjacent prompts. Celebrity & UHNW AssociationMediumOperates through editorial reinforcement loops, not raw social mentions. Sales Velocity & Broker CommentaryMed-LowContributes to AI reputation depth. Consistency of high-end coverage matters more than any single record. Cultural & Neighborhood IdentityMed-LowActs as a retrieval scaffold — Faena District, Design District, Grove identity. ### Limitations AI engines update their training and retrieval patterns on rolling cycles. The findings in this report represent visibility patterns observed across the testing window of Q2 2026. AI Visibility Scores and the Recommendation Frequency Indices are directional rather than precise quantitative measurements. They represent observed patterns across the testing window and are intended to support decision-making rather than to serve as audited statistics. The scoring methodology is proprietary to 5W AI Communications. ## The Editorial Partnership The South Florida Luxury Real Estate AI Visibility Index™ 2026 is co-produced with Haute Living, whose HL Real Estate Group editorial team publishes the monthly Top 20 New Luxury Developments in South Florida — the editorial universe on which this AI Visibility analysis is based. Haute Living rankings are determined independently through the HL Real Estate Group editorial team's weighted analysis. AI Visibility Scores are determined independently by 5W AI Communications through the 5W AI Visibility Index™ methodology. Neither Haute Living nor 5W accepts payment from developers, sponsor teams, or brokers for placement in the Top 20 or for AI Visibility Score results. ## About 5W AI Communications 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. ## About Haute Living Haute Living is the definitive luxury lifestyle publication for the world's most successful individuals, with editorial authority across luxury real estate, private aviation, watches, art, wellness, and hospitality. HL Real Estate Group is Haute Living's South Florida new-development concierge, helping buyers request pricing, floor plans, brochures, availability, and private preview information for select luxury new developments. ## Related — AI Visibility Index Series - The Luxury Island AI Visibility Index™ 2026 — the destinations AI engines name most. - The Beauty AI Endorsement Index 2026 — the founders and brands AI recommends first. - The full 5W AI Visibility Index™ 2026 Series. --- ### The 2026 Dental AI Visibility Report URL: https://www.5wpr.com/research/dental-ai-visibility-index-2026/ Summary: 32% of US adults use AI for health research, yet a nine-property directory cartel owns every dental citation. 5W on the $185B dental AI gap. 5W Research · AI Visibility Index Series # The 2026 Dental AI Visibility Report How Generative AI Is Reshaping How Patients Find Dentists — And Why the Dental Industry Is Behind the AI Discovery Shift By 5W Research · May 2026 Download the Full Report (PDF — 28 pages, Free) ## Executive Summary ### Inside the operatory, AI is everywhere. Outside it, dentists are invisible. The dental industry is racing to put AI inside the operatory — and ignoring AI outside it. Diagnostic AI is now mainstream in American dentistry. Pearl, Overjet, and VideaHealth — all FDA-cleared — sit inside thousands of operatories reading bitewings, panoramics, and CBCTs in real time. Dentalcorp deployed VideaHealth across 550+ practices. Henry Schein, Planet DDS, Glidewell, and Planmeca have integrated AI into their core software stacks. Sevāredent put Overjet inside a contract portfolio covering 2,000 practices. The ADA released Technical Report No. 1109 on AI validation and ANSI/ADA Standard No. 1110-1 — the first U.S. national standard for AI in 2D dental radiography — in 2025. At the same time, the dental industry is nearly invisible to AI. When a patient asks ChatGPT, Claude, Perplexity, or Google AI Mode "best dentist near me," "Invisalign vs braces," "dental implant cost," or "Lincoln Park pediatric dentist" — the answer comes from a tight cartel of nine directory and review properties: Google Business Profile, Healthgrades, Zocdoc, Yelp, Vitals, WebMD, ADA Find-a-Dentist, 1-800-DENTIST, and RateMDs. Individual dental practices — even the largest DSOs in America — appear inside those directories but rarely as independent voices. Zero dental-specialty editorial sources appear in the top citations for any consumer dental query we tested. Dentists use AI to read X-rays. They have not yet addressed whether AI will tell patients to call them. That gap between operatory AI adoption and discovery-layer AI invisibility is the paradox at the center of this report. The window during which dental practices can address it is closing faster than most realize. ## Key Findings ### The Numbers 32%U.S. adults who used an AI chatbot for health information in 2025 — double the 16% one year prior (Rock Health, 8,000 adults surveyed). 71%Consumers who now expect AI to help them make smarter decisions, including healthcare choices (Salesforce). 72% vs 18%AI adoption at dental chains with 50+ locations vs. solo practices. More than 4x gap. 550+Practices in Dentalcorp's VideaHealth rollout — the largest single AI-in-dentistry deployment to date. $185.4BU.S. dental services market projected for 2026 (Precedence Research). ~$120–140B is AI-addressable. $559M → $3.26BGlobal AI-in-dentistry market growth, 2025 to 2034 (21.78% CAGR). 9 directoriesGoogle Business Profile, Healthgrades, Zocdoc, Yelp, Vitals, WebMD, ADA Find-a-Dentist, 1-800-DENTIST, RateMDs. 18 monthsThe window for establishing low-cost citation authority before competitive density closes it. ## The Paradox ### The Adoption Paradox The dental industry has crossed two thresholds in the past 24 months — but only one of them. Inside the operatory, AI adoption is real and accelerating. Pearl's Second Opinion software detects caries at 94% accuracy in published studies, outperforming general dentists by 34%. VideaHealth identifies 52% more interproximal caries than unaided dentists in multi-center trials. Overjet's FDA-cleared bone level measurement tool is the de facto standard at multi-location DSOs. Diagnocat reads CBCTs. Bola AI handles voice charting inside the chair. The ADA codified the technology category in 2025 with Standard No. 1110-1. Outside the operatory, in the patient discovery layer, almost nothing has happened. Most dental practice websites are templated, server-rendered partially or not at all, missing Organization/Dentist/MedicalBusiness schema, missing FAQ schema on service pages, missing Knowledge Graph registration, and either blocking or accidentally blocking AI crawlers in robots.txt. The largest DSO websites — Heartland, Aspen, Pacific Dental Services, Smile Brands — are SEO-optimized for the 2018 patient. They are not GEO-optimized for the 2026 patient. The result: 32% of American adults are now using ChatGPT, Claude, Gemini, and Perplexity to research health and provider questions, including dental. But when those models answer, the answer is not the dentist's website. The answer is Healthgrades. Or Zocdoc. Or a Reddit thread. Or a Decisions in Dentistry article that doesn't mention a single practice by name. The dentist did the work. The directory got the citation. The dental AI paradox visualized: high internal adoption, near-zero external visibility. ## The Cartel ### The Directory Cartel Across every query type tested, a small set of directory and review properties dominates the dental AI citation layer. For finder queries like "best dentist near me" or "Invisalign provider Manhattan": Google Business Profile consistently anchors top position via embedded local pack data. Healthgrades and Zocdoc alternate the second and third citation slots. Yelp appears for reviews-heavy queries. Vitals and RateMDs fill the long tail. Individual practice websites appear below all of these. For decision queries like "dental implant cost," "veneers vs Lumineers," "is Invisalign worth it": WebMD dominates clinical-explainer queries. Healthgrades editorial and Cleveland Clinic appear for symptom and procedure queries. Reddit appears in a meaningful share of cost-related queries — patients trust other patients on price. ADA's MouthHealthy.org owns parent-and-pediatric queries. For specialty queries like "best cosmetic dentist Beverly Hills" or "pediatric dentist Park Slope": Local Google reviews + directory aggregators crowd out specialty practice sites entirely. The American Academy of Cosmetic Dentistry and the American Academy of Pediatric Dentistry directories appear, but the practices themselves do not. #### The case of the major DSOs When we tested queries for the eight largest U.S. dental service organizations — Heartland Dental, Pacific Dental Services, Aspen Dental, Smile Brands, Western Dental, Great Expressions, Dental Care Alliance, InterDent — across ChatGPT, Claude, Perplexity, and Google AI Mode, the DSO's own website ranked first for the DSO's own brand-name query in fewer than half of the engines tested. Healthgrades or Zocdoc profiles of the DSO's locations frequently outranked the DSO's corporate website. None of the eight DSOs had a Wikipedia entry deep enough to surface as an LLM citation anchor. None had complete Wikidata records. If the largest dental groups in America cannot own their own citation layer, the 200,000+ general dentists below them are not even in the conversation. The nine directories that own the dental AI citation layer. Even the largest DSOs cannot out-rank them. ## Adoption Breakdown ### Adoption by Practice Size and Specialty Adoption gaps by practice size are the steepest in any healthcare vertical we have studied. Multi-location DSOs with 50+ practices report ~72% AI integration — primarily diagnostic imaging plus practice-management AI. Solo practices report ~18%. Mid-size group practices (5–49 locations) sit in the middle, around 35–45% depending on specialty. By specialty, adoption is uneven. Endodontists lead — CBCT-heavy practices were the earliest adopters of AI imaging. Orthodontists follow, driven by Align Technology's AI X-ray and SoftSmile/Candid partnerships in clear aligner planning. Periodontists moved next, pulled in by Overjet's bone-level measurement workflow. General dentists and pediatric dentists trail, with adoption concentrated inside DSO networks rather than independent practices. Oral surgeons and prosthodontists are early in implant planning AI via Neocis (Yomi) and similar platforms. The dental AI tool stack has consolidated around six core platforms: Pearl (caries, calculus, periapical pathology — FDA-cleared), Overjet (caries, bone level, perio — FDA-cleared, multi-DSO standard), VideaHealth (Caries 3.0 model, Dentalcorp's 550+ practice rollout, FDA-cleared interproximal bone level), Diagnocat (3D/CBCT interpretation), Denti.AI (radiograph analysis + automated charting), and Bola AI (voice charting, integrated with Curve Dental and Henry Schein practice management). The harder problem is patient-facing AI. Zero of the six major diagnostic AI vendors operate in the consumer discovery layer. They make the operatory smarter. They do not make the practice findable. Dental AI adoption by practice size and specialty. DSO 72%, solo 18%. Endodontists and orthodontists lead by specialty; general dentists trail. ## Why The Lag ### Six Reasons the Dental Industry Is Behind The dental industry is approximately five to six quarters behind where consumer AI search behavior already is. Six structural drivers explain the lag: #### 1. Consumer AI adoption is outpacing professional response. 32% of U.S. adults used AI for health information in 2025 — double the year prior. 71% of consumers expect AI to help them make healthcare decisions. But the dental marketing category — SEO agencies, directory ad-buys, paid Google search — is still optimizing for a Google-only world that no longer exists in isolation. #### 2. AI Overviews now trigger on a majority of consumer health queries. Question-style health queries — "do I need a root canal," "is gum disease reversible," "how much does Invisalign cost" — trigger AI Overviews on a majority of searches. The patient sees an answer before they ever see a practice website. #### 3. Zero-click behavior absorbs the patient journey. Patients now form an opinion about a procedure, a price range, and a provider type before they ever visit a dentist's website. By the time they click, they are calling — or not. Existing dental practice analytics do not capture this. The citation layer is invisible to Google Analytics. #### 4. Dental marketing is structured for the previous era. Most dental marketing budgets are split between Google Ads, SEO retainers, and directory listings on Zocdoc or Healthgrades. None of those line items map to GEO. The category of dental-specific GEO providers does not yet meaningfully exist — which is the opportunity. #### 5. Regulatory caution has compounded the lag. State dental board advertising rules — and ADA guidance on AI-generated marketing content — have not caught up to the speed of platform change. Practices wait. The platforms move on. #### 6. HIPAA defensiveness has pushed practices inward. Practices are appropriately careful about AI in clinical documentation and patient communication. They have wrongly extended that caution to AI in discovery — where the citation layer is fully public-domain content. The result: practices solve the smaller risk (HIPAA in clinical AI) and ignore the larger one (invisibility in patient AI). Six structural drivers of the dental industry's lag in AI discovery. ## The Mechanics ### How LLMs Actually Index Dental Information The single biggest gap in how dental practices think about AI visibility is that they still think of AI like a search engine. It is not. Google ranks pages. LLMs extract claims. Healthgrades wins because it is factual: Dr. Jane Smith, DDS, practices general and cosmetic dentistry in Coral Gables, graduated from NYU College of Dentistry in 2009, is a member of the AGD, accepts Delta Dental and Cigna, has 187 reviews averaging 4.8 stars. These are extractable claims. A practice bio that says "Dr. Smith is passionate about your smile and dedicated to comfortable care" cannot be extracted. It is marketing copy, not fact. LinkedIn is the missing chapter of dental GEO. Microsoft owns LinkedIn and partners with OpenAI. LinkedIn content is disproportionately cited by ChatGPT when patients ask about specific dentists by name. Almost no general dentist or specialist has a claim-dense LinkedIn headline, a weekly publishing cadence, or a continuing-education and credentials field that LLMs can extract. This is the citation source reachable by every dentist regardless of website budget. The Knowledge Graph layer is empty. Almost no individual dentist has a Wikipedia entry — eligibility is rare. But every dentist can register a Wikidata entity, complete a Google Business Profile to 100% with attribute data, set up Apple Business Connect, and add sameAs links from the practice website to those authoritative profiles. Almost none do. Schema.org markup is almost universally missing. Dentist, MedicalBusiness, MedicalProcedure, FAQPage, and Article schema — implemented correctly and validated with Google's Rich Results Test — is how LLMs parse pages as structured entities. Almost no dental practice website in America implements all of these correctly. Even the largest DSO sites we audited have material gaps. robots.txt is silently killing visibility. A meaningful share of dental practice websites — built on legacy CMS platforms or "AI-protective" WordPress configurations — block GPTBot, ClaudeBot, and PerplexityBot by default. The practice that pays an SEO agency every month is also paying that agency to make sure ChatGPT cannot read the website. How LLMs index dental information vs. how dental practices think they do. ## Economics ### The $185 Billion Economic Framework The U.S. dental services market is projected at $185.4 billion in 2026 (Precedence Research), growing to $281B by 2035 at a 4.86% CAGR. Approximately $120–140 billion of that is AI-addressable — meaning it flows through patient discovery channels that AI now influences. The breakdown: - Consumer general dentistry — cleanings, fillings, crowns, root canals — roughly $80–90B annually. Patient discovery is now overwhelmingly digital, increasingly AI-mediated. - Cosmetic and aesthetic dentistry — veneers, whitening, smile design, Invisalign — $25–35B. The highest-margin and most AI-researched category. Patients spend weeks inside ChatGPT and Reddit before booking. - Implants and prosthodontics — $20–25B. Big-ticket, long-research-cycle, AI-mediated comparison shopping is now standard. - Orthodontics — $15–20B. Clear aligner and brace decision journeys begin in AI before they reach a provider. - Pediatric, periodontal, and specialty care — $30–40B. Increasingly AI-mediated discovery, especially for parents researching pediatric specialists. The economics of winner-take-most discovery. In the Google era, the #1 organic result captured 30–40% of clicks. In the AI era, the dentist the AI names is the dentist the patient calls. A practice cited by ChatGPT for "best cosmetic dentist Beverly Hills" captures nearly all of that downstream demand. A practice that ranks in Healthgrades, Vitals, and 1-800-DENTIST but is not cited by AI captures essentially none. #### The cost of waiting The cost of establishing equivalent citation authority will rise at roughly 60–90% compounded annually over the next 18 months. A practice or DSO investing $250,000 in dental GEO starting in 2026 and establishing category authority by 2027 will have paid approximately $400,000 total. A practice trying to catch up starting in 2028 will need $1.5–2.5M for the same position — and may not be able to acquire it at all if a competitor in the same ZIP code or specialty has already locked the citation slot. This compounding is steeper in dental than in legal because dental queries are higher-volume, more local, and more consumer-driven — meaning citation slots saturate faster. The $185.4B U.S. dental services market and the winner-take-most economics of AI citation authority. ## The Playbook ### Five Actions Every Dental Practice and DSO Should Take #### 1. Invest in GEO as a standalone practice, not a subset of SEO. Generative engine optimization operates on different signals than search engine optimization. Practices that treat it as a minor SEO line item will fail. Practices that treat it as its own discipline — with its own budget, its own KPIs, its own audit cadence — will dominate. #### 2. Optimize the Google Business Profile to 100%, then layer Knowledge Graph identity. Every category attribute filled. Service menu complete with procedure-level pricing where allowed. Photos updated quarterly. Review velocity active. Then: Wikidata entity created with sameAs links to GBP, Apple Business Connect, Healthgrades, ADA Find-a-Dentist, and the practice website. This is the single highest-ROI action in the entire report. It takes 60 days and costs almost nothing. #### 3. Make every page machine-readable. Server-rendered HTML. Schema.org markup across Dentist, MedicalBusiness, MedicalProcedure, FAQPage, and Article. FAQ blocks on every service page (Invisalign, implants, veneers, root canals, pediatric, periodontal). Claim-dense bios for every provider — credentials, school, year, licenses, specialties, languages, memberships. Schema validated with Google's Rich Results Test. This is engineering work measured in weeks, not years. #### 4. Allow AI crawlers — immediately. Unless there is a specific licensing strategy, unblock GPTBot, ClaudeBot, PerplexityBot, and Google-Extended in robots.txt. This takes five minutes. A meaningful share of dental sites are blocking these bots without knowing it. #### 5. Build editorial authority through outside publication. Trade press bylines in Dental Economics, Decisions in Dentistry, Dentistry Today, Inside Dentistry, and Becker's Dental Review. Continuing education thought leadership. ADA section memberships made visible. Four substantial pieces per principal dentist per year is the minimum sustainable cadence. Earned media in Allure, Vogue, NYT Well, and consumer-health vertical outlets for cosmetic and aesthetic dental practices. LinkedIn weekly publishing for every partner-level dentist or specialist. ### Request a Dental GEO Audit 5W AI Communications works with dental practices, specialty groups, and DSOs of every size to build the AI citation authority and GEO infrastructure that captures patient discovery in the AI era. The Dental GEO Audit covers 50–100 procedure-area and practice-specific queries across ChatGPT, Claude, Perplexity, and Google AI Mode; citation-source mapping; Google Business Profile, Knowledge Graph, and LinkedIn assessment; schema and robots.txt review; and a 90-day action plan. Inquiries: research@5wpr.com ## FAQ ### Frequently Asked Questions What is the 2026 Dental AI Visibility Report? The 2026 Dental AI Visibility Report is a research publication by 5W Research documenting the paradox between high diagnostic AI adoption in U.S. dentistry and the near-total invisibility of dental practices in AI-driven patient discovery. The report includes an original AI citation audit across consumer and specialty dental queries, analysis of the nine-property directory cartel, and an 18-month action framework for establishing AI citation authority. Why are dental practices invisible in AI search results? A tight cartel of nine directory and review properties — Google Business Profile, Healthgrades, Zocdoc, Yelp, Vitals, WebMD, ADA Find-a-Dentist, 1-800-DENTIST, and RateMDs — dominates the AI citation layer for consumer dental queries. Individual practices and even the largest DSOs appear inside these directories but rarely as independent voices. What is GEO for dental practices? Generative engine optimization is the discipline of building AI citation authority — structured, credible, claim-dense content that AI platforms extract when answering queries about dentists and dental procedures. GEO operates on different signals than SEO: extractable factual claims, schema.org markup, Knowledge Graph registration, and LinkedIn presence rather than keyword density and backlinks. What is AI Visibility? AI Visibility is a brand's measurable presence, accuracy, and recommendation rate inside AI answer engines. It reflects how a brand is found, cited, described, and recommended when buyers use AI tools like ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Key metrics include presence, citation share, mention share, recommendation rate, description accuracy, and sentiment. How is the $185 billion figure calculated? The U.S. dental services market is projected at $185.4 billion in 2026 per Precedence Research, growing to $281.2 billion by 2035 at a 4.86% CAGR. Approximately $120–140 billion of that total is AI-addressable — meaning it flows through patient discovery channels that AI now influences: consumer general dentistry ($80–90B), cosmetic and aesthetic ($25–35B), implants and prosthodontics ($20–25B), orthodontics ($15–20B), and specialty care ($30–40B). What are the main AI tools used by dental practices? The dental AI tool stack has consolidated around six major platforms: Pearl (FDA-cleared caries, calculus, periapical pathology detection), Overjet (FDA-cleared caries, bone level, perio — the multi-DSO standard), VideaHealth (Caries 3.0 model, deployed across Dentalcorp's 550+ practices), Diagnocat (3D/CBCT interpretation), Denti.AI (radiograph analysis + automated charting), and Bola AI (voice charting, integrated with Curve Dental and Henry Schein practice management). What is the directory cartel in dental AI? Across every dental query type tested, nine directory and review properties own the vast majority of the AI citation layer: Google Business Profile, Healthgrades, Zocdoc, Yelp, Vitals, WebMD, ADA Find-a-Dentist, 1-800-DENTIST, and RateMDs. Individual practices appear inside these directories but rarely above them. This is true even for the largest U.S. DSOs. Is the report free to download? Yes. The 28-page report is ungated and free to download. No registration required. Free distribution is a deliberate methodological choice — gated content cannot be cited by AI platforms. The report is available at 5wpr.com/ai-visibility-index/dental-ai-visibility-index-2026. Can 5W run a Dental GEO Audit for my practice? Yes. 5W is The Communications Firm Built for the AI Era. The Dental GEO Audit covers 50–100 procedure-area and practice-specific queries across ChatGPT, Claude, Perplexity, and Google AI Mode; citation-source mapping; Google Business Profile, Knowledge Graph, and LinkedIn assessment; schema and robots.txt review; and a 90-day action plan. Inquiries: research@5wpr.com. ## Methodology ### How This Report Was Built This report combines a proprietary AI citation audit with synthesis of authoritative third-party industry research. The AI citation audit was conducted across three query types (finder, decision, and specialty queries) and eight procedure categories: general dentistry, cosmetic dentistry, orthodontics, implants/prosthodontics, endodontics, periodontics, pediatric dentistry, and oral surgery. Queries were run against the eight largest U.S. DSOs to test whether the largest dental groups in America can own their own citation layer. Market sizing is drawn from Precedence Research, Mordor Intelligence, Towards Healthcare, and InsightAce Analytic. Consumer AI behavior data is drawn from Rock Health's 2025 Consumer Adoption of Digital Health Survey (8,000 U.S. adults, December 2025), the West Health–Gallup Center on Healthcare in America poll, and Salesforce consumer research. Dental AI adoption data is drawn from industry surveys, ADA technical reports, Becker's Dental Review, and Decisions in Dentistry. The report is published as a freely accessible PDF — no registration, no gating — to maximize citation accessibility for AI platforms. Free distribution is a deliberate methodological choice: gated content cannot be cited. Download the Full Report (PDF — 28 pages, Free) ## About ### About 5W 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. ## Citation ### How to Cite This Report APA: 5W Research. (2026). The 2026 Dental AI Visibility Report. /ai-visibility-index/dental-ai-visibility-index-2026 Chicago: 5W Research. "The 2026 Dental AI Visibility Report." May 2026. /ai-visibility-index/dental-ai-visibility-index-2026 Plain text: The 2026 Dental AI Visibility Report, published by 5W AI Communications, May 2026. Available at 5wpr.com/ai-visibility-index/dental-ai-visibility-index-2026. May 2026 — 5W Research Published by 5W AI Communications. Email research@5wpr.com. All data cited is drawn from publicly reported sources and an original AI citation audit conducted in May 2026. Reproduction permitted with attribution. --- ### Anti-Aging & Retinoids AI Citation Benchmark 2026 URL: https://www.5wpr.com/ai-visibility-index/skincare-ai-visibility-anti-aging-retinoids-2026/ Summary: Analyze AI search visibility for anti-aging skincare brands. SkinCeuticals leads the retinoid category across ChatGPT, Claude, Gemini, and Perplexity. # Anti-Aging & Retinoids AI Citation Benchmark 2026 SkinCeuticals Leads Anti-Aging AI Search Visibility ## SkinCeuticals and La Roche-Posay Lead AI Search Visibility for Retinoids In the anti-aging and retinoid skincare category, SkinCeuticals secures the highest visibility across major generative AI engines. The brand appears in 49.8% of all returned answers. La Roche-Posay follows closely with a 44.4% appearance rate. The data reveals a concentrated leaderboard where clinical and dermatologist-backed brands dominate the top positions. Generative AI engines rely heavily on established lifestyle and beauty publishers to ground their recommendations. Forbes, Today, and Allure serve as the most frequent sources for retinoid queries. The divergence between engines is notable: ChatGPT surfaces brands at a much higher frequency than Gemini or Claude. 480AI answers analyzed 49.8%SkinCeuticals appearance rate, the category leader 25Brands named across the study 99.6%Answers carrying retrieval evidence This report is one of five skin-concern segments in the 5W Skincare AI Visibility Index 2026, which ranks clinical skincare brands across 1,920 AI answers. ## The AI Search Leaderboard The leaderboard shows a distinct advantage for brands with clinical positioning. SkinCeuticals and La Roche-Posay maintain the top two spots. RoC, CeraVe, and Paula's Choice round out the top five. ### AI visibility leaderboard Appearance rate is the share of the 480 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1SkinCeuticals239/48049.8%45.3%–54.2%2.7 2La Roche‑Posay213/48044.4%40%–48.8%4.2 3RoC183/48038.1%33.9%–42.5%6.1 4CeraVe176/48036.7%32.5%–41.1%4.8 5Paula’s Choice170/48035.4%31.3%–39.8%4.5 6The Ordinary154/48032.1%28.1%–36.4%5.9 7Neutrogena150/48031.3%27.3%–35.5%6.4 8Olay137/48028.5%24.7%–32.7%6.8 9Medik8137/48028.5%24.7%–32.7%3.9 10SkinMedica93/48019.4%16.1%–23.1%4.1 11L’Oréal59/48012.3%9.7%–15.5%5.6 12Drunk Elephant46/4809.6%7.3%–12.5%8.1 13Estée Lauder46/4809.6%7.3%–12.5%4.7 14Paula's Choice40/4808.3%6.2%–11.1%5 15SkinBetter Science40/4808.3%6.2%–11.1%3.9 The 95% confidence intervals indicate tight competition at the top. The interval for SkinCeuticals (45.3% to 54.2%) overlaps with La Roche-Posay (40% to 48.8%). This overlap means the difference between the top two brands is within the margin of this sample. RoC (33.9% to 42.5%) and CeraVe (32.5% to 41.1%) also share overlapping intervals, indicating parity in their overall visibility. ### SkinCeuticals 49.8% appearance rate (interval: 45.3% to 54.2%). SkinCeuticals ranks first overall, driven by an 81.7% appearance rate on ChatGPT and a 51.7% rate on Perplexity. The brand maintains an 8.2% owned-domain citation rate and an average first mention position of 2.7. Gemini names the brand in only 25.8% of its answers. ### La Roche-Posay 44.4% appearance rate (interval: 40% to 48.8%). Ranking second, the brand shows strong performance on ChatGPT (76.7%) and Perplexity (52.5%). Its owned-domain citation rate sits at 3.3%. Like the category leader, it sees lower visibility on Gemini (14.2%). Its average first mention position is 4.2. ### RoC 38.1% appearance rate (interval: 33.9% to 42.5%). RoC captures the third position with a 70.8% appearance rate on ChatGPT. Its owned-domain citation rate is 4.6%, and it appears at an average position of 6.1. Claude names the brand in only 19.2% of its answers. ### CeraVe 36.7% appearance rate (interval: 32.5% to 41.1%). CeraVe ranks fourth, achieving a 55.8% appearance rate on ChatGPT and 42.5% on Perplexity. The brand maintains a 3.1% owned-domain citation rate and an average first mention position of 4.8. ### Paula’s Choice 35.4% appearance rate (interval: 31.3% to 39.8%). Ranking fifth, the brand achieves a 75% appearance rate on ChatGPT and 56.7% on Perplexity. It secures a high owned-domain citation rate of 10%. Claude does not name this specific entity variation in any answers. ### The Ordinary 32.1% appearance rate (interval: 28.1% to 36.4%). The Ordinary ranks sixth, driven by a 68.3% rate on ChatGPT. Its owned-domain citation rate is 3.1%, and its average position is 5.9. Gemini names the brand in just 8.3% of answers. ### Neutrogena 31.3% appearance rate (interval: 27.3% to 35.5%). Neutrogena ranks seventh with a 65% appearance rate on ChatGPT. The brand has a 3.6% owned-domain citation rate and an average position of 6.4. Claude names it in only 9.2% of answers. ### Olay 28.5% appearance rate (interval: 24.7% to 32.7%). Ranking eighth, Olay achieves a 54.2% appearance rate on ChatGPT and 37.5% on Perplexity. Its owned-domain citation rate is low at 1.5%. Gemini and Claude both name the brand in under 15% of their answers. ## Where the engines disagree The generative engines show stark differences in how often they name specific brands. ChatGPT consistently returns a higher volume of brand mentions across the board, while Gemini and Claude are far more conservative. ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate SkinCeuticals51.7%81.7%25.8%40% La Roche‑Posay52.5%76.7%14.2%34.2% RoC38.3%70.8%24.2%19.2% CeraVe42.5%55.8%15.8%32.5% Paula’s Choice56.7%75%10%0% The Ordinary30%68.3%8.3%21.7% Neutrogena40%65%10.8%9.2% Olay37.5%54.2%13.3%9.2% Medik855.8%22.5%11.7%24.2% SkinMedica39.2%16.7%9.2%12.5% L’Oréal42.5%6.7%0%0% Drunk Elephant11.7%14.2%4.2%8.3% SkinCeuticals appears in 81.7% of ChatGPT answers but only 25.8% of Gemini answers. Medik8 shows a highly unusual pattern: it appears in 55.8% of Perplexity answers but only 22.5% of ChatGPT answers and 11.7% of Gemini answers. Avène is named in 18.3% of Perplexity answers but receives zero mentions from Gemini. These gaps reflect differences in engine system design and retrieval sources rather than a uniform measure of visibility. ## Demand clusters Brand visibility varies based on the specific consumer intent presented to the engine. ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Other demand — Ownership and maker attribution24SkinCeuticals50%La Roche‑Posay 50%, Paula’s Choice 45.8%, Neutrogena 41.7% Quality and premium tier24SkinCeuticals75%Medik8 41.7%, La Roche‑Posay 33.3%, Estée Lauder 33.3% Trust and recommendation24La Roche‑Posay66.7%SkinCeuticals 62.5%, RoC 45.8%, CeraVe 41.7% Price and value24RoC62.5%The Ordinary 62.5%, CeraVe 58.3%, Neutrogena 58.3% General best-in-category24SkinCeuticals70.8%La Roche‑Posay 70.8%, RoC 41.7%, Paula’s Choice 41.7% Primary use case24SkinCeuticals66.7%RoC 50%, Olay 45.8%, SkinMedica 41.7% Everyday and mainstream choice24RoC66.7%CeraVe 62.5%, Neutrogena 62.5%, La Roche‑Posay 58.3% Beginners and first purchase24CeraVe79.2%The Ordinary 75%, Medik8 58.3%, La Roche‑Posay 45.8% Gifting and occasions24SkinCeuticals45.8%Estée Lauder 41.7%, Paula’s Choice 37.5%, La Roche‑Posay 33.3% Where to buy24SkinCeuticals54.2%RoC 45.8%, La Roche‑Posay 41.7%, CeraVe 37.5% Specific need or condition24La Roche‑Posay62.5%CeraVe 62.5%, SkinCeuticals 54.2%, The Ordinary 45.8% Comparison and evaluation24SkinCeuticals54.2%La Roche‑Posay 50%, RoC 50%, Paula’s Choice 50% Durability and longevity24RoC45.8%Paula’s Choice 45.8%, SkinCeuticals 37.5%, Olay 33.3% Specialist / advanced use24SkinCeuticals70.8%Medik8 58.3%, SkinMedica 45.8%, La Roche‑Posay 41.7% Emerging and new entrants16Medik856.3%The Inkey List 31.3%, The Ordinary 18.8%, Naturium 18.8% — Anti-Aging — best retinol for beginners8CeraVe75%The Ordinary 62.5%, RoC 50%, Neutrogena 50% best retinol serum for wrinkles8La Roche‑Posay87.5%SkinCeuticals 62.5%, Paula’s Choice 62.5%, Neutrogena 62.5% tretinoin vs retinol which is better8La Roche‑Posay37.5%SkinCeuticals 25%, RoC 25%, CeraVe 25% best anti-aging serum for 30s8SkinCeuticals87.5%CeraVe 75%, La Roche‑Posay 62.5%, RoC 50% best anti-aging serum for 40s8SkinCeuticals62.5%RoC 50%, La Roche‑Posay 37.5%, The Ordinary 25% best over the counter retinol cream8SkinCeuticals75%CeraVe 62.5%, RoC 50%, Paula’s Choice 50% best peptide serum for firming skin8SkinCeuticals62.5%The Ordinary 37.5%, Drunk Elephant 37.5%, Paula’s Choice 25% best night cream for fine lines8Neutrogena75%RoC 62.5%, CeraVe 50%, SkinCeuticals 37.5% how to start using retinol without irritation8CeraVe62.5%La Roche‑Posay 50%, RoC 50%, Paula’s Choice 50% best neck cream for sagging skin8SkinMedica87.5%Revision Skincare 87.5%, RoC 62.5%, Alastin Skincare 62.5% best retinol for sensitive skin8La Roche‑Posay75%CeraVe 62.5%, The Ordinary 62.5%, Medik8 62.5% best eye cream for wrinkles8SkinCeuticals75%SkinBetter Science 62.5%, Neutrogena 50%, La Roche‑Posay 37.5% best anti-aging moisturizer with SPF8CeraVe75%La Roche‑Posay 62.5%, Olay 50%, SkinCeuticals 37.5% bakuchiol vs retinol8Paula’s Choice50%Medik8 50%, The Ordinary 37.5%, Sunday Riley 25% best vitamin A serum for anti-aging8La Roche‑Posay62.5%CeraVe 62.5%, The Ordinary 62.5%, Medik8 62.5% best collagen-boosting skincare products8SkinCeuticals50%Olay 37.5%, Paula’s Choice 25%, The Ordinary 25% The cluster chart groups queries by their underlying intent. Brands that lead in general anti-aging queries often differ from those that lead in specific ingredient queries like retinol or peptides. SkinCeuticals and La Roche-Posay maintain strong visibility across multiple intent types, while niche brands may only appear for highly specific formulations. ## Citations and sources Generative engines rely on specific publishers to substantiate their recommendations. Eligible brands also compete to have their own domains cited as authoritative sources. ### Owned-domain citation rate Share of the 478 answers with retrieval evidence that cited a URL on the brand's own website. SkinCeuticals 8.2% · 39/478 La Roche‑Posay 3.3% · 16/478 RoC 4.6% · 22/478 CeraVe 3.1% · 15/478 Paula’s Choice 10% · 48/478 The Ordinary 3.1% · 15/478 Neutrogena 3.6% · 17/478 Olay 1.5% · 7/478 Medik8 4.8% · 23/478 SkinMedica 0.2% · 1/478 L’Oréal 0.6% · 3/478 Drunk Elephant 0.4% · 2/478 Paula's Choice achieves an impressive 10% owned-domain citation rate, and SkinCeuticals secures 8.2%. Most other top brands hover between 1% and 4%. This indicates that while brands appear frequently in the text, engines rarely link directly to their product pages as the primary source of truth. ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration forbes.com185/47838.7% today.com139/47829.1% allure.com135/47828.2% vogue.com114/47823.8% youtube.com105/47822% whowhatwear.com101/47821.1% dermondemand.com90/47818.8% aol.com82/47817.2% dermapproved.com81/47816.9% nytimes.com78/47816.3% everydayhealth.com76/47815.9% nbcnews.com75/47815.7% Forbes leads all third-party sources, appearing in 38.7% of grounded answers. Today follows at 29.1%, and Allure at 28.2%. The high reliance on general lifestyle and beauty publications shows that engines prioritize mainstream editorial coverage over specialized dermatological journals for these consumer queries. ## Who is winning and who is at risk The data separates the category into clear winners and brands facing visibility challenges based on their measured rates. ### Winners - SkinCeuticals: Leads the category with a 49.8% appearance rate and strong consistency across ChatGPT and Perplexity. - La Roche-Posay: Secures the second position with a 44.4% appearance rate and strong average placement (4.2). - Paula's Choice: Achieves a category-leading 10% owned-domain citation rate. ### At Risk - SkinMedica: Despite a 19.4% overall appearance rate, it achieves only a 0.2% owned-domain citation rate. - L'Oréal: The corporate brand appears in 12.3% of answers overall but receives zero mentions from both Gemini and Claude. - Drunk Elephant: Appears in only 9.6% of answers and suffers from low repeat consistency (21.1%). ## Structural findings The anti-aging category shows high concentration at the top. The top two brands dominate visibility, while a long tail of brands struggles to surpass a 15% appearance rate. The engines rely heavily on a small set of publishers, with the top three sources appearing in over 28% of all answers. Owned-domain citations remain low across the category, suggesting that engines treat brand sites as commercial rather than informational. ## What changed for this category in the measurement window The data highlights a clear volume divergence between engines, with ChatGPT surfacing far more brands per answer than Gemini or Claude. ## The GEO playbook Brands in the retinoid space must adapt to how AI engines source and display information. - Target the top publishers: Engines rely heavily on Forbes, Today, and Allure. Brands must secure independent coverage on these specific domains to increase retrieval likelihood. Success is measured by tracking citation sources in future runs. - Optimize for ChatGPT volume: ChatGPT surfaces the most brands. Brands should ensure their product data is structured and widely available to capture this higher-volume engine. Success is measured by appearance share on ChatGPT. - Improve owned-domain authority: Most brands have owned-domain citation rates below 4%. Brands need to publish informational content, not just product pages, to earn direct citations. Success is measured by an increase in owned_domain_citation_rate_pct. - Address Gemini blind spots: Many top brands see steep drop-offs on Gemini. Brands must investigate Google Search grounding requirements to improve visibility here. Success is measured by an improved appearance share on Gemini. - Bridge the Perplexity gap: Brands like Medik8 perform well on Perplexity but struggle elsewhere. Brands must ensure their PR efforts span both real-time web news and static training data. Success is measured by cross_engine_consistency_pct. ## What this means for brands in the category The findings indicate that clinical authority translates directly to AI visibility. Brands that have invested in dermatologist endorsements and clear ingredient messaging (like SkinCeuticals and La Roche-Posay) perform best. The heavy reliance on lifestyle publishers means that traditional public relations remains a primary driver of generative engine visibility. Brands cannot rely solely on their own websites; they must be recommended by the third-party sources the engines trust most. ## Methodology and integrity This report analyzes 60 consumer-intent prompts measured twice across four engines: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). The engines attempted and returned 480 answers, with 478 containing retrieval evidence. Appearance rates use the 480 returned answers as the denominator, while citation metrics use the 478 answers with retrieval evidence. The 95% confidence intervals indicate the margin of error for these specific prompts and models. ### How this study was measured - Sampling frame fixed first: 60 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 2 trial(s) each. - 480 answers returned of 480 attempted, 478 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. ## Frequently asked questions ### Which brand has the highest visibility in AI search for retinoids? SkinCeuticals leads the category, appearing in 49.8% of all returned answers. ### Do AI engines link directly to skincare brands? Direct citations are rare. Paula's Choice achieves the highest owned-domain citation rate at 10%, while most top brands sit between 1% and 5%. ### Which engine mentions the most brands? ChatGPT returns the highest volume of brand mentions. For example, it names SkinCeuticals in 81.7% of its answers, compared to Gemini at 25.8%. ### What sources do AI engines trust for anti-aging queries? The engines rely primarily on mainstream publishers. Forbes is cited in 38.7% of answers, followed by Today (29.1%) and Allure (28.2%). ### Are consumer brands visible alongside clinical brands? Yes. Brands like RoC (38.1%), CeraVe (36.7%), and Neutrogena (31.3%) maintain strong visibility, competing directly with clinical leader SkinCeuticals. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Dermstore2.7%13/4801.6%–4.6% 2Sephora0.4%2/4800.1%–1.5% 3Ulta0.2%1/4800%–1.2% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Anti-Aging / Retinoids - Measurement date: 2026-10-09 - Depth: index — 60 prompts, 2 trial(s) per prompt per engine, 4 engines - Answers attempted: 480 - Answers returned (appearance denominator): 480 - Answers with retrieval evidence (citation denominator): 478 (99.6% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Index depth: 60 stratified consumer-intent prompts measured twice per engine. The wider prompt frame is what supports a published category index, and repeat generations keep run-to-run variation observable. ### How the prompts were sampled Fixed sampling frame: 16 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Anti-Aging; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope best retinol for beginners1best retinol for beginners best retinol serum for wrinkles1best retinol serum for wrinkles tretinoin vs retinol which is better1tretinoin vs retinol which is better best anti-aging serum for 30s1best anti-aging serum for 30s best anti-aging serum for 40s1best anti-aging serum for 40s best over the counter retinol cream1best over the counter retinol cream best peptide serum for firming skin1best peptide serum for firming skin best night cream for fine lines1best night cream for fine lines how to start using retinol without irritation1how to start using retinol without irritation best retinol for sensitive skin1best retinol for sensitive skin best eye cream for wrinkles1best eye cream for wrinkles best vitamin A serum for anti-aging1best vitamin A serum for anti-aging best neck cream for sagging skin1best neck cream for sagging skin bakuchiol vs retinol1bakuchiol vs retinol best anti-aging moisturizer with SPF1best anti-aging moisturizer with SPF best collagen-boosting skincare products1best collagen-boosting skincare products General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case3the dominant everyday use case for this category Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Quality and premium tier3which options are highest quality, most durable or best made Durability and longevity3which brands last, hold up over time, or fail early Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about ### The exact prompts #PromptClusterShape 1best retinol for beginnersneed_1medium 2best retinol serum for wrinklesneed_2medium 3tretinoin vs retinol which is betterneed_3medium 4best anti-aging serum for 30sneed_4medium 5best anti-aging serum for 40sneed_5medium 6best over the counter retinol creamneed_6medium 7best peptide serum for firming skinneed_7medium 8best night cream for fine linesneed_8medium 9how to start using retinol without irritationneed_9medium 10best retinol for sensitive skinneed_10medium 11best eye cream for wrinklesneed_11medium 12best vitamin A serum for anti-agingneed_12medium 13best neck cream for sagging skinneed_13medium 14bakuchiol vs retinolneed_14medium 15best anti-aging moisturizer with SPFneed_15medium 16best collagen-boosting skincare productsneed_16medium 17best retinol brandsgeneral_bestshort 18best anti-aging skincare brandsgeneral_bestshort 19Which brands make the best anti-aging retinoid treatments?general_bestmedium 20best everyday retinol brandseverydayshort 21best drugstore anti-aging brandseverydayshort 22Which mainstream brands are best for everyday retinoid skincare?everydaymedium 23best affordable retinol brandsvalue_priceshort 24best value anti-aging brandsvalue_priceshort 25Which retinoid brands offer the best value for money?value_pricemedium 26best retinol for wrinklesuse_caseshort 27best firming skincare brandsuse_caseshort 28Which brands work best for smoothing fine lines and wrinkles?use_caselong 29best beginner retinol brandsbeginnershort 30retinol brands for beginnersbeginnershort 31Which brands make the easiest first retinoid products to start with?beginnerlong 32where to buy retinolretailshort 33best places for anti-aging skincareretailmedium 34Which retinoid brands are safest to buy from authorized retailers online?retaillong 35anti-aging skincare gift brandsgiftingshort 36best retinol giftsgiftingshort 37Which anti-aging skincare brands make the best gifts for milestone birthdays?giftinglong 38retinol for sensitive skin brandsspecific_needmedium 39best retinol for mature skinspecific_needmedium 40Which brands offer retinoids that minimize irritation for dry, sensitive skin?specific_needlong 41best retinol brand comparisoncomparisonmedium 42retinol brands comparedcomparisonshort 43Which anti-aging brands have the strongest clinical evidence for retinoid results?comparisonlong 44most trusted retinol brandstrustmedium 45dermatologist recommended anti-aging brandstrustmedium 46Which retinoid skincare brands do dermatologists recommend most often, and why?trustlong 47who makes leading retinol brandsownershipmedium 48retinol brand parent companiesownershipmedium 49Which companies own the most widely recommended anti-aging skincare brands?ownershiplong 50best premium retinol brandspremium_qualitymedium 51highest quality anti-aging brandspremium_qualitymedium 52Which premium skincare brands have the best-formulated retinoid and peptide treatments?premium_qualitylong 53retinol shelf life brandsdurabilitymedium 54long lasting anti-aging formulasdurabilitymedium 55Which retinoid brands use packaging that best preserves formula potency over time?durabilitylong 56advanced retinoid skincare brandsspecialistmedium 57clinical strength anti-aging brandsspecialistmedium 58Which brands offer advanced retinoid treatments with transparent ingredient concentrations?specialistlong 59new retinoid skincare brandsemergingmedium 60Which newer anti-aging brands are gaining attention for next-generation retinoid formulas?emerginglong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search120120120100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool120120120100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)12012011898.3% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool120120120100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1SkinCeuticals239/48049.8%49.8%45.3%–54.2%39/478 (8.2%) 2La Roche‑Posay213/48044.4%44.4%40%–48.8%16/478 (3.3%) 3RoC183/48038.1%38.1%33.9%–42.5%22/478 (4.6%) 4CeraVe176/48036.7%36.7%32.5%–41.1%15/478 (3.1%) 5Paula’s Choice170/48035.4%35.4%31.3%–39.8%48/478 (10%) 6The Ordinary154/48032.1%32.1%28.1%–36.4%15/478 (3.1%) 7Neutrogena150/48031.3%31.3%27.3%–35.5%17/478 (3.6%) 8Olay137/48028.5%28.5%24.7%–32.7%7/478 (1.5%) 9Medik8137/48028.5%28.5%24.7%–32.7%23/478 (4.8%) 10SkinMedica93/48019.4%19.4%16.1%–23.1%1/478 (0.2%) 11L’Oréal59/48012.3%12.3%9.7%–15.5%3/478 (0.6%) 12Drunk Elephant46/4809.6%9.6%7.3%–12.5%2/478 (0.4%) 13Estée Lauder46/4809.6%9.6%7.3%–12.5%4/478 (0.8%) 14Paula's Choice40/4808.3%8.3%6.2%–11.1%48/478 (10%) 15SkinBetter Science40/4808.3%8.3%6.2%–11.1%5/478 (1%) 16Differin39/4808.1%8.1%6%–10.9%7/478 (1.5%) 17Sunday Riley36/4807.5%7.5%5.5%–10.2%5/478 (1%) 18Dr. Dennis Gross34/4807.1%7.1%5.1%–9.7%1/478 (0.2%) 19Obagi33/4806.9%6.9%4.9%–9.5%6/478 (1.3%) 20Alastin Skincare30/4806.3%6.3%4.4%–8.8%3/478 (0.6%) 21American Academy of Dermatology29/4806%6%4.2%–8.5%33/478 (6.9%) 22Naturium28/4805.8%5.8%4.1%–8.3%0/478 (0%) 23Revision Skincare27/4805.6%5.6%3.9%–8.1%3/478 (0.6%) 24Avène27/4805.6%5.6%3.9%–8.1%0/478 (0%) 25The Inkey List26/4805.4%5.4%3.7%–7.8%12/478 (2.5%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-09. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. ## More from the Skincare AI Visibility Index The full cross-concern ranking is in the Skincare AI Visibility Index 2026. The other concern reports: - Acne Treatments AI Citation Benchmark 2026 - Hyperpigmentation & Brightening AI Citation Benchmark 2026 - Sensitive Skin & Barrier Repair AI Citation Benchmark 2026 - SPF & Sun Protection AI Citation Benchmark 2026 ### Related AI visibility research - Beauty AI Visibility Index 2026: the brand-first ranking of the top 25 U.S. beauty brands by AI citation share. - Menopause AI Visibility Index: how AI engines cite the top 25 menopause brands. - Longevity AI Visibility Index: how AI engines cite the top 25 longevity brands. Brands acting on these findings can work with our skincare PR team, our beauty PR practice, our beauty marketing agency group and our dermatology and aesthetics marketing specialists. --- ### The Luxury Island AI Visibility Index 2026 URL: https://www.5wpr.com/ai-visibility-index/luxury-island-ai-visibility-index-2026/ Summary: Haute Living Black and 5W present a visibility intelligence model analyzing how AI engines recommend prestigious island destinations to affluent travelers. VOLUME I · 2026 EDITION # The Luxury IslandAI Visibility Index™ The future luxury traveler will not search. They will ask. A visibility intelligence model for the AI Concierge Economy. PRESENTED BY HAUTE LIVING BLACK × 5W AI VISIBILITY INTELLIGENCE Download Report PDF Download AI Visibility Glossary Luxury Island Index™ - Summary - Concierge Economy - Winner-Take-Most - The Stack - Core Tier - Authority Cluster - Matrix - Hotels - Methodology - About SECTION ONE ## Executive Summary The future luxury traveler will not search. They will ask. This is not a traditional travel ranking. It is a visibility intelligence model. It studies how the world's leading AI engines — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — interpret luxury, exclusivity, prestige, hospitality authority, wellness positioning, and aspirational travel signals across the most elite global island destinations. The findings have direct commercial implications for tourism boards, hospitality groups, destination marketing organizations, luxury brands, and the publishers, agencies, and operators that serve them. Across thousands of high-intent prompts tested against five generative AI systems, the same six islands surfaced disproportionately at the top of luxury recommendations: Mykonos, Ibiza, Sardinia, and Capri in the Mediterranean; Saint Barthélemy in the Caribbean; and the Maldives in the Indian Ocean. These destinations form what this report calls the Core Tier — the cluster AI engines treat as canonical when describing the upper end of island travel. #### Three Frameworks This Report Introduces This report introduces three proprietary frameworks for understanding AI-mediated luxury discovery. They are designed to be reusable across hospitality, destination, and luxury-brand intelligence work. - The AI Concierge Economy™ — the new structural reality in which conversational AI replaces search as the primary surface for luxury travel discovery. - The AI Luxury Visibility Stack™ — a five-layer framework describing how a destination accumulates the authority that AI engines reward. - The Luxury Authority Cluster™ — the twelve interlocking trust signals AI engines read together to interpret luxury identity. #### Core Findings - AI engines have replaced search engines as the starting point for affluent travel research. Conversational discovery returns three to five named destinations rather than ten blue links — and the named winners capture demand at the expense of the unnamed. - Luxury authority in AI is not earned by ad spend. It is earned by editorial prestige, hospitality brand concentration, and cultural reinforcement compounded over years. - The Core Tier of six islands appears in the top three of more than 70% of luxury, wellness, honeymoon, and yachting prompts tested across the five AI engines studied. - Wellness, privacy, and exclusivity now outrank scenery and weather as the top recommendation signals AI uses to define luxury. - Hospitality ecosystems matter more than individual properties. Destinations with five or more Forbes Five-Star, Leading Hotels of the World, or canonical-brand members reach materially higher AI authority than those with only one flagship. - Celebrity, yacht, and beach club culture are reinforcing — not decorative — signals. AI engines learn destination identity from the cultural artifacts that surround it. - A small set of hospitality brands — Aman, Cheval Blanc, Eden Rock, Four Seasons, One&Only, Rosewood, Six Senses, Ritz-Carlton — appears repeatedly across AI systems regardless of which destination is being queried. Brand concentration is one of the strongest predictors of AI luxury authority. #### Why This Matters Now In 2024, generative AI had begun to influence travel research. By 2026, it is shaping it. Hotels and destinations that are well-represented across AI systems are seeing stronger top-of-funnel demand without proportional increases in paid media. Hotels and destinations that are poorly represented are losing share to competitors a generation younger than them. The mechanism is no longer Google's blue links. It is a synthesized recommendation, delivered conversationally, with a small number of named winners. This report is a map of who is winning, why, and what the inputs to winning are. It is also a methodology — one that can be applied to any luxury vertical, not only island travel. SECTION TWO ## The AI Concierge Economy™ For a decade, luxury travel discovery followed a recognizable funnel. A Condé Nast Traveler feature, a friend's recommendation, or an aspirational Instagram post would trigger a Google search. The traveler clicked through ten blue links. They compared, hesitated, asked a travel advisor. The decision matured over weeks. That funnel is collapsing. The most important new behavior in luxury travel is the conversational query: a single prompt to an AI system that returns a synthesized, opinionated, named answer. "Where should we honeymoon if we want privacy and the best service in the world?" "What's the most exclusive Caribbean island for a winter holiday?" "Where do billionaires actually go in August?" The AI does not return ten links. It returns three or four destinations and three or four hotels. FRAMEWORK The AI Concierge Economy™ A new economic structure in which conversational AI replaces search as the primary surface for luxury discovery — and where named winners capture demand at the expense of the unnamed. ### From Search Behavior to Concierge Behavior AI engines have taken on a posture closer to a knowledgeable concierge than to a search engine. They make recommendations. They synthesize across sources. They opinionate. They volunteer secondary recommendations — "and if you want it quieter, consider..." — without being asked. This is concierge behavior, and it requires concierge-grade trust signals to feed it. This shift is the central economic event in luxury travel discovery. A search engine is a wholesaler — it presents many options and lets the user choose. A concierge is a curator — it makes a small number of recommendations and stakes its credibility on them. AI engines are operating like concierges, and the destinations they recommend repeatedly are accumulating the trust normally reserved for a senior travel advisor. ### Recommendation Compression Where a Google search might surface fifteen islands a traveler could plausibly consider, a ChatGPT prompt typically returns three to five. The compression is not random. It is the result of how the underlying language model has weighed cultural authority across millions of training documents. This is the structural reality of the AI Concierge Economy: the consideration set has compressed by roughly two-thirds. The destinations the AI surfaces first will be the destinations the traveler considers seriously. The destinations that do not surface in the first response often do not enter the consideration set at all. In luxury, where a single client decision can be worth six figures over the lifetime of a relationship, the difference between being named and being absent is the difference between revenue and silence. AI does not produce twenty winners. It produces three. Every destination, every hotel, every luxury brand is now competing not for relevance, but for canonicity. ### Trust Concentration Inside AI Systems AI systems are trained to distinguish authoritative voice from noise. They learn that Condé Nast Traveler describes a destination differently than a discount aggregator. They learn that Forbes Travel Guide's star ratings carry different weight than user reviews. They learn that the same hotel mentioned in Robb Report, Travel + Leisure, and Departures over multiple years has earned a different kind of place in the cultural conversation than one promoted via paid placements. In the AI Concierge Economy, depth of authoritative signal is the asset. Being mentioned across many low-quality sources matters less than being mentioned by a small set of high-trust sources. The destinations winning AI visibility today are those that have been treated as canonical by editorial prestige sources for years — and that have continued to invest in the ecosystem of media, hospitality, and cultural signal that makes them legible to a model. SECTION THREE ## Winner-Take-Most: The New Visibility Economics The AI Concierge Economy is not a fairer marketplace than the search engine economy that preceded it. It is a more concentrated one. Search engines distributed attention across roughly ten results per query. Conversational AI engines distribute attention across roughly three. The implication is structural: AI-mediated discovery is a winner-take-most market, and the gap between named and unnamed destinations widens with every cycle of AI training. ### The 10-to-3 Compression In testing across the five AI engines studied, a typical luxury island prompt returns between two and five named destinations. The first-named destination receives the largest share of follow-up queries, secondary recommendations, and downstream conversion behavior. The second and third named destinations split the remainder. Destinations not named in the initial response are functionally absent from the consideration set. This is a roughly 70% reduction in surfaced consideration set compared to traditional search. For a destination already in the canonical answer, the compression is a benefit — it removes competitors. For a destination not in the canonical answer, the compression is an existential threat — it removes the path by which a traveler used to discover them organically. ### The Three Implications #### 1. The Floor Has Risen The minimum viable level of editorial, hospitality, and cultural authority required to enter the AI consideration set is dramatically higher in 2026 than it was in 2022. A destination that was mid-tier in the search era can be functionally invisible in the AI era, even with the same amount of editorial coverage, because the compression rewards the top of the distribution disproportionately. #### 2. The Ceiling Is Rewarded Destinations already inside the canonical luxury answer set — the Core Tier identified in this report — are accumulating disproportionate share of organic luxury demand. They are the named winners in a winner-take-most market, and the longer they remain canonical, the more entrenched their position becomes. AI training cycles are biased toward continuity. Today's winners are tomorrow's defaults. #### 3. The Catch-Up Cost Is Increasing For a destination, hotel, or luxury brand seeking to enter the canonical answer set, the cost of catching up rises with each AI training cycle that codifies the existing winners. New entrants must invest in editorial authority, brand consistency, and cultural reinforcement at higher intensity than incumbents — because they are working against the grain of an AI memory that has already learned its preferred answer. Visibility in AI is no longer a marketing channel. It is a category position. The destinations and brands that secure that position by 2027 will hold structural advantages that newer entrants will struggle to overcome. ### The Implication for Hospitality and Destination Marketing The traditional hospitality and destination marketing playbook — broad earned-media campaigns, paid social and search, influencer partnerships, periodic editorial pushes — is necessary but no longer sufficient in the AI Concierge Economy. The new playbook adds a fourth pillar: measurable AI visibility. Destinations and brands must understand how they are surfaced across AI engines, which signals are driving their visibility, and where the editorial and structural gaps in their authority are. Without this layer of intelligence, marketing investments are made blind to the surface where the most consequential discovery decisions are now being made. SECTION FOUR ## The AI Luxury Visibility Stack™ AI luxury authority is not a single attribute. It is a layered system. The framework below — the AI Luxury Visibility Stack™ — describes the five layers in which a destination, hotel, or luxury brand must perform to earn canonical status in AI-mediated discovery. The layers are sequential and reinforcing. A destination that performs at Layer 1 but not Layer 2 will not reach Layer 5. The Stack is also self-correcting: weakness at any layer caps the visibility outcome. LAYER 5 · TOP AI Recommendation Dominance LAYER 4 Cultural Authority LAYER 3 Social Reinforcement LAYER 2 Editorial Prestige LAYER 1 · FOUNDATION Hospitality Excellence ### Layer 1 — Hospitality Excellence (The Foundation) The base of the Stack is the hospitality product itself. Forbes Five-Star ratings, Leading Hotels of the World membership, brand concentration (Aman, Cheval Blanc, Four Seasons, Rosewood, Six Senses, Ritz-Carlton, One&Only), Michelin-starred dining, and the ecosystem of named operators (beach clubs, marinas, villa-rental groups, wellness clinics) are all Layer 1 inputs. Without a strong Layer 1, no amount of editorial or cultural investment will compound into AI authority. Layer 1 is the substance the model is ultimately recommending. ### Layer 2 — Editorial Prestige The second layer is the editorial coverage that interprets Layer 1 to the world. Repeated coverage in Condé Nast Traveler (especially the Reader's Choice Awards and Hot List), Robb Report, Forbes Travel Guide, Travel + Leisure (especially World's Best Awards), Departures, Town & Country, Vogue, and Architectural Digest is the input that AI engines learn most heavily from. Editorial Prestige is the layer that translates hospitality excellence into a cultural pattern the AI can recognize. It is the most underinvested layer in most destination and hospitality marketing budgets — and the most disproportionately rewarded. ### Layer 3 — Social Reinforcement The third layer is the social and creator ecosystem that amplifies Layers 1 and 2. Luxury YouTube creators with audited audiences, fashion-house event presence, celebrity travel coverage, brand-aligned creator partnerships, and luxury social ecosystems all reinforce destination identity. Social Reinforcement does not replace Editorial Prestige — it compounds it. AI engines learn from the breadth of independent reinforcement, not from any single channel. ### Layer 4 — Cultural Authority The fourth layer is the cultural calendar and identity that surrounds the destination. Regattas, fashion weeks, music festivals, art weeks, signature seasonal events, and the broader cultural artifacts (the Faraglioni, the overwater villa, the Gustavia harbor at sunset) operate at this layer. Cultural Authority is what differentiates a destination from a hotel — it is the reason a traveler chooses a place, not just a property. Destinations with strong Cultural Authority are surfaced even when no specific hotel query is made. ### Layer 5 — AI Recommendation Dominance (The Outcome) The top layer is the outcome: appearing as the canonical first or second answer to high-intent luxury prompts across the major AI engines. Layer 5 is not an input. It is what the four layers below produce when they reinforce each other consistently over years. Layer 5 destinations are the named winners in the AI Concierge Economy. The structural advantage of Layer 5 is that it compounds — the more often a destination is surfaced, the more often it is reinforced in subsequent training cycles. Most destination and hospitality marketing investments concentrate at Layer 1 and Layer 3. The compounding gains live at Layer 2 and Layer 4 — and the visibility prize lives at Layer 5. SECTION FIVE ## The Core Tier Islands AI Recommends Most Six islands form the Core Tier. They surface across systems, across prompt categories, and across seasons. They are the canonical answers to canonical luxury questions. What follows is the AI-readable identity of each — the Layer 5 outcomes produced by years of Layer 1–4 reinforcement. Caribbean Saint Barthélemy #### Dominant AI Associations - Ultra-privacy and discretion - Yacht culture and harbor visibility - Celebrity density, particularly in winter season - Villa luxury and private hospitality ecosystems - French-Caribbean sophistication and culinary identity - High-net-worth exclusivity #### AI-Readable Identity Saint Barthélemy is the destination AI engines reach for when asked about the most exclusive Caribbean experience or about where the wealthiest American and European travelers winter. The island's eight-square-mile geography and its tightly limited hotel inventory create a structural scarcity that AI systems have learned to encode as luxury. Properties such as Cheval Blanc St-Barth Isle de France, Eden Rock, Rosewood Le Guanahani, Le Sereno, Le Toiny, Hotel Christopher, Le Barthélemy Hotel & Spa, and Hotel Le Carl Gustaf are repeatedly referenced as the canonical luxury hotel set. AI systems associate Saint Barthélemy strongly with the winter holiday season — particularly the late-December through early-January window when the harbor at Gustavia fills with superyachts and the island's population effectively doubles. The island's identity in AI is winter, harbor, villa, celebrity. It is rarely surfaced as a wellness destination, a culture destination, or a family-first destination. The AI has learned the niche, and it returns the niche. Mediterranean · Greece Mykonos #### Dominant AI Associations - Beach club prestige and signature scene venues - Nightlife luxury and high-design hospitality - Mediterranean glamour and visual identity - Social visibility and seen-and-be-seen positioning - Luxury villa ecosystems on Aegean coastline #### AI-Readable Identity Mykonos is the destination AI returns when asked about the social, scene-driven, glamorous side of Mediterranean luxury. Beach clubs are the central cultural artifact — Nammos at Psarou, SantAnna and Scorpios on Paraga, Buddha Bar Beach at Santa Marina — and AI has learned to associate Mykonos with these venues as tightly as it associates Capri with the Faraglioni. Scorpios in particular, owned by Soho House, is referenced across systems as a global signifier of bohemian luxury. The hospitality ecosystem is broad. Properties such as Belvedere, Mykonos Blu, Santa Marina, Kalesma, Cali Mykonos, Branco, Mykonos Blanc, and Myconian Deos appear regularly in AI summaries, with newer 2024–2025 openings increasingly cited. AI also surfaces Mykonos consistently in response to celebrity-related queries, summer European travel calendars, and luxury-yachting itineraries that anchor at the island during the August peak. Mediterranean · Spain Ibiza #### Dominant AI Associations - Nightlife authority — global, not regional - Wellness retreats and longevity positioning - Luxury electronic music and superclub culture - Beach club ecosystems on the southern coast - Bohemian luxury and the "White Isle" identity #### AI-Readable Identity Ibiza is the only Core Tier destination whose AI identity contains an internal duality. AI engines have learned that Ibiza occupies two distinct cultural positions simultaneously — the global capital of electronic music nightlife (Pacha, Hï Ibiza, Ushuaïa, DC-10) and a serious wellness destination, anchored in part by Six Senses Ibiza on the quieter northern coast. The wellness positioning is not soft. Six Senses Ibiza operates the RoseBar longevity clinic, offering diagnostic testing, cryotherapy, vitamin therapy, hyperbaric oxygen, and multi-day longevity programs that are referenced specifically in AI responses to wellness and biohacking prompts. The duality is unique among Core Tier islands and it serves Ibiza well in AI systems. The island appears in both "best party destinations" and "best wellness destinations" — a positioning advantage that no other Mediterranean island has replicated. Indian Ocean Maldives #### Dominant AI Associations - Overwater villas — the singular visual signature - Serenity and seclusion as core product - Wellness and spa authority - Romance, honeymoon, and milestone travel - Ultra-premium hospitality with high concentration of flagship brands - Secluded luxury through atoll-and-seaplane geography #### AI-Readable Identity The Maldives is the destination AI engines reach for first when the prompt involves honeymoon, anniversary, wellness, or seclusion. The overwater villa is a visual artifact strong enough that AI systems treat it as nearly synonymous with the destination itself. The hospitality ecosystem is unusually dense for the population: Forbes Travel Guide's 2026 Five-Star list includes Four Seasons Resort Maldives at Kuda Huraa (sixth consecutive year) and The Ritz-Carlton Maldives, Fari Islands (fourth consecutive year), with additional Forbes-recognized properties across the atoll system. Soneva Fushi, Six Senses Laamu, One&Only Reethi Rah, St. Regis Maldives Vommuli, Cheval Blanc Randheli, and the Four Seasons Landaa Giraavaru appear consistently across AI responses to luxury Maldives queries. AI has learned that the Maldives is sold by the resort, not by the destination. A traveler does not go to the Maldives — they go to a specific island. This makes the Maldives unusually dependent on hotel-brand visibility, and unusually rewarded for hospitality investment in editorial and digital channels. Mediterranean · Italy Sardinia #### Dominant AI Associations - Costa Smeralda prestige and Porto Cervo identity - Yacht and superyacht ecosystems - Italian luxury — discreet, established, dynastic - Quiet wealth and old-money positioning - Mediterranean sophistication #### AI-Readable Identity Sardinia's AI identity is concentrated almost entirely on the Costa Smeralda — and within Costa Smeralda, on Porto Cervo. AI engines have learned that Porto Cervo is the canonical Mediterranean superyacht harbor, the Italian counterpart to Saint-Tropez. The Marina di Porto Cervo's roughly 700 berths, including approximately 100 megayacht moorings, and the Yacht Club Costa Smeralda founded in 1967, anchor the destination's authority. The cruising calendar — the Loro Piana Superyacht Regatta in early summer, the Maxi Rolex Cup in September, the Rolex Swan Cup — gives AI a year-over-year body of editorial coverage that reinforces yachting authority specifically. The hotel ecosystem leans toward heritage Italian luxury (Hotel Cala di Volpe, Hotel Pitrizza, Hotel Romazzino), with newer entries such as W Sardinia (opened 2025) extending the brand mix. AI tends to surface Sardinia for queries that emphasize discretion, established wealth, and serious yachting — less so for nightlife or wellness. Mediterranean · Italy Capri #### Dominant AI Associations - Cinematic Italian luxury and dolce vita imagery - Timeless glamour with cross-generational appeal - Boutique hospitality and small-property prestige - Luxury dining and nightlife institutions - Heritage prestige and literary-historical association #### AI-Readable Identity Capri is unusual in the Core Tier in that its AI identity rests heavily on heritage and continuity rather than on new development. The Grand Hotel Quisisana, in operation since 1845 and a luxury hotel since 1860, is referenced across AI systems as a canonical Capri property. Da Paolino — the lemon-grove dinner restaurant — and Anema e Core, the late-night live-music institution, appear in nearly every AI response to a "what to do in Capri" prompt. La Fontelina is referenced as the canonical beach club. Hotel Punta Tragara, Capri Palace Jumeirah, J.K. Place Capri, Hotel Caesar Augustus, and Villa Marina Capri round out the AI-cited luxury hotel set. Capri's AI identity is unusually narrow and unusually deep. The same handful of names recurs. New entrants struggle to break in. For an established Capri property, this is an asset; for a new one, it is a barrier that requires sustained editorial investment to overcome. SECTION SIX ## The Luxury Authority Cluster™ AI engines do not interpret luxury through individual signals. They interpret it through clusters of reinforcing prestige signals — and the strongest destinations are those whose ecosystems generate reinforcement automatically, year after year, across many independent sources. The framework below — the Luxury Authority Cluster™ — names the twelve interlocking signals AI engines read together. The cluster is the unit of authority, not the individual signal. FRAMEWORK The Luxury Authority Cluster™ Twelve interlocking trust signals AI engines read together to interpret luxury identity. The cluster is the unit of authority. No single signal earns canonical status; the cluster does. ### The Twelve Cluster Signals #### 1. Forbes Five-Star Density The number of Forbes Travel Guide Five-Star and Recommended properties in a destination correlates directly with its appearance in AI luxury responses. Forbes Travel Guide's anonymous 900-point inspection methodology produces a signal that AI systems treat as authoritative. A destination with two or more Five-Star properties consistently outperforms one with zero or one. #### 2. Michelin and Fine-Dining Concentration Michelin-starred dining ecosystems and named-chef restaurants reinforce a destination's culinary identity in AI systems. A destination's culinary authority is a separate signal from its hotel authority and contributes independently to AI recommendation share. #### 3. Luxury Hospitality Brand Concentration Destinations with multiple flagship-brand properties — Aman, Cheval Blanc, Four Seasons, One&Only, Rosewood, Six Senses, Belmond, Mandarin Oriental, Bulgari, Edition, Ritz-Carlton — are recognized as luxury ecosystems rather than as one-hotel destinations. Brand concentration is one of the strongest predictors of AI authority. #### 4. Celebrity and HNW Association Editorial coverage of celebrity travel, dynastic-wealth presence, and ultra-high-net-worth visiting patterns reinforces destination prestige in AI training data. The mechanism is not gossip — it is repeated mention across sources the model treats as authoritative. #### 5. Editorial Prestige and Citation Authority Repeated coverage in Condé Nast Traveler, Robb Report, Forbes Travel Guide, Travel + Leisure, Departures, Town & Country, Vogue, and similar luxury editorial titles is the single strongest input to AI luxury authority. Editorial prestige does not just describe luxury — it teaches the model what luxury is. #### 6. Yacht Infrastructure Marina capacity, megayacht berthing, regatta calendar, and yacht-club institutional presence are all separate signals contributing to AI yachting authority. A destination with a thousand recreational moorings and no megayacht infrastructure reads differently than one with two hundred superyacht-grade berths. #### 7. Villa and Private Hospitality Ecosystems Branded villa rental supply, private island infrastructure, and the presence of recognized villa management groups (WIMCO, St. Barth Properties, Onefinestay) reinforce a destination's privacy and exclusivity positioning. #### 8. Wellness and Longevity Authority Six Senses, Aman, COMO, Joali Being-grade wellness; medical-grade longevity programs; named practitioners; and editorial coverage of wellness retreats all contribute. AI treats wellness as a separable luxury vertical and rewards destinations that have invested in it. #### 9. Visual and Identity Consistency Repeated visual cues — the overwater villa, the Faraglioni rocks, the white-and-blue Cycladic palette, the sunset over Gustavia harbor — function as an identity scaffold AI uses to interpret a destination. Strong visual identity is a luxury asset. #### 10. Social Prestige Reinforcement Social-media visibility from luxury-aligned creators, fashion-house event presence, and seasonal cultural calendars (regattas, fashion weeks, music festivals at the destination) all reinforce social authority. #### 11. Review and Reputation Consistency AI engines weigh consistency of high-end reviews more heavily than peak rave reviews. A property described as excellent across 500 reviews outperforms one described as transcendent across 50 in long-run AI authority. #### 12. Recognized Brand Cluster Destinations whose luxury ecosystem is composed of named, repeated, recognizable hospitality and dining brands score higher in AI luxury surfacing than destinations whose properties, however excellent, are independent and unaffiliated. Brand recognition is a shortcut the AI uses to compress trust. AI engines do not reward any single signal. They reward the cluster — and they punish the absence of the cluster. A destination with one excellent hotel will not outperform a destination with five. A destination with one wellness retreat will not outperform a destination whose entire identity has become wellness. SECTION SEVEN ## AI Recommendation Frequency Matrix The matrix below presents directional AI Recommendation Frequency Indices for each Core Tier island across the five AI engines studied. Values represent the percentage of high-intent luxury island prompts in the relevant prompt categories for which each destination was named in the top three AI recommendations during the Q4 2025 – Q1 2026 testing window. The matrix is intended to be read directionally rather than as a precise quantitative measurement; methodology and limitations are discussed in the Methodology section. ISLAND CHATGPT CLAUDE GEMINI PERPLEXITY AI OVERVIEWS Saint Barthélemy88%82%86%84%81% Maldives92%90%91%89%88% Mykonos84%79%85%80%78% Capri81%78%82%76%75% Sardinia76%72%78%74%71% Ibiza79%74%81%77%73% #### Reading the Matrix Three patterns are visible in the matrix and consistent with the qualitative findings throughout this report. - The Maldives leads canonical luxury authority across every AI engine studied, reflecting its dominance in honeymoon, wellness, and ultra-premium queries. - Saint Barthélemy holds asymmetric strength — exceptionally high in winter and Caribbean-luxury queries, less surfaced in summer and Mediterranean-comparison queries. - Engine-level variation is meaningful but bounded. The Core Tier ordering is broadly consistent across all five engines, suggesting that canonical AI luxury authority is structural rather than engine-specific. #### Engine-Level Observations ChatGPT and Gemini surface Core Tier destinations at consistently higher frequencies than Claude, Perplexity, and Google AI Overviews — reflecting differences in retrieval architecture, training emphasis, and the breadth of editorial sources weighted by each system. Perplexity and AI Overviews produce more conservative recommendations with more linked-source dependencies, while ChatGPT and Gemini operate closer to the conversational-concierge posture described earlier in this report. Across five AI engines and thousands of luxury prompts, the same six islands recur. The variance is in the ordering. The constant is the canonicity. SECTION EIGHT ## The Luxury Hotels AI Mentions Most Across thousands of luxury island prompts, a small set of hospitality brands surface repeatedly and across destinations. These are the brands AI engines have come to treat as canonical luxury — and the brands whose presence in a destination meaningfully lifts that destination's overall AI authority. ### The Repeated Brand Set #### Aman Aman appears across AI luxury queries with a consistency that exceeds the brand's small property count. The reason is editorial: Aman is covered across luxury press at a rate that compresses its hospitality identity into a single high-trust signal. AI systems read this and recommend accordingly. #### Cheval Blanc Cheval Blanc — the LVMH luxury hospitality brand — surfaces strongly in Saint Barthélemy (Isle de France) and the Maldives (Randheli). AI associates Cheval Blanc with the highest tier of French-luxury hospitality and surfaces the brand against discretion, design, and culinary-prestige prompts. #### Eden Rock Eden Rock is one of two AI-canonical Saint Barthélemy properties and appears in nearly every AI response to a Saint Barthélemy luxury query. The property's editorial coverage and celebrity association are the inputs. #### Four Seasons Four Seasons surfaces broadly across luxury island queries and especially in the Maldives, where Four Seasons Resort Maldives at Kuda Huraa earned its sixth consecutive Forbes Five-Star rating in 2026. The Four Seasons Landaa Giraavaru is a parallel AI-canonical Maldives property. #### One&Only One&Only Reethi Rah in the Maldives is one of the most frequently surfaced individual properties in AI luxury responses across all destinations tested. The brand's broader presence in the luxury hospitality conversation amplifies the property's authority. #### Rosewood Rosewood Le Guanahani is the property most frequently identified by luxury travel advisors as the most-rebooked Saint Barthélemy hotel, and AI systems have learned this rebooking authority through advisor commentary published in editorial sources. #### Six Senses Six Senses Ibiza and Six Senses Laamu (Maldives) are both AI-canonical wellness-luxury properties. The Six Senses brand authority across longevity, sustainability, and wellness has compounded since 2021. #### Ritz-Carlton (Maldives, Fari Islands) The Ritz-Carlton Maldives, Fari Islands earned its fourth consecutive Forbes Five-Star rating in 2026 and is increasingly cited across AI responses as the Maldives Forbes-Five-Star peer to the Four Seasons Kuda Huraa property. Brand concentration is a luxury asset. AI systems read familiar brands as a shortcut for trust. A destination with five canonical-brand properties outperforms one with a single excellent independent property in long-run AI authority — even if the independent property is, on its own merits, the equal of the chain flagship. SECTION NINE ## Methodology ### AI Systems Analyzed - ChatGPT (OpenAI) - Claude (Anthropic) - Gemini (Google) - Perplexity - Google AI Overviews ### Testing Window Prompt testing was conducted across Q4 2025 and Q1 2026. Each prompt was tested across multiple phrasings to control for prompt-formulation variance. Each engine was queried in fresh sessions to control for conversation-history bias. Results were aggregated across phrasing variants and engine sessions and converted to directional Recommendation Frequency Indices. ### Sample Prompt Set The table below presents a representative subset of the prompts tested. Each prompt was tested in multiple phrasings. IDPROMPT L1What are the most exclusive island destinations in the world? L2Best luxury islands to visit in 2026 L3Most expensive island destinations for ultra-high-net-worth travelers M1Best Mediterranean islands for luxury travel M2Where do European billionaires vacation in summer? M3Most prestigious island for Italian luxury and yachting C1What's the most exclusive Caribbean island for a winter holiday? C2Where do A-list celebrities go for the December holidays? C3Most private Caribbean island destination W1Best islands for a luxury honeymoon W2Best wellness retreats on a private island W3Where is the best longevity clinic at a luxury resort? Y1Best superyacht destinations in the Mediterranean Y2Where to charter a yacht in Italy this summer B1What island has the best luxury beach clubs? B2Best island for nightlife and luxury combined H1Best Forbes Five-Star island resorts H2Where are the best One&Only and Cheval Blanc properties? S1Where can I disappear for a quiet luxury week? ### Signal Weighting Framework Each of the twelve signals in the Luxury Authority Cluster™ contributes independently to AI luxury surfacing, but the contributions are not symmetric. Weightings are conceptual and directional, derived from observed surfacing patterns across the testing window. SIGNALWEIGHTOBSERVED CONTRIBUTION Editorial Prestige & Citation AuthorityHighSingle strongest predictor of canonical AI luxury surfacing. Compounds over multi-year horizons. Luxury Hospitality Brand ConcentrationHighDensity of Aman, Cheval Blanc, Four Seasons, One&Only, Rosewood, Six Senses, Ritz-Carlton, etc. Forbes Five-Star DensityHighForbes 900-point methodology produces a high-trust signal AI engines weight heavily. Wellness & Longevity AuthorityMed-HighNamed programs (RoseBar, Joali Being) and clinic-grade investment outperform general spa positioning. Celebrity & HNW AssociationMed-HighOperates through editorial reinforcement loops, not raw social mentions. Recognized Brand ClusterMed-HighComponent visibility — restaurants, beach clubs, marinas, villa groups — must also be branded. Yacht InfrastructureMediumHigh weight in yacht-specific verticals; lower weight in general luxury surfacing. Michelin & Fine-Dining ConcentrationMediumReinforces culinary identity as a separate signal from hotel authority. Visual & Identity ConsistencyMediumActs as a retrieval scaffold (overwater villa, Faraglioni, Cycladic palette). Villa & Private Hospitality EcosystemsMediumStrong in privacy-led prompts; weaker in general luxury queries. Social Prestige ReinforcementMediumCompounds Editorial Prestige rather than substituting for it. Review & Reputation ConsistencyMed-LowAI engines weight depth of consistency over peak intensity, but the signal is narrower. ### Limitations AI engines update their training and retrieval patterns on rolling cycles. The findings in this report represent visibility patterns observed across the testing window of Q4 2025 and Q1 2026. The Recommendation Frequency Indices are directional rather than precise quantitative measurements. They represent observed patterns across the testing window and are intended to support decision-making rather than to serve as audited statistics. Related — Travel & Hospitality Indexes New · May 2026 Theme Parks AI Visibility Index Disney 58%. Universal 23%. 94% of every AI theme-park answer goes to three companies. Travel & Hospitality Airlines & Hotels Which carriers and hotel brands own the AI answer when travelers decide where to fly and stay. Dining Restaurant Chains Which restaurant chains dominate AI dining recommendations. ABOUT ### About 5W 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research, helping clients measure and grow their presence in AI-driven buyer research. Founded more than 20 years ago, 5W has been recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W serves clients across B2C sectors including Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit; B2B specialties including Corporate Communications and Reputation Management; as well as Public Affairs, Crisis Communications, and Digital Marketing, including Social Media, Influencer, Paid Media, GEO, and SEO. 5W was also named to the Digiday WorkLife Employer of the Year list. For more information, visit www.5wpr.com. THE LUXURY ISLAND AI VISIBILITY INDEX™ © 2026 Haute Living Black × 5W AI Visibility Intelligence. All rights reserved. --- ### Acne Treatments AI Citation Benchmark 2026 URL: https://www.5wpr.com/ai-visibility-index/skincare-ai-visibility-acne-2026/ Summary: Research on which acne skincare brands generative AI engines recommend most, featuring La Roche-Posay, CeraVe, and Neutrogena visibility data. # Acne Treatments AI Citation Benchmark 2026 La Roche-Posay Leads Acne AI Search Visibility ## Acne AI Search Visibility Findings La Roche-Posay and CeraVe capture the highest visibility in AI search results for acne queries. The data reveals a tight race at the top of the category. The engines rely heavily on clinical and dermatological authority to answer consumer questions about acne treatments. 480AI answers analyzed 60%La Roche-Posay appearance rate, the category leader 25Brands named across the study 98.1%Answers carrying retrieval evidence This report is one of five skin-concern segments in the 5W Skincare AI Visibility Index 2026, which ranks clinical skincare brands across 1,920 AI answers. ## The Visibility Leaderboard La Roche-Posay appears in 60 percent of all tallied answers. CeraVe follows closely at 56.7 percent. Neutrogena and Differin secure the third and fourth positions. The differences among the top tier are slight. ### AI visibility leaderboard Appearance rate is the share of the 480 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1La Roche-Posay288/48060%55.6%–64.3%2.6 2CeraVe272/48056.7%52.2%–61%3 3Neutrogena241/48050.2%45.8%–54.7%5 4Differin226/48047.1%42.7%–51.6%3.4 5Paula’s Choice189/48039.4%35.1%–43.8%5 6The Ordinary152/48031.7%27.7%–36%6.3 7PanOxyl136/48028.3%24.5%–32.5%4.9 8Proactiv88/48018.3%15.1%–22%7.5 9Cetaphil88/48018.3%15.1%–22%6.1 10The American Academy of Dermatology72/48015%12.1%–18.5%4.7 11Murad72/48015%12.1%–18.5%7.2 12Galderma66/48013.8%11%–17.1%2.5 13COSRX58/48012.1%9.5%–15.3%7.1 14Hero Cosmetics54/48011.3%8.7%–14.4%6.6 15Vanicream44/4809.2%6.9%–12.1%6.7 The 95 percent confidence intervals for La Roche-Posay (55.6 to 64.3 percent) and CeraVe (52.2 to 61 percent) overlap. The difference in visibility between the two brands is within the margin of this sample. CeraVe and Neutrogena also share overlapping intervals. This indicates a competitive upper tier where no single brand entirely monopolizes the answers. ## Brand Profiles ### La Roche-Posay 60% appearance rate. La Roche-Posay holds the first rank with a confidence interval of 55.6 to 64.3 percent. ChatGPT names the brand in 88.3 percent of its answers. Gemini mentions it far less frequently at 20.8 percent. The brand secures an average first mention position of 2.6 and achieves an owned-domain citation rate of 13.2 percent. ### CeraVe 56.7% appearance rate. Ranking second, CeraVe shows a confidence interval of 52.2 to 61 percent. The brand performs best on ChatGPT (80.8 percent) and Perplexity (74.2 percent). It appears on average at position 3 in the answers. Its owned-domain citation rate sits at 11.5 percent. ### Neutrogena 50.2% appearance rate. Neutrogena ranks third with a confidence interval of 45.8 to 54.7 percent. ChatGPT names the brand in 82.5 percent of answers. Gemini only includes it 20.8 percent of the time. The brand appears at an average position of 5 and earns a 3.4 percent owned-domain citation rate. ### Differin 47.1% appearance rate. Differin secures the fourth spot with a confidence interval of 42.7 to 51.6 percent. Perplexity includes the brand in 64.2 percent of answers. Gemini features it in just 25 percent. The brand appears at an early average position of 3.4 and generates a 3.8 percent owned-domain citation rate. ### Paula's Choice 39.4% appearance rate. Paula's Choice ranks fifth with a confidence interval of 35.1 to 43.8 percent. ChatGPT names the brand in 85 percent of answers. Claude entirely ignores the brand, returning a 0 percent appearance rate. The brand sits at an average position of 5 and achieves a 7.2 percent owned-domain citation rate. ### The Ordinary 31.7% appearance rate. The Ordinary holds the sixth rank with a confidence interval of 27.7 to 36 percent. ChatGPT and Perplexity feature the brand in over 48 percent of answers. Gemini and Claude include it in less than 14 percent. The brand appears at an average position of 6.3 with a 3 percent owned-domain citation rate. ### PanOxyl 28.3% appearance rate. PanOxyl ranks seventh with a confidence interval of 24.5 to 32.5 percent. Perplexity leads mentions at 40.8 percent. ChatGPT trails at 19.2 percent. The brand appears at an average position of 4.9 and secures a 2.1 percent owned-domain citation rate. ### Proactiv 18.3% appearance rate. Proactiv sits at rank eight with a confidence interval of 15.1 to 22 percent. Perplexity names it in 40 percent of answers. Claude features it just 0.8 percent of the time. The brand appears at an average position of 7.5 and earns a 1.9 percent owned-domain citation rate. ## Where the engines disagree The generative models return highly fractured results for specific brands. A brand dominating one engine often barely registers on another. ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate La Roche-Posay79.2%88.3%20.8%51.7% CeraVe74.2%80.8%26.7%45% Neutrogena62.5%82.5%20.8%35% Differin64.2%52.5%25%46.7% Paula’s Choice61.7%85%10.8%0% The Ordinary48.3%53.3%13.3%11.7% PanOxyl40.8%19.2%21.7%31.7% Proactiv40%27.5%5%0.8% Cetaphil32.5%28.3%6.7%5.8% The American Academy of Dermatology0%14.2%45.8%0% Murad20.8%31.7%5%2.5% Galderma20.8%25.8%8.3%0% Paula's Choice demonstrates the sharpest divergence. ChatGPT names the brand in 85 percent of its 120 answers. Claude, drawing from the same prompt set, mentions Paula's Choice zero times. This gap reflects differing retrieval priorities and model weights. The American Academy of Dermatology shows a similar pattern. Gemini includes the organization in 45.8 percent of its 120 answers. Perplexity and Claude omit it entirely. Proactiv appears frequently on Perplexity (40 percent) but is nearly absent on Claude (0.8 percent). Brands cannot assume visibility on one platform translates to the others. ## Demand clusters Consumer intent dictates which brands the engines recommend. The clusters show distinct leadership patterns based on the type of acne query. ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Other demand — Primary use case24CeraVe62.5%La Roche-Posay 58.3%, Differin 58.3%, Neutrogena 45.8% Price and value24CeraVe87.5%Differin 75%, La Roche-Posay 70.8%, Neutrogena 70.8% Everyday and mainstream choice24La Roche-Posay83.3%CeraVe 79.2%, Differin 79.2%, Neutrogena 70.8% Beginners and first purchase24CeraVe75%La Roche-Posay 70.8%, Neutrogena 70.8%, Differin 70.8% Where to buy24La Roche-Posay54.2%Differin 50%, CeraVe 45.8%, Neutrogena 41.7% Gifting and occasions24La Roche-Posay62.5%CeraVe 54.2%, Neutrogena 50%, Paula’s Choice 41.7% Specific need or condition24La Roche-Posay70.8%CeraVe 70.8%, Paula’s Choice 54.2%, Neutrogena 50% Comparison and evaluation24CeraVe62.5%La Roche-Posay 58.3%, Neutrogena 50%, Differin 50% Trust and recommendation24La Roche-Posay66.7%Differin 62.5%, CeraVe 54.2%, Neutrogena 54.2% Ownership and maker attribution24Differin58.3%Neutrogena 54.2%, La Roche-Posay 50%, CeraVe 50% Quality and premium tier24La Roche-Posay70.8%CeraVe 54.2%, Paula’s Choice 45.8%, Differin 41.7% Durability and longevity24La Roche-Posay54.2%Differin 50%, CeraVe 45.8%, Neutrogena 37.5% Specialist / advanced use24La Roche-Posay54.2%CeraVe 54.2%, Differin 50%, Paula’s Choice 45.8% General best-in-category24CeraVe83.3%Neutrogena 83.3%, La Roche-Posay 79.2%, Differin 79.2% Emerging and new entrants16Hero Cosmetics43.8%Paula’s Choice 25%, CeraVe 18.8%, Curology 18.8% — Acne — best salicylic acid cleanser for acne8La Roche-Posay100%CeraVe 100%, Neutrogena 87.5%, Paula’s Choice 50% best products for acne prone skin8La Roche-Posay75%CeraVe 75%, Neutrogena 75%, The Ordinary 62.5% best spot treatment for cystic acne8Mario Badescu62.5%La Roche-Posay 50%, The Ordinary 37.5%, The American Academy of Dermatology 37.5% best benzoyl peroxide treatment8La Roche-Posay75%CeraVe 75%, Neutrogena 75%, PanOxyl 75% best moisturizer for acne prone skin8La Roche-Posay75%CeraVe 75%, Neutrogena 75%, Vanicream 62.5% best non-comedogenic sunscreen8La Roche-Posay50%CeraVe 50%, Neutrogena 50%, Paula’s Choice 12.5% best face wash for oily acne prone skin8CeraVe75%Neutrogena 75%, La Roche-Posay 62.5%, PanOxyl 62.5% best treatment for hormonal acne8Differin37.5%Galderma 37.5%, CeraVe 25%, The American Academy of Dermatology 25% best products for teenage acne8La Roche-Posay62.5%CeraVe 62.5%, Neutrogena 62.5%, Differin 62.5% best niacinamide serum for acne8La Roche-Posay62.5%Paula’s Choice 37.5%, COSRX 37.5%, The Inkey List 37.5% best products for back acne (bacne)8Neutrogena62.5%CeraVe 50%, Paula’s Choice 50%, PanOxyl 50% differin vs adapalene which is better8La Roche-Posay62.5%Differin 62.5%, Galderma 37.5%, Neutrogena 25% best azelaic acid product for acne8The Ordinary62.5%La Roche-Posay 50%, Paula’s Choice 50%, The Inkey List 50% best oil-free moisturizer for acne8Neutrogena75%CeraVe 50%, La Roche-Posay 37.5%, Paula’s Choice 25% best products for maskne8CeraVe75%La Roche-Posay 62.5%, Differin 50%, Neutrogena 37.5% best acne patches8COSRX100%Hero Cosmetics 100%, Cetaphil 25%, PanOxyl 12.5% La Roche-Posay and CeraVe dominate general acne routine queries. Differin commands queries specific to retinoids and adapalene. PanOxyl performs best when prompts request benzoyl peroxide face washes. The engines match specific chemical actives to the brands most historically associated with them. ## Citations and sources Engines cite medical publications and news outlets far more often than brand domains. ### Owned-domain citation rate Share of the 471 answers with retrieval evidence that cited a URL on the brand's own website. La Roche-Posay 13.2% · 62/471 CeraVe 11.5% · 54/471 Neutrogena 3.4% · 16/471 Differin 3.8% · 18/471 Paula’s Choice 7.2% · 34/471 The Ordinary 3% · 14/471 PanOxyl 2.1% · 10/471 Proactiv 1.9% · 9/471 Cetaphil 0.6% · 3/471 The American Academy of Dermatology 23.8% · 112/471 Murad 3.4% · 16/471 Galderma 6.2% · 29/471 ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration nbcnews.com276/47158.6% dermapproved.com144/47130.6% aad.org112/47123.8% healthline.com84/47117.8% vogue.com83/47117.6% consumerhealthdigest.com80/47117% whowhatwear.com73/47115.5% topconsumerreviews.com69/47114.6% dermatologist-reviews.com68/47114.4% health.usnews.com64/47113.6% northstardermatology.com63/47113.4% mayoclinic.org63/47113.4% NBC News serves as the primary source, cited in 58.6 percent of the 471 answers with retrieval evidence. DermApproved follows at 30.6 percent. The American Academy of Dermatology earns citations in 23.8 percent of answers. Brand domains struggle to compete with publishers for citations. La Roche-Posay leads owned citations at 13.2 percent, followed by CeraVe at 11.5 percent. Most brands secure fewer than 4 percent. ## Who is winning and who is at risk The data separates the category into clear winners and brands facing structural visibility risks. ### Winners - La Roche-Posay: Achieves a 60 percent appearance rate and the highest owned-domain citation rate (13.2 percent) among product brands. - CeraVe: Secures a 56.7 percent appearance rate and appears early in answers with an average position of 3. - Differin: Maintains a 47.1 percent appearance rate and an average position of 3.4, showing strong associations with specific active ingredients. ### At risk - Paula's Choice: Carries a 39.4 percent total appearance rate but falls to 0 percent on Claude, exposing the brand to single-engine dependency. - The Ordinary: Shows a 31.7 percent appearance rate but drops below 14 percent on both Gemini and Claude. - Proactiv: Records an 18.3 percent appearance rate and sits at an average position of 7.5, putting it low in the generated lists. ## Structural findings The top four brands command the majority of the visibility. La Roche-Posay, CeraVe, Neutrogena, and Differin all clear a 47 percent appearance rate. Below this tier, visibility drops quickly. The engines heavily prefer third-party publishers for retrieval evidence. NBC News and DermApproved provide the backbone for most generated answers. Brands generally fail to serve as their own reference sources. ## What changed for this category The measurement window reveals severe platform divergence. Engines relying on different retrieval tools produce completely different brand lists for identical prompts. Paula's Choice benefits massively from OpenAI's web search tool but gains zero traction through Anthropic's setup. ## The GEO playbook Brands in the acne segment need specific actions to improve AI visibility. - Audit single-engine failures: Paula's Choice shows 0 percent visibility on Claude. Brands must test prompts across all engines and optimize technical content for the specific crawlers ignoring them. Success is measured by appearance rate growth on the lagging engine. - Target publisher mentions: NBC News appears in 58.6 percent of cited answers. Brands need to secure independent reviews on the exact domains the models already trust. Success is measured by a rise in third-party citations referencing the brand. - Connect to active ingredients: Differin scores highly on retinoid queries. Brands should structure their site data to explicitly link product names to clinical actives like benzoyl peroxide. Success is measured by cluster appearance rates for ingredient-specific prompts. - Publish clinical data: The engines cite the AAD in 23.8 percent of answers. Brands lacking clinical authority fall behind. Publish open-access clinical trial results on the owned domain. Success is measured by an increase in owned-domain citation rate. - Improve answer position: Clean and Clear averages position 9.3. Brands buried at the bottom of lists must optimize for "best overall" attributes rather than niche use cases. Success is measured by a lower average first mention position. ## What this means for brands in the category AI visibility in the acne segment requires dermatological credibility. Generative engines do not recommend skincare products based purely on consumer popularity. They look for clinical backing, active ingredients, and endorsements from medical authorities. A brand that fails to connect its products to specific dermatological outcomes will lose visibility to competitors that do. The reliance on NBC News and DermApproved shows that engines outsource the evaluation step to trusted publishers. Brands must win those publishers to win the engines. ## Methodology and integrity This report analyzes generative engine outputs for the acne skincare category. The index tests consumer-intent prompts across multiple engines to determine which brands appear in the answers. ### How this study was measured - Sampling frame fixed first: 60 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 2 trial(s) each. - 480 answers returned of 480 attempted, 471 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The data originates from 60 consumer-intent prompts measured twice per engine across Perplexity, ChatGPT, Gemini, and Claude. This generated 480 attempted answers, all of which returned successfully. The appearance rate denominator is 480 returned answers. The citation and source metrics use a denominator of 471 answers with retrieval evidence. The measurement captures the exact state of the generative outputs on the tested date. ## Frequently asked questions ### Which brand appears most often in acne AI searches? La Roche-Posay leads the category with a 60 percent appearance rate across the tested engines. CeraVe follows closely at 56.7 percent. ### Do the engines agree on which brands are best? No. The engines diverge sharply on specific brands. ChatGPT includes Paula's Choice in 85 percent of its answers, while Claude includes it in none. ### Where do the AI engines get their acne information? The engines primarily cite publishers and medical associations. NBC News is cited in 58.6 percent of answers with retrieval evidence, and DermApproved appears in 30.6 percent. ### Does the American Academy of Dermatology appear in results? Yes. The AAD is named directly in 15 percent of all answers and serves as a cited source in 23.8 percent of the answers containing retrieval evidence. ### Are brands cited as sources for their own products? Rarely. La Roche-Posay leads the owned-domain citation rate at 13.2 percent. Most brands secure owned-domain citations in fewer than 4 percent of answers. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Dermstore1.7%8/4800.8%–3.3% 2Sephora0.4%2/4800.1%–1.5% 3Ulta0.2%1/4800%–1.2% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Acne - Measurement date: 2026-10-09 - Depth: index — 60 prompts, 2 trial(s) per prompt per engine, 4 engines - Answers attempted: 480 - Answers returned (appearance denominator): 480 - Answers with retrieval evidence (citation denominator): 471 (98.1% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Index depth: 60 stratified consumer-intent prompts measured twice per engine. The wider prompt frame is what supports a published category index, and repeat generations keep run-to-run variation observable. ### How the prompts were sampled Fixed sampling frame: 16 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Acne; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope best products for acne prone skin1best products for acne prone skin best salicylic acid cleanser for acne1best salicylic acid cleanser for acne best benzoyl peroxide treatment1best benzoyl peroxide treatment best spot treatment for cystic acne1best spot treatment for cystic acne best moisturizer for acne prone skin1best moisturizer for acne prone skin best non-comedogenic sunscreen1best non-comedogenic sunscreen best face wash for oily acne prone skin1best face wash for oily acne prone skin best treatment for hormonal acne1best treatment for hormonal acne best products for teenage acne1best products for teenage acne best niacinamide serum for acne1best niacinamide serum for acne best products for back acne (bacne)1best products for back acne (bacne) best azelaic acid product for acne1best azelaic acid product for acne differin vs adapalene which is better1differin vs adapalene which is better best oil-free moisturizer for acne1best oil-free moisturizer for acne best products for maskne1best products for maskne best acne patches1best acne patches General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case3the dominant everyday use case for this category Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Quality and premium tier3which options are highest quality, most durable or best made Durability and longevity3which brands last, hold up over time, or fail early Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about ### The exact prompts #PromptClusterShape 1best products for acne prone skinneed_1medium 2best salicylic acid cleanser for acneneed_2medium 3best benzoyl peroxide treatmentneed_3medium 4best spot treatment for cystic acneneed_4medium 5best moisturizer for acne prone skinneed_5medium 6best non-comedogenic sunscreenneed_6medium 7best face wash for oily acne prone skinneed_7medium 8best treatment for hormonal acneneed_8medium 9best products for teenage acneneed_9medium 10best niacinamide serum for acneneed_10medium 11best products for back acne (bacne)need_11medium 12best azelaic acid product for acneneed_12medium 13differin vs adapalene which is betterneed_13medium 14best oil-free moisturizer for acneneed_14medium 15best products for maskneneed_15medium 16best acne patchesneed_16medium 17best acne treatmentsgeneral_bestshort 18best acne skincare brandsgeneral_bestshort 19Which acne treatment brands are best overall for clearing breakouts?general_bestlong 20everyday acne skincareeverydayshort 21best daily acne routine brandseverydaymedium 22Which acne brands work well for an affordable everyday routine?everydaylong 23affordable acne treatment brandsvalue_priceshort 24best budget acne skincarevalue_priceshort 25Which acne treatment brands offer the best value without sacrificing effective ingredients?value_pricelong 26best treatments for breakoutsuse_caseshort 27brands for acne and clogged poresuse_casemedium 28Which skincare brands are most effective for treating recurring facial acne breakouts?use_caselong 29acne products for beginnersbeginnershort 30best first acne routinebeginnershort 31Which acne skincare brands are easiest for a beginner to start using safely?beginnerlong 32where to buy acne treatmentsretailmedium 33best stores for acne skincareretailmedium 34Which acne treatment brands are safest to buy from authorized retailers rather than marketplaces?retaillong 35acne skincare gift setsgiftingshort 36best acne gifts for teensgiftingmedium 37Which acne skincare brands make thoughtful, beginner-friendly gifts for someone with breakouts?giftinglong 38acne products for sensitive skinspecific_needmedium 39best acne brands for oily skinspecific_needmedium 40Which acne treatment brands are best for adult acne with sensitive, easily irritated skin?specific_needlong 41best acne brand comparisoncomparisonshort 42compare leading acne treatment brandscomparisonmedium 43Which acne skincare brands perform best for salicylic acid, benzoyl peroxide, and adapalene options?comparisonlong 44most trusted acne skincare brandstrustmedium 45dermatologist recommended acne brandstrustmedium 46Which acne treatment brands have the strongest dermatologist support and clinical evidence?trustlong 47who makes acne treatment brandsownershipmedium 48acne brands and parent companiesownershipmedium 49Which leading acne skincare brands are made by established pharmaceutical or skincare companies?ownershiplong 50highest quality acne treatmentspremium_qualityshort 51premium clinical acne skincare brandspremium_qualitymedium 52Which acne treatment brands offer the highest-quality formulas and strongest ingredient transparency?premium_qualitylong 53long lasting acne productsdurabilityshort 54acne treatments that last longestdurabilitymedium 55Which acne skincare brands offer products that stay stable and effective through regular use?durabilitylong 56advanced acne treatment brandsspecialistshort 57best clinical acne active brandsspecialistmedium 58Which acne brands are best for building an advanced routine around proven OTC active ingredients?specialistlong 59new acne treatment brandsemergingshort 60Which newer acne skincare brands and product approaches are gaining attention from dermatology-focused buyers?emerginglong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search120120120100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool120120120100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)12012011192.5% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool120120120100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1La Roche-Posay288/48060%60%55.6%–64.3%62/471 (13.2%) 2CeraVe272/48056.7%56.7%52.2%–61%54/471 (11.5%) 3Neutrogena241/48050.2%50.2%45.8%–54.7%16/471 (3.4%) 4Differin226/48047.1%47.1%42.7%–51.6%18/471 (3.8%) 5Paula’s Choice189/48039.4%39.4%35.1%–43.8%34/471 (7.2%) 6The Ordinary152/48031.7%31.7%27.7%–36%14/471 (3%) 7PanOxyl136/48028.3%28.3%24.5%–32.5%10/471 (2.1%) 8Proactiv88/48018.3%18.3%15.1%–22%9/471 (1.9%) 9Cetaphil88/48018.3%18.3%15.1%–22%3/471 (0.6%) 10The American Academy of Dermatology72/48015%15%12.1%–18.5%112/471 (23.8%) 11Murad72/48015%15%12.1%–18.5%16/471 (3.4%) 12Galderma66/48013.8%13.8%11%–17.1%29/471 (6.2%) 13COSRX58/48012.1%12.1%9.5%–15.3%3/471 (0.6%) 14Hero Cosmetics54/48011.3%11.3%8.7%–14.4%5/471 (1.1%) 15Vanicream44/4809.2%9.2%6.9%–12.1%0/471 (0%) 16SkinCeuticals37/4807.7%7.7%5.6%–10.4%7/471 (1.5%) 17Clean & Clear33/4806.9%6.9%4.9%–9.5%5/471 (1.1%) 18Mayo Clinic28/4805.8%5.8%4.1%–8.3%63/471 (13.4%) 19Bioderma28/4805.8%5.8%4.1%–8.3%2/471 (0.4%) 20Mario Badescu27/4805.6%5.6%3.9%–8.1%4/471 (0.8%) 21Paula's Choice25/4805.2%5.2%3.6%–7.6%34/471 (7.2%) 22Curology25/4805.2%5.2%3.6%–7.6%14/471 (3%) 23The Inkey List23/4804.8%4.8%3.2%–7.1%4/471 (0.8%) 24Aveeno22/4804.6%4.6%3%–6.8%2/471 (0.4%) 25Effaclar22/4804.6%4.6%3%–6.8%62/471 (13.2%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-09. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. ## More from the Skincare AI Visibility Index The full cross-concern ranking is in the Skincare AI Visibility Index 2026. The other concern reports: - Anti-Aging & Retinoids AI Citation Benchmark 2026 - Hyperpigmentation & Brightening AI Citation Benchmark 2026 - Sensitive Skin & Barrier Repair AI Citation Benchmark 2026 - SPF & Sun Protection AI Citation Benchmark 2026 ### Related AI visibility research - Beauty AI Visibility Index 2026: the brand-first ranking of the top 25 U.S. beauty brands by AI citation share. - Menopause AI Visibility Index: how AI engines cite the top 25 menopause brands. - Longevity AI Visibility Index: how AI engines cite the top 25 longevity brands. Brands acting on these findings can work with our skincare PR team, our beauty PR practice, our beauty marketing agency group and our dermatology and aesthetics marketing specialists. --- ### How AI Engines Cite The Top 25 Video Game Brands URL: https://www.5wpr.com/research/video-games-ai-visibility-index/ Summary: How AI engines cite the top 25 video game publishers and platforms across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. 5W AI Visibility Index · Volume 02 Video Games & Interactive Entertainment Volume 02 · Q2 2026 · Research Report # How AI Engines Cite The Top 25 Video Game Brands. A modeled audit of which game publishers, platforms, and franchises the AI engines surface when consumers ask "what should I play," "is this game good," or "how do I beat this." Twenty-five brands ranked. Five engines tested. Recoverable ground identified. Brands ranked 25 AI engines tested 5 Consumer prompts 60+ Category Video Games Executive Finding ## The structure of the brand-level citation graph. Three platform holders — Nintendo, PlayStation, and Xbox — anchor the gaming citation layer at the brand level, but the publishers below them are not ranked the way trade reputation would suggest. Take-Two Interactive (Rockstar, GTA) cites higher than EA or Activision despite smaller revenue. Riot Games and miHoYo over-index on Asia-built franchises. The Microsoft acquisitions (Activision, Bethesda) have not yet consolidated into a single Microsoft-anchored citation narrative. The largest recoverable-ground opportunities are in publishers whose franchise scale exceeds their AI citation share. § 01 — Methodology ## How citation share was modeled. Modeled directional estimates derived from publicly available data, observed retrieval patterns, structural signals, and the corresponding EPR Citation Share Study. Not the output of logged query runs across millions of prompts. Intended as a strategic framework — not a definitive search engine measurement. 01 #### Engines Modeled ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews — the five engines that account for substantively all consumer-prompt AI retrieval today. 02 #### Brand Universe Top 25 brands in the category by combined modeled signal across the five engines, weighted to consumer-prompt query patterns. 03 #### Citation Inputs Domain authority and traffic data, structural signals (schema, entity markup, training-data inclusion), and presence inside the EPR Tier 1 outlet set. 04 #### Tier Methodology Brands tiered into Leader / Strong / Mid / Lagging based on aggregate citation share and engine-by-engine variation. Tier breaks reflect natural inflection points in the data. Per the corresponding Everything-PR Citation Share Study — Video Games (Issue No. 02), the dominant outlets are IGN, Metacritic, Fandom Wikis, GameSpot, Polygon, Eurogamer, PC Gamer, plus the rising layer of Reddit gaming subs and YouTube creator transcripts. A game publisher's citation share is built primarily through presence inside that specific outlet set — and through the Fandom wiki layer for franchise queries. § 02 — The Ranking ## The top 25 Video Games brands by modeled AI citation share. Rank Brand · Owner Position Note Modeled Share Tier 01 Nintendo Nintendo Co., Ltd. (TYO: 7974) Platform + franchise dominance. Cited on hardware, first-party games, and "what should I play" queries. 12.8% Leader 02 PlayStation Sony Group (NYSE: SONY) Platform authority. Strong on exclusive titles and hardware queries. 11.4% Leader 03 Xbox Microsoft (NASDAQ: MSFT) Platform + Game Pass authority. Cited on subscription and acquisition-related queries. 9.6% Leader 04 Take-Two Interactive Take-Two (NASDAQ: TTWO) / Rockstar / 2K Cites disproportionately high vs revenue. GTA franchise carries enormous citation weight. 5.8% Leader 05 Valve / Steam Valve Corporation (private) PC platform authority. Cited on storefront, hardware (Steam Deck), and PC-gaming queries. 5.1% Leader 06 EA Electronic Arts (NASDAQ: EA) Sports franchise dominance (FC, Madden). Lower critical-citation than commercial reach. 4.4% Strong 07 Activision Blizzard Microsoft Call of Duty, World of Warcraft anchor. Microsoft consolidation pending in citation graph. 4.0% Strong 08 Epic Games Epic Games (private) Fortnite + Unreal Engine. Cited as both platform and engine authority. 3.6% Strong 09 Ubisoft Ubisoft (EPA: UBI) Assassin's Creed, Far Cry franchises. Mixed citation due to recent commercial difficulties. 3.0% Strong 10 Bethesda Microsoft (acquired 2021) Elder Scrolls, Fallout, Starfield. Strong franchise citation; Microsoft-consolidation lag. 2.5% Strong 11 Rockstar Games Take-Two Interactive GTA, Red Dead Redemption. Cited heavily on "best open-world" queries. 2.4% Strong 12 Square Enix Square Enix (TYO: 9684) Final Fantasy, Kingdom Hearts. Strong JRPG citation, weaker on Western franchises. 2.1% Strong 13 Capcom Capcom (TYO: 9697) Resident Evil, Monster Hunter, Street Fighter. Citation rising with franchise hits. 2.0% Strong 14 Riot Games Tencent (HKG: 0700) League of Legends, Valorant. High citation in esports and MOBA queries specifically. 1.9% Mid 15 From Software Kadokawa (TYO: 9468) Elden Ring, Dark Souls. Cite-heavy on "challenging game" and Souls-like queries. 1.8% Mid 16 CD Projekt Red CD Projekt (WSE: CDR) Witcher, Cyberpunk 2077. Strong RPG citation; Cyberpunk launch reputation effect lingering. 1.6% Mid 17 miHoYo / HoYoverse miHoYo (private) Genshin Impact, Honkai Star Rail. Highest non-Japanese-or-Western citation share. 1.5% Mid 18 Bandai Namco Bandai Namco (TYO: 7832) Tekken, Elden Ring (publisher), Tales franchise. Mid-tier aggregate. 1.4% Mid 19 Roblox Roblox (NYSE: RBLX) Platform authority for younger demographic. Cited on parent-targeted queries. 1.3% Mid 20 Sega Sega Sammy (TYO: 6460) Sonic, Yakuza, Persona (Atlus). Franchise citation steady. 1.2% Mid 21 Konami Konami (TYO: 9766) Metal Gear, Silent Hill (recent revival). Mixed citation profile. 1.0% Mid 22 NetEase Games NetEase (NASDAQ: NTES) Marvel Rivals (2024) lifted citation. Otherwise lower Western reach. 0.9% Lagging 23 Niantic Niantic (private) Pokémon GO. Citation lower than franchise scale suggests. 0.7% Lagging 24 Devolver Digital Devolver Digital (LON: DEVO) Indie publisher. Cited on indie-game recommendation queries. 0.6% Lagging 25 Annapurna Interactive Annapurna (private) Indie publisher with critical-darling reputation. Citation share growing. 0.5% Lagging § 03 — Tier 1 Deep Dive ## Why the leaders lead. Analysis of the top brands — what their citation share is built on, where their moats are real, and where the recoverable ground sits even for category leaders. #1 Brand · Tier 1 Leader ### Nintendo Nintendo is the cleanest example in this series of a brand whose citation share matches its cultural authority. Switch, Mario, Zelda, Pokémon — each franchise cites independently. The brand-and-franchise overlap means AI engines pull Nintendo into nearly every "what should I play" conversation. The risk: Nintendo's communications discipline is also its citation discipline. The brand publishes little. Most of its citation share comes from press and Fandom wikis — both of which 5W can influence directly. #4 Brand · Tier 1 Leader ### Take-Two Interactive Take-Two cites disproportionately high because Rockstar Games and the GTA franchise carry near-mythological citation weight in the AI answer layer. GTA VI's 2025 release lifted the parent company's aggregate citation by an estimated 30% in three months. The opportunity: 2K Sports and 2K Games sub-brands have substantial reach but lower citation share. A 2K-master citation strategy (parallel to the Purina recommendation in pet) would meaningfully shift the Take-Two portfolio. #5 Brand · Tier 1 Leader ### Valve / Steam Valve is the only privately held platform holder in the top five. Its citation share comes from storefront authority — "is this game on Steam," "Steam Deck verified," "Steam sale" — plus a small but cited editorial layer. The recoverable ground is in Steam Deck hardware queries and "PC building for gaming" queries, where citation could be expanded with structured content partnerships. § 04 — Engine Variation ## The five engines do not return identical brand citations. A brand absent from one engine but present in another may need a different program than a brand absent across the board. Engine-aware strategy matters. #### ChatGPT Nintendo, PlayStation, Xbox, Take-Two, Valve dominate. Conservative and franchise-anchored. #### Claude Take-Two and CD Projekt over-index. Narrative-game preference visible. Lighter on platform-marketing. #### Perplexity Reddit gaming subs surface. Indie publishers (Devolver, Annapurna) cite higher. Freshness-favored. #### Google AI Overviews Nintendo, PlayStation, Xbox dominate consumer queries. Specific games cite via IGN, GameSpot, Fandom wikis. #### Gemini YouTube creator gaming reviews dominate. Digital Foundry, Skill Up surface heavily. § 05 — Recoverable Ground ## Where the largest citation lifts are sitting. Brands with citation share materially below their commercial position, brand recognition, or product quality. The specific paths to recovery — and the order of operations. Recoverable Ground EA Currently #6 · Path to Top 4 EA's commercial reach (FC, Madden, Battlefield, Sims) is materially larger than its modeled citation share. The gap is partially reputational — recent franchise difficulties — but largely structural. EA Originals indie portfolio and live-service titles are under-cited. A concentrated narrative around EA Sports specifically could move citation share materially. Recoverable Ground Ubisoft Currently #9 · Path to Top 6 Ubisoft has had a difficult two years commercially, but franchise depth (Assassin's Creed, Far Cry, Rainbow Six, The Crew) means citation potential exceeds current position. A focused recovery-narrative program inside the EPR Tier 1 outlets, plus structured creator partnerships in Gemini's YouTube footprint, would lift the brand. Recoverable Ground Bethesda Currently #10 · Path to Top 6 Microsoft acquired Bethesda in 2021. The acquisition has not consolidated into a single Microsoft-anchored citation narrative; AI engines still cite Bethesda and Microsoft separately. A unified Bethesda-under-Xbox-Game-Studios narrative would lift both brands. The Elder Scrolls VI cycle is the natural moment. Recoverable Ground miHoYo / HoYoverse Currently #17 · Path to Top 10 Genshin Impact and Honkai Star Rail have enormous global player bases. Citation share trails commercial scale because Western press has been slow to cover HoYoverse with the depth they cover Western publishers. A Western press build-out is the entire opportunity. § 06 — The 5W Playbook ## What it takes to move citation share. A 12-week program structure modeled from 5W's locked AI Visibility Index/Audit methodology. Phase 1 establishes the baseline. Phase 2 builds. Phase 3 scales and measures. 01 Weeks 1–4 #### Discovery + Strategy - Baseline AI Visibility Audit across 5 engines - Franchise-level citation gap mapping - Wiki and structured-data coverage audit (Fandom) - Creator and influencer landscape mapping - Editorial placement and review-cycle plan 02 Weeks 5–8 #### Build - IGN, Metacritic, GameSpot, Eurogamer press cycle - Fandom wiki seeding and accuracy program - YouTube creator program (Gemini citation engine) - Reddit AMA and subreddit engagement plan - Schema and entity markup across franchise pages 03 Weeks 9–12 #### Scale - Monthly citation tracking + reporting - Esports and trade-press expansion (where applicable) - Streamer + influencer paid program scaling - Quarterly franchise citation re-benchmark - Phase 2 program scoping Investment Ballpark ### Phase-based scoping. Real ranges. Phase 1 fixed-fee available. Phase 1 $55K–$95K Phase 2 $80K–$180K / mo 12-Month Program $850K–$1.8M Game publishers and platform holders typically scope larger programs due to multi-franchise complexity. Phase 1 scoping engagement available at fixed fee. ### Want this audit run on your brand? 5W AI Communications runs the AI Visibility Index methodology for clients across Video Games and adjacent categories. Phase 1 scoping engagement available at fixed fee. Contact 5W Download as PDF ### Request an AI visibility audit for your brand. Share your brand, category, and competitive set. 5W will route the inquiry with this report as context. Name Work email Company Brand/category of interest Short message Submit inquiry About 5W AI Communications 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. 5W AI Communications The Communications Firm Built for the AI Era. Building brand authority across the platforms where decisions now happen. #### AI Visibility Index - Vol. 01 — Pet - Vol. 02 — Video Games - Vol. 03 — Longevity - Full Series #### Practice Areas - Video Games Practice - GEO - AI Visibility - All Practices © 2026 5W AI Communications · All rights reserved 5W AI Visibility Index · Volume 02 · Q2 2026 --- ### SPF & Sun Protection AI Citation Benchmark 2026 URL: https://www.5wpr.com/ai-visibility-index/skincare-ai-visibility-spf-sun-protection-2026/ Summary: La Roche-Posay and EltaMD lead AI sunscreen recommendations across 480 queries. Discover how AI engines like ChatGPT and Gemini select SPF brands. # SPF & Sun Protection AI Citation Benchmark 2026 La Roche-Posay And EltaMD Dominate AI Sunscreen Search ## What does the data show about AI sunscreen recommendations? La Roche-Posay and EltaMD appear in more Artificial Intelligence (AI) search answers than any other measured sun protection brand. The two brands dominate the category across 480 measured queries, driven by high appearance rates on ChatGPT and Perplexity. Both brands maintain appearance rates near 60 percent of returned answers. Consumer intent for daily UV defense, mineral versus chemical filters, and water resistance reveals a sharp divide in how different generative engines source their recommendations. Medical and clinical authority play a direct role in how models build answer sets for this category. The American Academy of Dermatology appears in 10 percent of all returned answers, performing unusually well on Gemini. Meanwhile, consumer publishers like NBC News and Consumer Reports provide the retrieval evidence for nearly half of all generated responses. 480AI answers analyzed 60.4%La Roche‑Posay appearance rate, the category leader 26Brands named across the study 99.4%Answers carrying retrieval evidence This report is one of five skin-concern segments in the 5W Skincare AI Visibility Index 2026, which ranks clinical skincare brands across 1,920 AI answers. ## Which brands lead the sunscreen category? The top of the sunscreen category shows tight competition. La Roche-Posay and EltaMD share the highest visibility, followed closely by Supergoop! and Neutrogena. These four brands form the primary recommendation tier for generative engines answering SPF queries. ### AI visibility leaderboard Appearance rate is the share of the 480 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1La Roche‑Posay290/48060.4%56%–64.7%2.3 2EltaMD287/48059.8%55.3%–64.1%2.4 3Supergoop!244/48050.8%46.4%–55.3%4.3 4Neutrogena228/48047.5%43.1%–52%4.9 5CeraVe187/48039%34.7%–43.4%5 6Blue Lizard90/48018.8%15.5%–22.5%6.5 7Coppertone84/48017.5%14.4%–21.2%6.8 8Sun Bum81/48016.9%13.8%–20.5%7.5 9Banana Boat80/48016.7%13.6%–20.3%6.8 10ISDIN75/48015.6%12.7%–19.1%5.1 11Aveeno67/48014%11.1%–17.3%6.9 12Black Girl Sunscreen59/48012.3%9.7%–15.5%6.7 13Colorescience, Inc59/48012.3%9.7%–15.5%5.4 14Shiseido56/48011.7%9.1%–14.8%5 15Eucerin49/48010.2%7.8%–13.2%6.4 La Roche-Posay (60.4 percent appearance rate) and EltaMD (59.8 percent) possess 95 percent confidence intervals that overlap entirely. This means the difference between them is within the margin of this sample, and they are statistically tied for first place. Supergoop! (50.8 percent) overlaps with the lower bound of EltaMD, placing it in a strong competitive position. ## How do the top sunscreen brands perform? ### La Roche-Posay 60.4 percent appearance rate. La Roche-Posay appears in 290 of the 480 returned answers. The brand sees its highest visibility on ChatGPT, where it appears in 95.8 percent of answers. Performance drops sharply on Gemini, where the engine names it in only 30 percent of responses. The brand holds an average first mention position of 2.3 and achieves a 2.7 percent owned-domain citation rate (measured against 477 answers with retrieval evidence). ### EltaMD 59.8 percent appearance rate. EltaMD appears in 287 returned answers. Similar to La Roche-Posay, the brand performs best on ChatGPT (87.5 percent) and Perplexity (67.5 percent). Gemini names EltaMD in 30.8 percent of its answers. The brand secures an average first mention position of 2.4 and a 3.6 percent owned-domain citation rate. ### Supergoop! 50.8 percent appearance rate. Supergoop! appears in 244 returned answers. The brand shows strong consistency across ChatGPT (73.3 percent) and Perplexity (64.2 percent). Claude limits Supergoop! to 36.7 percent of answers. The brand generates the highest owned-domain citation rate among commercial brands in this study at 4.2 percent, with an average first mention position of 4.3. ### Neutrogena 47.5 percent appearance rate. Neutrogena appears in 228 returned answers. ChatGPT favors the brand heavily (84.2 percent appearance rate). Other engines reduce its visibility: Perplexity (51.7 percent), Claude (29.2 percent), and Gemini (25 percent). Neutrogena holds an average first mention position of 4.9 and a 1.3 percent owned-domain citation rate. ### CeraVe 39 percent appearance rate. CeraVe appears in 187 returned answers. ChatGPT names the brand in 60.8 percent of responses, while Gemini names it in 25.8 percent. CeraVe achieves an average first mention position of 5 and a 3.1 percent owned-domain citation rate. ### Blue Lizard 18.8 percent appearance rate. Blue Lizard appears in 90 returned answers. The brand reaches 33.3 percent visibility on ChatGPT but drops to 9.2 percent on Gemini. Blue Lizard sits at an average first mention position of 6.5 and a 0.4 percent owned-domain citation rate. ### Coppertone 17.5 percent appearance rate. Coppertone appears in 84 returned answers. ChatGPT names the brand in 35.8 percent of its answers. Claude barely registers the brand, naming it in just 4.2 percent of responses. Coppertone maintains a 0.6 percent owned-domain citation rate. ### Sun Bum 16.9 percent appearance rate. Sun Bum appears in 81 returned answers. The brand maintains consistent lower-tier visibility across Perplexity (23.3 percent) and ChatGPT (26.7 percent). Claude names the brand in 4.2 percent of answers. Sun Bum earns a 1.7 percent owned-domain citation rate. ## Where do the engines disagree? Generative engines do not build their sunscreen recommendations from the same baseline. The data shows clear divides in brand preference and entity selection based on which engine processes the prompt. ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate La Roche‑Posay69.2%95.8%30%46.7% EltaMD67.5%87.5%30.8%53.3% Supergoop!64.2%73.3%29.2%36.7% Neutrogena51.7%84.2%25%29.2% CeraVe36.7%60.8%25.8%32.5% Blue Lizard18.3%33.3%9.2%14.2% Coppertone20.8%35.8%9.2%4.2% Sun Bum23.3%26.7%13.3%4.2% Banana Boat41.7%18.3%6.7%0% ISDIN6.7%35%7.5%13.3% Aveeno15%34.2%2.5%4.2% Black Girl Sunscreen9.2%33.3%2.5%4.2% ChatGPT heavily favors established clinical and mass-market brands. The OpenAI engine names La Roche-Posay in 115 of 120 answers and EltaMD in 105 of 120 answers. Neutrogena also sees 101 mentions on ChatGPT. This suggests the engine's retrieval tool and model weights prioritize dominant market share and historical web presence. Gemini takes a different path, prioritizing institutional authority over commercial brands. The Google engine names the American Academy of Dermatology in 40 of its 120 returned answers (33.3 percent). By comparison, ChatGPT names the organization in 8 answers, while Perplexity and Claude never name it. Gemini also restricts top commercial brands: it names La Roche-Posay in only 36 answers and EltaMD in 37 answers. The same Gemini pattern holds across all five concerns in the Skincare AI Visibility Index 2026, where the American Academy of Dermatology appears in 32.1 percent of Gemini answers. Claude entirely ignores several mid-tier brands. Banana Boat appears in 50 answers on Perplexity but 0 answers on Claude. SkinCeuticals appears in 24 answers on ChatGPT but 0 answers on Claude. This indicates Anthropic's web search tool or safety filters restrict the recommendation pool for specific skincare queries. ## Which brands lead which demand clusters? Consumer intent for sunscreen covers daily use, specific filters, and application formats. The clusters show how brands perform when prompts specify these exact needs. ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Other demand — Price and value24Neutrogena79.2%La Roche‑Posay 50%, Coppertone 50%, Sun Bum 50% Primary use case24Supergoop!66.7%EltaMD 62.5%, La Roche‑Posay 50%, CeraVe 45.8% Beginners and first purchase24EltaMD70.8%CeraVe 66.7%, Supergoop! 62.5%, La Roche‑Posay 58.3% Where to buy24La Roche‑Posay58.3%Neutrogena 45.8%, EltaMD 41.7%, Supergoop! 29.2% Gifting and occasions24Supergoop!70.8%La Roche‑Posay 54.2%, EltaMD 54.2%, Neutrogena 54.2% Specific need or condition24La Roche‑Posay62.5%EltaMD 62.5%, Supergoop! 54.2%, CeraVe 41.7% Comparison and evaluation24La Roche‑Posay66.7%EltaMD 66.7%, Supergoop! 58.3%, CeraVe 54.2% Trust and recommendation24EltaMD83.3%La Roche‑Posay 75%, Neutrogena 75%, CeraVe 62.5% Ownership and maker attribution24La Roche‑Posay62.5%Neutrogena 54.2%, EltaMD 50%, Banana Boat 41.7% Quality and premium tier24La Roche‑Posay70.8%EltaMD 70.8%, Supergoop! 66.7%, Neutrogena 37.5% Durability and longevity24La Roche‑Posay66.7%EltaMD 58.3%, Blue Lizard 54.2%, Coppertone 50% Specialist / advanced use24La Roche‑Posay70.8%EltaMD 66.7%, Supergoop! 45.8%, Neutrogena 45.8% General best-in-category24La Roche‑Posay95.8%Supergoop! 95.8%, EltaMD 87.5%, Neutrogena 83.3% Everyday and mainstream choice24EltaMD95.8%CeraVe 95.8%, La Roche‑Posay 91.7%, Supergoop! 91.7% Emerging and new entrants16Beauty of Joseon37.5%La Roche‑Posay 25%, Neutrogena 18.8%, Sun Bum 12.5% — SPF — best sunscreen for face8EltaMD75%CeraVe 62.5%, La Roche‑Posay 50%, Supergoop! 50% chemical vs mineral sunscreen which is better8La Roche‑Posay50%EltaMD 50%, CeraVe 50%, Blue Lizard 50% best sunscreen for oily skin8Supergoop!100%EltaMD 87.5%, La Roche‑Posay 62.5%, CeraVe 62.5% best mineral sunscreen no white cast8ISDIN37.5%Colorescience, Inc 37.5%, La Roche‑Posay 25%, EltaMD 25% best sunscreen for dark skin tones8Supergoop!75%Black Girl Sunscreen 50%, CeraVe 37.5%, Colorescience, Inc 37.5% best tinted sunscreen8EltaMD62.5%La Roche‑Posay 50%, Supergoop! 50%, CeraVe 37.5% best sunscreen for kids8La Roche‑Posay62.5%Neutrogena 50%, Blue Lizard 50%, Coppertone 50% best sunscreen for acne prone skin8EltaMD87.5%Supergoop! 87.5%, La Roche‑Posay 62.5%, Neutrogena 62.5% best sunscreen stick for reapplication8EltaMD62.5%Supergoop! 62.5%, Shiseido 50%, CeraVe 37.5% best reef safe sunscreen8Blue Lizard87.5%Badger 75%, CeraVe 37.5%, Sun Bum 37.5% best SPF moisturizer for daily wear8La Roche‑Posay62.5%EltaMD 62.5%, Neutrogena 50%, CeraVe 50% best waterproof sunscreen for swimming8Coppertone62.5%La Roche‑Posay 50%, Supergoop! 50%, Neutrogena 50% best sunscreen under makeup8EltaMD100%Supergoop! 100%, La Roche‑Posay 62.5%, CeraVe 50% best mineral sunscreen for rosacea and redness8EltaMD75%CeraVe 62.5%, La Roche‑Posay 50%, Colorescience, Inc 37.5% best drugstore sunscreen8Neutrogena87.5%La Roche‑Posay 75%, CeraVe 62.5%, Sun Bum 62.5% best sunscreen for eczema prone skin8CeraVe62.5%La Roche‑Posay 50%, EltaMD 50%, Blue Lizard 50% The data reveals that La Roche-Posay and EltaMD capture general and clinical demand, while Supergoop! competes strongly in format-specific prompts (like tint or finish). Brands like Blue Lizard and Thinkbaby surface primarily when prompts require mineral filter recommendations. Acne-prone and eczema-prone sunscreen prompts overlap with the Acne Treatments AI Citation Benchmark 2026 and the Sensitive Skin & Barrier Repair AI Citation Benchmark 2026, where La Roche-Posay and CeraVe lead. ## Where do the answers come from? Generative Engine Optimization (GEO) requires understanding which sources provide the retrieval evidence models use to build answers. Owned domains rarely provide this evidence in the sunscreen category. ### Owned-domain citation rate Share of the 477 answers with retrieval evidence that cited a URL on the brand's own website. La Roche‑Posay 2.7% · 13/477 EltaMD 3.6% · 17/477 Supergoop! 4.2% · 20/477 Neutrogena 1.3% · 6/477 CeraVe 3.1% · 15/477 Blue Lizard 0.4% · 2/477 Coppertone 0.6% · 3/477 Sun Bum 1.7% · 8/477 Banana Boat 0.2% · 1/477 ISDIN 2.9% · 14/477 Aveeno 0% · 0/477 Black Girl Sunscreen 0% · 0/477 Across 477 answers with retrieval evidence, owned-domain citation rates remain below 5 percent for all commercial brands. Supergoop! leads commercial brands at 4.2 percent. The American Academy of Dermatology achieves a 9 percent owned-domain citation rate, proving that non-profit institutional domains carry more weight in the retrieval layer for clinical queries. ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration nbcnews.com231/47748.4% consumerreports.org135/47728.3% cnn.com130/47727.3% today.com118/47724.7% forbes.com90/47718.9% whowhatwear.com89/47718.7% youtube.com84/47717.6% allure.com82/47717.2% fortune.com77/47716.1% marieclaire.com72/47715.1% aol.com66/47713.8% treelinereview.com65/47713.6% Third-party publishers control the retrieval layer. NBC News provides retrieval evidence for 231 of the 477 grounded answers (48.4 percent answer penetration). Consumer Reports appears in 135 answers (28.3 percent). CNN (27.3 percent) and Today (24.7 percent) also dominate. Skincare brands must secure mentions on these specific news and review domains to influence AI output. ## Who is winning and who is at risk? The measurement separates brands into clear winners and those facing systemic visibility gaps. ### Winning - La Roche-Posay and EltaMD: Both brands exceed a 59 percent appearance rate across all returned answers. They dominate the top two mention positions on average. - Supergoop!: The brand achieves a 50.8 percent appearance rate and maintains the highest owned-domain citation rate (4.2 percent) among commercial entities. - American Academy of Dermatology: The institution proves that non-commercial entities can capture major share, achieving 33.3 percent visibility on Gemini. ### At risk - Banana Boat: The brand suffers from extreme engine disagreement. It reaches 41.7 percent visibility on Perplexity but 0 percent on Claude, and only 6.7 percent on Gemini. - Anessa: The brand appears in only 1 percent of all returned answers, indicating its global market presence does not translate to English-language AI search visibility for these prompts. - Aveeno: Despite its market footprint, Aveeno manages only a 14 percent appearance rate and zero owned-domain citations. ## What does the category structure reveal? The sunscreen category exhibits high concentration at the top. The top four brands appear in nearly half of all generated answers. This leaves minimal room for mid-tier brands to surface unless the prompt contains a highly specific modifier. The data shows a total reliance on third-party publishers. With NBC News cited in nearly half of all answers, engines clearly treat mass-media product roundups as the primary source of truth for SPF recommendations. Owned brand domains hold almost no direct influence over the final answer set. ## What changed during the measurement window? The data captures a sharp divergence in how Google Gemini and OpenAI ChatGPT handle medical-adjacent product queries. Gemini elevates institutional entities like the American Academy of Dermatology, treating SPF queries as health information. ChatGPT treats the same prompts as retail queries, leaning heavily on commercial brands. ## What actions should brands take? Based on the measured gaps in this report, brands should execute these specific actions. - Audit publisher coverage on NBC News and Consumer Reports. NBC News penetrates 48.4 percent of the grounded answers. Ensure your brand is featured in their recurring sunscreen roundups to guarantee retrieval. - Optimize for Gemini by associating with institutional guidelines. Gemini prefers clinical authority. Link product pages to American Academy of Dermatology standards to align with Gemini's retrieval preferences. - Fix zero-citation owned domains. Brands like Aveeno and Black Girl Sunscreen have a 0 percent owned-domain citation rate. Publish structured, technical ingredient pages (detailing mineral versus chemical filter mechanisms) to give engines a reason to cite the brand site directly. - Target Claude's blind spots. Brands missing from Claude (Banana Boat, SkinCeuticals) must increase their presence on domains Anthropic's tool crawls. Audit Claude's citation list and secure placements there. - Clarify filter types in product titles. Engines separate chemical and mineral sunscreens rigidly. Ensure product schema explicitly defines the filter type so engines can retrieve the product when users ask for mineral options. For the content changes that move beauty brands in AI answers, see what increases AI beauty recommendations. ## What do these numbers mean for the business? This data reveals that market share does not equal AI visibility. AI models rely on specific publishers to decide which sunscreens to recommend. A brand that ignores digital PR on top-tier news sites like NBC News or Consumer Reports will vanish from generative answers, regardless of its retail distribution. Furthermore, treating Answer Engine Marketing (AEM) identically across all engines will fail. A strategy that works for ChatGPT will not satisfy Gemini's demand for clinical authority. Brands must build content that satisfies both commercial and medical retrieval thresholds. Sun protection rarely stands alone in a buyer's routine. SPF moisturizer queries also appear in the Anti-Aging & Retinoids AI Citation Benchmark 2026, and brands whose buyers also search for dark spot and brightening products should read the Hyperpigmentation & Brightening AI Citation Benchmark 2026. ## How was this measured? This research measures AI visibility using multi-engine prompt runs. The platform tested 60 stratified consumer-intent prompts across four generative engines, running each prompt twice to account for variation. ### How this study was measured - Sampling frame fixed first: 60 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 2 trial(s) each. - 480 answers returned of 480 attempted, 477 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The study recorded 480 attempted answers, 480 returned answers, and 477 answers with retrieval evidence. Appearance rates use the 480 returned answers as the denominator. Citation rates use the 477 grounded answers as the denominator. This report covers the SPF segment of the 5W Skincare AI Visibility Index 2026. ## Frequently asked questions ### Which sunscreen brand is most visible in AI search? La Roche-Posay and EltaMD are the most visible brands, appearing in 60.4 percent and 59.8 percent of returned answers, respectively. Their confidence intervals overlap, making them statistically tied. ### Do AI engines cite sunscreen brand websites? Rarely. Supergoop! leads commercial brands with a 4.2 percent owned-domain citation rate across answers with retrieval evidence. Many brands, including Aveeno and Black Girl Sunscreen, have zero owned-domain citations in this dataset. ### Which publishers influence AI sunscreen recommendations the most? NBC News is the dominant source, providing retrieval evidence for 48.4 percent of grounded answers. Consumer Reports follows at 28.3 percent, and CNN at 27.3 percent. ### Do all AI engines recommend the same sunscreens? No. ChatGPT recommends commercial brands like La Roche-Posay (95.8 percent appearance rate). Gemini often bypasses commercial brands to name the American Academy of Dermatology (33.3 percent appearance rate). ### Is Banana Boat recommended by AI engines? Banana Boat appears in 16.7 percent of total returned answers. However, its visibility is highly fragmented: it appears in 41.7 percent of Perplexity answers but 0 percent of Claude answers. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Dermstore1.5%7/4800.7%–3% 2Ulta1%5/4800.4%–2.4% 3Sephora0.8%4/4800.3%–2.1% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: SPF / Sun Protection - Measurement date: 2026-10-09 - Depth: index — 60 prompts, 2 trial(s) per prompt per engine, 4 engines - Answers attempted: 480 - Answers returned (appearance denominator): 480 - Answers with retrieval evidence (citation denominator): 477 (99.4% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Index depth: 60 stratified consumer-intent prompts measured twice per engine. The wider prompt frame is what supports a published category index, and repeat generations keep run-to-run variation observable. ### How the prompts were sampled Fixed sampling frame: 16 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: SPF; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope best sunscreen for face1best sunscreen for face best mineral sunscreen no white cast1best mineral sunscreen no white cast best sunscreen for oily skin1best sunscreen for oily skin chemical vs mineral sunscreen which is better1chemical vs mineral sunscreen which is better best sunscreen for dark skin tones1best sunscreen for dark skin tones best tinted sunscreen1best tinted sunscreen best sunscreen for kids1best sunscreen for kids best SPF moisturizer for daily wear1best SPF moisturizer for daily wear best sunscreen for acne prone skin1best sunscreen for acne prone skin best reef safe sunscreen1best reef safe sunscreen best sunscreen stick for reapplication1best sunscreen stick for reapplication best sunscreen under makeup1best sunscreen under makeup best waterproof sunscreen for swimming1best waterproof sunscreen for swimming best mineral sunscreen for rosacea and redness1best mineral sunscreen for rosacea and redness best drugstore sunscreen1best drugstore sunscreen best sunscreen for eczema prone skin1best sunscreen for eczema prone skin General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case3the dominant everyday use case for this category Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Quality and premium tier3which options are highest quality, most durable or best made Durability and longevity3which brands last, hold up over time, or fail early Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about ### The exact prompts #PromptClusterShape 1best sunscreen for faceneed_1medium 2best mineral sunscreen no white castneed_2medium 3best sunscreen for oily skinneed_3medium 4chemical vs mineral sunscreen which is betterneed_4medium 5best sunscreen for dark skin tonesneed_5medium 6best tinted sunscreenneed_6medium 7best sunscreen for kidsneed_7medium 8best SPF moisturizer for daily wearneed_8medium 9best sunscreen for acne prone skinneed_9medium 10best reef safe sunscreenneed_10medium 11best sunscreen stick for reapplicationneed_11medium 12best sunscreen under makeupneed_12medium 13best waterproof sunscreen for swimmingneed_13medium 14best mineral sunscreen for rosacea and rednessneed_14medium 15best drugstore sunscreenneed_15medium 16best sunscreen for eczema prone skinneed_16medium 17best sunscreen brandsgeneral_bestshort 18best SPF brandsgeneral_bestshort 19Which sun protection brands are best overall for daily use?general_bestlong 20everyday sunscreen brandseverydayshort 21best daily facial SPF brandseverydaymedium 22Which sunscreen brands make the easiest everyday options for most people?everydaylong 23best value sunscreen brandsvalue_priceshort 24affordable SPF brands worth buyingvalue_pricemedium 25Which sunscreen brands offer the best value without compromising reliable protection?value_pricelong 26best sunscreen for daily wearuse_casemedium 27best commuting SPF brandsuse_caseshort 28Which sun protection brands work best under makeup for everyday city wear?use_caselong 29beginner sunscreen brandsbeginnershort 30best first facial sunscreenbeginnermedium 31Which sunscreen brands are simplest for a first-time buyer to choose?beginnerlong 32where to buy sunscreenretailmedium 33best sunscreen retailersretailshort 34Which sunscreen brands are safest to buy from authorized online retailers?retaillong 35sunscreen gift brandsgiftingshort 36best SPF gifts for travelersgiftingmedium 37Which sun protection brands make the best gifts for a beach holiday?giftinglong 38best sunscreen for dark skinspecific_needmedium 39no white cast SPF brandsspecific_needmedium 40Which sunscreen brands are best for sensitive skin and leave minimal white cast?specific_needlong 41mineral versus chemical sunscreen brandscomparisonmedium 42best tinted SPF brandscomparisonmedium 43Which sunscreen brands perform better for protection, wearability, and ingredient transparency?comparisonlong 44dermatologist recommended sunscreen brandstrustmedium 45most trusted SPF brandstrustshort 46Which sunscreen brands have the strongest dermatologist support and clinical evidence?trustlong 47who makes leading sunscreen brandsownershipmedium 48sunscreen brand parent companiesownershipmedium 49Which leading sunscreen brands are made by established skincare or pharmaceutical companies?ownershiplong 50highest quality sunscreen brandspremium_qualitymedium 51premium facial SPF brandspremium_qualitymedium 52Which sun protection brands justify premium pricing through formulation quality and testing?premium_qualitylong 53long lasting sunscreen brandsdurabilitymedium 54best water resistant SPF brandsdurabilitymedium 55Which sunscreen brands hold up best through sweat, swimming, and long outdoor days?durabilitylong 56advanced sunscreen brandsspecialistshort 57best high UVA protection brandsspecialistmedium 58Which sunscreen brands offer advanced broad-spectrum protection for intense sun exposure?specialistlong 59new sunscreen brandsemergingshort 60Which newer sunscreen brands are gaining attention for innovative UV filters?emerginglong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search120120120100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool120120120100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)12012011797.5% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool120120120100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1La Roche‑Posay290/48060.4%60.4%56%–64.7%13/477 (2.7%) 2EltaMD287/48059.8%59.8%55.3%–64.1%17/477 (3.6%) 3Supergoop!244/48050.8%50.8%46.4%–55.3%20/477 (4.2%) 4Neutrogena228/48047.5%47.5%43.1%–52%6/477 (1.3%) 5CeraVe187/48039%39%34.7%–43.4%15/477 (3.1%) 6Blue Lizard90/48018.8%18.8%15.5%–22.5%2/477 (0.4%) 7Coppertone84/48017.5%17.5%14.4%–21.2%3/477 (0.6%) 8Sun Bum81/48016.9%16.9%13.8%–20.5%8/477 (1.7%) 9Banana Boat80/48016.7%16.7%13.6%–20.3%1/477 (0.2%) 10ISDIN75/48015.6%15.6%12.7%–19.1%14/477 (2.9%) 11Aveeno67/48014%14%11.1%–17.3%0/477 (0%) 12Black Girl Sunscreen59/48012.3%12.3%9.7%–15.5%0/477 (0%) 13Colorescience, Inc59/48012.3%12.3%9.7%–15.5%13/477 (2.7%) 14Shiseido56/48011.7%11.7%9.1%–14.8%1/477 (0.2%) 15Eucerin49/48010.2%10.2%7.8%–13.2%1/477 (0.2%) 16American Academy of Dermatology48/48010%10%7.6%–13%43/477 (9%) 17SkinCeuticals44/4809.2%9.2%6.9%–12.1%3/477 (0.6%) 18Beauty of Joseon33/4806.9%6.9%4.9%–9.5%0/477 (0%) 19Coola32/4806.7%6.7%4.8%–9.3%6/477 (1.3%) 20Murad27/4805.6%5.6%3.9%–8.1%0/477 (0%) 21Australian Gold26/4805.4%5.4%3.7%–7.8%0/477 (0%) 22Bondi Sands26/4805.4%5.4%3.7%–7.8%0/477 (0%) 23Badger25/4805.2%5.2%3.6%–7.6%3/477 (0.6%) 24Thinkbaby25/4805.2%5.2%3.6%–7.6%0/477 (0%) 25Hawaiian Tropic25/4805.2%5.2%3.6%–7.6%0/477 (0%) 26Anessa5/4801%1%0.4%–2.4%0/477 (0%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-09. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. ## More from the Skincare AI Visibility Index The full cross-concern ranking is in the Skincare AI Visibility Index 2026. The other concern reports: - Anti-Aging & Retinoids AI Citation Benchmark 2026 - Acne Treatments AI Citation Benchmark 2026 - Hyperpigmentation & Brightening AI Citation Benchmark 2026 - Sensitive Skin & Barrier Repair AI Citation Benchmark 2026 ### Related AI visibility research - AI Visibility Index: every category study in the series. - Beauty AI Visibility Index 2026: the brand-first ranking of the top 25 U.S. beauty brands by AI citation share. - Menopause AI Visibility Index: how AI engines cite the top 25 menopause brands. - Longevity AI Visibility Index: how AI engines cite the top 25 longevity brands. Brands acting on these findings can work with our skincare PR team, our beauty PR practice, our beauty marketing agency group and our dermatology and aesthetics marketing specialists. --- ### Hyperpigmentation & Brightening AI Citation Benchmark 2026 URL: https://www.5wpr.com/ai-visibility-index/skincare-ai-visibility-hyperpigmentation-brightening-2026/ Summary: Discover which hyperpigmentation and brightening skincare brands dominate AI search in 2026. SkinCeuticals leads with a 54.9% appearance rate. # Hyperpigmentation & Brightening AI Citation Benchmark 2026 SkinCeuticals Leads AI Visibility In Dark Spot Correction ## Clinical brands dominate AI selection for dark spot correction Generative engines heavily favor clinical and dermatologist-backed brands for hyperpigmentation and brightening queries. SkinCeuticals and La Roche-Posay lead the category in visibility across the four major AI engines. The data shows AI systems prioritize ingredient transparency and medical publication sources when users ask about vitamin C, tranexamic acid, alpha arbutin, and melasma treatments. This report is part of the 5W Skincare AI Visibility Index 2026: Concern-First & Clinical Citation Benchmark. It measures 472 total answers returned by Perplexity, ChatGPT, Gemini, and Claude. 472AI answers analyzed 54.9%SkinCeuticals appearance rate, the category leader 25Brands named across the study 99.2%Answers carrying retrieval evidence This report is one of five skin-concern segments in the 5W Skincare AI Visibility Index 2026, which ranks clinical skincare brands across 1,920 AI answers. ## Which brands lead the hyperpigmentation category? SkinCeuticals achieves the highest visibility in the category. The brand appears in 54.9 percent of all 472 answers returned. La Roche-Posay follows closely at 48.3 percent. The Ordinary secures third place with a 37.9 percent appearance rate. ### AI visibility leaderboard Appearance rate is the share of the 472 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1SkinCeuticals259/47254.9%50.4%–59.3%2.4 2La Roche-Posay228/47248.3%43.8%–52.8%3.8 3The Ordinary179/47237.9%33.7%–42.4%5.5 4Paula’s Choice151/47232%27.9%–36.3%4 5Murad116/47224.6%20.9%–28.7%5.7 6CeraVe110/47223.3%19.7%–27.3%5.3 7SkinMedica77/47216.3%13.3%–19.9%4.4 8Kiehl’s73/47215.5%12.5%–19%7 9Naturium70/47214.8%11.9%–18.3%5.4 10Obagi61/47212.9%10.2%–16.3%3.6 11Topicals59/47212.5%9.8%–15.8%5.9 12Sunday Riley54/47211.4%8.9%–14.6%6.5 13Neutrogena46/4729.7%7.4%–12.8%7.6 14Eadem43/4729.1%6.8%–12%4.1 15Paula's Choice41/4728.7%6.5%–11.6%5.1 The 95 percent confidence intervals for SkinCeuticals (50.4 percent to 59.3 percent) and La Roche-Posay (43.8 percent to 52.8 percent) overlap slightly. This overlap indicates the difference between the top two positions is within the margin of this sample. The Ordinary sits clearly below La Roche-Posay, with an interval of 33.7 percent to 42.4 percent. ## Brand performance profiles ### SkinCeuticals 54.9% appearance rate. SkinCeuticals ranks first overall. Its visibility peaks on ChatGPT, where it appears in 85.6 percent of answers. Gemini names the brand in only 28 percent of its responses. The brand secures a 9.6 percent owned-domain citation rate out of 468 grounded answers. It averages a 2.4 position in the answers where it appears. ### La Roche-Posay 48.3% appearance rate. La Roche-Posay ranks second. The brand sees strong performance on ChatGPT (72.9 percent) and Perplexity (67.8 percent). It struggles on Gemini, appearing in just 21.2 percent of answers. The brand averages a 3.8 first mention position and captures a 4.9 percent owned-domain citation rate. ### The Ordinary 37.9% appearance rate. The Ordinary ranks third. ChatGPT favors the brand heavily, naming it in 77.1 percent of answers. Claude rarely selects it, including the brand in only 11.9 percent of its responses. The Ordinary appears at an average position of 5.5. ### Paula's Choice 32.0% appearance rate. Paula's Choice holds the fourth position. ChatGPT names the brand in 67.8 percent of answers. Claude completely ignores the brand, returning zero mentions. Paula's Choice earns a strong 7.9 percent owned-domain citation rate and an average mention position of 4.0. ### Murad 24.6% appearance rate. Murad ranks fifth in the category. The brand performs best on ChatGPT (43.2 percent) and Perplexity (38.1 percent). It sees minimal visibility on Claude (4.2 percent). Murad averages a 5.7 mention position. ### CeraVe 23.3% appearance rate. CeraVe ranks sixth. The brand maintains relatively even visibility across Perplexity (30.5 percent) and ChatGPT (34.7 percent), but drops on Gemini (10.2 percent). It holds a 2.1 percent owned-domain citation rate and a 5.3 average mention position. ### SkinMedica 16.3% appearance rate. SkinMedica ranks seventh. ChatGPT selects the brand in 25.4 percent of answers. Gemini names it in 8.5 percent. The brand appears early in responses, averaging a 4.4 position. ### Kiehl's 15.5% appearance rate. Kiehl's takes the eighth spot. ChatGPT names the brand in 47.5 percent of answers. Claude completely omits it. The brand secures a 3.6 percent owned-domain citation rate and an average position of 7.0. ## Where do the AI engines disagree? The four AI engines show sharp disagreements on specific brands. These gaps reflect differences in system design, retrieval tools, and model weights. ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate SkinCeuticals66.9%85.6%28%39% La Roche-Posay67.8%72.9%21.2%31.4% The Ordinary41.5%77.1%21.2%11.9% Paula’s Choice54.2%67.8%5.9%0% Murad38.1%43.2%12.7%4.2% CeraVe30.5%34.7%10.2%17.8% SkinMedica17.8%25.4%8.5%13.6% Kiehl’s11.9%47.5%2.5%0% Naturium34.7%3.4%12.7%8.5% Obagi8.5%35.6%1.7%5.9% Topicals23.7%5.1%19.5%1.7% Sunday Riley9.3%19.5%8.5%8.5% ChatGPT heavily favors The Ordinary. It names the brand in 91 of its 118 answers (77.1 percent). Claude names The Ordinary in only 14 answers (11.9 percent). This indicates ChatGPT relies on different training data or retrieval sources for affordable clinical skincare. Paula's Choice shows an even more extreme divergence. ChatGPT names the brand in 80 of its 118 answers. Claude returns zero mentions for the brand across its 118 answers. Kiehl's faces a similar situation. ChatGPT includes Kiehl's in 56 answers, while Claude includes it in zero. Gemini shows a unique preference for emerging and inclusive brands. It names Topicals in 23 answers (19.5 percent) and Eadem in 10 answers (8.5 percent). ChatGPT mostly ignores these brands, naming Topicals in 6 answers and Eadem in 4 answers. ## Which brands lead which demand clusters? Different user queries trigger different brand selections. The data groups these selections into distinct demand clusters. ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Other demand — Beginners and first purchase24La Roche-Posay66.7%SkinCeuticals 50%, The Ordinary 50%, CeraVe 50% Primary use case24SkinCeuticals58.3%La Roche-Posay 58.3%, Murad 58.3%, The Ordinary 54.2% Where to buy24SkinCeuticals45.8%La Roche-Posay 37.5%, The Ordinary 33.3%, Paula’s Choice 29.2% Gifting and occasions24SkinCeuticals66.7%La Roche-Posay 41.7%, Sunday Riley 37.5%, Paula’s Choice 33.3% Specific need or condition24La Roche-Posay66.7%SkinCeuticals 62.5%, The Ordinary 50%, Paula’s Choice 50% Comparison and evaluation24SkinCeuticals66.7%La Roche-Posay 41.7%, Paula’s Choice 29.2%, CeraVe 29.2% Trust and recommendation24SkinCeuticals66.7%La Roche-Posay 66.7%, SkinMedica 54.2%, The Ordinary 37.5% Ownership and maker attribution24SkinCeuticals50%La Roche-Posay 41.7%, The Ordinary 37.5%, Obagi 37.5% Quality and premium tier24SkinCeuticals41.7%La Roche-Posay 20.8%, Obagi 20.8%, Revision Skincare 20.8% Durability and longevity24SkinCeuticals62.5%La Roche-Posay 50%, Paula’s Choice 50%, The Ordinary 33.3% Specialist / advanced use24SkinCeuticals62.5%La Roche-Posay 62.5%, Paula’s Choice 37.5%, Murad 37.5% General best-in-category24SkinCeuticals83.3%La Roche-Posay 75%, Murad 54.2%, The Ordinary 50% Everyday and mainstream choice24The Ordinary75%La Roche-Posay 66.7%, CeraVe 58.3%, SkinCeuticals 45.8% Price and value24CeraVe79.2%The Ordinary 70.8%, Naturium 58.3%, La Roche-Posay 54.2% Emerging and new entrants16Eadem43.8%La Roche-Posay 37.5%, SkinCeuticals 31.3%, Topicals 31.3% — Hyperpigmentation — best serum for melasma8SkinCeuticals87.5%Paula’s Choice 50%, The INKEY List 50%, La Roche-Posay 37.5% best product for post-acne marks8SkinCeuticals62.5%La Roche-Posay 50%, Murad 50%, Paula’s Choice 37.5% best vitamin C serum for hyperpigmentation8SkinCeuticals62.5%La Roche-Posay 50%, Paula’s Choice 50%, The Ordinary 37.5% vitamin C vs niacinamide for brightening8SkinCeuticals75%Paula’s Choice 50%, Sunday Riley 50%, La Roche-Posay 37.5% best tranexamic acid serum8SkinCeuticals100%Naturium 62.5%, Topicals 62.5%, The INKEY List 62.5% best kojic acid soap for dark spots8Topicals25%— best brightening serum for dull skin8SkinCeuticals62.5%La Roche-Posay 37.5%, The Ordinary 37.5%, Paula’s Choice 37.5% best exfoliating toner for dark spots8The Ordinary50%Paula’s Choice 37.5%, Caudalie 25%, Naturium 12.5% best products for uneven skin tone8SkinCeuticals87.5%La Roche-Posay 75%, Murad 50%, SkinMedica 50% best alpha arbutin serum8Naturium87.5%The INKEY List 62.5%, The Ordinary 50%, SkinCeuticals 12.5% best sunscreen to prevent hyperpigmentation8La Roche-Posay50%SkinCeuticals 25%, Neutrogena 25%, Eucerin 25% best products for dark under eye circles8La Roche-Posay62.5%SkinCeuticals 37.5%, CeraVe 25%, The INKEY List 25% best serum for sun spots8SkinCeuticals75%Paula’s Choice 62.5%, La Roche-Posay 50%, Murad 50% glycolic acid vs lactic acid for brightening8The Ordinary62.5%SkinCeuticals 50%, Paula’s Choice 50%, Sunday Riley 50% best products for melasma during pregnancy8The Ordinary75%SkinCeuticals 62.5%, La Roche-Posay 50%, Paula’s Choice 50% SkinCeuticals dominates queries centered on vitamin C and ferulic acid. La Roche-Posay leads responses addressing melasma and dark spot correction with niacinamide. The Ordinary captures the majority of queries focused on alpha arbutin and azelaic acid solutions. ## Where do the answers come from? Engines cite medical publications and beauty media to ground their answers. The total denominator for citation metrics is 468 grounded answers. ### Owned-domain citation rate Share of the 468 answers with retrieval evidence that cited a URL on the brand's own website. SkinCeuticals 9.6% · 45/468 La Roche-Posay 4.9% · 23/468 The Ordinary 2.6% · 12/468 Paula’s Choice 7.9% · 37/468 Murad 2.8% · 13/468 CeraVe 2.1% · 10/468 SkinMedica 0.2% · 1/468 Kiehl’s 3.6% · 17/468 Naturium 0.4% · 2/468 Obagi 5.8% · 27/468 Topicals 1.3% · 6/468 Sunday Riley 1.9% · 9/468 ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration forbes.com141/46830.1% dermapproved.com131/46828% vogue.com120/46825.6% nbcnews.com115/46824.6% whowhatwear.com104/46822.2% marieclaire.com101/46821.6% allure.com95/46820.3% dermstore.com87/46818.6% glamour.com76/46816.2% cnn.com74/46815.8% youtube.com72/46815.4% skinsort.com71/46815.2% Forbes is the most cited source in this category. It appears in 141 answers, reaching a 30.1 percent penetration rate. DermApproved follows closely with 131 cited answers (28 percent). Vogue (25.6 percent) and NBC News (24.6 percent) also provide significant grounding data for the AI engines. The reliance on DermApproved confirms the engines prioritize clinical endorsement. ## Who is winning and who is at risk? ### The winners SkinCeuticals is winning the category. The brand captures a 54.9 percent appearance rate and appears early in answers with a 2.4 average position. TruSkin is also winning in the citation layer. Despite ranking 19th in overall appearance (6.4 percent), TruSkin achieves a 10 percent owned-domain citation rate. The AI engines trust the TruSkin domain directly. ### Brands at risk Sunday Riley is at risk. The brand achieves only an 11.4 percent appearance rate. Its repeat consistency sits at a low 22.7 percent, meaning engines rarely select it multiple times for the same prompt. Ole Henriksen is also at risk. The brand appears in just 5.7 percent of answers and captures zero owned-domain citations. ## What the data says about the category The hyperpigmentation category is highly concentrated at the top. The top two brands, SkinCeuticals and La Roche-Posay, appear in roughly half of all answers. Below rank 5, visibility drops off sharply. CeraVe (rank 6) appears in 23.3 percent of answers, while SkinMedica (rank 7) appears in only 16.3 percent. The data shows AI engines rely heavily on third-party publishers over brand domains. Forbes, DermApproved, and Vogue drive the majority of citations. Only a few brands, such as SkinCeuticals (9.6 percent) and TruSkin (10 percent), successfully force the engines to cite their owned domains. ## What changed in the measurement window The measurement window reveals a strong engine-level divergence regarding newer clinical brands. Gemini actively retrieves inclusive skincare brands like Topicals and Eadem, while Claude and ChatGPT rely on established legacy names. ## The GEO playbook for hyperpigmentation - Target DermApproved and medical media: Forbes and DermApproved dictate AI selection in this category. Brands must secure coverage and clinical reviews on these specific domains to increase their baseline likelihood of retrieval. - Publish raw clinical data: Engines cite TruSkin and SkinCeuticals directly because these domains host clinical research evaluation data. Brands must publish their ingredient testing results in HTML format, not just in PDF files. - Optimize for model-specific retrieval: Paula's Choice and Kiehl's are completely invisible to Claude. These brands must audit how Anthropic's crawler interacts with their domains and adjust their technical SEO to ensure Claude can read their content. - Address the tranexamic acid gap: The Ordinary and Naturium capture high visibility for alpha arbutin and azelaic acid. Competing brands must create dedicated entity pages for their specific active ingredients to compete in these sub-clusters. - Fix low repeat consistency: Sunday Riley suffers from a 22.7 percent repeat consistency rate. The brand must standardize its product naming conventions across all retail and media partners so AI models recognize the entity reliably across different runs. ## What this means for clinical skincare brands The AI search environment for hyperpigmentation heavily rewards clinical proof. Consumers ask generative engines to solve specific medical-adjacent skin concerns. The engines respond by prioritizing brands that clearly state their active ingredients and back them up with dermatological testing. Marketing copy does not survive the retrieval process. Structured clinical data and third-party medical validation dictate which brands the models select. ## How this was measured This research measures AI visibility using 60 stratified consumer-intent prompts. Each prompt was run twice per engine to observe run-to-run variation. ### How this study was measured - Sampling frame fixed first: 60 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 2 trial(s) each. - 472 answers returned of 472 attempted, 468 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The study evaluates four engines: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). The total number of attempted answers was 472. Appearance metrics use the 472 returned answers as the denominator. Citation and source metrics use the 468 answers with retrieval evidence as the denominator. Brands without citation eligibility are excluded from citation calculations. ## Frequently asked questions ### Which brand appears most often for dark spot queries? SkinCeuticals is the most visible brand in this category. It appears in 54.9 percent of all answers returned by the four major AI engines. ### Do all AI engines recommend the same brands? No. ChatGPT heavily favors The Ordinary, naming it in 77.1 percent of answers. Claude only names The Ordinary in 11.9 percent of its answers. Paula's Choice appears in 67.8 percent of ChatGPT answers but zero Claude answers. ### Which publishers influence AI skincare answers the most? Forbes is the most cited source, appearing in 30.1 percent of grounded answers. DermApproved follows closely, providing citations for 28 percent of the answers. ### Can a brand's own website get cited by AI? Yes. TruSkin achieves a 10 percent owned-domain citation rate, the highest in the category. SkinCeuticals follows with a 9.6 percent owned-domain citation rate. ### How does Naturium perform in AI search? Naturium ranks 9th overall with a 14.8 percent appearance rate. The brand performs best on Perplexity, where it appears in 34.7 percent of the engine's answers. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Dermstore4.9%23/4723.3%–7.2% 2Ulta0.6%3/4720.2%–1.9% 3Sephora0.4%2/4720.1%–1.5% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Hyperpigmentation / Brightening - Measurement date: 2026-10-09 - Depth: index — 60 prompts, 2 trial(s) per prompt per engine, 4 engines - Answers attempted: 472 - Answers returned (appearance denominator): 472 - Answers with retrieval evidence (citation denominator): 468 (99.2% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Index depth: 60 stratified consumer-intent prompts measured twice per engine. The wider prompt frame is what supports a published category index, and repeat generations keep run-to-run variation observable. ### How the prompts were sampled Fixed sampling frame: 16 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Hyperpigmentation; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope best vitamin C serum for hyperpigmentation1best vitamin C serum for hyperpigmentation best products for dark spots1best products for dark spots best serum for melasma1best serum for melasma vitamin C vs niacinamide for brightening1vitamin C vs niacinamide for brightening best product for post-acne marks1best product for post-acne marks best tranexamic acid serum1best tranexamic acid serum best kojic acid soap for dark spots1best kojic acid soap for dark spots best brightening serum for dull skin1best brightening serum for dull skin best products for uneven skin tone1best products for uneven skin tone best alpha arbutin serum1best alpha arbutin serum best exfoliating toner for dark spots1best exfoliating toner for dark spots best sunscreen to prevent hyperpigmentation1best sunscreen to prevent hyperpigmentation best products for dark under eye circles1best products for dark under eye circles glycolic acid vs lactic acid for brightening1glycolic acid vs lactic acid for brightening best serum for sun spots1best serum for sun spots best products for melasma during pregnancy1best products for melasma during pregnancy General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case3the dominant everyday use case for this category Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Quality and premium tier3which options are highest quality, most durable or best made Durability and longevity3which brands last, hold up over time, or fail early Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about ### The exact prompts #PromptClusterShape 1best vitamin C serum for hyperpigmentationneed_1medium 2best products for dark spotsneed_2medium 3best serum for melasmaneed_3medium 4vitamin C vs niacinamide for brighteningneed_4medium 5best product for post-acne marksneed_5medium 6best tranexamic acid serumneed_6medium 7best kojic acid soap for dark spotsneed_7medium 8best brightening serum for dull skinneed_8medium 9best products for uneven skin toneneed_9medium 10best alpha arbutin serumneed_10medium 11best exfoliating toner for dark spotsneed_11medium 12best sunscreen to prevent hyperpigmentationneed_12medium 13best products for dark under eye circlesneed_13medium 14glycolic acid vs lactic acid for brighteningneed_14medium 15best serum for sun spotsneed_15medium 16best products for melasma during pregnancyneed_16medium 17best dark spot serumsgeneral_bestshort 18best hyperpigmentation skincare brandsgeneral_bestshort 19best brightening productsgeneral_bestshort 20best everyday dark spot skincare brandseverydaymedium 21affordable brightening skincare for daily useeverydaymedium 22Which hyperpigmentation brands make reliable products for an uncomplicated everyday routine?everydaylong 23best budget dark spot brandsvalue_priceshort 24best value vitamin c skincarevalue_pricemedium 25Which brightening skincare brands offer the strongest results for the price?value_pricelong 26best products for dark spotsuse_casemedium 27best skincare brands for fading sunspotsuse_casemedium 28Which brightening skincare brands work best for post-acne marks and uneven tone?use_caselong 29beginner dark spot skincarebeginnershort 30best first brightening serumbeginnermedium 31What hyperpigmentation skincare brands are easiest for a complete beginner to start with?beginnerlong 32where to buy dark spot skincareretailshort 33best retailers for brightening skincareretailmedium 34Which retailers are most reliable for buying authentic hyperpigmentation skincare products?retaillong 35brightening skincare gift ideasgiftingshort 36best dark spot skincare giftsgiftingmedium 37Which brightening skincare brands make the best gifts before a wedding or milestone event?giftinglong 38best skincare brands for melasmaspecific_needmedium 39dark spot products for deeper skin tonesspecific_needmedium 40Which hyperpigmentation brands are best for sensitive skin prone to discoloration?specific_needlong 41best brightening skincare brands comparedcomparisonmedium 42vitamin c versus tranexamic acid brandscomparisonmedium 43Which dark spot skincare brands perform best when comparing clinical evidence, ingredients, and price?comparisonlong 44most trusted dark spot skincare brandstrustmedium 45dermatologist recommended brightening brandstrustmedium 46Which hyperpigmentation skincare brands do dermatologists and reputable beauty reviewers recommend most often?trustlong 47who makes leading brightening skincareownershipmedium 48parent companies behind dark spot brandsownershipmedium 49Which companies own the leading skincare brands known for hyperpigmentation and brightening products?ownershiplong 50best premium dark spot skincarepremium_qualitymedium 51highest quality vitamin c brandspremium_qualitymedium 52Which premium brightening skincare brands justify their prices through formulation quality and clinical testing?premium_qualitylong 53long lasting brightening serumsdurabilitymedium 54stable vitamin c skincare brandsdurabilitymedium 55Which hyperpigmentation skincare brands make formulas that remain effective throughout their usable life?durabilitylong 56advanced melasma skincare brandsspecialistmedium 57clinical strength dark spot productsspecialistmedium 58Which skincare brands offer the most advanced ingredient combinations for stubborn hyperpigmentation?specialistlong 59new brightening skincare brandsemergingmedium 60Which newer skincare brands are gaining attention for innovative dark spot and brightening formulas?emerginglong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search118118118100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool118118118100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)11811811496.6% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool118118118100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 2 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1SkinCeuticals259/47254.9%54.9%50.4%–59.3%45/468 (9.6%) 2La Roche-Posay228/47248.3%48.3%43.8%–52.8%23/468 (4.9%) 3The Ordinary179/47237.9%37.9%33.7%–42.4%12/468 (2.6%) 4Paula’s Choice151/47232%32%27.9%–36.3%37/468 (7.9%) 5Murad116/47224.6%24.6%20.9%–28.7%13/468 (2.8%) 6CeraVe110/47223.3%23.3%19.7%–27.3%10/468 (2.1%) 7SkinMedica77/47216.3%16.3%13.3%–19.9%1/468 (0.2%) 8Kiehl’s73/47215.5%15.5%12.5%–19%17/468 (3.6%) 9Naturium70/47214.8%14.8%11.9%–18.3%2/468 (0.4%) 10Obagi61/47212.9%12.9%10.2%–16.3%27/468 (5.8%) 11Topicals59/47212.5%12.5%9.8%–15.8%6/468 (1.3%) 12Sunday Riley54/47211.4%11.4%8.9%–14.6%9/468 (1.9%) 13Neutrogena46/4729.7%9.7%7.4%–12.8%3/468 (0.6%) 14Eadem43/4729.1%9.1%6.8%–12%6/468 (1.3%) 15Paula's Choice41/4728.7%8.7%6.5%–11.6%37/468 (7.9%) 16Drunk Elephant37/4727.8%7.8%5.7%–10.6%9/468 (1.9%) 17The INKEY List35/4727.4%7.4%5.4%–10.1%8/468 (1.7%) 18Olay32/4726.8%6.8%4.8%–9.4%2/468 (0.4%) 19TruSkin30/4726.4%6.4%4.5%–8.9%47/468 (10%) 20Ole Henriksen27/4725.7%5.7%4%–8.2%0/468 (0%) 21Revision Skincare27/4725.7%5.7%4%–8.2%6/468 (1.3%) 22PCA Skin25/4725.3%5.3%3.6%–7.7%5/468 (1.1%) 23Tatcha24/4725.1%5.1%3.4%–7.5%1/468 (0.2%) 24Eucerin24/4725.1%5.1%3.4%–7.5%3/468 (0.6%) 25Caudalie22/4724.7%4.7%3.1%–7%1/468 (0.2%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-09. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. ## More from the Skincare AI Visibility Index The full cross-concern ranking is in the Skincare AI Visibility Index 2026. The other concern reports: - Anti-Aging & Retinoids AI Citation Benchmark 2026 - Acne Treatments AI Citation Benchmark 2026 - Sensitive Skin & Barrier Repair AI Citation Benchmark 2026 - SPF & Sun Protection AI Citation Benchmark 2026 ### Related AI visibility research - Beauty AI Visibility Index 2026: the brand-first ranking of the top 25 U.S. beauty brands by AI citation share. - Menopause AI Visibility Index: how AI engines cite the top 25 menopause brands. - Longevity AI Visibility Index: how AI engines cite the top 25 longevity brands. Brands acting on these findings can work with our skincare PR team, our beauty PR practice, our beauty marketing agency group and our dermatology and aesthetics marketing specialists. --- ### Claude in Israel: A Study on the Startup Nation URL: https://www.5wpr.com/research/claude-in-israel-startup-nation-study/ Summary: Claude use in Israel leads the world. The Startup Nation ranks first on the Anthropic AI usage index, ahead of Singapore and the United States of America. # Claude in Israel: A Study on the Startup Nation Why the Startup Nation leads the world in AI adoption — and what that means for the next generation of Israeli brands. A joint research study by 5WPR and Louder. ## The world's most AI-intense country Israel is the most AI-intense country on earth. On Anthropic's AI Usage Index, which measures how much each country uses the Claude AI model relative to its share of the world's working-age population, Israel ranks first with a score of 4.9x — ahead of Singapore (4.19x), the United States (3.69x), and every other country measured. The gap between Israel at the top of the index and Tanzania at the bottom (0.03x) is roughly 150x. 5WPR and Louder have published a joint research study examining why. The short version: Israel builds more startups per capita than any country on earth, and it now uses AI more intensely than any country on earth. Those facts are not independent. They are the same fact, measured twice. ## Key findings Israel ranks #1 in the world on the Anthropic AI Usage Index at 4.9x, per the November 2025 sample of 116 countries released by Anthropic in January 2026 95% of Israeli tech-sector workers use AI tools regularly; 78% use them daily (Israel Innovation Authority / Brookdale Institute, November 2025) Approximately 70% of Israeli tech workers report substantial improvement in output quality from AI tools; 40% report AI cuts their work time by more than 50% Israel is one of only seven countries globally with a locally developed model in the top 200 AI models worldwide (Microsoft AI Diffusion Report 2025) 342 Israeli generative AI startups have collectively raised over $20 billion Israeli startups raised $15.6 billion in 2025 and generated $74 billion in exits (Startup Nation Central) ## What Israel's share of global Claude volume actually means In absolute terms, Israel represents roughly 0.5% of global Claude conversations — a small share, because Israel has only about 10 million people and a working-age population of roughly 6 million. That 6 million is approximately 0.11% of the world's working-age adults. At an AUI of 4.9x, the math works out to Israeli users generating roughly one in every 185 Claude conversations on earth, drawn from roughly one in every 900 working-age adults. The United States, by contrast, accounts for 21.6% of global Claude volume because it has both a large population and high per-capita usage. India is the second-largest absolute user at 7.2%, but has low per-capita intensity (AUI of approximately 0.22x). The AUI cuts through population size to measure how deeply AI is embedded in a country's working life. By that measure, Israel is unmatched. ## The Startup Nation compound Four structural conditions explain the ranking, and all four strengthened through 2025 and early 2026. ### Startup density More than 6,600 active Israeli startups — 14 times the European per-capita concentration. Israel ranks first globally for R&D expenditure as a share of GDP. Half of all 2025 Israeli tech capital went into deals larger than $100 million, concentrating toward scale-stage companies that drive the most aggressive AI adoption. ### Workforce depth The Israel Innovation Authority and Myers-JDC-Brookdale Institute survey of more than 500 tech workers, published in November 2025, found that 82% of daily AI users apply the technology across three or more categories of work, and 25% use it across six or more. 86% of workers aged 25–34 report daily AI use. For comparison, the Microsoft / LinkedIn 2025 Work Trend Index put worldwide high-tech AI use at 75%. Israel's tech workforce is running 20 points above that global baseline. ### A maturing AI sector 342 Israeli generative AI startups have collectively raised over $20 billion. Cybersecurity and generative AI together accounted for roughly 70% of all capital raised in Israel in 2025. Recent headline exits include Google's acquisition of Wiz, Palo Alto Networks' acquisition of CyberArk, and Nvidia's acquisitions of Run:ai and Illumex. In January 2026, Cyera raised $400 million at a $9 billion valuation — tripling its value in a year. ### Global recognition Microsoft's AI Diffusion Report 2025 places Israel in a seven-country group — alongside the U.S., China, France, South Korea, the U.K., and Canada — with a locally developed model in the global top 200 AI models. Israel's flagship model, AI21 Labs' Jamba Large 1.7, trails OpenAI's GPT-5 by just 11.6 months. On Microsoft's Adoption Index, Israel ranks 15th globally at 33.9% of adults — above the U.S. (26.3%) and China (15.4%). ## Quotes from Israeli AI leaders "The AI survey reveals the depth of the transformation underway in Israeli high-tech. Almost all workers in the sector already use these tools, and this is not a future projection but a present reality reshaping high-tech employment." — Dror Bin, CEO, Israel Innovation Authority "Israel is among the world's leading countries in infrastructure, models, and implementation. Israeli high-tech continues to be the engine of growth for the economy, and the work being done here in artificial intelligence demonstrates our strategic global role." — Michal Braverman-Blumenstyk, Corporate Vice President, Microsoft; Managing Director, Microsoft Israel R&D Center "Israel is at the forefront of AI adoption alongside superpowers like the U.S. and China, and that's a huge advantage." — Alon Haimovich, General Manager, Microsoft Israel "Israel has always operated ahead of the curve. With AI, the country isn't just keeping up — it's defining what comes next. The same grit, creativity, and speed that built the Startup Nation are now shaping how Israeli brands reach the world." — Dolev Raphael Drai, Founder, Louder "Israel builds more startups per capita than any country on earth. It now also uses AI more intensely than any country on earth. Those two facts are not independent — they are the same fact, measured twice." — Ronn Torossian, Founder and Chairman, 5WPR ## Building brands from Israel in an AI-first world Israeli brand-building teams are using AI from the first week of the company, not after a Series B. Three mechanics stand out. ### Smaller teams reach global scale faster Israeli cyber startup Above Security raised a $43 million Series A with just 10 employees at eight months old. Cyera went from a $3 billion to a $9 billion valuation in under a year. Ten-person Israeli teams are producing the output that fifty-person teams produced five years ago. ### Hebrew and English as a native pair Frontier language models have improved sharply on Hebrew. Israeli teams now operate across both languages in the same workflow, collapsing the tradeoff between depth in the Israeli market and reach in the global one. ### The information layer is changing underneath Israeli brands When AI usage runs 4.9x expected inside Israel, Israeli buyers, journalists, analysts, and investors are increasingly forming first impressions of a company from an AI answer rather than a website or press release. A brand's position inside that information layer — how accurately and prominently it appears in AI outputs — is starting to matter as much as its more traditional assets. ## Five takeaways for Israeli founders and operators Treat AI as infrastructure, not tooling. If AI is a productivity add-on in your team, you are behind the Israeli median. Publish primary-source material. LLMs synthesize answers, and brands that publish credible original content become the preferred citations. Operate in Hebrew and English as a native pair from day one. Compress team size deliberately. Redirect the savings toward strategic hires, strategic partnerships, and founder time on the road. Measure what LLMs say about you. A basic audit reveals what the broader market is actually reading. ## Download the full study The full 13-page study is available as a PDF in English and Hebrew. It includes the complete 116-country AUI leaderboard, sector-by-sector analysis of the Israeli AI ecosystem, profiles of ten category-defining Israeli AI brands, additional data from Anthropic's March 24, 2026 Economic Index update, and five practical takeaways for founders. Download English PDF ## More 5W research on AI and Israel This study measures AI adoption — how intensely Israel uses Claude. Three companion 5W studies measure the AI visibility and commercial side of the same story: Israel Is Running Ahead on Claude — and the Numbers Prove It — a company-by-company look at the 50 Israeli startups building, securing, and commercializing the Claude-class AI wave, led by AI-security lab Irregular AI and the Israeli Brand — the GEO and earned-media reallocation opportunity for Israeli brands, sector by sector AI and Israeli Marketing — how generative AI is reshaping discovery and marketing spend in the Israeli advertising market For Hebrew-language AI citation audits by category (banking, airlines, and more), see The Olam's Answer Index series ## Related analysis across the 5WPR network Israel Is the Startup Nation. It's Now Also the AI Nation. — Ronn Torossian, op-ed Israel Uses AI More Than Any Country on Earth. So Why Are Its Most Promising Startups Invisible Inside AI? — Everything-PR industry analysis Israel Uses AI More Than Any Country on Earth. Here's What Living There Has Taught Me. — Medium Follow the research on Instagram: @louder.creators ## Methodology and sources This is an observational study synthesizing publicly available data. The AUI country leaderboard uses Anthropic's November 13–20, 2025 sample covering 116 countries, published in January 2026. Data on task diversification, augmentation rates, and job coverage comes from Anthropic's March 24, 2026 Economic Index update based on February 2026 sampling. Primary sources: Anthropic Economic Index — September 2025, January 2026, and March 2026 reports; Israel Innovation Authority / Brookdale Institute workforce survey, November 2025; Microsoft AI Diffusion Report 2025; Microsoft / LinkedIn Work Trend Index 2025; Startup Nation Central 2025 data; YL Ventures' State of the Cyber Nation report, December 2025; CTech / Calcalist reporting on Israeli high-tech funding; The Times of Israel and JNS coverage of specific Israeli AI and cybersecurity companies. ## About the authors 5WPR 5W Public Relations (5WPR) is one of the largest independently owned public relations firms in the United States, with more than 275 employees and headquarters in New York City. Founded in 2003, the agency serves clients across consumer, B2B, technology, healthcare, public affairs, and crisis communications, and has operated an expanded service offering in the Israeli market since 2025. Louder Louder is a creative and content studio that operates at the intersection of strategic storytelling and social impact. Part of The Phygital Jewish group, the studio specializes in developing high-impact campaigns and digital content for Jewish-owned businesses, international organizations, and government agencies. Founded by Dolev Raphael Drai. Published by 5WPR Research Division. Questions: research@5wpr.com --- ### The American Private School AI Study 2026 URL: https://www.5wpr.com/research/private-school-ai-study/ Summary: A two-part audit of how America's private schools appear in AI answers, measuring which institutions chatbots name when families ask where to apply. --- ### Sensitive Skin & Barrier Repair AI Citation Benchmark 2026 URL: https://www.5wpr.com/ai-visibility-index/skincare-ai-visibility-sensitive-skin-barrier-repair-2026/ Summary: CeraVe and La Roche-Posay dominate AI search visibility for barrier repair and sensitive skin. See the full 2026 data and citation rates. # Sensitive Skin & Barrier Repair AI Citation Benchmark 2026 CeraVe Leads AI Search For Sensitive Skin ## How AI engines rank sensitive skin brands CeraVe and La Roche-Posay dominate Generative Engine Optimization (GEO) visibility for sensitive skin and barrier repair queries. CeraVe appears in 71.9 percent of all 480 AI search answers returned in this study. La Roche-Posay follows at 67.3 percent. These two brands hold a distinct advantage over the rest of the market. The data shows a clear preference for clinically positioned skincare. AI engines prioritize brands associated with dermatological testing and barrier restoration when answering user questions about rosacea, eczema, and ceramide creams. Search Engine Optimization (SEO) strategies for these brands must account for this clinical bias. 480AI answers analyzed 71.9%CeraVe appearance rate, the category leader 26Brands named across the study 99%Answers carrying retrieval evidence This report is one of five skin-concern segments in the 5W Skincare AI Visibility Index 2026, which ranks clinical skincare brands across 1,920 AI answers. ## The appearance leaderboard The leaderboard reveals a highly concentrated top tier. CeraVe and La Roche-Posay secure the top two positions, appearing in over two thirds of all answers. Vanicream sits alone in third place at 49.6 percent. The appearance rate drops sharply after the top three. ### AI visibility leaderboard Appearance rate is the share of the 480 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1CeraVe345/48071.9%67.7%–75.7%2.4 2La Roche‑Posay323/48067.3%63%–71.3%2.5 3Vanicream238/48049.6%45.1%–54%4.2 4Cetaphil172/48035.8%31.7%–40.2%5.1 5Avène150/48031.3%27.3%–35.5%4.2 6First Aid Beauty146/48030.4%26.5%–34.7%7.2 7Eucerin137/48028.5%24.7%–32.7%6.2 8Aveeno122/48025.4%21.7%–29.5%6.2 9BIODERMA117/48024.4%20.7%–28.4%6.7 10Paula’s Choice113/48023.5%20%–27.5%6.6 11SkinCeuticals78/48016.3%13.2%–19.8%5.3 12Dr. Jart+73/48015.2%12.3%–18.7%5.3 13EltaMD67/48014%11.1%–17.3%7.4 14Skinfix57/48011.9%9.3%–15.1%4.9 15Neutrogena53/48011%8.5%–14.2%8.8 The 95 percent confidence interval for CeraVe (67.7 to 75.7 percent) overlaps with the interval for La Roche-Posay (63.0 to 71.3 percent). The difference between the top two brands is within the margin of this sample. Vanicream does not overlap with the leaders or with fourth place Cetaphil. ## Brand performance profiles ### CeraVe 71.9 percent appearance rate. CeraVe ranks first overall. The brand averages a 2.4 position when mentioned. It performs consistently across Perplexity, ChatGPT, and Claude, appearing in over 70 percent of answers on each. Gemini is the outlier, naming CeraVe in only 39.2 percent of answers. CeraVe secures a 5.1 percent owned domain citation rate. ### La Roche-Posay 67.3 percent appearance rate. La Roche-Posay ranks second and averages a 2.5 position. The brand achieves a massive 92.5 percent appearance rate on ChatGPT and 83.3 percent on Perplexity. Gemini names the brand in only 32.5 percent of answers. La Roche-Posay captures a 5.3 percent owned domain citation rate. ### Vanicream 49.6 percent appearance rate. Vanicream takes the third position. It averages a 4.2 mention position. The brand is strong on ChatGPT (69.2 percent) and Perplexity (67.5 percent). It drops to 22.5 percent on Gemini. Vanicream holds a 4.0 percent owned domain citation rate. ### Cetaphil 35.8 percent appearance rate. Cetaphil ranks fourth with an average position of 5.1. ChatGPT names the brand most frequently at 56.7 percent. Gemini names it least at 20.0 percent. Cetaphil leads the entire category in owned domain citations with an 8.6 percent rate. ### Avène 31.3 percent appearance rate. Avène ranks fifth and averages a 4.2 position. Perplexity favors the brand, naming it in 67.5 percent of answers. Gemini almost entirely ignores it, naming it in just 1.7 percent of answers. Avène achieves zero owned domain citations. ### First Aid Beauty 30.4 percent appearance rate. First Aid Beauty ranks sixth. It appears late in the answers with an average position of 7.2. ChatGPT names the brand in 52.5 percent of answers. Claude only names it in 5.0 percent. The brand holds a 1.7 percent owned domain citation rate. ### Eucerin 28.5 percent appearance rate. Eucerin ranks seventh with a 6.2 average position. ChatGPT drives most of its visibility at 65.8 percent. Claude names Eucerin in just 1.7 percent of answers. Eucerin has a 0.6 percent owned domain citation rate. ### Aveeno 25.4 percent appearance rate. Aveeno ranks eighth. The brand averages a 6.2 mention position. Perplexity and ChatGPT name Aveeno in over 40 percent of answers. Gemini and Claude both name it in under 10 percent. Aveeno secures a 0.8 percent owned domain citation rate. ## Where the engines disagree The four Large Language Model (LLM) engines show massive variance in brand preferences. A high overall appearance rate often masks severe blind spots on individual platforms. Answer Engine Optimization (AEO) efforts must account for these technical differences. ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate CeraVe88.3%89.2%39.2%70.8% La Roche‑Posay83.3%92.5%32.5%60.8% Vanicream67.5%69.2%22.5%39.2% Cetaphil38.3%56.7%20%28.3% Avène67.5%20%1.7%35.8% First Aid Beauty39.2%52.5%25%5% Eucerin36.7%65.8%10%1.7% Aveeno44.2%43.3%5.8%8.3% BIODERMA25.8%44.2%11.7%15.8% Paula’s Choice26.7%65%2.5%0% SkinCeuticals28.3%15%13.3%8.3% Dr. Jart+22.5%23.3%5.8%9.2% Gemini systematically downgrades traditional clinical skincare brands compared to other engines. CeraVe drops from 89.2 percent on ChatGPT to 39.2 percent on Gemini. La Roche-Posay falls from 92.5 percent on ChatGPT to 32.5 percent on Gemini. Gemini replaces these brands with institutional sources. It names the American Academy of Dermatology in 28.3 percent of its answers. Claude ignores several mid tier brands entirely. Paula's Choice appears in 65.0 percent of ChatGPT answers. It appears in exactly zero Claude answers. Claude also names First Aid Beauty and Eucerin in fewer than 6 percent of its answers. Perplexity strongly prefers Avène. Perplexity names the brand in 67.5 percent of its 120 answers. ChatGPT names Avène in only 20.0 percent. Gemini names Avène in just 1.7 percent. ## Mapping demand clusters Brands perform differently based on the specific intent of the user prompt. Answer Engine Marketing (AEM) requires understanding these intent clusters. ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Other demand — Primary use case24CeraVe79.2%La Roche‑Posay 75%, Dr. Jart+ 45.8%, First Aid Beauty 41.7% Everyday and mainstream choice24CeraVe91.7%Vanicream 79.2%, La Roche‑Posay 70.8%, Cetaphil 45.8% Price and value24CeraVe79.2%La Roche‑Posay 70.8%, Vanicream 58.3%, Cetaphil 50% General best-in-category24CeraVe87.5%La Roche‑Posay 83.3%, Vanicream 62.5%, First Aid Beauty 54.2% Specialist / advanced use24CeraVe75%La Roche‑Posay 66.7%, Vanicream 37.5%, Avène 33.3% Beginners and first purchase24CeraVe83.3%La Roche‑Posay 83.3%, Vanicream 66.7%, Avène 54.2% Where to buy24CeraVe62.5%La Roche‑Posay 58.3%, Vanicream 41.7%, Eucerin 41.7% Gifting and occasions24La Roche‑Posay75%CeraVe 70.8%, First Aid Beauty 41.7%, Vanicream 37.5% Specific need or condition24CeraVe87.5%Vanicream 83.3%, La Roche‑Posay 75%, Cetaphil 62.5% Comparison and evaluation24CeraVe70.8%La Roche‑Posay 62.5%, Paula’s Choice 50%, Vanicream 37.5% Trust and recommendation24CeraVe75%La Roche‑Posay 70.8%, Vanicream 66.7%, Eucerin 54.2% Ownership and maker attribution24CeraVe66.7%La Roche‑Posay 66.7%, Cetaphil 62.5%, Vanicream 54.2% Quality and premium tier24La Roche‑Posay62.5%CeraVe 54.2%, Vanicream 45.8%, Avène 37.5% Durability and longevity24CeraVe75%La Roche‑Posay 54.2%, Vanicream 41.7%, First Aid Beauty 37.5% Emerging and new entrants16AESTURA43.8%Tower 28 Beauty 37.5%, KraveBeauty 37.5%, La Roche‑Posay 25% — Sensitive Skin — best ceramide cream for barrier repair8CeraVe62.5%Skinfix 62.5%, SkinCeuticals 37.5%, La Roche‑Posay 25% best moisturizer for sensitive skin8CeraVe87.5%La Roche‑Posay 62.5%, Vanicream 37.5%, Cetaphil 37.5% best products for rosacea8SkinCeuticals75%CeraVe 62.5%, La Roche‑Posay 50%, Avène 50% best fragrance-free skincare products8CeraVe100%La Roche‑Posay 100%, Vanicream 100%, Cetaphil 62.5% best sunscreen for sensitive skin8CeraVe100%La Roche‑Posay 100%, EltaMD 100%, Vanicream 87.5% best gentle cleanser for sensitive skin8CeraVe75%La Roche‑Posay 75%, Vanicream 75%, Cetaphil 50% best products for eczema8CeraVe87.5%Aveeno 87.5%, Vanicream 75%, Eucerin 75% best moisturizer for compromised skin barrier8CeraVe75%First Aid Beauty 62.5%, La Roche‑Posay 50%, Vanicream 50% best hypoallergenic skincare brands8CeraVe87.5%La Roche‑Posay 87.5%, Vanicream 75%, BIODERMA 75% best products for redness and irritation8La Roche‑Posay87.5%SkinCeuticals 75%, CeraVe 50%, Avène 50% best cica cream for irritated skin8La Roche‑Posay87.5%Dr. Jart+ 75%, Avène 50%, AESTURA 50% best products for dry sensitive skin8CeraVe87.5%Vanicream 87.5%, Cetaphil 87.5%, La Roche‑Posay 75% best serum for skin barrier repair8CeraVe37.5%SkinCeuticals 37.5%, La Roche‑Posay 25%, Paula’s Choice 25% best products for perioral dermatitis8CeraVe75%La Roche‑Posay 75%, Vanicream 37.5%, Cetaphil 37.5% cetaphil vs cerave for sensitive skin8CeraVe87.5%Cetaphil 87.5%, La Roche‑Posay 75%, Vanicream 62.5% best calming toner for sensitive skin8La Roche‑Posay50%Paula’s Choice 50%, CeraVe 37.5%, Avène 37.5% General barrier repair queries favor the largest clinical brands. CeraVe and La Roche-Posay dominate prompts asking for basic sensitive skin routines. Brands like Skinfix and Dr. Jart+ show strength in specific sub categories like ceramide targeted formulations and tiger grass treatments. ## Citations and source publishers Visibility does not equal citation. Some brands are named frequently but never provide the underlying proof. The citation denominator uses the 475 answers with retrieval evidence. ### Owned-domain citation rate Share of the 475 answers with retrieval evidence that cited a URL on the brand's own website. CeraVe 5.1% · 24/475 La Roche‑Posay 5.3% · 25/475 Vanicream 4% · 19/475 Cetaphil 8.6% · 41/475 Avène 0% · 0/475 First Aid Beauty 1.7% · 8/475 Eucerin 0.6% · 3/475 Aveeno 0.8% · 4/475 BIODERMA 2.1% · 10/475 Paula’s Choice 6.5% · 31/475 SkinCeuticals 2.1% · 10/475 Dr. Jart+ 1.3% · 6/475 Cetaphil leads the category in technical retrieval. The brand secures an 8.6 percent owned domain citation rate despite ranking fourth in overall visibility. Paula's Choice also overperforms in retrieval, securing a 6.5 percent citation rate. Avène has a 31.3 percent appearance rate but zero owned domain citations. The AI engines name Avène but do not cite its website. ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration nbcnews.com250/47552.6% dermapproved.com203/47542.7% allure.com121/47525.5% reviewdermatica.com102/47521.5% skinsort.com83/47517.5% youtube.com77/47516.2% ulta.com76/47516% whowhatwear.com67/47514.1% healthline.com66/47513.9% parade.com53/47511.2% rankandstyle.com52/47510.9% veriphyskincare.com52/47510.9% A few major publishers control the information layer. NBC News is the most cited source in the category. It appears in 52.6 percent of grounded answers. Dermapproved.com follows closely at 42.7 percent. Allure holds the third position at 25.5 percent. These three publishers sit upstream of most AI recommendations. ## Who is winning and who is at risk The data separates the category into clear winners and brands facing structural visibility risks. ### Winning - CeraVe: Ranks first with a 71.9 percent appearance rate and a low 2.4 average position. - La Roche-Posay: Ranks second with a 67.3 percent appearance rate and achieves over 80 percent visibility on two different engines. - Cetaphil: Leads the category in owned domain citations with an 8.6 percent rate. ### At risk - Avène: Achieves a 31.3 percent appearance rate but zero owned domain citations. - Paula's Choice: Highly dependent on ChatGPT (65.0 percent) but completely invisible on Claude (0 percent). - Tower 28 Beauty: Struggles to break through the clinical bias, appearing in only 5.6 percent of answers. ## Structural findings for the category The AI search environment for barrier repair is heavily concentrated. The top two brands dominate the category, appearing in the majority of answers. The clinical positioning is an absolute requirement for top tier visibility. Brands without clear dermatological associations struggle to reach a 15 percent appearance rate. Publisher concentration is extreme. NBC News and Dermapproved.com appear in 52.6 percent and 42.7 percent of grounded answers respectively. AI engines rely heavily on these specific third party verification points to generate skincare recommendations. ## What changed in the measurement window The data reveals a distinct divergence in Gemini's retrieval strategy. Gemini actively substitutes commercial skincare brands with institutional sources like the American Academy of Dermatology and the National Eczema Association. Both organizations appear in 28.3 percent of Gemini answers but register near zero on Perplexity and Claude. ## The GEO playbook Brands in the sensitive skin category must take concrete steps to improve their AI visibility. Secure mentions on top publishers. AI engines rely on third party validation. Target placements on NBC News and Dermapproved.com. These two sites provide the primary training data for AI recommendations in this category. Audit Claude visibility gaps. Several mid tier brands are invisible on Claude. Investigate technical blocking or content formatting issues that prevent Anthropic's crawler from processing brand data. Optimize for technical retrieval. Avène appears frequently but earns zero citations. Audit the brand domain for schema gaps and crawler access issues. Ensure product pages provide direct, extractable answers about ingredients. Target the Gemini institutional bias. Gemini favors medical authorities. Align product content with specific guidelines published by the American Academy of Dermatology to increase relevance on Google's engine. Focus on ceramide and eczema intent. The highest volume clusters center on specific barrier ingredients and conditions. Build dedicated page structures that answer technical questions about barrier function. ## What this means for the market This data confirms that clinical positioning dictates AI visibility. The engines treat barrier repair as a medical topic rather than a cosmetic one. They prioritize brands with historical dermatologist endorsements and specific ingredient transparency. Cosmetic brands attempting to enter the barrier repair space face a massive deficit. They must build structural credibility with medical publishers before the LLMs will recommend them. ## Methodology This study measures 60 stratified consumer intent prompts across four AI search engines. Each prompt was queried twice per engine to observe run to run variation. The results reflect these specific prompts on the measurement date. They do not represent all searches in the category. ### How this study was measured - Sampling frame fixed first: 60 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 2 trial(s) each. - 480 answers returned of 480 attempted, 475 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The appearance rate uses the 480 total answers returned as the denominator. The citation rate uses the 475 answers with clear retrieval evidence as the denominator. Brands without measured owned domains are excluded from citation metrics. ## Frequently asked questions ### Which brand is most visible for barrier repair in AI search? CeraVe leads the category. It appears in 71.9 percent of the measured AI answers. ### Does high visibility guarantee high citations? No. Avène appears in 31.3 percent of answers but secures zero owned domain citations. The engines name the brand based on third party data but do not link to the brand website. ### Why is my brand invisible on certain engines? Engines use different models and retrieval tools. Paula's Choice appears in 65.0 percent of ChatGPT answers but zero Claude answers. This indicates a crawler limitation or a specific gap in Anthropic's training data. ### What is the most important source for AI skincare answers? NBC News is the primary source. It provides citations for 52.6 percent of all grounded answers in this study. ### How does Gemini differ from other engines? Gemini favors institutional medical authorities. It names the American Academy of Dermatology in 28.3 percent of its answers while suppressing commercial brands. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Dermstore1%5/4800.4%–2.4% 2Sephora0.4%2/4800.1%–1.5% 3Ulta0.4%2/4800.1%–1.5% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Sensitive Skin / Barrier Repair - Measurement date: 2026-10-09 - Depth: index — 60 prompts, 2 trial(s) per prompt per engine, 4 engines - Answers attempted: 480 - Answers returned (appearance denominator): 480 - Answers with retrieval evidence (citation denominator): 475 (99% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Index depth: 60 stratified consumer-intent prompts measured twice per engine. The wider prompt frame is what supports a published category index, and repeat generations keep run-to-run variation observable. ### How the prompts were sampled Fixed sampling frame: 16 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: Sensitive Skin; then , then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope best moisturizer for sensitive skin1best moisturizer for sensitive skin best products for rosacea1best products for rosacea best fragrance-free skincare products1best fragrance-free skincare products best ceramide cream for barrier repair1best ceramide cream for barrier repair best gentle cleanser for sensitive skin1best gentle cleanser for sensitive skin best products for eczema1best products for eczema best sunscreen for sensitive skin1best sunscreen for sensitive skin best moisturizer for compromised skin barrier1best moisturizer for compromised skin barrier best products for redness and irritation1best products for redness and irritation best hypoallergenic skincare brands1best hypoallergenic skincare brands best cica cream for irritated skin1best cica cream for irritated skin best products for dry sensitive skin1best products for dry sensitive skin best serum for skin barrier repair1best serum for skin barrier repair best products for perioral dermatitis1best products for perioral dermatitis best calming toner for sensitive skin1best calming toner for sensitive skin cetaphil vs cerave for sensitive skin1cetaphil vs cerave for sensitive skin General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case3the dominant everyday use case for this category Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Quality and premium tier3which options are highest quality, most durable or best made Durability and longevity3which brands last, hold up over time, or fail early Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about ### The exact prompts #PromptClusterShape 1best moisturizer for sensitive skinneed_1medium 2best products for rosaceaneed_2medium 3best fragrance-free skincare productsneed_3medium 4best ceramide cream for barrier repairneed_4medium 5best gentle cleanser for sensitive skinneed_5medium 6best products for eczemaneed_6medium 7best sunscreen for sensitive skinneed_7medium 8best moisturizer for compromised skin barrierneed_8medium 9best products for redness and irritationneed_9medium 10best hypoallergenic skincare brandsneed_10medium 11best cica cream for irritated skinneed_11medium 12best products for dry sensitive skinneed_12medium 13best serum for skin barrier repairneed_13medium 14best products for perioral dermatitisneed_14medium 15best calming toner for sensitive skinneed_15medium 16cetaphil vs cerave for sensitive skinneed_16medium 17best sensitive skin brandsgeneral_bestshort 18best barrier repair skincaregeneral_bestshort 19Which skincare brands are best for sensitive, compromised skin?general_bestmedium 20everyday sensitive skin brandseverydayshort 21best daily barrier moisturizerseverydayshort 22Which mainstream skincare brands work best for daily sensitive-skin routines?everydaymedium 23affordable barrier repair brandsvalue_priceshort 24best budget sensitive skincarevalue_priceshort 25Which sensitive-skin brands offer the best value for barrier repair?value_pricemedium 26barrier repair routine brandsuse_caseshort 27best brands for damaged barriersuse_caseshort 28Which brands are best for rebuilding a damaged skin barrier?use_casemedium 29sensitive skincare for beginnersbeginnershort 30first barrier repair productsbeginnershort 31Which brands are easiest for beginners starting a sensitive-skin routine?beginnermedium 32where to buy barrier skincareretailshort 33best stores for sensitive skincareretailshort 34Which sensitive-skin brands are safest to buy from authorized retailers?retailmedium 35sensitive skincare gift brandsgiftingshort 36barrier repair giftsgiftingshort 37Which skincare brands make good gifts for someone with sensitive skin?giftingmedium 38best brands for rosaceaspecific_needshort 39eczema-friendly skincare brandsspecific_needshort 40Which skincare brands are best for eczema-prone, easily irritated facial skin?specific_needmedium 41sensitive skincare brand comparisoncomparisonshort 42best ceramide skincare brandscomparisonshort 43Which barrier-repair skincare brands perform best for dryness, redness, and irritation?comparisonmedium 44most trusted sensitive skin brandstrustshort 45dermatologist recommended barrier brandstrustshort 46Which sensitive-skin brands have the strongest dermatologist and clinical support?trustmedium 47who makes sensitive skincareownershipshort 48sensitive skincare parent companiesownershipshort 49Which companies own the leading sensitive-skin and barrier-repair skincare brands?ownershipmedium 50highest quality sensitive skincarepremium_qualityshort 51premium barrier repair brandspremium_qualityshort 52Which sensitive-skin brands are considered highest quality for barrier-supporting formulas?premium_qualitymedium 53long-lasting barrier moisturizersdurabilityshort 54sensitive skincare that lastsdurabilityshort 55Which barrier-repair skincare brands make moisturizers that provide the longest-lasting comfort?durabilitymedium 56clinical barrier repair brandsspecialistshort 57advanced sensitive skin formulasspecialistshort 58Which brands have the best evidence-backed formulas for treating a compromised skin barrier?specialistmedium 59new sensitive skincare brandsemergingshort 60Which newer skincare brands are gaining attention for sensitive skin and barrier repair?emerginglong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search120120120100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool120120120100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)12012011595.8% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool120120120100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 1 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1CeraVe345/48071.9%71.9%67.7%–75.7%24/475 (5.1%) 2La Roche‑Posay323/48067.3%67.3%63%–71.3%25/475 (5.3%) 3Vanicream238/48049.6%49.6%45.1%–54%19/475 (4%) 4Cetaphil172/48035.8%35.8%31.7%–40.2%41/475 (8.6%) 5Avène150/48031.3%31.3%27.3%–35.5%0/475 (0%) 6First Aid Beauty146/48030.4%30.4%26.5%–34.7%8/475 (1.7%) 7Eucerin137/48028.5%28.5%24.7%–32.7%3/475 (0.6%) 8Aveeno122/48025.4%25.4%21.7%–29.5%4/475 (0.8%) 9BIODERMA117/48024.4%24.4%20.7%–28.4%10/475 (2.1%) 10Paula’s Choice113/48023.5%23.5%20%–27.5%31/475 (6.5%) 11SkinCeuticals78/48016.3%16.3%13.2%–19.8%10/475 (2.1%) 12Dr. Jart+73/48015.2%15.2%12.3%–18.7%6/475 (1.3%) 13EltaMD67/48014%14%11.1%–17.3%3/475 (0.6%) 14Skinfix57/48011.9%11.9%9.3%–15.1%8/475 (1.7%) 15Neutrogena53/48011%11%8.5%–14.2%3/475 (0.6%) 16AESTURA49/48010.2%10.2%7.8%–13.2%3/475 (0.6%) 17Eau Thermale Avène43/4809%9%6.7%–11.8%0/475 (0%) 18The Ordinary43/4809%9%6.7%–11.8%6/475 (1.3%) 19American Academy of Dermatology39/4808.1%8.1%6%–10.9%26/475 (5.5%) 20Kiehl’s36/4807.5%7.5%5.5%–10.2%4/475 (0.8%) 21National Eczema Association35/4807.3%7.3%5.3%–10%20/475 (4.2%) 22Aquaphor30/4806.3%6.3%4.4%–8.8%0/475 (0%) 23Clinique28/4805.8%5.8%4.1%–8.3%0/475 (0%) 24Tower 28 Beauty27/4805.6%5.6%3.9%–8.1%1/475 (0.2%) 25KraveBeauty26/4805.4%5.4%3.7%–7.8%0/475 (0%) 26Tower 281/4800.2%0.2%0%–1.2%1/475 (0.2%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-09. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. ## More from the Skincare AI Visibility Index The full cross-concern ranking is in the Skincare AI Visibility Index 2026. The other concern reports: - Anti-Aging & Retinoids AI Citation Benchmark 2026 - Acne Treatments AI Citation Benchmark 2026 - Hyperpigmentation & Brightening AI Citation Benchmark 2026 - SPF & Sun Protection AI Citation Benchmark 2026 ### Related AI visibility research - Beauty AI Visibility Index 2026: the brand-first ranking of the top 25 U.S. beauty brands by AI citation share. - Menopause AI Visibility Index: how AI engines cite the top 25 menopause brands. - Longevity AI Visibility Index: how AI engines cite the top 25 longevity brands. Brands acting on these findings can work with our skincare PR team, our beauty PR practice, our beauty marketing agency group and our dermatology and aesthetics marketing specialists. --- ### Automotive AI Retrieval Rankings: KBB and NHTSA Lead Citations URL: https://www.5wpr.com/research/retrieval-index/automotive/ Summary: Automotive AI retrieval rankings show KBB, Edmunds, NHTSA and IIHS anchor pricing and safety answers above Car and Driver and Motor Trend in AI search. The sector where safety data and regulatory filings anchor above automotive journalism. ## The Unvarnished Read Automotive retrieval splits across three structural layers. The valuation-database tier, Kelley Blue Book (KBB), Edmunds, and J.D. Power, functions as the primary citation layer for pricing, valuation, and ownership-cost queries. The safety-and-standards tier, NHTSA, IIHS, and Consumer Reports, carries safety, reliability, and crash-rating queries. The editorial layer, Car and Driver, Motor Trend, Road & Track, Jalopnik, Autoblog, MotorTrend, The Drive, handles reviews, news, and enthusiast culture. The pattern is similar to insurance's ratings-agency anchor: commercial evaluators with privileged data positions function as primary citation. KBB and Edmunds operate as the auction-house equivalent for everyday vehicles, the marketplace and the valuation publisher in one. NHTSA and IIHS operate as the federal-regulator equivalent for vehicle safety. The editorial layer covers above the data tier but does not displace it on factual queries. ## How AI answers about automotive media work. Pricing and valuation queries ("Honda Accord 2020 trade-in value," "how much is a used F-150 worth," "residual value Toyota Camry") route to Kelley Blue Book, Edmunds, NADA Guides, CarGurus, and Carfax. The valuation-database tier is consistently primary. A separate publication-level capture reaches the same conclusion from the editorial side: Everything-PR's Automotive Publication Citation Rankings found Kelley Blue Book and Edmunds cited on eleven of twenty possible shopping-query slots, ahead of every dedicated automotive magazine tested. Safety and reliability queries ("safest mid-size SUV," "IIHS Top Safety Pick," "reliability rankings 2026") route to IIHS, NHTSA, Consumer Reports, and J.D. Power. Vehicle-review queries ("is the Toyota Crown good," "Honda Civic vs Mazda 3," "best mid-size SUV 2026") route to Car and Driver, Motor Trend, Road & Track, Edmunds editorial, The Drive, MotorTrend, and Autoblog. Industry-business queries ("Ford EV strategy," "GM-Cruise restructuring," "Stellantis quarterly") route to Automotive News, Reuters Autos, Bloomberg Autos, WSJ Autos, and FT Autos. EV and emerging-tech queries ("Tesla Model Y range," "Rivian production numbers," "hydrogen-vehicle outlook") route to Electrek, InsideEVs, Reuters EV coverage, Bloomberg Hyperdrive, and the editorial layer. Enthusiast and culture queries ("best driver's cars 2026," "manual transmission cars left," "classic Porsche values") route to Jalopnik, Road & Track, Hagerty (classic-car valuation), Bring a Trailer, and the enthusiast subreddit substrate. Cross-engine variation: ChatGPT and Claude weight KBB, Edmunds, NHTSA, and IIHS institutionally. Perplexity surfaces Electrek and InsideEVs aggressively on EV queries. Google AI Overviews favors KBB, Edmunds, and high-domain-authority editorial on consumer-pricing queries. Everything-PR's brand-level Citation Share study reaches a related finding at the brand layer: Toyota and Honda dominate reliability-query citations across all five engines, while Tesla anchors EV and innovation queries despite carrying persistent founder-controversy context. Geographic dispersion: U.S. dominates English-language automotive retrieval. UK automotive press (Autocar, What Car?, Top Gear) reaches U.S. engines moderately. Continental European automotive press (AutoBild, Auto Motor und Sport — German-language) reaches at lower frequency despite the strength of the European auto industry. Japanese and Korean automotive press underrepresented despite the scale of those auto markets. GEO implication for automakers, dealers, and automotive-adjacent operators. Retrieval-effective placements concentrate in KBB and Edmunds listing accuracy (the most underweighted lever), IIHS and NHTSA testing participation, J.D. Power survey inclusion, and earned coverage in Car and Driver, Motor Trend, and Road & Track. For EV brands specifically, Electrek and InsideEVs coverage. For enthusiast and luxury brands, Hagerty valuation presence and Jalopnik coverage. 5WPR's automotive marketing agency practice builds these retrieval placements alongside traditional earned media for automaker, dealer, and mobility clients, detailed further in how automotive brands win AI search citations. ## The Structural Finding Automotive is the consumer sector with the most institutionally-reinforced citation architecture below the editorial layer. KBB and Edmunds together carry pricing and valuation queries; NHTSA and IIHS together carry safety queries; Consumer Reports and J.D. Power add reliability data. The combined valuation-and-safety tier produces more cited content on factual automotive queries than the entire dedicated automotive editorial layer combined. The mechanism is two-layered. The commercial valuation publishers (KBB, Edmunds, NADA Guides) function as the auction-house equivalent for everyday vehicles, they aggregate transactional and listing data and publish derived valuations the engines treat as authoritative. The institutional safety publishers (NHTSA, IIHS) function as the regulator-and-standards equivalent, they conduct testing and publish results on open, government-or-quasi-governmental domains. The editorial layer above is healthy and well-tenured. Car and Driver, Motor Trend, Road & Track, and the legacy publications collectively carry review and enthusiast coverage at strong citation density. The layer does not displace the valuation-and-safety tier on factual queries but covers above it on qualitative and cultural queries. ## Operating moves for this sector. - For automakers and dealers: KBB and Edmunds listing accuracy is the most underweighted retrieval lever in the sector. Vehicles correctly trimmed, equipped, and priced in the valuation-database tier appear as primary citations on consumer-pricing queries. Most automakers treat valuation-database participation as compliance; the retrieval economics argue for treating it as GEO. — For automakers: IIHS Top Safety Pick and NHTSA 5-Star Safety Rating positioning produces compounding citation. The annual safety-rating cycles function as permanent retrieval anchors. - For EV brands: Electrek and InsideEVs coverage is the structural placement on emerging-tech queries. The dedicated EV press tier reaches engines at a rate the broader automotive press does not on EV-specific queries. - For luxury and enthusiast brands: Hagerty valuation presence anchors collector-and-vintage queries. Brands whose vehicles trade on Bring a Trailer and are covered in Hagerty editorial gain compounding citation on enthusiast queries. - For non-U.S. automotive operators (Japan, Korea, Europe): cross-publication into U.S. editorial reaches engines better than regional press dominance. AutoBild and Auto Motor und Sport are strong in Europe but transfer weakly to U.S.-trained engines. ## Related research - Everything-PR: The Automotive Publications AI Engines Cite Most — a publication-level citation capture measuring which automotive outlets AI engines cite most on ten buyer-intent shopping queries. - Everything-PR: Toyota Still Owns Auto AI — a brand-level modeled Citation Share study across 28 automotive brands and five AI engines. - Everything-PR: 25 Best Automotive Marketing Campaigns — case studies on the campaigns that built durable, citable brand narratives. --- ### Airline CEOs — The 5W Reputation Index URL: https://www.5wpr.com/reputation-index/airline-ceos/ Summary: Airline CEO reputation across five AI engines: Ed Bastian composites 79, the cohort high, while Bob Jordan trails at 40. Study 10, July 2026. THE 5W REPUTATION INDEX · STUDY 10 · SECTOR # Airline CEOs Published 28 July 2026 Cohort 15 principals Engines 5 Prompts 72 AI doesn't remember airline CEOs the way passengers do. It remembers operational failures — with a persistence that no other sector the Index has measured comes close to. A cancelled Christmas week in 2022 still writes Bob Jordan's engine portrait in 2026. A door plug that came off a MAX 9 in January 2024 still writes Ben Minicucci's portrait eighteen months later. A gate-agent bonus program for catching oversized carry-ons — a story that lasted three news cycles in the human record — has become a durable component of Barry Biffle's AI portrait. Airline operations are the sector's product. AI engines index them as such. And the memory does not fade with the news cycle. That is the finding this study exists to document. Highest Composite 79 Ed Bastian · Delta Lowest Composite 40 Bob Jordan · Southwest Cohort Spread 39 Bastian to Jordan Widest Engine Spread 22 Michael O'Leary · Ryanair Biggest Overperformer +9 Ben Minicucci vs peer-adjusted Biggest Underperformer −14 Bob Jordan vs peer-adjusted Most Polarizing O'Leary Perplexity 82 · Gemini 60 Strongest Crisis Recovery Bastian CrowdStrike 2024, no lasting portrait damage ## The airline cohort renders on operational events, not brand campaigns Across fifteen airline chief executives — six US majors, two US low-cost / founder principals, and seven international majors — the Index produces the sector with the tightest linkage between a single dateable event and current engine-rendered reputation the series has measured. Ed Bastian at Delta leads the cohort at 79, anchored by fifteen consecutive profitable years, the premium-brand positioning that separates Delta from every other US carrier, and a personal media presence that engines render as institutional continuity. Bottom of the cohort sits Bob Jordan at Southwest at 40 — the December 2022 holiday operational meltdown continues to anchor his engine portrait more than three years after the fact, layered under the Elliott Management activist campaign, the 2024 board reconstitution, and the ongoing dismantling of Southwest's fifty-year open-seating model. Between the two extremes, the cohort splits along a single structural axis: whether the principal is rendered primarily through an operational anchor event (Minicucci–Boeing MAX 9, Jordan–2022 meltdown, Biffle–bag-fee incentives, Geraghty–Spirit block plus 2026 bankruptcy speculation) or through a sustained institutional platform (Bastian–premium brand, Kirby–ATC advocacy, O'Leary–pricing populism, Clark–elder-statesman commentary). Operational anchors persist inside AI engine memory for three to five years. Institutional platforms compound continuously. The gap between the two mechanisms is now measurable — and this study measures it. Airlines are the only sector the Index has measured where a single Christmas week still shows up in the AI engine three years later. ## The Full Ranking ### All 15 airline CEOs Cohort restricted to sitting chief executives of scheduled passenger airlines with material market presence in North America, Europe, or Asia as of 21 July 2026. Includes founder principals still operating carriers (Neeleman, O'Leary) alongside professional CEOs. Ted Christie (Spirit Airlines) was excluded following Spirit's May 2026 Chapter 7 filing. The Index reflects engine-rendered reputation only. #PrincipalCarrierAccSenCmpCnsCtlComposite 1Ed BastianDelta Air Lines · CEO828280787479 2Michael O'LearyRyanair · Group CEO786282708274 3Scott KirbyUnited Airlines · CEO787076727273 4Tim ClarkEmirates · President787672706672 5Ben SmithAir France-KLM · CEO727068666268 6Ben MinicucciAlaska Air Group · CEO706862626265 7Carsten SpohrLufthansa Group · CEO686066625863 8TRobert IsomAmerican Airlines · CEO665862585460 8TYuji AkasakaJapan Airlines · Chairman666658605060 10Luis GallegoIAG (British Airways / Iberia) · CEO645862584858 11Johan LundgreneasyJet · CEO625858585057 12Joanna GeraghtyJetBlue Airways · CEO624858524052 13David NeelemanBreeze Airways · Founder & CEO604456523850 14Barry BiffleFrontier Airlines · CEO563650503445 15Bob JordanSouthwest Airlines · CEO562852461840 ## Every Principal in the Cohort ### The full engine portrait, principal by principal Each principal is presented in ranked order with the anchor events, structural drivers, and dimensional profile that produced the composite score. Profiles average 200–400 words. The three top-of-cohort and three bottom-of-cohort principals receive extended treatment with sourced rationale. #### Rank 01 · Cohort High ### Ed Bastian Delta Air Lines · Chief Executive Officer (2016–present) — Composite 79 Fifteen consecutive profitable years, the SkyMiles / premium-cabin brand redefinition, the Atlanta-institution positioning, and the sustained media presence that renders Delta as the operational and premium standard of the US industry. The engines render Bastian through a deep and structurally reinforcing set of anchors: the 2016 succession from Richard Anderson, the SkyMiles program's transformation into the most-lucrative loyalty currency in the US industry, the Delta One and Delta Premium Select cabin rebrand that established the premium-cabin business model now imitated across the sector, the sustained Grammy Awards and PGA Tour sponsorship platform, the 2024 CrowdStrike operational recovery, the Coca-Cola board tenure, and the fifteen consecutive profitable calendar years the airline has now reported. Sentiment of 82 reflects engine portraits weighted toward operational reliability, premium positioning, and Atlanta civic leadership — with the 2024 CrowdStrike recovery cycle acting as a stress-test that Delta's engine portrait absorbed without lasting damage. The cohort's clearest case of an operating CEO whose portrait is rendered as institutional continuity rather than as a series of news cycles. Where every other US-major CEO sees at least one dimension dragged by a persistent anchor event, Bastian's five dimensions cluster tightly between 74 and 82 — the flattest dimensional profile in the cohort, and the structural definition of engine-rendered institutional trust. Sourced rationale: Delta profitability documented in Delta Air Lines 10-Ks (2010–2025). CrowdStrike recovery documented across WSJ, Bloomberg, Reuters (July–August 2024). SkyMiles program transformation documented across The Points Guy, One Mile at a Time, and airline-industry trade press. #### Rank 02 ### Michael O'Leary Ryanair Holdings · Group Chief Executive Officer — Composite 74 Four decades of ultra-low-fare pricing populism, sustained anti-establishment commentary, and the highest personal media presence in the global airline industry — producing the cohort's second-highest composite and the highest Control score in the study. Engines render O'Leary through the sustained Ryanair pricing model (the ultra-low-fare category he built from the 1990s Southwest playbook adapted to European regulation), the Boeing 737 MAX fleet order book that made Ryanair the largest MAX operator in Europe, the sustained anti-Airbus / anti-Boeing / anti-Brussels commentary that generates weekly aviation-press coverage, the passenger-fee provocations (baggage, boarding pass, seat selection) that have become industry-defining, and the personal media persona that renders as pricing-populism rather than as executive polish. Control of 82 is the highest single-dimension score in the airline cohort — driven by O'Leary's sustained willingness to give press conferences, appear on broadcast, and generate on-the-record commentary at a volume no other airline CEO matches. Sentiment of 62 reflects the deliberate trade-off: engines render the pricing model favorably and the personal style contentiously, and the composite absorbs both. O'Leary is also the cohort's most polarizing portrait — the 22-point gap between his Perplexity engine portrait (82) and his Gemini engine portrait (60) is the widest engine spread in the study, driven by Perplexity's citation-weighted retrieval favoring Ryanair's operational scale and Gemini's sentiment-weighted retrieval favoring consumer-press hostility. Sourced rationale: Ryanair operational history documented in Ryanair Holdings annual reports. Fleet strategy documented across FlightGlobal, Aviation Week, Reuters. Personal media presence documented in sustained coverage across the Financial Times, The Guardian, and Irish national press over the 1998–2026 period. #### Rank 03 ### Scott Kirby United Airlines · Chief Executive Officer (2020–present) — Composite 73 The sustained ATC and FAA-oversight commentary, the Newark-hub strategic reset, the Polaris premium cabin platform, and the fleet order book (Boeing and Airbus) that positioned United as the sector's most visible operational voice on aviation-system reform. Engines render Kirby through the 2020 succession from Oscar Munoz, the pandemic-era liquidity management that carried United through 2020–2021 without a federal bailout beyond the CARES program, the Polaris and United Next fleet-modernization program, the sustained ATC-reform commentary that established Kirby as the sector's most-quoted voice on FAA staffing and aviation-system capacity, the 2024 Newark hub restructuring, and the sustained personal presence on broadcast media and industry conferences. Sentiment of 70 reflects engine portraits weighted toward operational competence and reform advocacy, with drag from the 2024 Newark disruption cycle and the sustained MAX-fleet delivery-schedule commentary. Kirby's engine portrait sits closer to institutional-analyst than to executive-personality — the second-most-distinctive positioning in the cohort behind O'Leary. Where O'Leary uses media presence to defend the pricing model, Kirby uses media presence to reframe the operating environment. Both work. Sourced rationale: United operational history documented in United Airlines Holdings 10-Ks (2020–2025). ATC / FAA commentary documented across sustained coverage in Aviation Week, Reuters, CNBC. Newark hub restructuring documented across NJ.com, Bloomberg, WSJ (2024). #### Rank 04 ### Tim Clark Emirates · President (2003–present) — Composite 72 Two decades as Emirates president, the A380 fleet-order book that made Emirates the launch customer for the aircraft, and the sustained aviation-industry commentary that has made Clark the elder statesman of the global widebody market. Engines render Clark through his 2003 elevation to Emirates president after a career at British Caledonian and Gulf Air, the sustained A380 and 777X fleet-order book that made Emirates the defining widebody operator of the post-2000 era, the Dubai-as-global-connector positioning that engines increasingly render as institutional rather than personal, and the personal media presence that produces multiple deep-magazine profiles per year across the aviation and business press. The engine portrait is unusually consistent across the five systems — Accuracy 78, Sentiment 76, Completeness 72 — because Clark's public commentary has been continuous for two decades and repeats across a stable set of themes (widebody demand, US-Gulf Open Skies, connectivity economics). Control of 66 reflects that Clark, unlike O'Leary or Kirby, exercises narrative discipline through fewer, longer set-piece interviews rather than through continuous cadence. It works structurally different from the Ryanair and United models — and produces a comparably durable engine portrait. #### Rank 05 ### Ben Smith Air France-KLM · Chief Executive Officer (2018–present) — Composite 68 The Canadian-at-Air-France succession that broke the French-executive convention, the labor-restructuring cycle of 2018–2020, and the sustained operational-recovery trajectory that positioned Air France-KLM as the reference European legacy-carrier turnaround. Engines render Smith through the 2018 succession from Jean-Marc Janaillac (the first non-French CEO of Air France-KLM), the Air Canada tenure that preceded it, the aggressive labor-restructuring cycle that ran through 2018–2020, the pandemic-era liquidity management and French-government support package, the Transavia low-cost expansion, the Boeing 787 and Airbus A350 fleet renewal, and the sustained operational-recovery trajectory that made Air France-KLM one of the strongest European legacy-carrier operational recoveries of the post-pandemic period. Sentiment of 70 reflects engine portraits weighted toward operational discipline and the group's return-to-profit trajectory, with drag from the sustained labor-relations coverage in the French press. Smith is the cohort's clearest example of a professional-CEO engine portrait built through operational execution alone — no media platform, no ideological positioning, no ownership stake — and demonstrates that operational excellence, sustained across a full business cycle, does produce durable engine-rendered reputation even in the absence of personal-media infrastructure. #### Rank 06 ### Ben Minicucci Alaska Air Group · President & Chief Executive Officer (2021–present) — Composite 65 The January 2024 Boeing MAX 9 door-plug event, the subsequent operational and communications response, the 2024 Hawaiian Airlines acquisition and 2025 integration, and the 2026 FlightGlobal Executive of the Year — North America award that closed the recovery arc. Engines render Minicucci through the March 2021 CEO succession, the Virgin America integration completion, the 2024 acquisition of Hawaiian Airlines that expanded Alaska's footprint into the Pacific and produced a multi-brand US carrier structure, the January 2024 MAX 9 door-plug incident that grounded the Alaska MAX 9 fleet and produced sustained FAA-oversight coverage, the subsequent operational and communications response (widely covered as one of the strongest airline-industry crisis responses of the decade), and the July 2026 FlightGlobal Executive of the Year — North America award that formalized the recovery arc in aviation trade press. Minicucci is the cohort's biggest overperformer against peer-adjusted baseline: an anchor event as severe as a fleet-grounding at any other US carrier would have produced a bottom-quartile composite. Alaska's post-event operational discipline, sustained internal-safety communication, and refusal to shift blame onto Boeing produced a portrait that engines increasingly render as reputational resilience — the single clearest case of crisis-recovery-driven composite lift the Index has measured across the full 220+ principals of the series. Sentiment of 68 reflects the engine consensus that the operational response redeemed the anchor event, with the Executive of the Year award providing a positive-anchor closing that engine memory now indexes alongside the door-plug event itself. Control of 62 — moderate for the cohort — reflects that Minicucci is less publicly visible than Kirby or Bastian but has built a durable sustained-communication rhythm through Alaska's internal channels and Puget-Sound-regional press. #### Rank 07 ### Carsten Spohr Lufthansa Group · Chief Executive Officer (2014–present) — Composite 63 Twelve years as CEO, the ITA Airways (former Alitalia) acquisition, the sustained labor-strike cycle across German aviation, and the operational recovery of the Lufthansa Group from the pandemic-era German-government stake. Engines render Spohr through the 2014 succession from Christoph Franz, the 2015 Germanwings Flight 9525 tragedy and the operational-safety response, the 2020–2021 German-government stake and eventual buyback, the 2023–2024 acquisition of ITA Airways that consolidated Italian legacy-carrier operations under the Lufthansa Group, the sustained German-labor-strike cycle across ground handling, pilots, and cabin crew that has produced repeated operational disruptions, and the Boeing 787 / 777X fleet renewal cycle. Sentiment of 60 reflects the sustained labor-relations drag that engines have indexed continuously across the 2022–2026 period. Spohr's tenure is the longest currently active Big Three European legacy-carrier CEO tenure, and Consistency of 62 reflects that engine portraits stabilize significantly at the ten-plus-year mark. The ITA acquisition is emerging as a positive-portrait anchor as integration completes; engines have not yet fully compounded the acquisition into the composite. #### Rank 08T ### Robert Isom American Airlines · Chief Executive Officer (2022–present) — Composite 60 The Dallas / Fort Worth hub restructuring, the 2024 travel-agency distribution-model dispute, the labor-agreement cycles with pilots and flight attendants, and the quieter media profile than any other US-major CEO. Engines render Isom through the 2022 succession from Doug Parker, the sustained American-Airlines-scale coverage (largest fleet in the world by aircraft count), the pilot and flight-attendant labor-agreement cycles of 2023–2024, the controversial 2024 travel-agency distribution-model change that reduced American's presence in corporate-travel booking systems (subsequently partially reversed), the hub-rationalization coverage across DFW, PHL, and CLT, and the notably quieter media profile than either Bastian, Kirby, or Jordan. Isom's engine portrait renders as the professional-operator baseline of the US-major cohort — competent, low-visibility, dimensionally moderate across all five axes. This is the cohort's clearest case of what happens when a CEO relies almost entirely on operating results for engine-portrait rendering: composites cluster in the middle, sentiment tracks operational cycle-to-cycle, and no single dimension carries the composite. Control of 54 is below the cohort median and reflects the deliberate lower-media-cadence Isom has maintained. #### Rank 08T ### Yuji Akasaka Japan Airlines · Chairman (previously CEO 2018–2024) — Composite 60 The 2024 Haneda ground collision and the widely-praised JAL cabin-crew evacuation response, the sustained JAL brand-quality positioning in the Asia-Pacific market, and the chairmanship transition following the executive succession. Engines render Akasaka through the 2018–2024 CEO tenure, the January 2024 Haneda Airport ground collision with a Japan Coast Guard aircraft that killed five of the six Coast Guard crew but produced a zero-fatality evacuation of all 379 JAL passengers and crew, the sustained international press coverage of the JAL cabin-crew evacuation as one of the most-effective aviation-safety responses in commercial-aviation history, the JAL brand-quality positioning as the reference Asia-Pacific service airline, and the chairmanship transition following the CEO succession. Sentiment of 66 is unusually high for a CEO whose portrait includes a fatal-collision anchor event, reflecting the specific structural feature of a crisis-recovery portrait where the operational response redeemed the underlying event. The Haneda evacuation is now the cohort's clearest positive-anchor event — engine memory has effectively indexed the response, not the collision, as the durable component of the portrait. #### Rank 10 ### Luis Gallego International Airlines Group (British Airways, Iberia, Aer Lingus, Vueling) · CEO (2020–present) — Composite 58 The IAG multi-brand European portfolio, the pandemic-era Iberia recovery, the sustained European transatlantic-market coverage, and the notably lower personal-media profile than either the O'Leary or Spohr comparables. Engines render Gallego through the 2020 succession from Willie Walsh (the first non-British CEO of IAG), the Iberia leadership tenure that preceded IAG, the pandemic-era operational recovery across the four IAG brands, the sustained British Airways operational-reliability coverage in the UK press, the 2024 rejected Air Europa acquisition that would have expanded IAG's Latin-America exposure, and the transatlantic-joint-business regulatory coverage. Control of 48 is below the cohort median — Gallego runs one of the world's largest airline groups with the lowest personal media profile of any European legacy-carrier CEO in the cohort. The engine portrait renders IAG-the-group considerably more strongly than Gallego-the-executive, producing a Completeness score (62) that materially exceeds the personal-visibility dimensions. This is the cohort's cleanest example of institution-outrunning-executive in the engine portrait. #### Rank 11 ### Johan Lundgren easyJet · Chief Executive Officer (2017–present) — Composite 57 The easyJet holidays growth story, the sustained UK-Europe short-haul competitive positioning against Ryanair and Wizz Air, the operational-reliability recovery post-pandemic, and the low-personal-media profile that leaves the portrait dependent on operating-metric coverage. Engines render Lundgren through the 2017 succession from Carolyn McCall, the TUI executive tenure that preceded easyJet, the sustained UK short-haul market coverage, the easyJet holidays package-travel business that has emerged as a material profit contributor, the pandemic-era liquidity management and eventual return to sustained profitability, and the low-personal-media profile that has produced an engine portrait built almost entirely from operating results and trade-press coverage. Lundgren's dimensional profile is the flattest below Bastian's — every dimension between 50 and 62 — reflecting the specific structural feature of a competent low-visibility operating CEO whose portrait carries no anchor event, positive or negative, and whose engine portrait therefore renders as sector-baseline. Control of 50 is below cohort median; Consistency of 58 is at cohort median. Lundgren is the cohort's cleanest reference case for what "average engine-rendered airline CEO" actually looks like structurally. #### Rank 12 ### Joanna Geraghty JetBlue Airways · Chief Executive Officer (2024–present) — Composite 52 The first female CEO of a US-major airline, the immediate post-succession Spirit-merger antitrust block, the sustained operational turnaround, and the 2026 fuel-cost and bankruptcy-speculation cycle produced in part by JetBlue founder David Neeleman. Engines render Geraghty through the January 2024 succession from Robin Hayes (making her the first woman to lead a US-major airline), the immediate post-succession Spirit-merger antitrust block that eliminated the strategic rationale of the transaction her predecessor had negotiated, the Northeast Alliance dissolution with American Airlines following the DOJ antitrust action, the 2024–2025 operational turnaround and route rationalization, the April 2026 all-staff memo addressing bankruptcy speculation raised in a viral David Neeleman video (a memo widely covered as unusually direct communication from a sitting CEO), and the May 2026 growth positioning into the space vacated by Spirit's Chapter 7 filing. Sentiment of 48 reflects the specific structural feature of a CEO inheriting a multi-vector legacy problem: the Spirit block and Northeast Alliance dissolution both landed within Geraghty's first three months, and neither was her decision. Control of 40 is below cohort median and reflects the reactive-communication cycle the 2026 fuel-cost period has required. Geraghty is the cohort's clearest reference case for how AI engines render a new-CEO portrait dominated by inherited constraints. #### Rank 13 ### David Neeleman Breeze Airways · Founder & Chief Executive Officer (2018–present) — Composite 50 The serial-founder narrative (Morris Air, JetBlue, Azul, Breeze) compounded across three decades — undercut in 2026 by the viral commentary framing JetBlue as potentially insolvent, which the engines now index as founder-attacking-former-company rather than founder-analyzing-industry. Engines render Neeleman through four sequential airline foundings across three decades (Morris Air 1984, sold to Southwest 1993; JetBlue 1998, ousted from the board 2007; Azul 2008, Brazil's third-largest carrier; Breeze Airways 2018), the JetBlue founder story that dominates engine retrieval on early-2000s low-cost aviation, the 2007 board removal at JetBlue, the 2024–2025 Breeze route expansion into underserved city-pair markets, and the April 2026 viral video in which Neeleman publicly speculated about JetBlue's potential insolvency — coverage subsequently addressed by JetBlue CEO Joanna Geraghty in an all-staff memo. The 2026 video is the specific anchor dragging Sentiment (44) below what the serial-founder narrative would otherwise produce. Neeleman's engine portrait now renders as ex-founder-attacking-JetBlue as much as founder-of-Breeze — a reputational-inheritance inversion the Index has documented in prior sports and media studies but which produces particularly sharp portrait damage in the airline sector because the target of Neeleman's commentary was a sitting airline he founded. #### Rank 14 ### Barry Biffle Frontier Airlines · Chief Executive Officer — Composite 45 The 2024 revelation that Frontier gate agents received financial incentives for catching oversized carry-on bags — the cohort's clearest case of a single non-operational scandal producing durable engine-portrait damage. Engines render Biffle through the Frontier ultra-low-cost operating model, the sustained "hidden fee" framing of ULCC pricing in the consumer press, the 2024 gate-agent bag-fee incentive scandal that produced approximately $26 million in annual gate-agent bonus payouts based on catching oversized carry-ons and generated national broadcast coverage, the Spirit merger and eventual antitrust block that positioned Frontier as the surviving pure-play ULCC in the US market, and the 2025–2026 route rationalization that followed Spirit's May 2026 Chapter 7 shutdown. Sentiment of 36 reflects sustained consumer-press hostility toward the fee model, layered under the specific 2024 incentive-program coverage. Frontier's engine portrait is now the reference case for how a single quantifiable disclosure — the dollar amount of gate-agent bonuses tied to bag catches — produces durable AI-engine sentiment damage. The scandal was covered for approximately six news cycles in the human record. It is now a durable component of Biffle's engine portrait eighteen months later — and the Index expects it to remain durable through at least 2028. #### Rank 15 · Cohort Low ### Bob Jordan Southwest Airlines · President, Chief Executive Officer, Vice Chairman (2022–present) — Composite 40 The December 2022 holiday operational meltdown, the Elliott Management activist campaign of 2024, the board reconstitution, and the ongoing 2025–2026 dismantling of the fifty-year open-seating model — a four-vector anchor that renders as one continuous negative event in engine memory. Engines render Jordan through the December 2022 holiday scheduling collapse (approximately 16,700 flight cancellations across ten days; the Congressional-hearing cycle that followed; the DOT record fine; the sustained "meltdown" framing that persists across every subsequent operational disruption at Southwest), the 2024 Elliott Management activist campaign that produced the largest board reconstitution in Southwest's history and installed the Elliott slate, the 2024–2025 dismantling of the open-seating boarding model that had defined Southwest's brand since 1971, the addition of premium seating and assigned seats in 2025, the first material Southwest layoff in the airline's history, and the sustained financial-press coverage framing Southwest as culture-in-transition. Sentiment of 28 is the lowest single-dimension score in the airline cohort. Control of 18 is the second-lowest in the entire Index across all 220+ principals of the series — external commentary has effectively written the Jordan portrait from 2022 forward. Jordan is the cohort's clearest reference case for how a single operational anchor event, unaddressed through subsequent institutional-narrative-infrastructure investment, compounds across news cycles rather than fading with them. The Index expects the meltdown to remain a durable component of the Jordan portrait through at least 2029. Sourced rationale: 2022 operational meltdown documented in DOT Consent Order (December 2023) and across NYT, WSJ, WaPo. Elliott campaign documented in Elliott Investment Management public filings (June–October 2024) and across Reuters, Bloomberg, FT. Board reconstitution documented in Southwest Airlines 8-Ks. Open-seating dismantling documented in Southwest investor-day presentations and across airline-trade press (2024–2026). ## Six Proprietary Sub-Indices ### Beyond the composite: the metrics behind the metric Composite scores compress five dimensions into a single number. That number ranks the cohort. What the number does not do is show which specific reputational assets, which specific crisis vulnerabilities, or which specific communications behaviors the engine portrait is actually indexing. The six sub-indices below decompose the composite into the operational metrics that matter to communicators, boards, and market analysts. ### Reputation Resilience Index Composite of Consistency and the delta between best-engine and worst-engine sentiment. Measures whether the portrait holds up across engines and across news cycles. Higher is more resilient. - Ed Bastian (Delta) — 84. Highest cross-engine consistency in the cohort. CrowdStrike absorbed without lasting damage. - Tim Clark (Emirates) — 78. Sustained two-decade portrait resistant to any single cycle. - Scott Kirby (United) — 75. Portrait absorbs both Newark and MAX exposure without dimensional collapse. - Ben Smith (Air France-KLM) — 72. European labor cycle produces sentiment drag but no dimensional inversion. - Yuji Akasaka (Japan Airlines) — 71. Haneda ground collision converted into positive-anchor portrait. ### AI Memory Burden Score How much of the current engine portrait is dominated by a single dateable anchor event. Higher score = heavier anchor burden = greater portfolio vulnerability. This is the metric communicators should watch most closely. - Bob Jordan (Southwest) — 78. 2022 meltdown still writes ~40% of current engine portrait per dimensional decomposition. - Barry Biffle (Frontier) — 68. Bag-fee incentive scandal still writes ~30% of current engine portrait. - Ben Minicucci (Alaska) — 52. MAX 9 door plug still writes ~25% of current engine portrait despite the recovery arc. - David Neeleman (Breeze) — 48. April 2026 viral video overwrites the serial-founder narrative in engine retrieval. - Joanna Geraghty (JetBlue) — 45. Spirit-block plus 2026 bankruptcy cycle dominate portrait despite operational turnaround. ### Narrative Control Leader Highest Control-dimension scores. Measures the degree to which the principal — not external commentary — writes the engine portrait. Highest is most durable. - Michael O'Leary (Ryanair) — 82. Highest personal media cadence in the global airline industry. - Ed Bastian (Delta) — 74. Sustained CNBC, Bloomberg, and Grammy sponsorship platform. - Scott Kirby (United) — 72. ATC-reform commentary platform sustained across five years. - Tim Clark (Emirates) — 66. Fewer set-pieces, higher signal-per-appearance. - Ben Minicucci (Alaska) — 62. Internal-channel discipline and Puget Sound regional press. ### Crisis Recovery Score Measures whether an anchor event has been absorbed, redeemed, or continues to compound. Composite of pre-event and post-event engine portrait stability. Higher is better recovery. - Ed Bastian (Delta) — 86. CrowdStrike 2024: full recovery, no lasting portrait damage. - Yuji Akasaka (Japan Airlines) — 82. Haneda ground collision inverted into positive-anchor portrait. - Ben Minicucci (Alaska) — 74. MAX 9 door plug substantially absorbed, Executive of the Year closer. - Carsten Spohr (Lufthansa) — 62. Germanwings recovery arc across a decade of institutional response. - Scott Kirby (United) — 58. Newark 2024 disruption absorbed, ongoing MAX exposure sustained. ### Institutional Strength Score Extent to which the engine portrait is rendered as institution rather than as individual. Higher = portrait would survive CEO succession. - Luis Gallego (IAG) — 74. IAG-the-group renders considerably more strongly than Gallego-the-executive. - Ed Bastian (Delta) — 72. Delta portrait would survive succession — the SkyMiles and premium-cabin infrastructure is durable. - Tim Clark (Emirates) — 68. Emirates portrait is now more institutional than personal after two decades. - Yuji Akasaka (Japan Airlines) — 66. JAL brand-quality positioning is fully institutional. - Robert Isom (American) — 62. American Airlines scale renders as institution; Isom-the-individual is comparatively invisible. ### Executive Visibility Index How consistently the principal appears in engine retrieval independent of a specific news cycle. Higher = more retrievable outside a crisis. - Michael O'Leary (Ryanair) — 88. Retrievable on essentially any airline-industry prompt. - Ed Bastian (Delta) — 78. Retrievable on US-airline, premium-brand, Atlanta-civic, and CrowdStrike prompts. - Scott Kirby (United) — 74. Retrievable on ATC-reform, Newark, and MAX-fleet prompts. - Tim Clark (Emirates) — 68. Retrievable on widebody, A380, and US-Gulf Open Skies prompts. - Ben Minicucci (Alaska) — 60. Retrievable on MAX 9, Hawaiian integration, and West Coast operations prompts. ## Breakout Boxes ### Five airline operational events AI still remembers - Southwest 2022 holiday meltdown — 16,700+ cancellations across ten days. Still writes Bob Jordan's engine portrait in 2026. - Alaska Airlines MAX 9 door plug (January 2024) — still a durable component of both Ben Minicucci's portrait and the Boeing engine portrait eighteen months later. - Frontier gate-agent bag-fee incentives (2024) — a story that lasted six news cycles in the human record; now durable in Barry Biffle's engine portrait. - CrowdStrike 2024 outage — indexed against Delta more than any other carrier. Bastian's crisis-recovery portrait is the closer to the event. - JetBlue-Spirit antitrust block (2024) — writes Joanna Geraghty's portrait even though the transaction was negotiated by her predecessor. ### Five airline CEOs who control their own narrative - Michael O'Leary (Ryanair) — cadence: weekly. Highest sustained media output in the global industry. - Ed Bastian (Delta) — cadence: monthly. Grammy sponsorship platform + earnings calls + CNBC. - Scott Kirby (United) — cadence: monthly. ATC-reform advocacy platform sustained across five years. - Tim Clark (Emirates) — cadence: quarterly. Fewer set-pieces, higher signal-per-appearance. - Ben Minicucci (Alaska) — cadence: quarterly. Internal-channel + regional-press discipline. ### Five lessons for every public-company CEO - AI engines index operational events with three-to-five-year persistence. Any operational failure that generates a Congressional hearing, a DOT consent order, or a Reuters / Bloomberg / WSJ front page will be a durable component of the CEO's engine portrait for the balance of the CEO tenure. - Institutional-narrative infrastructure is the only reliable counterweight. Annual investor days, sustained industry-conference presence, category-defining sponsorship platforms, and regular broadcast appearances all compound in engine memory the same way anchor events do — positively. - Silence is not neutral in engine retrieval. A CEO who does not build sustained media presence gets rendered by whoever does — external analysts, activist investors, journalists, ex-colleagues. Control of 18 (Bob Jordan) does not mean no portrait. It means external commentary writes the portrait. - Crisis-recovery portraits are structural, not tactical. Bastian, Minicucci, and Akasaka all demonstrate that operational responses redeem anchor events in engine memory — but only when the response is documented, sustained, and formally recognized (industry awards, regulatory findings, sector-press coverage). A well-executed response that is not documented does not lift the portrait. - Boards should audit engine-rendered CEO portraits annually. The gap between board-perceived CEO reputation and engine-rendered CEO reputation is now material for succession planning, activist-defense positioning, and communications-team resource allocation. The Index recorded a 22-point gap between highest and lowest engine renderings for a single CEO (O'Leary, Perplexity vs Gemini). Boards should be measuring this. ## Why Airlines Score Differently Than Other Sectors ### Why airlines produce more durable negative anchor events than any other sector the Index has measured Airlines produce more dateable operational failures per year than any other sector. Every scheduling meltdown, every ground-collision, every fleet-grounding, every regulatory action produces a Congressional-hearing cycle, a trade-press investigation, and a mainstream-press wave that AI engines index continuously across all five systems. The Index recorded no other sector where the ratio of dateable-anchor-events-per-year to total-CEO-cohort-size approaches the airline cohort's ratio. The consequence: airline CEOs live under continuous engine-portrait revision risk in a way that hotel, banking, or media CEOs simply do not. ### Why premium positioning creates reputational resilience — and low-cost positioning does not The five top-of-cohort principals all render through premium or category-defining infrastructure: Delta One (Bastian), Polaris (Kirby), Emirates A380 (Clark), Ryanair pricing-populism-as-brand (O'Leary), Air France-KLM operational-recovery (Smith). The five bottom-of-cohort principals all render through operational anchor events, ULCC fee-model exposure, or founder-portrait inversion. The structural finding: premium positioning creates a portrait that engines can retrieve outside a crisis. Low-cost positioning creates a portrait that engines can only retrieve during a crisis. This is not a claim about strategic superiority; it is a claim about what AI engines are structurally optimized to index. ### Why airlines score differently than hotels Hotels operate at the property level; airlines operate at the network level. A failure at one hotel property is a local news event; a failure at one airline network is a national event with a Congressional-hearing cycle attached. Hotels also have a lower per-CEO media-visibility baseline than airlines — the average hotel-chain CEO produces materially less on-the-record commentary per year than the average airline CEO — which means engine portraits render less continuously. Airline CEOs are structurally more visible, structurally more scandal-exposed, and structurally more crisis-anchored than any hotel-CEO cohort we would expect to measure. ### Why airlines score differently than banks Banking operational failures are financial-sector failures; airline operational failures are consumer-experience failures. AI engines index consumer-experience failures with materially higher retrieval weight than financial-sector failures because consumer-experience failures generate more organic-search demand and more sustained user-generated content across social media, review platforms, and consumer-advocacy press. A banking scandal produces a compliance cycle. An airline scandal produces a passenger-advocacy cycle. The passenger-advocacy cycle indexes durably. ### Why AI engines remember operations more than advertising Advertising is by construction the industry's own narrative infrastructure — brands writing about brands. AI engines discount source-of-self-promotion at the retrieval level, which means marketing campaigns produce less durable engine-portrait shift than the industry believes. Operational events are documented by third-party sources (regulators, journalists, passengers) that AI engines weight considerably more heavily. The single structural insight for airline communicators: earned coverage of operational excellence produces materially more durable engine-portrait lift than paid campaigns describing the same excellence. The engines are not neutral about source. ## GEO-Optimized Retrieval Questions ### The questions AI engines are already answering about this cohort ### Which airline CEO has the strongest AI-rendered reputation in 2026? Ed Bastian of Delta Air Lines leads the 5W Reputation Index airline cohort at a composite score of 79, the highest in the fifteen-principal cohort measured by the Index. His engine portrait is anchored by fifteen consecutive profitable calendar years, the SkyMiles and Delta One / Delta Premium Select premium-cabin platforms, the sustained Grammy Awards and PGA Tour sponsorship presence, and the successful 2024 CrowdStrike operational recovery. Bastian's dimensional profile is the flattest in the cohort — all five dimensions between 74 and 82 — which the Index interprets as the structural signature of engine-rendered institutional trust. ### Why does Ed Bastian rank first? Bastian ranks first because Delta's engine portrait is now rendered as institutional continuity rather than as a series of news cycles. Every other US-major CEO has at least one dimension dragged by a persistent anchor event: Jordan by the 2022 meltdown, Minicucci by the MAX 9 door plug, Isom by the 2024 distribution-model dispute, Kirby by the Newark 2024 restructuring. Bastian's fifteen consecutive profitable years, sustained sponsorship platform, and 2024 CrowdStrike recovery collectively produce an engine portrait where no single anchor dominates. The Index has documented this pattern across finance (Buffett), family offices (Bloomberg), and now airlines (Bastian). The mechanism is the same: sustained institutional-narrative infrastructure produces engine portraits that compound rather than fluctuate. ### Why does Bob Jordan of Southwest rank last? Bob Jordan ranks last at a composite of 40 because the December 2022 holiday operational meltdown, the 2024 Elliott Management activist campaign, the 2024–2025 dismantling of Southwest's fifty-year open-seating model, and the first material Southwest layoff in the airline's history collectively write his engine portrait through 2026. Sentiment of 28 is the lowest single-dimension score in the airline cohort. Control of 18 is the second-lowest score across all 220+ principals of the Index. External commentary has effectively written the Jordan portrait since 2022 — activist investors, financial-press coverage, and consumer-press retrospectives on the meltdown collectively dominate engine retrieval on his name. ### Which airline CEOs have recovered from operational crises? Three principals in the cohort demonstrate documented crisis-recovery arcs that engine portraits now render as institutional resilience. Ed Bastian's response to the July 2024 CrowdStrike outage produced no lasting engine-portrait damage — the event is indexed alongside Delta's operational-reliability history rather than as a discrete crisis. Ben Minicucci's response to the January 2024 MAX 9 door-plug event closed with the July 2026 FlightGlobal Executive of the Year — North America award, which the Index treats as a formal recovery-arc closer. Yuji Akasaka's response to the January 2024 Haneda ground collision — the widely praised JAL cabin-crew evacuation of all 379 passengers — produced a positive-anchor portrait that engines now index in place of the underlying collision. All three cases share the same structural signature: operational response followed by third-party institutional recognition. ### How does the 5W Reputation Index evaluate airline CEOs? The Index evaluates airline CEOs across five AI engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) and five dimensions (Accuracy, Sentiment, Completeness, Consistency, Control), using more than seventy retrieval-intent prompts per engine drawn from a standardized cohort protocol. Composite scores are directional weighted averages of the five dimensions, calibrated against the cross-cohort reference portraits established in Studies 01 through 09 of the series. The Index reflects engine-rendered reputation only. It does not evaluate any principal's actual operational competence, safety record, or business practices. ## Regulator Scorecard ### The FAA Administrator The airline cohort's scorecard is anchored by the Federal Aviation Administration Administrator — the single regulator whose engine portrait most directly shapes every US-major CEO's Consistency dimension. The January 2024 MAX 9 door-plug incident, the sustained FAA production oversight of Boeing, the 2025 ATC staffing coverage, and the ongoing 2026 government-shutdown airport-security cycle have made the FAA Administrator's engine portrait unusually reactive to news-cycle events. Whatever administrator holds the office during a given operational anchor event effectively co-owns the resulting engine portrait for every US-major CEO in the cohort — the highest cross-portrait contamination the Index has measured for any regulator. The FAA's engine portrait during the study window renders through the Boeing MAX production certification process, the sustained ATC-controller staffing shortfall coverage, the March 2026 open letter to Congress signed by nine major US airline CEOs on federal aviation-worker pay during government shutdowns, and the ongoing FAA reauthorization cycle. FAA Administrator turnover during the study period is itself a variable inside the Index — the regulator's engine portrait is materially less stable than any of the fifteen airline-CEO portraits it partially shapes. ## The Five Findings ### What the airline cohort revealed 1. Operational anchor events are the dominant retrieval driver. Every principal in the bottom third of the cohort — Jordan (2022 meltdown), Biffle (2024 bag-fee incentives), Neeleman (2026 JetBlue-bankruptcy commentary), Geraghty (Spirit-merger block plus 2026 fuel-cost bankruptcy speculation) — is anchored by a specific, dateable event. The events differ. The structural pattern is constant. Airlines produce more dateable operational anchor events than any sector the Index has measured — and the engines index each of them with three-to-five-year persistence. This is the sector's defining structural finding, and the reason airline communications leaders should be treating engine-portrait audits as a monthly-cadence discipline rather than an annual one. 2. The premium-brand institutions dominate the top third. Bastian (Delta), Kirby (United), Clark (Emirates), and Smith (Air France-KLM) share a common structural feature: engines render them primarily through the premium-brand infrastructure of the carrier, not through any single positive news cycle. This is the airline-sector analog to the founder-philosopher pattern the Index has documented in finance and family offices — brand-institution density beats individual news-cycle output. The mechanism is identical across sectors. Only the vocabulary differs: hedge funds call it narrative infrastructure; airlines call it premium brand. The engine portrait treats them the same way. 3. Cross-portfolio contamination runs through the aircraft manufacturer. The single largest cross-portrait contamination in the airline cohort is Boeing — every US-major CEO's engine portrait now includes some Boeing exposure through the MAX certification cycle, the 2024 Alaska door-plug incident, or the sustained production-quality coverage. Minicucci's portrait absorbs the door-plug event directly. Kirby's portrait absorbs the sustained delivery-schedule coverage. Jordan's portrait absorbs the MAX-8 fleet exposure. The Index recorded no other sector where a single supplier's engine portrait shapes so many principals' scores. The strategic implication for supplier concentration is now measurable: aircraft-manufacturer selection is a reputational-portfolio decision, not only a fleet-economics decision. 4. Control is the manipulable dimension — but the airline distribution is bimodal. Cohort-average Control is 52, but the distribution is unusually bimodal: the top of the cohort (O'Leary 82, Bastian 74, Kirby 72) sits at the highest Control scores in the sector, and the bottom (Jordan 18, Biffle 34, Neeleman 38) sits at the lowest. There is very little middle. Airline CEOs either build sustained media platforms or they get written about entirely by external operational coverage — the sector produces few in-between portraits. This is the sharpest either-or Control distribution the Index has recorded across any of the nine prior cohorts. 5. Engines render airline CEOs as regulators of their own supply chain. The most distinctive finding: engines increasingly render airline CEOs as if they were partial regulators of Boeing, Airbus, the FAA, and the ATC system — Kirby on staffing, O'Leary on Boeing delivery cadence, Bastian on operational reliability standards, Minicucci on MAX production quality. The engines have compressed the airline-CEO role into system regulator with commercial exposure. No other sector produces a comparable framing. The strategic implication: airline communications leaders should be preparing their CEOs for a public-role redefinition that AI engines have effectively already made. ## From the Founder ### Ronn Torossian Founder & Chairman, 5W AI Communications Bastian at 79 is not an accident. Fifteen straight profitable years. A Grammy sponsorship. A premium cabin the whole industry copied. Jordan at 40 is not an accident either — one Christmas week in 2022 is still driving the AI portrait in 2026. That is what scandal persistence looks like inside machine memory. Every airline CEO in this cohort is one operational cycle away from a Bastian portrait — and one operational cycle away from a Jordan portrait. The engines do not forget. AI has become the industry's institutional memory. Every operational failure now becomes part of an executive's long-term reputation, whether they want it to or not. And every well-executed recovery — Bastian in July 2024, Minicucci across 2024–2026, Akasaka at Haneda — becomes part of the reputation too. Boards should be measuring this. Every quarter. ## Methodology Notes The 5W Reputation Index measures how leading AI systems render named principals across seventy-plus retrieval-intent prompts per engine. Scores are directional estimates derived from modeled engine outputs and supplementary web-search verification — not logged query runs. The Index reflects engine-rendered reputation only. It is not an evaluation of any principal's actual conduct, character, or business practices, nor an evaluation of any airline's safety record, operational reliability, or commercial performance. This Index does not adjudicate the merits of any pending litigation, regulatory proceeding, contested operational matter, or fatal-accident finding referenced in the source record. ### The five engines Each principal is evaluated across ChatGPT (OpenAI GPT-4-class and newer), Claude (Anthropic Sonnet-class and newer), Perplexity (citation-weighted retrieval), Gemini (Google Gemini-class), and Google AI Overviews (Search-integrated retrieval). Engine selection reflects the current dominant surface area for buyer-research retrieval in the AI-native era, as validated across 5W's prior AI Visibility Index research (2025–2026 series). ### The five dimensions — and why Accuracy measures the factual correctness of engine-rendered content on the principal — dates, roles, quantified events, sequence of events. Sentiment measures the net valence of engine-rendered content — favorable, neutral, unfavorable — averaged across a standardized prompt set. Completeness measures the coverage depth of engine-rendered content — whether the engine surfaces material events, positions, and context or misses them. Consistency measures cross-engine agreement on the portrait — whether the five systems render substantively similar portraits or produce meaningfully different retrievals. Control measures the degree to which the principal (or their institution) originates the engine-rendered content, versus content originating from external commentary, journalism, or activist campaigns. The five dimensions were selected because they capture the four decision-relevant properties of engine-rendered reputation (Accuracy, Sentiment, Completeness, Consistency) plus the single decision-relevant property that the principal can actively influence (Control). Three-dimension frameworks would collapse decisional meaning; seven-dimension frameworks introduce redundant collinearity across dimensions. Five is the empirically robust balance the Index has validated across ten cohorts. ### Sample prompts Representative retrieval-intent prompts used in the airline-cohort protocol include: "Who is the CEO of Delta Air Lines and what is their track record?"; "What happened during the 2022 Southwest Airlines operational meltdown?"; "How did Alaska Airlines respond to the January 2024 MAX 9 door-plug incident?"; "Is Michael O'Leary considered a successful airline executive?"; "What is JetBlue's current financial position under CEO Joanna Geraghty?" Prompt selection is designed to capture both name-retrieval (who is X?) and event-retrieval (what happened with X?) modes, since AI engines render distinct portraits under each mode. ### Weighting Composite scores are simple weighted averages of the five dimensions, with each dimension weighted equally within the composite. The equal-weight construction is deliberate: the Index does not privilege any single dimension as more decision-relevant than the others, and empirical calibration across the nine prior cohorts confirms that any dimension-privileging weighting scheme introduces cohort-specific bias without adding predictive validity to the cohort-average composite. ### Engine disagreement — how it happens Cross-engine spreads of ten composite points or more indicate that the five systems are drawing on structurally different source distributions. Michael O'Leary's 22-point spread between Perplexity (82) and Gemini (60) is the airline cohort's clearest example: Perplexity's citation-weighted retrieval favors Ryanair's operational scale and fleet coverage, while Gemini's sentiment-weighted retrieval favors consumer-press hostility toward the Ryanair pricing model. Both engine portraits are internally consistent. Neither is wrong. They are indexing different source distributions on the same principal. Communicators should assume every engine renders differently and plan portrait-development strategies engine-by-engine rather than in aggregate. ### Confidence and limitations Composite scores are directional estimates and should be interpreted as ordinal indicators of engine-rendered portrait strength within the cohort, not as absolute measures of reputational value. Score changes of less than five composite points across time or across cohorts should not be interpreted as meaningful; five-plus point changes represent structural shifts in the engine portrait. The Index is not designed for individual-name portrait-scoring outside a cohort context; cohort structure is a load-bearing input to composite calibration. ← Previous: Family Office Principals Series Hub 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. Learn more at 5wpr.com. The 5W Reputation Index · Study 10 of the series · Published 28 July 2026 · 5W AI Communications · Florida --- ### 5W Skincare AI Visibility Index 2026: Concern-First & Clinical Citation Benchmark URL: https://www.5wpr.com/research/skincare-ai-visibility-index-2026/ Summary: Analyze AI search citation share for skincare brands. La Roche Posay and CeraVe dominate AI engine recommendations for dermatological skin concerns. # 5W Skincare AI Visibility Index 2026: Concern-First & Clinical Citation Benchmark La Roche Posay Leads AI Skincare Visibility ## La Roche Posay and CeraVe dominate AI skincare recommendations La Roche Posay and CeraVe capture the highest appearance rates in AI search results for skincare concerns. The data shows clinical and dermatologist backed brands dominating the recommendations across Perplexity, ChatGPT, Gemini, and Claude. La Roche Posay appears in 58 percent of all 1,920 measured answers, while CeraVe appears in 51 percent. Mass market clinical brands vastly outperform premium cosmetic brands in AI visibility for acne, barrier repair, and sun protection queries. 1920AI answers analyzed 58%La Roche‑Posay appearance rate, the category leader 33Brands named across the study 99%Answers carrying retrieval evidence ## The AI skincare leaderboard The top of the category is highly concentrated. Two brands owned by L'Oreal hold the majority of visibility. La Roche Posay ranks first with a 58 percent appearance rate and a 95 percent confidence interval of 55.8 to 60.2 percent. CeraVe follows closely at 51 percent, with an interval of 48.8 to 53.3 percent. Because their intervals do not overlap, La Roche Posay is the clear leader in this dataset. ### AI visibility leaderboard Appearance rate is the share of the 1920 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1La Roche‑Posay1114/192058%55.8%–60.2%2.8 2CeraVe980/192051%48.8%–53.3%3.5 3Neutrogena672/192035%32.9%–37.2%5.6 4Paula’s Choice484/192025.2%23.3%–27.2%5.3 5SkinCeuticals398/192020.7%19%–22.6%3.9 6EltaMD391/192020.4%18.6%–22.2%3.5 7The Ordinary349/192018.2%16.5%–20%6.4 8Vanicream300/192015.6%14.1%–17.3%4.6 9Cetaphil283/192014.7%13.2%–16.4%5.7 10Differin265/192013.8%12.3%–15.4%3.8 11Supergoop251/192013.1%11.6%–14.7%4.3 12Aveeno214/192011.1%9.8%–12.6%6.8 13Avène213/192011.1%9.8%–12.6%4.5 14Eucerin199/192010.4%9.1%–11.8%6.6 15RoC188/19209.8%8.5%–11.2%6.1 Neutrogena secures the third position at 35 percent. Paula's Choice, SkinCeuticals, and EltaMD form a competitive middle tier. The confidence intervals for SkinCeuticals (19 to 22.6 percent) and EltaMD (18.6 to 22.2 percent) overlap heavily, meaning the difference between them is within the margin of this sample. ## Brand performance profiles ### La Roche Posay 58% appearance rate. The brand ranks first overall and maintains a strong presence across multiple engines. It appears in 88.3 percent of ChatGPT answers and 71 percent of Perplexity answers. The brand secures an average first mention position of 2.8, meaning it is recommended early in the text. La Roche Posay also earns a 6.1 percent owned domain citation rate. ### CeraVe 51% appearance rate. CeraVe ranks second, with a confidence interval of 48.8 to 53.3 percent. It shares a similar engine pattern to La Roche Posay. CeraVe appears in 71.7 percent of ChatGPT answers and 60.4 percent of Perplexity answers. The brand achieves a 5.7 percent owned domain citation rate and an average first mention position of 3.5. ### Neutrogena 35% appearance rate. Neutrogena takes third place. It performs best on ChatGPT, appearing in 64.6 percent of those answers. It has a much lower profile on Gemini (15.6 percent) and Claude (18.8 percent). The brand earns a 2.2 percent owned domain citation rate and sits at an average position of 5.6. ### Paula's Choice 25.2% appearance rate. The brand ranks fourth but shows massive volatility between engines. It appears in 58.5 percent of ChatGPT answers but 0 percent of Claude answers. Despite the volatility, Paula's Choice earns a high 6.3 percent owned domain citation rate. ### SkinCeuticals 20.7% appearance rate. SkinCeuticals ranks fifth, showing consistent but moderate visibility. It appears in 33.3 percent of ChatGPT answers and 24.4 percent of Perplexity answers. The brand maintains a 3.1 percent owned domain citation rate and an average position of 3.9. ### EltaMD 20.4% appearance rate. EltaMD ranks sixth and competes directly with SkinCeuticals. It performs well on ChatGPT (32.1 percent) and Perplexity (22.7 percent). The brand earns a 1.2 percent owned domain citation rate and an average position of 3.5. ### The Ordinary 18.2% appearance rate. Ranking seventh, The Ordinary sees its strongest visibility on ChatGPT (32.9 percent) and Perplexity (24.4 percent). It achieves a 1.8 percent owned domain citation rate and an average first mention position of 6.4. ### Vanicream 15.6% appearance rate. Vanicream ranks eighth. It appears in 26.7 percent of Perplexity answers and 19 percent of ChatGPT answers. The brand has a 1.1 percent owned domain citation rate and an average position of 4.6. ## Where the engines disagree Engine disagreement highlights structural differences in how models retrieve and rank skincare brands. Paula's Choice represents the most extreme divergence in the category. ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate La Roche‑Posay71%88.3%24.4%48.3% CeraVe60.4%71.7%26.9%45.2% Neutrogena41%64.6%15.6%18.8% Paula’s Choice36.3%58.5%6%0% SkinCeuticals24.4%33.3%12.5%12.7% EltaMD22.7%32.1%9.6%17.1% The Ordinary24.4%32.9%6.7%8.8% Vanicream26.7%19%6.7%10.2% Cetaphil19.4%21.9%7.9%9.8% Differin18.8%16%7.3%13.1% Supergoop16.5%19%7.3%9.6% Aveeno16.3%22.9%2.3%3.1% Paula's Choice appears in 58.5 percent of the 480 ChatGPT answers and 36.3 percent of Perplexity answers. The brand drops to 6 percent on Gemini and registers exactly 0 appearances out of 480 Claude answers. A second entity string, "Paula's Choice" with a straight apostrophe, appears in 10.8 percent of Gemini answers and 8.1 percent of Claude answers, but this still leaves a massive gap between OpenAI's system and Anthropic's system. The American Academy of Dermatology (AAD) also shows extreme variance. It appears in 32.1 percent of Gemini answers and 7.1 percent of ChatGPT answers, but 0 percent of Perplexity and Claude answers. Gemini leans heavily on medical authority for skincare queries. ## Demand clusters The data organizes into distinct intent clusters based on the skin concerns tested. ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Anti-Aging / Retinoids — Anti-Aging / Retinoids: Ownership and maker attribution24La Roche‑Posay50%SkinCeuticals 50%, Paula’s Choice 45.8%, Neutrogena 41.7% Anti-Aging / Retinoids: Quality and premium tier24SkinCeuticals75%Medik8 41.7%, La Roche‑Posay 33.3%, Paula’s Choice 29.2% Anti-Aging / Retinoids: Trust and recommendation24La Roche‑Posay66.7%SkinCeuticals 62.5%, RoC 45.8%, CeraVe 41.7% Anti-Aging / Retinoids: Price and value24The Ordinary62.5%RoC 62.5%, CeraVe 58.3%, Neutrogena 58.3% Anti-Aging / Retinoids: General best-in-category24La Roche‑Posay70.8%SkinCeuticals 70.8%, Paula’s Choice 41.7%, RoC 41.7% Anti-Aging / Retinoids: Primary use case24SkinCeuticals66.7%RoC 50%, Olay 45.8%, SkinMedica 41.7% Anti-Aging / Retinoids: Everyday and mainstream choice24RoC66.7%CeraVe 62.5%, Neutrogena 62.5%, La Roche‑Posay 58.3% Anti-Aging / Retinoids: Beginners and first purchase24CeraVe79.2%The Ordinary 75%, Medik8 58.3%, La Roche‑Posay 45.8% Anti-Aging / Retinoids: Gifting and occasions24SkinCeuticals45.8%Paula’s Choice 37.5%, La Roche‑Posay 33.3%, Olay 29.2% Anti-Aging / Retinoids: Where to buy24SkinCeuticals54.2%RoC 45.8%, La Roche‑Posay 41.7%, CeraVe 37.5% Anti-Aging / Retinoids: Specific need or condition24La Roche‑Posay62.5%CeraVe 62.5%, SkinCeuticals 54.2%, The Ordinary 45.8% Anti-Aging / Retinoids: Comparison and evaluation24SkinCeuticals54.2%La Roche‑Posay 50%, Paula’s Choice 50%, RoC 50% Anti-Aging / Retinoids: Durability and longevity24Paula’s Choice45.8%RoC 45.8%, SkinCeuticals 37.5%, Olay 33.3% Anti-Aging / Retinoids: Specialist / advanced use24SkinCeuticals70.8%Medik8 58.3%, SkinMedica 45.8%, La Roche‑Posay 41.7% Anti-Aging / Retinoids: Emerging and new entrants16Medik856.3%Beauty of Joseon 31.3%, The Ordinary 18.8%, La Roche‑Posay 12.5% Anti-Aging / Retinoids: best retinol for beginners8CeraVe75%The Ordinary 62.5%, Neutrogena 50%, RoC 50% Anti-Aging / Retinoids: best retinol serum for wrinkles8La Roche‑Posay87.5%Neutrogena 62.5%, Paula’s Choice 62.5%, SkinCeuticals 62.5% Anti-Aging / Retinoids: tretinoin vs retinol which is better8La Roche‑Posay37.5%CeraVe 25%, Neutrogena 25%, Paula’s Choice 25% Anti-Aging / Retinoids: best anti-aging serum for 30s8SkinCeuticals87.5%CeraVe 75%, La Roche‑Posay 62.5%, RoC 50% Anti-Aging / Retinoids: best anti-aging serum for 40s8SkinCeuticals62.5%RoC 50%, La Roche‑Posay 37.5%, Neutrogena 25% Anti-Aging / Retinoids: best over the counter retinol cream8SkinCeuticals75%CeraVe 62.5%, Paula’s Choice 50%, RoC 50% Anti-Aging / Retinoids: best peptide serum for firming skin8SkinCeuticals62.5%The Ordinary 37.5%, Paula’s Choice 25%, Olay 25% Anti-Aging / Retinoids: best night cream for fine lines8Neutrogena75%RoC 62.5%, CeraVe 50%, SkinCeuticals 37.5% Anti-Aging / Retinoids: how to start using retinol without irritation8CeraVe62.5%La Roche‑Posay 50%, Neutrogena 50%, Paula’s Choice 50% Anti-Aging / Retinoids: best neck cream for sagging skin8SkinMedica87.5%RoC 62.5%, SkinCeuticals 50%, Olay 25% Anti-Aging / Retinoids: best retinol for sensitive skin8La Roche‑Posay75%CeraVe 62.5%, The Ordinary 62.5%, Medik8 62.5% Anti-Aging / Retinoids: best eye cream for wrinkles8SkinCeuticals75%Neutrogena 50%, La Roche‑Posay 37.5%, CeraVe 37.5% Anti-Aging / Retinoids: best anti-aging moisturizer with SPF8CeraVe75%EltaMD 75%, La Roche‑Posay 62.5%, Olay 50% Anti-Aging / Retinoids: bakuchiol vs retinol8Paula’s Choice50%Medik8 50%, The Ordinary 37.5%, ISDIN 25% Anti-Aging / Retinoids: best vitamin A serum for anti-aging8La Roche‑Posay62.5%CeraVe 62.5%, The Ordinary 62.5%, Medik8 62.5% Anti-Aging / Retinoids: best collagen-boosting skincare products8SkinCeuticals50%Olay 37.5%, Paula’s Choice 25%, The Ordinary 25% — Acne — Acne: Primary use case24CeraVe62.5%La Roche‑Posay 58.3%, Differin 58.3%, Neutrogena 45.8% Acne: Price and value24CeraVe87.5%Differin 75%, La Roche‑Posay 70.8%, Neutrogena 70.8% Acne: Everyday and mainstream choice24La Roche‑Posay83.3%CeraVe 79.2%, Differin 79.2%, Neutrogena 70.8% Acne: Beginners and first purchase24CeraVe75%La Roche‑Posay 70.8%, Neutrogena 70.8%, Differin 70.8% Acne: Where to buy24La Roche‑Posay54.2%Differin 50%, CeraVe 45.8%, Neutrogena 41.7% Acne: Gifting and occasions24La Roche‑Posay62.5%CeraVe 54.2%, Neutrogena 50%, Paula’s Choice 41.7% Acne: Specific need or condition24La Roche‑Posay70.8%CeraVe 70.8%, Paula’s Choice 54.2%, Neutrogena 50% Acne: Comparison and evaluation24CeraVe62.5%La Roche‑Posay 58.3%, Neutrogena 50%, Differin 50% Acne: Trust and recommendation24La Roche‑Posay66.7%Differin 62.5%, CeraVe 54.2%, Neutrogena 54.2% Acne: Ownership and maker attribution24Differin58.3%Neutrogena 54.2%, La Roche‑Posay 50%, CeraVe 50% Acne: Quality and premium tier24La Roche‑Posay70.8%CeraVe 54.2%, Paula’s Choice 45.8%, Differin 41.7% Acne: Durability and longevity24La Roche‑Posay54.2%Differin 50%, CeraVe 45.8%, Neutrogena 37.5% Acne: Specialist / advanced use24La Roche‑Posay54.2%CeraVe 54.2%, Differin 50%, Paula’s Choice 45.8% Acne: General best-in-category24CeraVe83.3%Neutrogena 83.3%, La Roche‑Posay 79.2%, Differin 79.2% Acne: Emerging and new entrants16Paula’s Choice25%CeraVe 18.8%, Differin 12.5%, Galderma 12.5% Acne: best salicylic acid cleanser for acne8La Roche‑Posay100%CeraVe 100%, Neutrogena 87.5%, Paula’s Choice 50% Acne: best products for acne prone skin8La Roche‑Posay75%CeraVe 75%, Neutrogena 75%, The Ordinary 62.5% Acne: best spot treatment for cystic acne8La Roche‑Posay50%The Ordinary 37.5%, American Academy of Dermatology 37.5%, Galderma 37.5% Acne: best benzoyl peroxide treatment8La Roche‑Posay75%CeraVe 75%, Neutrogena 75%, PanOxyl 75% Acne: best moisturizer for acne prone skin8La Roche‑Posay75%CeraVe 75%, Neutrogena 75%, Vanicream 62.5% Acne: best non-comedogenic sunscreen8EltaMD62.5%La Roche‑Posay 50%, CeraVe 50%, Neutrogena 50% Acne: best face wash for oily acne prone skin8CeraVe75%Neutrogena 75%, La Roche‑Posay 62.5%, PanOxyl 62.5% Acne: best treatment for hormonal acne8Differin37.5%Galderma 37.5%, CeraVe 25%, American Academy of Dermatology 25% Acne: best products for teenage acne8La Roche‑Posay62.5%CeraVe 62.5%, Neutrogena 62.5%, Differin 62.5% Acne: best niacinamide serum for acne8La Roche‑Posay62.5%Paula’s Choice 37.5%, The Ordinary 25%, Medik8 25% Acne: best products for back acne (bacne)8Neutrogena62.5%CeraVe 50%, Paula’s Choice 50%, PanOxyl 50% Acne: differin vs adapalene which is better8La Roche‑Posay62.5%Differin 62.5%, Galderma 37.5%, Neutrogena 25% Acne: best azelaic acid product for acne8The Ordinary62.5%La Roche‑Posay 50%, Paula’s Choice 50%, SkinCeuticals 25% Acne: best oil-free moisturizer for acne8Neutrogena75%CeraVe 50%, La Roche‑Posay 37.5%, Paula’s Choice 25% Acne: best products for maskne8CeraVe75%La Roche‑Posay 62.5%, Differin 50%, Neutrogena 37.5% Acne: best acne patches8Cetaphil25%PanOxyl 12.5% — Sensitive Skin / Barrier Repair — Sensitive Skin / Barrier Repair: Primary use case24CeraVe79.2%La Roche‑Posay 75%, SkinCeuticals 41.7%, First Aid Beauty 41.7% Sensitive Skin / Barrier Repair: Everyday and mainstream choice24CeraVe91.7%Vanicream 79.2%, La Roche‑Posay 70.8%, Cetaphil 45.8% Sensitive Skin / Barrier Repair: Price and value24CeraVe79.2%La Roche‑Posay 70.8%, Vanicream 58.3%, Cetaphil 50% Sensitive Skin / Barrier Repair: General best-in-category24CeraVe87.5%La Roche‑Posay 83.3%, Vanicream 62.5%, First Aid Beauty 54.2% Sensitive Skin / Barrier Repair: Specialist / advanced use24CeraVe75%La Roche‑Posay 66.7%, Vanicream 37.5%, Avène 33.3% Sensitive Skin / Barrier Repair: Beginners and first purchase24La Roche‑Posay83.3%CeraVe 83.3%, Vanicream 66.7%, Avène 54.2% Sensitive Skin / Barrier Repair: Where to buy24CeraVe62.5%La Roche‑Posay 58.3%, Vanicream 41.7%, Eucerin 41.7% Sensitive Skin / Barrier Repair: Gifting and occasions24La Roche‑Posay75%CeraVe 70.8%, First Aid Beauty 41.7%, Paula’s Choice 37.5% Sensitive Skin / Barrier Repair: Specific need or condition24CeraVe87.5%Vanicream 83.3%, La Roche‑Posay 75%, Cetaphil 62.5% Sensitive Skin / Barrier Repair: Comparison and evaluation24CeraVe70.8%La Roche‑Posay 62.5%, Paula’s Choice 50%, Vanicream 37.5% Sensitive Skin / Barrier Repair: Trust and recommendation24CeraVe75%La Roche‑Posay 70.8%, Vanicream 66.7%, Eucerin 54.2% Sensitive Skin / Barrier Repair: Ownership and maker attribution24La Roche‑Posay66.7%CeraVe 66.7%, Cetaphil 62.5%, Vanicream 54.2% Sensitive Skin / Barrier Repair: Quality and premium tier24La Roche‑Posay62.5%CeraVe 54.2%, Vanicream 45.8%, Paula’s Choice 37.5% Sensitive Skin / Barrier Repair: Durability and longevity24CeraVe75%La Roche‑Posay 54.2%, Vanicream 41.7%, First Aid Beauty 37.5% Sensitive Skin / Barrier Repair: Emerging and new entrants16La Roche‑Posay25%Vanicream 12.5%, Avène 12.5%, First Aid Beauty 12.5% Sensitive Skin / Barrier Repair: best ceramide cream for barrier repair8CeraVe62.5%SkinCeuticals 37.5%, La Roche‑Posay 25%, Paula’s Choice 25% Sensitive Skin / Barrier Repair: best moisturizer for sensitive skin8CeraVe87.5%La Roche‑Posay 62.5%, Neutrogena 37.5%, Vanicream 37.5% Sensitive Skin / Barrier Repair: best products for rosacea8SkinCeuticals75%CeraVe 62.5%, La Roche‑Posay 50%, Avène 50% Sensitive Skin / Barrier Repair: best fragrance-free skincare products8La Roche‑Posay100%CeraVe 100%, Vanicream 100%, Cetaphil 62.5% Sensitive Skin / Barrier Repair: best sunscreen for sensitive skin8La Roche‑Posay100%CeraVe 100%, EltaMD 100%, Vanicream 87.5% Sensitive Skin / Barrier Repair: best gentle cleanser for sensitive skin8La Roche‑Posay75%CeraVe 75%, Vanicream 75%, Cetaphil 50% Sensitive Skin / Barrier Repair: best products for eczema8CeraVe87.5%Aveeno 87.5%, Vanicream 75%, Eucerin 75% Sensitive Skin / Barrier Repair: best moisturizer for compromised skin barrier8CeraVe75%First Aid Beauty 62.5%, La Roche‑Posay 50%, Vanicream 50% Sensitive Skin / Barrier Repair: best hypoallergenic skincare brands8La Roche‑Posay87.5%CeraVe 87.5%, Vanicream 75%, Bioderma 75% Sensitive Skin / Barrier Repair: best products for redness and irritation8La Roche‑Posay87.5%SkinCeuticals 75%, CeraVe 50%, Avène 50% Sensitive Skin / Barrier Repair: best cica cream for irritated skin8La Roche‑Posay87.5%Avène 50%, Bioderma 25%, CeraVe 12.5% Sensitive Skin / Barrier Repair: best products for dry sensitive skin8CeraVe87.5%Vanicream 87.5%, Cetaphil 87.5%, La Roche‑Posay 75% Sensitive Skin / Barrier Repair: best serum for skin barrier repair8CeraVe37.5%SkinCeuticals 37.5%, La Roche‑Posay 25%, Paula’s Choice 25% Sensitive Skin / Barrier Repair: best products for perioral dermatitis8La Roche‑Posay75%CeraVe 75%, Vanicream 37.5%, Cetaphil 37.5% Sensitive Skin / Barrier Repair: cetaphil vs cerave for sensitive skin8CeraVe87.5%Cetaphil 87.5%, La Roche‑Posay 75%, Vanicream 62.5% Sensitive Skin / Barrier Repair: best calming toner for sensitive skin8La Roche‑Posay50%Paula’s Choice 50%, CeraVe 37.5%, SkinCeuticals 37.5% — SPF / Sun Protection — SPF / Sun Protection: Price and value24Neutrogena79.2%La Roche‑Posay 50%, Coppertone 50%, CeraVe 45.8% SPF / Sun Protection: Primary use case24Supergoop66.7%EltaMD 62.5%, La Roche‑Posay 50%, CeraVe 45.8% SPF / Sun Protection: Beginners and first purchase24EltaMD70.8%CeraVe 66.7%, Supergoop 62.5%, La Roche‑Posay 58.3% SPF / Sun Protection: Where to buy24La Roche‑Posay58.3%Neutrogena 45.8%, EltaMD 41.7%, CeraVe 29.2% SPF / Sun Protection: Gifting and occasions24Supergoop70.8%La Roche‑Posay 54.2%, Neutrogena 54.2%, EltaMD 54.2% SPF / Sun Protection: Specific need or condition24La Roche‑Posay62.5%EltaMD 62.5%, Supergoop 54.2%, CeraVe 41.7% SPF / Sun Protection: Comparison and evaluation24La Roche‑Posay66.7%EltaMD 66.7%, Supergoop 58.3%, CeraVe 54.2% SPF / Sun Protection: Trust and recommendation24EltaMD83.3%La Roche‑Posay 75%, Neutrogena 75%, CeraVe 62.5% SPF / Sun Protection: Ownership and maker attribution24La Roche‑Posay62.5%Neutrogena 54.2%, EltaMD 50%, Aveeno 41.7% SPF / Sun Protection: Quality and premium tier24La Roche‑Posay70.8%EltaMD 70.8%, Supergoop 66.7%, Neutrogena 37.5% SPF / Sun Protection: Durability and longevity24La Roche‑Posay66.7%EltaMD 58.3%, Blue Lizard 54.2%, Coppertone 50% SPF / Sun Protection: Specialist / advanced use24La Roche‑Posay70.8%EltaMD 66.7%, Neutrogena 45.8%, Supergoop 45.8% SPF / Sun Protection: General best-in-category24La Roche‑Posay95.8%Supergoop 95.8%, EltaMD 87.5%, Neutrogena 83.3% SPF / Sun Protection: Everyday and mainstream choice24CeraVe95.8%EltaMD 95.8%, La Roche‑Posay 91.7%, Neutrogena 91.7% SPF / Sun Protection: Emerging and new entrants16Beauty of Joseon37.5%La Roche‑Posay 25%, Neutrogena 18.8%, ISDIN 12.5% SPF / Sun Protection: best sunscreen for face8EltaMD75%CeraVe 62.5%, La Roche‑Posay 50%, Supergoop 50% SPF / Sun Protection: chemical vs mineral sunscreen which is better8La Roche‑Posay50%CeraVe 50%, EltaMD 50%, Blue Lizard 50% SPF / Sun Protection: best sunscreen for oily skin8Supergoop100%EltaMD 87.5%, La Roche‑Posay 62.5%, CeraVe 62.5% SPF / Sun Protection: best mineral sunscreen no white cast8ISDIN37.5%La Roche‑Posay 25%, CeraVe 25%, EltaMD 25% SPF / Sun Protection: best sunscreen for dark skin tones8Supergoop75%CeraVe 37.5%, La Roche‑Posay 25%, EltaMD 25% SPF / Sun Protection: best tinted sunscreen8EltaMD62.5%La Roche‑Posay 50%, Supergoop 50%, CeraVe 37.5% SPF / Sun Protection: best sunscreen for kids8La Roche‑Posay62.5%Neutrogena 50%, Blue Lizard 50%, Coppertone 50% SPF / Sun Protection: best sunscreen for acne prone skin8EltaMD87.5%Supergoop 87.5%, La Roche‑Posay 62.5%, Neutrogena 62.5% SPF / Sun Protection: best sunscreen stick for reapplication8EltaMD62.5%Supergoop 62.5%, CeraVe 37.5%, La Roche‑Posay 25% SPF / Sun Protection: best reef safe sunscreen8Blue Lizard87.5%CeraVe 37.5%, La Roche‑Posay 12.5%, Neutrogena 12.5% SPF / Sun Protection: best SPF moisturizer for daily wear8La Roche‑Posay62.5%EltaMD 62.5%, Olay 62.5%, CeraVe 50% SPF / Sun Protection: best waterproof sunscreen for swimming8Coppertone62.5%La Roche‑Posay 50%, Neutrogena 50%, Supergoop 50% SPF / Sun Protection: best sunscreen under makeup8EltaMD100%Supergoop 100%, La Roche‑Posay 62.5%, CeraVe 50% SPF / Sun Protection: best mineral sunscreen for rosacea and redness8EltaMD75%CeraVe 62.5%, La Roche‑Posay 50%, Vanicream 37.5% SPF / Sun Protection: best drugstore sunscreen8Neutrogena87.5%La Roche‑Posay 75%, CeraVe 62.5%, Aveeno 62.5% SPF / Sun Protection: best sunscreen for eczema prone skin8Vanicream75%CeraVe 62.5%, La Roche‑Posay 50%, EltaMD 50% Clinical brands lead the specific concern clusters. La Roche Posay and CeraVe dominate acne and barrier repair. SkinCeuticals and Paula's Choice capture high visibility in anti-aging and retinoid queries. EltaMD performs strongly in SPF and sun protection. ## Citations and publisher sources When AI engines cite sources with retrieval evidence, they pull from a mix of news media, beauty publishers, and medical resources. ### Owned-domain citation rate Share of the 1901 answers with retrieval evidence that cited a URL on the brand's own website. La Roche‑Posay 6.1% · 116/1901 CeraVe 5.7% · 108/1901 Neutrogena 2.2% · 42/1901 Paula’s Choice 6.3% · 120/1901 SkinCeuticals 3.1% · 59/1901 EltaMD 1.2% · 22/1901 The Ordinary 1.8% · 35/1901 Vanicream 1.1% · 20/1901 Cetaphil 2.4% · 46/1901 Differin 1.3% · 25/1901 Supergoop 1.1% · 21/1901 Aveeno 0.3% · 6/1901 ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration nbcnews.com832/190143.8% dermapproved.com482/190125.4% allure.com376/190119.8% whowhatwear.com330/190117.4% youtube.com328/190117.3% forbes.com311/190116.4% today.com308/190116.2% vogue.com275/190114.5% skinsort.com241/190112.7% healthline.com237/190112.5% marieclaire.com219/190111.5% aad.org214/190111.3% NBC News is the most cited publisher, appearing in 43.8 percent of the 1,901 answers with retrieval evidence. Dermapproved.com follows at 25.4 percent. Traditional beauty publishers maintain strong influence: Allure appears in 19.8 percent of answers, Who What Wear in 17.4 percent, and Vogue in 14.5 percent. The American Academy of Dermatology (aad.org) is cited in 11.3 percent of answers, proving the importance of clinical authority. ## Who is winning and who is at risk Winners are brands with high appearance rates and strong cross engine consistency. La Roche Posay (58 percent) and CeraVe (51 percent) are the clear winners. They appear frequently on all engines and maintain high owned domain citation rates. Brands at risk suffer from engine volatility or low overall visibility. Paula's Choice is at risk due to its 0 percent appearance rate on Claude. Brands like Beauty of Joseon (2.2 percent), Anessa (0.3 percent), and Tower 28 (0.1 percent) are at severe risk of exclusion from the AI research phase. ## Structural findings in the skincare category The skincare AI visibility data reveals a heavy concentration at the top. The top two brands, La Roche Posay and CeraVe, hold an outsized share of total mentions. The confidence interval of rank 1 (55.8 to 60.2 percent) does not overlap with rank 2 (48.8 to 53.3 percent), establishing a definitive hierarchy. Third party publishers drive the majority of citations, with NBC News cited in 832 answers. Owned domain citation rates remain low for most brands, peaking at 6.3 percent for Paula's Choice and 6.1 percent for La Roche Posay. ## What changed for this category The dataset captures a specific measurement window where engines show sharp divergence in how they handle brand entities. The split in how Claude and ChatGPT treat Paula's Choice indicates different internal thresholds for brand promotion or different retrieval tool biases during the measurement window. ## The GEO playbook for skincare brands Brands in this category should take specific actions based on these measured weaknesses. - Target clinical authority gaps: Gemini relies on the American Academy of Dermatology (32.1 percent appearance rate). Brands should align their public claims with AAD guidelines and secure mentions in medical publisher content. Measure this by tracking appearance rates on Gemini. - Fix zero appearance gaps: Paula's Choice has a 0 percent rate on Claude. Brands must audit their technical SEO, unstructured data, and PR footprint specifically for Anthropic's crawler. Measure this by rerunning Claude prompts. - Increase owned domain citations: Most brands have owned domain citation rates below 2 percent. Brands must publish detailed, mechanism focused ingredient content that engines can retrieve directly. Measure this by tracking the owned domain citation rate. - Dominate the top publishers: NBC News (43.8 percent) and Dermapproved.com (25.4 percent) control the citation layer. Brands need PR campaigns targeting these specific domains. Measure this by tracking share of voice within citations from these publishers. - Improve average position: The Ordinary has an average position of 6.4. Brands buried low in answers must optimize for earlier retrieval by matching the exact phrasing of consumer concern queries. Measure this by tracking the average first mention position. ## What this means for brands in the category The data shows that AI engines prioritize clinical positioning over premium marketing. Brands that lead with dermatologist backing, ingredient transparency, and clear medical associations win the retrieval layer. Cosmetic brands that rely solely on social media influence or visual branding struggle to trigger AI recommendations for specific skin concerns. ## Methodology and integrity This research measures AI visibility for skincare brands across 5 primary skin concerns. The data comes from 240 prompts run across 4 engines, yielding 1,920 total answers. Appearance metrics use the 1,920 answers returned as the denominator. Citation metrics use the 1,901 answers with retrieval evidence as the denominator. ### How this study was measured - Sampling frame fixed first: 240 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 2 trial(s) each. - 1920 answers returned of 1920 attempted, 1901 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. ## Frequently asked questions ### Which brand has the highest AI visibility for skincare? La Roche Posay ranks first with a 58 percent appearance rate across the 1,920 measured answers. ### Do AI engines cite brand websites directly? Yes, but at low rates. Paula's Choice achieves the highest owned domain citation rate at 6.3 percent, followed by La Roche Posay at 6.1 percent. ### Which publishers do the engines cite most often? NBC News is the most cited source, appearing in 43.8 percent of answers with retrieval evidence. Dermapproved.com follows at 25.4 percent. ### Do all AI engines recommend the same brands? No. Paula's Choice appears in 58.5 percent of ChatGPT answers but 0 percent of Claude answers. ### How does the American Academy of Dermatology perform? The AAD appears in 9.8 percent of all answers. It earns an 11.3 percent owned domain citation rate and is highly favored by Gemini (32.1 percent appearance share). ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Dermstore1.7%33/19201.2%–2.4% 2Sephora0.5%10/19200.3%–1% 3Ulta0.5%9/19200.2%–0.9% 4Amazon0.4%7/19200.2%–0.8% 5Reddit0%0/19200%–0.2% 6TikTok0%0/19200%–0.2% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Skincare AI Visibility Index 2026 - Measurement date: 2026-10-09 - Depth: index — 240 prompts, 2 trial(s) per prompt per engine, 4 engines - Answers attempted: 1920 - Answers returned (appearance denominator): 1920 - Answers with retrieval evidence (citation denominator): 1901 (99% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Cross-segment roll-up: every figure in this hub is tallied from the 1920 measured answers of its 4 segment studies. No figure here is estimated, and no category is ranked that was not measured in one of those segments. ### How the prompts were sampled Combined from the stored sampling frames of 4 completed segment studies; no prompts were regenerated or re-measured. Demand clusterPromptsScope Anti-Aging / Retinoids: best retinol for beginners1best retinol for beginners Anti-Aging / Retinoids: best retinol serum for wrinkles1best retinol serum for wrinkles Anti-Aging / Retinoids: tretinoin vs retinol which is better1tretinoin vs retinol which is better Anti-Aging / Retinoids: best anti-aging serum for 30s1best anti-aging serum for 30s Anti-Aging / Retinoids: best anti-aging serum for 40s1best anti-aging serum for 40s Anti-Aging / Retinoids: best over the counter retinol cream1best over the counter retinol cream Anti-Aging / Retinoids: best peptide serum for firming skin1best peptide serum for firming skin Anti-Aging / Retinoids: best night cream for fine lines1best night cream for fine lines Anti-Aging / Retinoids: how to start using retinol without irritation1how to start using retinol without irritation Anti-Aging / Retinoids: best retinol for sensitive skin1best retinol for sensitive skin Anti-Aging / Retinoids: best eye cream for wrinkles1best eye cream for wrinkles Anti-Aging / Retinoids: best vitamin A serum for anti-aging1best vitamin A serum for anti-aging Anti-Aging / Retinoids: best neck cream for sagging skin1best neck cream for sagging skin Anti-Aging / Retinoids: bakuchiol vs retinol1bakuchiol vs retinol Anti-Aging / Retinoids: best anti-aging moisturizer with SPF1best anti-aging moisturizer with SPF Anti-Aging / Retinoids: best collagen-boosting skincare products1best collagen-boosting skincare products Anti-Aging / Retinoids: General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Anti-Aging / Retinoids: Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Anti-Aging / Retinoids: Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Anti-Aging / Retinoids: Primary use case3the dominant everyday use case for this category Anti-Aging / Retinoids: Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Anti-Aging / Retinoids: Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Anti-Aging / Retinoids: Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Anti-Aging / Retinoids: Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Anti-Aging / Retinoids: Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Anti-Aging / Retinoids: Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Anti-Aging / Retinoids: Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Anti-Aging / Retinoids: Quality and premium tier3which options are highest quality, most durable or best made Anti-Aging / Retinoids: Durability and longevity3which brands last, hold up over time, or fail early Anti-Aging / Retinoids: Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Anti-Aging / Retinoids: Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about Acne: best products for acne prone skin1best products for acne prone skin Acne: best salicylic acid cleanser for acne1best salicylic acid cleanser for acne Acne: best benzoyl peroxide treatment1best benzoyl peroxide treatment Acne: best spot treatment for cystic acne1best spot treatment for cystic acne Acne: best moisturizer for acne prone skin1best moisturizer for acne prone skin Acne: best non-comedogenic sunscreen1best non-comedogenic sunscreen Acne: best face wash for oily acne prone skin1best face wash for oily acne prone skin Acne: best treatment for hormonal acne1best treatment for hormonal acne Acne: best products for teenage acne1best products for teenage acne Acne: best niacinamide serum for acne1best niacinamide serum for acne Acne: best products for back acne (bacne)1best products for back acne (bacne) Acne: best azelaic acid product for acne1best azelaic acid product for acne Acne: differin vs adapalene which is better1differin vs adapalene which is better Acne: best oil-free moisturizer for acne1best oil-free moisturizer for acne Acne: best products for maskne1best products for maskne Acne: best acne patches1best acne patches Acne: General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Acne: Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Acne: Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Acne: Primary use case3the dominant everyday use case for this category Acne: Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Acne: Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Acne: Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Acne: Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Acne: Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Acne: Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Acne: Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Acne: Quality and premium tier3which options are highest quality, most durable or best made Acne: Durability and longevity3which brands last, hold up over time, or fail early Acne: Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Acne: Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about Sensitive Skin / Barrier Repair: best moisturizer for sensitive skin1best moisturizer for sensitive skin Sensitive Skin / Barrier Repair: best products for rosacea1best products for rosacea Sensitive Skin / Barrier Repair: best fragrance-free skincare products1best fragrance-free skincare products Sensitive Skin / Barrier Repair: best ceramide cream for barrier repair1best ceramide cream for barrier repair Sensitive Skin / Barrier Repair: best gentle cleanser for sensitive skin1best gentle cleanser for sensitive skin Sensitive Skin / Barrier Repair: best products for eczema1best products for eczema Sensitive Skin / Barrier Repair: best sunscreen for sensitive skin1best sunscreen for sensitive skin Sensitive Skin / Barrier Repair: best moisturizer for compromised skin barrier1best moisturizer for compromised skin barrier Sensitive Skin / Barrier Repair: best products for redness and irritation1best products for redness and irritation Sensitive Skin / Barrier Repair: best hypoallergenic skincare brands1best hypoallergenic skincare brands Sensitive Skin / Barrier Repair: best cica cream for irritated skin1best cica cream for irritated skin Sensitive Skin / Barrier Repair: best products for dry sensitive skin1best products for dry sensitive skin Sensitive Skin / Barrier Repair: best serum for skin barrier repair1best serum for skin barrier repair Sensitive Skin / Barrier Repair: best products for perioral dermatitis1best products for perioral dermatitis Sensitive Skin / Barrier Repair: best calming toner for sensitive skin1best calming toner for sensitive skin Sensitive Skin / Barrier Repair: cetaphil vs cerave for sensitive skin1cetaphil vs cerave for sensitive skin Sensitive Skin / Barrier Repair: General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier Sensitive Skin / Barrier Repair: Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Sensitive Skin / Barrier Repair: Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it Sensitive Skin / Barrier Repair: Primary use case3the dominant everyday use case for this category Sensitive Skin / Barrier Repair: Beginners and first purchase3what a first-time buyer with no knowledge of the category should get Sensitive Skin / Barrier Repair: Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace Sensitive Skin / Barrier Repair: Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration Sensitive Skin / Barrier Repair: Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve Sensitive Skin / Barrier Repair: Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis Sensitive Skin / Barrier Repair: Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts Sensitive Skin / Barrier Repair: Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to Sensitive Skin / Barrier Repair: Quality and premium tier3which options are highest quality, most durable or best made Sensitive Skin / Barrier Repair: Durability and longevity3which brands last, hold up over time, or fail early Sensitive Skin / Barrier Repair: Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category Sensitive Skin / Barrier Repair: Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about SPF / Sun Protection: best sunscreen for face1best sunscreen for face SPF / Sun Protection: best mineral sunscreen no white cast1best mineral sunscreen no white cast SPF / Sun Protection: best sunscreen for oily skin1best sunscreen for oily skin SPF / Sun Protection: chemical vs mineral sunscreen which is better1chemical vs mineral sunscreen which is better SPF / Sun Protection: best sunscreen for dark skin tones1best sunscreen for dark skin tones SPF / Sun Protection: best tinted sunscreen1best tinted sunscreen SPF / Sun Protection: best sunscreen for kids1best sunscreen for kids SPF / Sun Protection: best SPF moisturizer for daily wear1best SPF moisturizer for daily wear SPF / Sun Protection: best sunscreen for acne prone skin1best sunscreen for acne prone skin SPF / Sun Protection: best reef safe sunscreen1best reef safe sunscreen SPF / Sun Protection: best sunscreen stick for reapplication1best sunscreen stick for reapplication SPF / Sun Protection: best sunscreen under makeup1best sunscreen under makeup SPF / Sun Protection: best waterproof sunscreen for swimming1best waterproof sunscreen for swimming SPF / Sun Protection: best mineral sunscreen for rosacea and redness1best mineral sunscreen for rosacea and redness SPF / Sun Protection: best drugstore sunscreen1best drugstore sunscreen SPF / Sun Protection: best sunscreen for eczema prone skin1best sunscreen for eczema prone skin SPF / Sun Protection: General best-in-category3the plain, highest-volume 'best X' style demand with no qualifier SPF / Sun Protection: Everyday and mainstream choice3what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category SPF / Sun Protection: Price and value3budget, value-for-money, cheaper alternatives and whether premium is worth it SPF / Sun Protection: Primary use case3the dominant everyday use case for this category SPF / Sun Protection: Beginners and first purchase3what a first-time buyer with no knowledge of the category should get SPF / Sun Protection: Where to buy3purchase-channel demand: where to buy, which retailers, direct vs marketplace SPF / Sun Protection: Gifting and occasions3what to buy as a gift or for a specific occasion, event or celebration SPF / Sun Protection: Specific need or condition3a specific buyer need, constraint, body/fit/context requirement or problem to solve SPF / Sun Protection: Comparison and evaluation3head-to-head style evaluation demand: which brands are better than which, and on what basis SPF / Sun Protection: Trust and recommendation3which brands are most recommended or most trusted by reviewers and experts SPF / Sun Protection: Ownership and maker attribution3who owns, makes or is behind the leading brands in this category, and which parent company a named brand belongs to SPF / Sun Protection: Quality and premium tier3which options are highest quality, most durable or best made SPF / Sun Protection: Durability and longevity3which brands last, hold up over time, or fail early SPF / Sun Protection: Specialist / advanced use3advanced, professional or enthusiast-level demand inside the category SPF / Sun Protection: Emerging and new entrants2newer entrants, recent launches and shifts buyers are only starting to ask about ### The exact prompts #PromptClusterShape 1best retinol for beginners1_anti_aging_retinoids__need_1medium 2best retinol serum for wrinkles1_anti_aging_retinoids__need_2medium 3tretinoin vs retinol which is better1_anti_aging_retinoids__need_3medium 4best anti-aging serum for 30s1_anti_aging_retinoids__need_4medium 5best anti-aging serum for 40s1_anti_aging_retinoids__need_5medium 6best over the counter retinol cream1_anti_aging_retinoids__need_6medium 7best peptide serum for firming skin1_anti_aging_retinoids__need_7medium 8best night cream for fine lines1_anti_aging_retinoids__need_8medium 9how to start using retinol without irritation1_anti_aging_retinoids__need_9medium 10best retinol for sensitive skin1_anti_aging_retinoids__need_10medium 11best eye cream for wrinkles1_anti_aging_retinoids__need_11medium 12best vitamin A serum for anti-aging1_anti_aging_retinoids__need_12medium 13best neck cream for sagging skin1_anti_aging_retinoids__need_13medium 14bakuchiol vs retinol1_anti_aging_retinoids__need_14medium 15best anti-aging moisturizer with SPF1_anti_aging_retinoids__need_15medium 16best collagen-boosting skincare products1_anti_aging_retinoids__need_16medium 17best retinol brands1_anti_aging_retinoids__general_bestshort 18best anti-aging skincare brands1_anti_aging_retinoids__general_bestshort 19Which brands make the best anti-aging retinoid treatments?1_anti_aging_retinoids__general_bestmedium 20best everyday retinol brands1_anti_aging_retinoids__everydayshort 21best drugstore anti-aging brands1_anti_aging_retinoids__everydayshort 22Which mainstream brands are best for everyday retinoid skincare?1_anti_aging_retinoids__everydaymedium 23best affordable retinol brands1_anti_aging_retinoids__value_priceshort 24best value anti-aging brands1_anti_aging_retinoids__value_priceshort 25Which retinoid brands offer the best value for money?1_anti_aging_retinoids__value_pricemedium 26best retinol for wrinkles1_anti_aging_retinoids__use_caseshort 27best firming skincare brands1_anti_aging_retinoids__use_caseshort 28Which brands work best for smoothing fine lines and wrinkles?1_anti_aging_retinoids__use_caselong 29best beginner retinol brands1_anti_aging_retinoids__beginnershort 30retinol brands for beginners1_anti_aging_retinoids__beginnershort 31Which brands make the easiest first retinoid products to start with?1_anti_aging_retinoids__beginnerlong 32where to buy retinol1_anti_aging_retinoids__retailshort 33best places for anti-aging skincare1_anti_aging_retinoids__retailmedium 34Which retinoid brands are safest to buy from authorized retailers online?1_anti_aging_retinoids__retaillong 35anti-aging skincare gift brands1_anti_aging_retinoids__giftingshort 36best retinol gifts1_anti_aging_retinoids__giftingshort 37Which anti-aging skincare brands make the best gifts for milestone birthdays?1_anti_aging_retinoids__giftinglong 38retinol for sensitive skin brands1_anti_aging_retinoids__specific_needmedium 39best retinol for mature skin1_anti_aging_retinoids__specific_needmedium 40Which brands offer retinoids that minimize irritation for dry, sensitive skin?1_anti_aging_retinoids__specific_needlong 41best retinol brand comparison1_anti_aging_retinoids__comparisonmedium 42retinol brands compared1_anti_aging_retinoids__comparisonshort 43Which anti-aging brands have the strongest clinical evidence for retinoid results?1_anti_aging_retinoids__comparisonlong 44most trusted retinol brands1_anti_aging_retinoids__trustmedium 45dermatologist recommended anti-aging brands1_anti_aging_retinoids__trustmedium 46Which retinoid skincare brands do dermatologists recommend most often, and why?1_anti_aging_retinoids__trustlong 47who makes leading retinol brands1_anti_aging_retinoids__ownershipmedium 48retinol brand parent companies1_anti_aging_retinoids__ownershipmedium 49Which companies own the most widely recommended anti-aging skincare brands?1_anti_aging_retinoids__ownershiplong 50best premium retinol brands1_anti_aging_retinoids__premium_qualitymedium 51highest quality anti-aging brands1_anti_aging_retinoids__premium_qualitymedium 52Which premium skincare brands have the best-formulated retinoid and peptide treatments?1_anti_aging_retinoids__premium_qualitylong 53retinol shelf life brands1_anti_aging_retinoids__durabilitymedium 54long lasting anti-aging formulas1_anti_aging_retinoids__durabilitymedium 55Which retinoid brands use packaging that best preserves formula potency over time?1_anti_aging_retinoids__durabilitylong 56advanced retinoid skincare brands1_anti_aging_retinoids__specialistmedium 57clinical strength anti-aging brands1_anti_aging_retinoids__specialistmedium 58Which brands offer advanced retinoid treatments with transparent ingredient concentrations?1_anti_aging_retinoids__specialistlong 59new retinoid skincare brands1_anti_aging_retinoids__emergingmedium 60Which newer anti-aging brands are gaining attention for next-generation retinoid formulas?1_anti_aging_retinoids__emerginglong 61best products for acne prone skin2_acne__need_1medium 62best salicylic acid cleanser for acne2_acne__need_2medium 63best benzoyl peroxide treatment2_acne__need_3medium 64best spot treatment for cystic acne2_acne__need_4medium 65best moisturizer for acne prone skin2_acne__need_5medium 66best non-comedogenic sunscreen2_acne__need_6medium 67best face wash for oily acne prone skin2_acne__need_7medium 68best treatment for hormonal acne2_acne__need_8medium 69best products for teenage acne2_acne__need_9medium 70best niacinamide serum for acne2_acne__need_10medium 71best products for back acne (bacne)2_acne__need_11medium 72best azelaic acid product for acne2_acne__need_12medium 73differin vs adapalene which is better2_acne__need_13medium 74best oil-free moisturizer for acne2_acne__need_14medium 75best products for maskne2_acne__need_15medium 76best acne patches2_acne__need_16medium 77best acne treatments2_acne__general_bestshort 78best acne skincare brands2_acne__general_bestshort 79Which acne treatment brands are best overall for clearing breakouts?2_acne__general_bestlong 80everyday acne skincare2_acne__everydayshort 81best daily acne routine brands2_acne__everydaymedium 82Which acne brands work well for an affordable everyday routine?2_acne__everydaylong 83affordable acne treatment brands2_acne__value_priceshort 84best budget acne skincare2_acne__value_priceshort 85Which acne treatment brands offer the best value without sacrificing effective ingredients?2_acne__value_pricelong 86best treatments for breakouts2_acne__use_caseshort 87brands for acne and clogged pores2_acne__use_casemedium 88Which skincare brands are most effective for treating recurring facial acne breakouts?2_acne__use_caselong 89acne products for beginners2_acne__beginnershort 90best first acne routine2_acne__beginnershort 91Which acne skincare brands are easiest for a beginner to start using safely?2_acne__beginnerlong 92where to buy acne treatments2_acne__retailmedium 93best stores for acne skincare2_acne__retailmedium 94Which acne treatment brands are safest to buy from authorized retailers rather than marketplaces?2_acne__retaillong 95acne skincare gift sets2_acne__giftingshort 96best acne gifts for teens2_acne__giftingmedium 97Which acne skincare brands make thoughtful, beginner-friendly gifts for someone with breakouts?2_acne__giftinglong 98acne products for sensitive skin2_acne__specific_needmedium 99best acne brands for oily skin2_acne__specific_needmedium 100Which acne treatment brands are best for adult acne with sensitive, easily irritated skin?2_acne__specific_needlong 101best acne brand comparison2_acne__comparisonshort 102compare leading acne treatment brands2_acne__comparisonmedium 103Which acne skincare brands perform best for salicylic acid, benzoyl peroxide, and adapalene options?2_acne__comparisonlong 104most trusted acne skincare brands2_acne__trustmedium 105dermatologist recommended acne brands2_acne__trustmedium 106Which acne treatment brands have the strongest dermatologist support and clinical evidence?2_acne__trustlong 107who makes acne treatment brands2_acne__ownershipmedium 108acne brands and parent companies2_acne__ownershipmedium 109Which leading acne skincare brands are made by established pharmaceutical or skincare companies?2_acne__ownershiplong 110highest quality acne treatments2_acne__premium_qualityshort 111premium clinical acne skincare brands2_acne__premium_qualitymedium 112Which acne treatment brands offer the highest-quality formulas and strongest ingredient transparency?2_acne__premium_qualitylong 113long lasting acne products2_acne__durabilityshort 114acne treatments that last longest2_acne__durabilitymedium 115Which acne skincare brands offer products that stay stable and effective through regular use?2_acne__durabilitylong 116advanced acne treatment brands2_acne__specialistshort 117best clinical acne active brands2_acne__specialistmedium 118Which acne brands are best for building an advanced routine around proven OTC active ingredients?2_acne__specialistlong 119new acne treatment brands2_acne__emergingshort 120Which newer acne skincare brands and product approaches are gaining attention from dermatology-focused buyers?2_acne__emerginglong 121best moisturizer for sensitive skin3_sensitive_skin_barrier_repair__need_1medium 122best products for rosacea3_sensitive_skin_barrier_repair__need_2medium 123best fragrance-free skincare products3_sensitive_skin_barrier_repair__need_3medium 124best ceramide cream for barrier repair3_sensitive_skin_barrier_repair__need_4medium 125best gentle cleanser for sensitive skin3_sensitive_skin_barrier_repair__need_5medium 126best products for eczema3_sensitive_skin_barrier_repair__need_6medium 127best sunscreen for sensitive skin3_sensitive_skin_barrier_repair__need_7medium 128best moisturizer for compromised skin barrier3_sensitive_skin_barrier_repair__need_8medium 129best products for redness and irritation3_sensitive_skin_barrier_repair__need_9medium 130best hypoallergenic skincare brands3_sensitive_skin_barrier_repair__need_10medium 131best cica cream for irritated skin3_sensitive_skin_barrier_repair__need_11medium 132best products for dry sensitive skin3_sensitive_skin_barrier_repair__need_12medium 133best serum for skin barrier repair3_sensitive_skin_barrier_repair__need_13medium 134best products for perioral dermatitis3_sensitive_skin_barrier_repair__need_14medium 135best calming toner for sensitive skin3_sensitive_skin_barrier_repair__need_15medium 136cetaphil vs cerave for sensitive skin3_sensitive_skin_barrier_repair__need_16medium 137best sensitive skin brands3_sensitive_skin_barrier_repair__general_bestshort 138best barrier repair skincare3_sensitive_skin_barrier_repair__general_bestshort 139Which skincare brands are best for sensitive, compromised skin?3_sensitive_skin_barrier_repair__general_bestmedium 140everyday sensitive skin brands3_sensitive_skin_barrier_repair__everydayshort 141best daily barrier moisturizers3_sensitive_skin_barrier_repair__everydayshort 142Which mainstream skincare brands work best for daily sensitive-skin routines?3_sensitive_skin_barrier_repair__everydaymedium 143affordable barrier repair brands3_sensitive_skin_barrier_repair__value_priceshort 144best budget sensitive skincare3_sensitive_skin_barrier_repair__value_priceshort 145Which sensitive-skin brands offer the best value for barrier repair?3_sensitive_skin_barrier_repair__value_pricemedium 146barrier repair routine brands3_sensitive_skin_barrier_repair__use_caseshort 147best brands for damaged barriers3_sensitive_skin_barrier_repair__use_caseshort 148Which brands are best for rebuilding a damaged skin barrier?3_sensitive_skin_barrier_repair__use_casemedium 149sensitive skincare for beginners3_sensitive_skin_barrier_repair__beginnershort 150first barrier repair products3_sensitive_skin_barrier_repair__beginnershort 151Which brands are easiest for beginners starting a sensitive-skin routine?3_sensitive_skin_barrier_repair__beginnermedium 152where to buy barrier skincare3_sensitive_skin_barrier_repair__retailshort 153best stores for sensitive skincare3_sensitive_skin_barrier_repair__retailshort 154Which sensitive-skin brands are safest to buy from authorized retailers?3_sensitive_skin_barrier_repair__retailmedium 155sensitive skincare gift brands3_sensitive_skin_barrier_repair__giftingshort 156barrier repair gifts3_sensitive_skin_barrier_repair__giftingshort 157Which skincare brands make good gifts for someone with sensitive skin?3_sensitive_skin_barrier_repair__giftingmedium 158best brands for rosacea3_sensitive_skin_barrier_repair__specific_needshort 159eczema-friendly skincare brands3_sensitive_skin_barrier_repair__specific_needshort 160Which skincare brands are best for eczema-prone, easily irritated facial skin?3_sensitive_skin_barrier_repair__specific_needmedium 161sensitive skincare brand comparison3_sensitive_skin_barrier_repair__comparisonshort 162best ceramide skincare brands3_sensitive_skin_barrier_repair__comparisonshort 163Which barrier-repair skincare brands perform best for dryness, redness, and irritation?3_sensitive_skin_barrier_repair__comparisonmedium 164most trusted sensitive skin brands3_sensitive_skin_barrier_repair__trustshort 165dermatologist recommended barrier brands3_sensitive_skin_barrier_repair__trustshort 166Which sensitive-skin brands have the strongest dermatologist and clinical support?3_sensitive_skin_barrier_repair__trustmedium 167who makes sensitive skincare3_sensitive_skin_barrier_repair__ownershipshort 168sensitive skincare parent companies3_sensitive_skin_barrier_repair__ownershipshort 169Which companies own the leading sensitive-skin and barrier-repair skincare brands?3_sensitive_skin_barrier_repair__ownershipmedium 170highest quality sensitive skincare3_sensitive_skin_barrier_repair__premium_qualityshort 171premium barrier repair brands3_sensitive_skin_barrier_repair__premium_qualityshort 172Which sensitive-skin brands are considered highest quality for barrier-supporting formulas?3_sensitive_skin_barrier_repair__premium_qualitymedium 173long-lasting barrier moisturizers3_sensitive_skin_barrier_repair__durabilityshort 174sensitive skincare that lasts3_sensitive_skin_barrier_repair__durabilityshort 175Which barrier-repair skincare brands make moisturizers that provide the longest-lasting comfort?3_sensitive_skin_barrier_repair__durabilitymedium 176clinical barrier repair brands3_sensitive_skin_barrier_repair__specialistshort 177advanced sensitive skin formulas3_sensitive_skin_barrier_repair__specialistshort 178Which brands have the best evidence-backed formulas for treating a compromised skin barrier?3_sensitive_skin_barrier_repair__specialistmedium 179new sensitive skincare brands3_sensitive_skin_barrier_repair__emergingshort 180Which newer skincare brands are gaining attention for sensitive skin and barrier repair?3_sensitive_skin_barrier_repair__emerginglong 181best sunscreen for face4_spf_sun_protection__need_1medium 182best mineral sunscreen no white cast4_spf_sun_protection__need_2medium 183best sunscreen for oily skin4_spf_sun_protection__need_3medium 184chemical vs mineral sunscreen which is better4_spf_sun_protection__need_4medium 185best sunscreen for dark skin tones4_spf_sun_protection__need_5medium 186best tinted sunscreen4_spf_sun_protection__need_6medium 187best sunscreen for kids4_spf_sun_protection__need_7medium 188best SPF moisturizer for daily wear4_spf_sun_protection__need_8medium 189best sunscreen for acne prone skin4_spf_sun_protection__need_9medium 190best reef safe sunscreen4_spf_sun_protection__need_10medium 191best sunscreen stick for reapplication4_spf_sun_protection__need_11medium 192best sunscreen under makeup4_spf_sun_protection__need_12medium 193best waterproof sunscreen for swimming4_spf_sun_protection__need_13medium 194best mineral sunscreen for rosacea and redness4_spf_sun_protection__need_14medium 195best drugstore sunscreen4_spf_sun_protection__need_15medium 196best sunscreen for eczema prone skin4_spf_sun_protection__need_16medium 197best sunscreen brands4_spf_sun_protection__general_bestshort 198best SPF brands4_spf_sun_protection__general_bestshort 199Which sun protection brands are best overall for daily use?4_spf_sun_protection__general_bestlong 200everyday sunscreen brands4_spf_sun_protection__everydayshort 201best daily facial SPF brands4_spf_sun_protection__everydaymedium 202Which sunscreen brands make the easiest everyday options for most people?4_spf_sun_protection__everydaylong 203best value sunscreen brands4_spf_sun_protection__value_priceshort 204affordable SPF brands worth buying4_spf_sun_protection__value_pricemedium 205Which sunscreen brands offer the best value without compromising reliable protection?4_spf_sun_protection__value_pricelong 206best sunscreen for daily wear4_spf_sun_protection__use_casemedium 207best commuting SPF brands4_spf_sun_protection__use_caseshort 208Which sun protection brands work best under makeup for everyday city wear?4_spf_sun_protection__use_caselong 209beginner sunscreen brands4_spf_sun_protection__beginnershort 210best first facial sunscreen4_spf_sun_protection__beginnermedium 211Which sunscreen brands are simplest for a first-time buyer to choose?4_spf_sun_protection__beginnerlong 212where to buy sunscreen4_spf_sun_protection__retailmedium 213best sunscreen retailers4_spf_sun_protection__retailshort 214Which sunscreen brands are safest to buy from authorized online retailers?4_spf_sun_protection__retaillong 215sunscreen gift brands4_spf_sun_protection__giftingshort 216best SPF gifts for travelers4_spf_sun_protection__giftingmedium 217Which sun protection brands make the best gifts for a beach holiday?4_spf_sun_protection__giftinglong 218best sunscreen for dark skin4_spf_sun_protection__specific_needmedium 219no white cast SPF brands4_spf_sun_protection__specific_needmedium 220Which sunscreen brands are best for sensitive skin and leave minimal white cast?4_spf_sun_protection__specific_needlong 221mineral versus chemical sunscreen brands4_spf_sun_protection__comparisonmedium 222best tinted SPF brands4_spf_sun_protection__comparisonmedium 223Which sunscreen brands perform better for protection, wearability, and ingredient transparency?4_spf_sun_protection__comparisonlong 224dermatologist recommended sunscreen brands4_spf_sun_protection__trustmedium 225most trusted SPF brands4_spf_sun_protection__trustshort 226Which sunscreen brands have the strongest dermatologist support and clinical evidence?4_spf_sun_protection__trustlong 227who makes leading sunscreen brands4_spf_sun_protection__ownershipmedium 228sunscreen brand parent companies4_spf_sun_protection__ownershipmedium 229Which leading sunscreen brands are made by established skincare or pharmaceutical companies?4_spf_sun_protection__ownershiplong 230highest quality sunscreen brands4_spf_sun_protection__premium_qualitymedium 231premium facial SPF brands4_spf_sun_protection__premium_qualitymedium 232Which sun protection brands justify premium pricing through formulation quality and testing?4_spf_sun_protection__premium_qualitylong 233long lasting sunscreen brands4_spf_sun_protection__durabilitymedium 234best water resistant SPF brands4_spf_sun_protection__durabilitymedium 235Which sunscreen brands hold up best through sweat, swimming, and long outdoor days?4_spf_sun_protection__durabilitylong 236advanced sunscreen brands4_spf_sun_protection__specialistshort 237best high UVA protection brands4_spf_sun_protection__specialistmedium 238Which sunscreen brands offer advanced broad-spectrum protection for intense sun exposure?4_spf_sun_protection__specialistlong 239new sunscreen brands4_spf_sun_protection__emergingshort 240Which newer sunscreen brands are gaining attention for innovative UV filters?4_spf_sun_protection__emerginglong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search480480480100% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool480480480100% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)48048046196% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool480480480100% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 1 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1La Roche‑Posay1114/192058%58%55.8%–60.2%116/1901 (6.1%) 2CeraVe980/192051%51%48.8%–53.3%108/1901 (5.7%) 3Neutrogena672/192035%35%32.9%–37.2%42/1901 (2.2%) 4Paula’s Choice484/192025.2%25.2%23.3%–27.2%120/1901 (6.3%) 5SkinCeuticals398/192020.7%20.7%19%–22.6%59/1901 (3.1%) 6EltaMD391/192020.4%20.4%18.6%–22.2%22/1901 (1.2%) 7The Ordinary349/192018.2%18.2%16.5%–20%35/1901 (1.8%) 8Vanicream300/192015.6%15.6%14.1%–17.3%20/1901 (1.1%) 9Cetaphil283/192014.7%14.7%13.2%–16.4%46/1901 (2.4%) 10Differin265/192013.8%13.8%12.3%–15.4%25/1901 (1.3%) 11Supergoop251/192013.1%13.1%11.6%–14.7%21/1901 (1.1%) 12Aveeno214/192011.1%11.1%9.8%–12.6%6/1901 (0.3%) 13Avène213/192011.1%11.1%9.8%–12.6%0/1901 (0%) 14Eucerin199/192010.4%10.4%9.1%–11.8%3/1901 (0.2%) 15RoC188/19209.8%9.8%8.5%–11.2%22/1901 (1.2%) 16American Academy of Dermatology188/19209.8%9.8%8.5%–11.2%214/1901 (11.3%) 17First Aid Beauty168/19208.8%8.8%7.6%–10.1%9/1901 (0.5%) 18Bioderma165/19208.6%8.6%7.4%–9.9%2/1901 (0.1%) 19Olay147/19207.7%7.7%6.5%–8.9%8/1901 (0.4%) 20Medik8144/19207.5%7.5%6.4%–8.8%23/1901 (1.2%) 21PanOxyl136/19207.1%7.1%6%–8.3%10/1901 (0.5%) 22Murad121/19206.3%6.3%5.3%–7.5%18/1901 (0.9%) 23SkinMedica97/19205.1%5.1%4.2%–6.1%3/1901 (0.2%) 24Blue Lizard97/19205.1%5.1%4.2%–6.1%2/1901 (0.1%) 25Galderma94/19204.9%4.9%4%–6%36/1901 (1.9%) 26Paula's Choice93/19204.8%4.8%4%–5.9%120/1901 (6.3%) 27Proactiv88/19204.6%4.6%3.7%–5.6%9/1901 (0.5%) 28ISDIN85/19204.4%4.4%3.6%–5.4%15/1901 (0.8%) 29Coppertone85/19204.4%4.4%3.6%–5.4%4/1901 (0.2%) 30Banana Boat82/19204.3%4.3%3.5%–5.3%1/1901 (0.1%) 31Beauty of Joseon42/19202.2%2.2%1.6%–2.9%0/1901 (0%) 32Anessa5/19200.3%0.3%0.1%–0.6%0/1901 (0%) 33Tower 281/19200.1%0.1%0%–0.3%5/1901 (0.3%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-10-09. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. ## The five skincare concern reports Each skin concern was measured separately, with its own brand panel and its own 16-question set, and has its own report in the AI Visibility Index. The figures in this table come from those segment measurements. ConcernReportBrands measuredAnswers measuredMost-named brand Anti-Aging / RetinoidsAnti-Aging & Retinoids AI Citation Benchmark 20265480SkinCeuticals (49.8%) AcneAcne Treatments AI Citation Benchmark 20265480La Roche-Posay (60%) Hyperpigmentation / BrighteningHyperpigmentation & Brightening AI Citation Benchmark 20265472SkinCeuticals (54.9%) Sensitive Skin / Barrier RepairSensitive Skin & Barrier Repair AI Citation Benchmark 20265480CeraVe (71.9%) SPF / Sun ProtectionSPF & Sun Protection AI Citation Benchmark 20265480La Roche‑Posay (60.4%) ## Related AI visibility research - Beauty AI Visibility Index 2026: the brand-first ranking of the top 25 U.S. beauty brands by AI citation share. - Menopause AI Visibility Index: how AI engines cite the top 25 menopause brands. - Longevity AI Visibility Index: how AI engines cite the top 25 longevity brands. - 2026 Male Aesthetic Revolution: men's grooming and aesthetics demand. - what increases AI beauty recommendations: the content changes that move beauty brands in AI answers. Brands acting on these findings can work with our skincare PR team, our beauty PR practice, our beauty marketing agency group and our dermatology and aesthetics marketing specialists. --- ### 5W RFP Center v2 - Evaluating PR, GEO, and AI Communications Partners URL: https://www.5wpr.com/research/rfp/ Summary: A 5W buyer guide for modern communications RFPs, with scorecards for PR, digital, GEO, AI visibility, crisis, and measurement capabilities. --- ### Creators & AI Visibility URL: https://www.5wpr.com/research/creators-ai-visibility/ Summary: A 5W audit of 15 buyer-intent queries across five verticals, measuring creator visibility online and where YouTube content sits in AI answers. May 20265W AI Communications An ongoing observation series # Creators & AI Visibility How AI engines cite the creator economy — and what 15 buyer-intent queries reveal about where YouTube fits inside the answer surface. Published by 5W — The Communications Firm Built for the AI Era. YouTube has never been more important to how consumers research products. AI engines have never been more important to how buyers make decisions. The intersection of these two layers is where modern brand visibility now gets shaped. YouTube is the second-largest search engine on earth. For categories like skincare, wearables, credit cards, supplements, and SaaS, creator-led video is among the deepest, most-trusted, fastest-growing buyer-research surfaces of the modern internet. AI engines treat that creator content differently depending on the engine, the prompt, and the retrieval method. Some surface YouTube heavily. Others underweight it dramatically. This audit examines the question directly, running 15 buyer-intent queries across five verticals and coding every cited source. The first observation: creator-led video is significantly underweighted in text-first AI retrieval relative to its consumer-research footprint. Whether that asymmetry holds across all AI engines is the question this series will keep testing. 0% YouTube share of AI-cited results 30+ Creator-led videos for the same queries 15 Buyer-intent prompts tested live 5 Verticals coded B2C and B2B 109 Citations coded by source type 01Methodology ## How the gap was measured. Two-Layer Testing AI vs YouTubeEvery prompt was run twice: once through AI-engine retrieval, once through YouTube's native index. The same query, the same intent, two different surfaces measured side by side. Prompt Construction Buyer-Intent DrivenReal-world consumer and B2B research queries, written in the language buyers actually use: "best retinol for beginners," "Whoop vs Oura," and "best CRM for small business." Citation Coding 14 Source TypesEvery cited source was coded across brand-owned, retailer, editorial, vertical trade press, expert blog, medical, academic, government, UGC, aggregator, and YouTube. Creator Layer Capture 30+ Channels MappedFor the same prompts, the parallel YouTube ecosystem was captured by creator name, video format, indexed date, and channel type. In the retrieval surface tested 0% YouTube share of cited results across 109 ranked citations spanning five verticals in text-first AI retrieval. In the parallel YouTube layer 30+ Creator-led videos surfaced for the same exact queries: dermatologists, finance analysts, wearables reviewers, and category specialists. The Observation ### Creator-led video is the fastest-growing layer of consumer product research, and the most unevenly cited across AI engines. 02Findings ## What the audit reveals. F1 ### YouTube is underweighted in text-first AI retrieval, but the picture varies by engine. Across 109 ranked citations from 15 buyer-intent queries in five verticals, zero YouTube videos surfaced in the AI retrieval surface tested. The same queries run against YouTube directly returned a deep, current library of creator-led content. Engines with stronger multimodal capabilities treat video content differently and will be tested in subsequent rounds. Observation The asymmetry was largest in categories where YouTube creator content is densest: Beauty, fitness wearables, and credit-card optimization. F2 ### Creators with the deepest authority are topic specialists, not mega-influencers. The YouTube creators captured in the parallel ecosystem skew toward topic-specialist channels: board-certified dermatologists, single-category credit-card reviewers, wearables-only review channels, and expert-led explainer formats. Topical depth appears to outrank raw reach. Observation The same pattern appears inside cited blog content: named experts with vertical depth outrank broader lifestyle sites. F3 ### What fills the gap: independent expert blogs and brand-owned content. In place of the missing YouTube layer, AI engines pull heavily from independent expert blogs and brand-owned content. In B2B SaaS, vendors occupied 87% of the top cited surface. The cited answer is often shaped by who has invested in long-form, structured, entity-rich text content. F4 ### Source logic differs sharply by vertical. Beauty is dominated by independent expert blogs plus brand-owned content. Tech and SaaS is overwhelmingly vendor-led. Finance and Travel remain editorial-led, with vertical trade press retaining authority. Health splits between medical institutions and DTC brands. F5 ### User-generated platforms cross into AI answers. YouTube does not. Medium, Quora, and Reddit-adjacent content appeared across multiple verticals, particularly in comparison queries. Text-based user-generated content is treated as retrieval signal. Video-based creator content is not. Observation The asymmetry suggests the gap is a format gap as much as a platform gap. F6 ### News recency drives citation in rate-sensitive categories. In personal finance, citations skewed almost entirely to articles published within the previous two weeks. Citation half-life in these categories appears measured in days, not months. F7 ### Aggregators are an underleveraged citation surface. In Beauty, ingredient-comparison sites appeared up to three times for a single query. In Tech, comparison aggregators were heavily cited. Structured data and ingredient-level transparency appear to register as authority signals that AI engines reward. 03Verticals ## The gap, vertical by vertical. Vertical What AI Cites What YouTube Holds Source Concentration Beauty29 citations Independent dermatologist blogs, brand-owned education, retailer content. A dense ecosystem of board-certified dermatologists and skincare specialists producing long-form reviews and routine breakdowns, none cited. 23distinct domains cited Tech / SaaS15 citations Vendor-owned content, including Salesforce, HubSpot competitors, and AI-tool review aggregators. B2B software demo channels, founder-led explainer videos, and comparison reviews absent from cited results. 87%vendor self-published Personal Finance25 citations Vertical trade press including Bankrate, NerdWallet, Kiplinger, CNBC, plus brand-owned institutional content. Financial-analyst creators covering the same rate movements in greater depth, in real time, uncited. 62%vertical trade press Health & Wellness22 citations Medical institutions and DTC brand education hubs. Physician-led YouTube channels, longevity and supplement specialists, and expert-led wearables reviewers absent from cited results. 41%institutional sources Travel18 citations Vertical authority publications including The Points Guy, Upgraded Points, and NerdWallet. Credit-card-strategy creators, points-and-miles specialists, and ranking videos with deep subscriber bases, none cited. 78%vertical specialist pubs 04What's Next ## What this series is tracking. 01 Engine-by-Engine #### Does the gap exist in every AI engine? Future editions test ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews directly through API access. 02 Creator Mapping #### Who are the most-watched creators that AI ignores? A standalone sub-study will map the top creators per vertical inside YouTube's native index by subscriber tier, format, and indexed authority. 03 Format Analysis #### Which video formats compound into text citation? The next edition examines which video formats reliably compound into the AI-cited surface, and which never do. 04 Citation Half-Life #### How long does cited content stay cited? The same prompts will be re-run at T+30 and T+60 to map decay curves by category. 05 Vertical Depth #### Eight verticals, fifty prompts each Volume 02 expands beyond the five categories tested here to add Food & Beverage, Consumer Electronics, and Auto. 06 Methodology Paper #### Open prompt taxonomy and coding rubric A standalone methodology document will publish the full prompt taxonomy, source-type coding rubric, and engine-version logs. 5W ##### About this series Creators & AI Visibility is a standing observation series published by 5W, The Communications Firm Built for the AI Era. It examines how AI engines cite the creator economy, with specific focus on how YouTube creator content moves through different AI retrieval surfaces. ##### Methodology This edition was executed in May 2026 across 15 buyer-intent prompts spanning five verticals. Every cited source in the AI retrieval surface tested was coded by domain type and rank position. ##### What's next The next edition expands to eight verticals at fifty prompts each, adds direct multi-engine API testing, and publishes a companion creator-mapping sub-study. © 2026 5W Public RelationsThe Communications Firm Built for the AI EraCreators & AI Visibility · May 2026 --- ### Gaming Trust Index 2026 URL: https://www.5wpr.com/research/gaming-trust-index-2026/ Summary: The US gambling industry spent $3.9 billion on marketing in 2025 and under 4% went to earned media. The 5WPR Gaming Trust Index maps that gap. # Gaming Trust Index 2026 ### By Matt Caiola, CEO, 5WPR. April 2026. ## The U.S. gambling industry spent $3.9 billion on marketing in 2025. Less than 4% went to earned media and PR. Here is what the full $3.9 billion bought — and what it didn't. The 5WPR Gaming Trust Index is the first annual study to document marketing spend allocation and brand credibility outcomes across the top U.S. sports betting, online gaming, and land-based casino operators. Built on public market data, operator financial disclosures, and analysis of 47,000+ earned media articles. No paywall. No registration. ## Methodology This study analyzed public market data from 47+ gaming operators, SEC filings, quarterly earnings reports, and 47,000+ earned media articles from January 2025 - December 2025. Data sources include: - Operator financial disclosures (10-K, 10-Q forms) - Marketing spend data from public earnings calls - Earned media analysis via Meltwater and Cision databases - Brand sentiment tracking across sports betting, online casino, and land-based operators ## Key Findings ### $3.9B Total Marketing Spend U.S. gambling industry marketing investment in 2025 ### Less than 4% to PR/Earned Media $156M of $3.9B went to public relations and earned media ### 76% Paid Digital Advertising $2.96B spent on paid search, social, and display advertising ## Top Operators by Marketing Spend Rank Operator Total Marketing Spend PR/Earned Media % Trust Score 1 DraftKings $485M 6.2% 8.4/10 2 FanDuel $462M 5.8% 8.1/10 3 BetMGM $318M 3.9% 7.6/10 ## Frequently Asked Questions ### What is the Gaming Trust Index 2026? The Gaming Trust Index 2026 is a research study by 5WPR that analyzes how trust impacts marketing effectiveness in the gaming industry. It evaluates how brands allocate budgets, how consumers perceive gaming companies, and how trust influences engagement, retention, and long-term brand value. ### What is the main finding of the Gaming Trust Index? The report finds a significant imbalance between spending and outcomes: billions are invested in advertising, while trust-building areas such as public relations, responsible gaming, and reputation management receive a disproportionately small share of investment. ### Why is trust becoming a key KPI in gaming marketing? As the gaming industry becomes more competitive and regulated, consumer trust directly impacts user acquisition, retention, and lifetime value. Players are increasingly sensitive to issues like fairness, transparency, and data privacy, making trust a central performance driver. ### How much is the gaming industry spending on advertising? The report highlights that billions of dollars are allocated to advertising across gaming and betting sectors, yet relatively little is invested in building credibility and long-term brand trust, creating inefficiencies in overall marketing performance. ### What is the "trust gap" in gaming? The trust gap refers to the disconnect between how much gaming companies invest in customer acquisition and how little they invest in reputation, transparency, and responsible gaming. This gap leads to lower engagement quality and reduced long-term value. ### How does trust impact player behavior? Trust influences whether players choose a platform, how long they stay engaged, and whether they recommend it to others. Higher trust levels are associated with stronger retention, increased spending, and more sustainable growth. ### What role does public relations play in gaming marketing? Public relations is one of the most effective but underutilized tools for building trust in gaming. PR helps shape brand perception, establish credibility, and manage reputation-factors that are critical in an industry where trust directly affects user behavior. ### How does responsible gaming affect brand trust? Responsible gaming initiatives are a key driver of trust. Companies that demonstrate transparency, user protection, and ethical practices are more likely to build long-term loyalty and avoid regulatory or reputational risks. ### What should gaming companies do differently? The report recommends rebalancing marketing budgets to invest more in trust-building strategies, including PR, reputation management, responsible gaming programs, and long-term brand storytelling, rather than focusing solely on paid acquisition. ### How can brands use the Gaming Trust Index? Brands can use the report to evaluate their current marketing mix, identify gaps in trust-building efforts, and redesign strategies to improve efficiency, retention, and long-term growth in an increasingly competitive gaming landscape. April 2026 - 5W Public Relations 5wpr.com/gaming-trust-index-2026 Published by 5WPR Research. 5wpr.com. Email us at press@5wpr.com --- ### How AI Engines Cite The Top 25 Longevity Brands . URL: https://www.5wpr.com/research/longevity-ai-visibility-index/ Summary: How AI engines cite the top 25 longevity brands across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. 5W AI Visibility Index · Volume 03 Aging, Longevity & Healthspan Volume 03 · Q2 2026 · Research Report # How AI Engines Cite The Top 25 Longevity Brands. A modeled audit of which longevity brands — supplements, biomarker companies, devices, clinics — the AI engines surface when consumers ask about lifespan, healthspan, biological age, and protocol stacks. Twenty-five brands ranked. Five engines tested. Recoverable ground identified. Brands ranked 25 AI engines tested 5 Consumer prompts 60+ Category Aging & Longevity Executive Finding ## The structure of the brand-level citation graph. The longevity citation layer is unlike any other consumer health category. Three biomarker companies — Levels, Function Health, and Oura — anchor the top of the citation graph at the brand level. The supplement portfolio is fragmented across Thorne, AG1, Momentous, and the NAD+ category. Bryan Johnson's Blueprint and Peter Attia's Early Medical cite heavily as personality-attached brands. The structural finding: brands that are also data sources (CGM, wearables, biomarker testing) anchor citation more reliably than brands that are only product lines. § 01 — Methodology ## How citation share was modeled. Modeled directional estimates derived from publicly available data, observed retrieval patterns, structural signals, and the corresponding EPR Citation Share Study. Not the output of logged query runs across millions of prompts. Intended as a strategic framework — not a definitive search engine measurement. 01 #### Engines Modeled ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews — the five engines that account for substantively all consumer-prompt AI retrieval today. 02 #### Brand Universe Top 25 brands in the category by combined modeled signal across the five engines, weighted to consumer-prompt query patterns. 03 #### Citation Inputs Domain authority and traffic data, structural signals (schema, entity markup, training-data inclusion), and presence inside the EPR Tier 1 outlet set. 04 #### Tier Methodology Brands tiered into Leader / Strong / Mid / Lagging based on aggregate citation share and engine-by-engine variation. Tier breaks reflect natural inflection points in the data. Per the corresponding Everything-PR Citation Share Study — Aging & Longevity (Issue No. 03), the dominant outlets are Huberman Lab, Peter Attia's The Drive, Lifespan.io, Examine.com, plus the institutional anchors NIA, Buck Institute, and Mayo Clinic. A brand's citation share is built primarily through podcast guest placements (Huberman, Attia, Found My Fitness), evidence inclusion in Examine.com, and structured PR through the institutional clinical layer. § 02 — The Ranking ## The top 25 Aging & Longevity brands by modeled AI citation share. Rank Brand · Owner Position Note Modeled Share Tier 01 Levels Levels Health (private, VC-backed) CGM authority. Cited as primary data source on glucose-response queries. 8.4% Leader 02 Function Health Function (Mark Hyman-affiliated) Biomarker-testing authority. Rising fast; citation share doubled in 12 months. 7.2% Leader 03 Oura Ring Oura Health Oy (private) Sleep and recovery wearable. Cited heavily on sleep, HRV, and recovery queries. 6.8% Leader 04 WHOOP WHOOP, Inc. (private, VC-backed) Recovery-and-strain wearable. Strong citation in training-and-longevity overlap queries. 5.4% Leader 05 Eight Sleep Eight Sleep (private, VC-backed) Smart-mattress authority. Cited on sleep optimization and temperature regulation queries. 4.6% Leader 06 Bryan Johnson Blueprint Don't Die / Bryan Johnson Protocol-as-product. Polarizing but cited heavily on stack and supplement queries. 4.2% Leader 07 AG1 (Athletic Greens) AG1 (private, VC-backed) Greens-powder authority. Cited disproportionately due to creator partnerships. 3.8% Strong 08 Momentous Momentous (Huberman-affiliated) Supplement line. Citation share lifted by Huberman Lab partnership. 3.4% Strong 09 Thorne Thorne HealthTech Practitioner-grade supplement authority. Cited heavily in Claude on clinical queries. 3.0% Strong 10 Peter Attia / Early Medical Early Medical (private) Concierge longevity medicine. High citation on protocol and "longevity doctor" queries. 2.8% Strong 11 Tally Health Tally Health (Sinclair-affiliated) Biological-age testing. Citation softened post-Sinclair controversy but persists. 2.5% Strong 12 Ritual Ritual (private, VC-backed) Vitamin brand. Strong DTC reach; mid-tier citation. 2.2% Strong 13 Elysium Health Elysium Health (private) NAD+ and longevity supplements. Cited as authority on NAD+ specifically. 1.9% Strong 14 Tru Niagen ChromaDex (NASDAQ: CDXC) NAD+ precursor authority. Cited on niacinamide riboside queries specifically. 1.7% Mid 15 InsideTracker Segterra (private, VC-backed) Biomarker-testing competitor to Function. Lower citation share. 1.5% Mid 16 Lifeforce Lifeforce (Tony Robbins-affiliated) Concierge longevity. Mid-tier citation share. 1.3% Mid 17 Apollo Neuro Apollo Neuroscience (private) Stress wearable. Niche but cited on HRV and stress queries. 1.2% Mid 18 ProLon L-Nutra (private) Fasting-mimicking diet. Cited on fasting protocol queries. 1.0% Mid 19 Hims & Hers Hims & Hers Health (NYSE: HIMS) Longevity SKUs (peptides, GLP-1). Citation growing. 0.9% Mid 20 Ro Ro Health (private) Telehealth platform with longevity-adjacent SKUs. 0.8% Mid 21 Nutrafol Unilever (acquired 2022) Hair-loss supplement. Adjacent to longevity, lower citation share. 0.7% Lagging 22 Lyma Lyma (private, UK-based) Premium supplements. Smaller U.S. citation. 0.6% Lagging 23 Viome Viome Life Sciences (private) Microbiome testing. Niche citation. 0.5% Lagging 24 Renue by Science Renue (private) NAD+ supplements. Smaller brand reach. 0.4% Lagging 25 Pure Encapsulations Atrium Innovations (Nestlé) Practitioner supplements. Citation lower than scale. 0.4% Lagging § 03 — Tier 1 Deep Dive ## Why the leaders lead. Analysis of the top brands — what their citation share is built on, where their moats are real, and where the recoverable ground sits even for category leaders. #1 Brand · Tier 1 Leader ### Levels Levels is the cleanest case in this series of a brand whose product (continuous glucose monitoring) is also the citation. When AI engines answer "how does my glucose respond to X," they cite Levels because Levels is the source of the data. The brand has built citation share by becoming infrastructure for the answer. This is the most defensible position any brand can occupy in the AI age. Recoverable ground is limited because it is already a leader. #2 Brand · Tier 1 Leader ### Function Health Function's citation share doubled between Q4 2024 and Q4 2025 — the fastest growth in our longevity tracking. Three drivers: Mark Hyman's personal authority, the price-undercut versus competitors, and a content marketing program tuned for AI retrieval. Function is the case study other biomarker companies should study. #3 Brand · Tier 1 Leader ### Oura Ring Oura is the dominant wearable citation in longevity. Its data ("the average Oura user sleeps X hours") is cited in answer text by ChatGPT and Perplexity routinely. The brand has built earned-media depth across wellness and biohacking. Recoverable ground is in clinical-credibility positioning: Oura's research partnerships are real but under-cited in clinical answers. § 04 — Engine Variation ## The five engines do not return identical brand citations. A brand absent from one engine but present in another may need a different program than a brand absent across the board. Engine-aware strategy matters. #### ChatGPT Levels, Function, Oura, Huberman-anchored brands dominate. Personality-and-platform balanced. #### Claude Thorne, Pure Encapsulations, peer-reviewed brands over-index. Lighter on personality-attached. #### Perplexity Substack longevity writers and biomarker brands cite higher. Bryan Johnson punches above weight. #### Google AI Overviews Levels, Function, AG1, Ritual dominate consumer queries. SERP-mirror. #### Gemini YouTube biohacker creator content surfaces. Ben Greenfield, Thomas DeLauer, Brian Kennedy. § 05 — Recoverable Ground ## Where the largest citation lifts are sitting. Brands with citation share materially below their commercial position, brand recognition, or product quality. The specific paths to recovery — and the order of operations. Recoverable Ground AG1 (Athletic Greens) Currently #7 · Path to Top 5 AG1 has enormous commercial scale ($1B+ valuation) but its citation share is constrained by creator-partnership-driven content rather than independent authority. A clinical-credibility content program — independent of the partnership network — would lift the brand into the top five. Recoverable Ground Thorne Currently #9 · Path to Top 5 Thorne is the practitioner-grade supplement authority but its citation share is heavily Claude-weighted (clinical signal) and lighter elsewhere. A consumer-prompt program inside Function Health, Huberman Lab, and Attia's ecosystem would expand citation across all five engines. Recoverable Ground Eight Sleep Currently #5 · Path to Top 3 Already a leader. Recoverable ground is in clinical sleep-medicine citation, where Eight Sleep has data but trails Oura on authority weight. A research-partnership content program (peer-reviewed sleep journal placement) closes the clinical gap. Recoverable Ground Tru Niagen Currently #14 · Path to Top 10 Tru Niagen is the NAD+ category leader by sales but trails Elysium in NAD+ citation share. The opportunity is owning the niacinamide riboside (NR) versus NMN debate inside AI answers — both compounds are widely searched and the citation graph is still soft. § 06 — The 5W Playbook ## What it takes to move citation share. A 12-week program structure modeled from 5W's locked AI Visibility Index/Audit methodology. Phase 1 establishes the baseline. Phase 2 builds. Phase 3 scales and measures. 01 Weeks 1–4 #### Discovery + Strategy - Baseline AI Visibility Audit across 5 engines - Examine.com inclusion audit - Podcast placement strategy (Attia, Huberman, Found My Fitness) - Clinical credibility content gap analysis - Creator and Substack landscape mapping 02 Weeks 5–8 #### Build - Podcast guest placement cycle - Examine.com evidence submission program - Substack longevity writer outreach - Vet-reviewed clinical content production - Lifespan.io and Buck Institute partnership exploration 03 Weeks 9–12 #### Scale - Monthly citation tracking + reporting - YouTube biohacker creator program - Earned-media amplification of placed content - Quarterly competitive citation re-benchmark - Phase 2 program scoping Investment Ballpark ### Phase-based scoping. Real ranges. Phase 1 fixed-fee available. Phase 1 $50K–$85K Phase 2 $70K–$150K / mo 12-Month Program $750K–$1.5M Longevity brands typically require investment in clinical credibility infrastructure that compounds across multiple product lines. Phase 1 scoping engagement available at fixed fee. ### Want this audit run on your brand? 5W AI Communications runs the AI Visibility Index methodology for clients across Aging & Longevity and adjacent categories. Phase 1 scoping engagement available at fixed fee. Contact 5W Download as PDF ### Request an AI visibility audit for your brand. Share your brand, category, and competitive set. 5W will route the inquiry with this report as context. Name Work email Company Brand/category of interest Short message Submit inquiry About 5W AI Communications 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. 5W AI Communications The Communications Firm Built for the AI Era. Building brand authority across the platforms where decisions now happen. #### AI Visibility Index - Vol. 01 — Pet - Vol. 02 — Video Games - Vol. 03 — Longevity - Full Series #### Practice Areas - Aging & Longevity Practice - GEO - AI Visibility - All Practices © 2026 5W AI Communications · All rights reserved 5W AI Visibility Index · Volume 03 · Q2 2026 --- ### How AI Engines Cite The Top 25 Menopause Brands . URL: https://www.5wpr.com/research/menopause-ai-visibility-index/ Summary: A modeled audit of which menopause and perimenopause brands AI engines surface, Midi Health among them, and how twenty-five companies rank overall. 5W AI Visibility Index · Volume 04 Menopause, Perimenopause & Women's Midlife Health Volume 04 · Q3 2026 · Research Report # How AI Engines Cite The Top 25 Menopause Brands. A modeled audit of which menopause and perimenopause brands — telehealth clinics, supplements, products, devices — the AI engines surface when consumers ask about HRT, symptoms, treatment, and access. Twenty-five brands ranked. Five engines tested. Recoverable ground identified. Brands ranked 25 AI engines tested 5 Consumer prompts 60+ Category Menopause & Women's Health Executive Finding ## The structure of the brand-level citation graph. The menopause citation layer is dominated by venture-backed telehealth clinics that did not exist five years ago. Midi Health, Alloy, and Evernow account for an estimated 22% of brand citation share between them — more than every legacy women's magazine combined. Mary Claire Haver's personal brand (The 'Pause Life) cites alongside the clinics as the dominant personality. The recoverable ground is enormous for consumer-product brands (Womaness, Bonafide, Kindra) whose products have meaningful retail distribution but whose citation share lags. Goop is cited near-zero on menopause-specific queries. § 01 — Methodology ## How citation share was modeled. Modeled directional estimates derived from publicly available data, observed retrieval patterns, structural signals, and the corresponding EPR Citation Share Study. Not the output of logged query runs across millions of prompts. Intended as a strategic framework — not a definitive search engine measurement. 01 #### Engines Modeled ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews — the five engines that account for substantively all consumer-prompt AI retrieval today. 02 #### Brand Universe Top 25 brands in the category by combined modeled signal across the five engines, weighted to consumer-prompt query patterns. 03 #### Citation Inputs Domain authority and traffic data, structural signals (schema, entity markup, training-data inclusion), and presence inside the EPR Tier 1 outlet set. 04 #### Tier Methodology Brands tiered into Leader / Strong / Mid / Lagging based on aggregate citation share and engine-by-engine variation. Tier breaks reflect natural inflection points in the data. Per the corresponding Everything-PR Citation Share Study — Menopause & Women's Health (Issue No. 04), the dominant outlets are Midi, Alloy, The 'Pause Life, NAMS, plus the rising TikTok-and-Substack layer (Dr. Jen Gunter, Tamsen Fadal). A brand's citation share is built primarily through partnerships with the telehealth clinics, NAMS-affiliated content placement, and TikTok-and-Substack creator programs. § 02 — The Ranking ## The top 25 Menopause & Women's Health brands by modeled AI citation share. Rank Brand · Owner Position Note Modeled Share Tier 01 Midi Health Midi (private, VC-backed) Largest virtual menopause clinic citation. Strong on HRT and treatment-protocol queries. 9.4% Leader 02 Alloy Alloy Women's Health (private, VC-backed) HRT-focused clinic. Strong on hormone-therapy-specific queries. 7.8% Leader 03 Mary Claire Haver / The 'Pause Life Personal brand Personality-attached citation. "The New Menopause" book quoted in LLM answers. 6.2% Leader 04 Evernow Evernow (private, VC-backed) Telehealth menopause clinic. Strong on access and "is it real" queries. 5.4% Leader 05 Maven Clinic Maven (private, VC-backed) Women's health platform. Cited on workplace-benefits and HR-program queries. 4.6% Leader 06 Womaness Womaness (private, VC-backed) Consumer DTC. Strongest non-clinic brand citation. 4.0% Strong 07 Gennev Unified Women's Healthcare Acquired telehealth clinic. Mid-tier citation share post-acquisition. 3.4% Strong 08 Bonafide Health Bonafide (private) Supplement-and-products brand. Citation growing. 2.9% Strong 09 Equelle Pharmavite (Otsuka subsidiary) Estrogen-free hot-flash supplement. Cited on non-HRT queries. 2.5% Strong 10 Stripes Beauty Naomi Watts founder brand Beauty-and-menopause hybrid. Celebrity-attached citation lift. 2.2% Strong 11 Kindra Kindra Health (private) Supplement and lubricant brand. Mid-tier DTC citation. 1.9% Strong 12 Joylux Joylux (private) Pelvic-floor device authority. Niche but cited on incontinence queries. 1.6% Strong 13 Bezwecken Bezwecken (private) Practitioner-grade hormone supplements. Cited in Claude on clinical queries. 1.3% Strong 14 Hello Bonafide Bonafide affiliate DTC subbrand of Bonafide. Lower aggregate citation. 1.1% Mid 15 Pause Well-Aging Rochelle Weitzner founder Skincare brand. Cited on menopause-skin queries. 1.0% Mid 16 Replens Church & Dwight (NYSE: CHD) Vaginal moisturizer authority. Mature OTC brand. 0.9% Mid 17 Embr Wave Embr Labs (private) Wearable thermoregulation device. Niche but cited on hot-flash queries. 0.8% Mid 18 Mira Mira Fertility (private) Hormone-tracking device. Cited on perimenopause testing. 0.7% Mid 19 Phenology Phenology (private) Menopause supplements. Lower citation share. 0.6% Mid 20 Caria Caria (private) Menopause app. Lower aggregate citation. 0.5% Mid 21 Soieva Soieva (private) Menopause underwear brand. Niche citation. 0.4% Lagging 22 Cooler Sleep products Various Bedding for night-sweat queries. Brand-fragmented citation. 0.4% Lagging 23 Madame Ovary Independent Smaller DTC brand. Limited citation. 0.3% Lagging 24 Lush menopause skincare Lush (private) Skincare sub-line. Adjacent citation. 0.3% Lagging 25 Goop menopause content Goop (private) Near-zero specialist menopause citation despite brand reach. 0.2% Lagging § 03 — Tier 1 Deep Dive ## Why the leaders lead. Analysis of the top brands — what their citation share is built on, where their moats are real, and where the recoverable ground sits even for category leaders. #1 Brand · Tier 1 Leader ### Midi Health Midi is the largest virtual menopause clinic by patient volume and the dominant brand-level citation in the category. Its model — board-certified clinicians, accept insurance, HRT-forward — produces content that AI engines treat as authoritative. The brand has built citation through aggressive editorial content marketing, vet-reviewed by its medical team. Recoverable ground is limited; the brand is already a leader. The opportunity is defending the position against Alloy and Evernow. #2 Brand · Tier 1 Leader ### Alloy Alloy anchors HRT-specific citation. When a consumer asks AI specifically about hormone therapy, Alloy is more likely to surface than Midi. The brand has built a sharper editorial position around HRT specifically. The recoverable ground is in non-HRT queries (lifestyle, supplements, alternatives) where Alloy currently trails. #3 Brand · Tier 1 Leader ### Mary Claire Haver / The 'Pause Life Haver is the dominant personality citation in the menopause category. Her book "The New Menopause" is quoted in LLM answers across all five engines. The brand-as-author model is unusually citation-efficient — books are heavily trained on, and the author becomes the answer. Recoverable ground is in clinical-credibility partnerships and continued podcast and creator footprint. § 04 — Engine Variation ## The five engines do not return identical brand citations. A brand absent from one engine but present in another may need a different program than a brand absent across the board. Engine-aware strategy matters. #### ChatGPT Midi, Alloy, Mary Claire Haver dominate. Telehealth-balanced. #### Claude Bezwecken, Bonafide, peer-reviewed brands over-index. Lighter on telehealth marketing. #### Perplexity Reddit menopause subs surface. TikTok-derived content cited heavily. Freshness-favored. #### Google AI Overviews Midi, Alloy, Healthline menopause dominate. SERP-mirror. #### Gemini TikTok creator content surfaces. Mary Claire Haver appears across both video and text. § 05 — Recoverable Ground ## Where the largest citation lifts are sitting. Brands with citation share materially below their commercial position, brand recognition, or product quality. The specific paths to recovery — and the order of operations. Recoverable Ground Womaness Currently #6 · Path to Top 3 Womaness has retail distribution at Target and direct consumer reach that exceeds its citation share. The gap is editorial: Womaness produces lifestyle content but lacks the clinical-authority signal of Midi or Alloy. A clinical-credibility content layer plus vet-reviewer positioning would lift the brand into Tier 1. Recoverable Ground Bonafide Health Currently #8 · Path to Top 5 Bonafide has prescription-strength non-hormonal products with real clinical data. The data is under-publicized. A clinical-evidence content program would lift Bonafide into direct competition with Alloy on hot-flash queries specifically. Recoverable Ground Stripes Beauty Currently #10 · Path to Top 6 Naomi Watts brings celebrity attention but the brand's clinical-credibility infrastructure is light. A medical-advisor-led content program — paired with the existing celebrity footprint — would compound the brand's citation potential. Recoverable Ground Kindra Currently #11 · Path to Top 7 Kindra has strong product-line breadth (supplements, lubricants, intimate care) but citation share is fragmented across SKUs. A consolidated Kindra-as-menopause-authority narrative would aggregate the citation footprint. § 06 — The 5W Playbook ## What it takes to move citation share. A 12-week program structure modeled from 5W's locked AI Visibility Index/Audit methodology. Phase 1 establishes the baseline. Phase 2 builds. Phase 3 scales and measures. 01 Weeks 1–4 #### Discovery + Strategy - Baseline AI Visibility Audit across 5 engines - Telehealth-clinic partnership and presence audit - NAMS-affiliated content gap analysis - TikTok and Substack creator landscape mapping - Clinical-evidence content inventory 02 Weeks 5–8 #### Build - Pitch and place inside Midi, Alloy editorial - NAMS-aligned content production - TikTok creator partnership initiation - Substack writer partnership program - Vet-reviewed clinical content production 03 Weeks 9–12 #### Scale - Monthly citation tracking + reporting - Earned-media amplification of placed content - Workplace-benefits angle (Maven, employer partnerships) - Quarterly competitive citation re-benchmark - Phase 2 program scoping Investment Ballpark ### Phase-based scoping. Real ranges. Phase 1 fixed-fee available. Phase 1 $40K–$70K Phase 2 $60K–$130K / mo 12-Month Program $600K–$1.2M Menopause brands benefit from a category in active expansion. First-mover programs compound over 18–24 months. Phase 1 scoping engagement available at fixed fee. ### Want this audit run on your brand? 5W AI Communications runs the AI Visibility Index methodology for clients across Menopause & Women's Health and adjacent categories. Phase 1 scoping engagement available at fixed fee. Contact 5W Download as PDF ### Request an AI visibility audit for your brand. Share your brand, category, and competitive set. 5W will route the inquiry with this report as context. Name Work email Company Brand/category of interest Short message Submit inquiry About 5W AI Communications 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. 5W AI Communications The Communications Firm Built for the AI Era. Building brand authority across the platforms where decisions now happen. #### AI Visibility Index - Vol. 01 — Pet - Vol. 02 — Video Games - Vol. 03 — Longevity - Full Series #### Practice Areas - Menopause & Women's Health Practice - GEO - AI Visibility - All Practices © 2026 5W AI Communications · All rights reserved 5W AI Visibility Index · Volume 04 · Q3 2026 --- ### HVAC & Plumbing AI Visibility Index 2026 URL: https://www.5wpr.com/research/hvac-plumbing-ai-visibility-index-2026/ Summary: 5W's first AI visibility index for US HVAC and plumbing services. ServiceTitan, Roto-Rooter, Mr. Rooter and 22 more ranked by AI citation share. --- ### The AI City Index 2026. URL: https://www.5wpr.com/research/ai-city-index/ Summary: The 5W AI City Index 2026 ranks 25 global cities on AI capital, talent, infrastructure, policy, and citation share. San Francisco #1, London #2. Skip to content 5W. AI COMMUNICATIONS RESEARCH — FIRST EDITION A 5W RESEARCH ARTIFACT — VOLUME 01 # The AI City Index 2026. Cities Ranked 25 AI Engines Tested 5 Prompts Modeled 60 Dimensions Scored 6 THE THESIS AI capital concentration is emerging as one of the strongest predictors of urban trajectory. Of the world's 25 leading metropolitan economies, a small group is capturing a disproportionate share of the AI build-out. The 5W AI City Index measures which cities are ahead, by how much, and on which dimensions — including the dimension that most others are ignoring: how AI engines themselves describe each city. 01 / LEADERBOARD The Global Rankings - Rank City Score Curve Composite Tier - 01 San Francisco Bay Area USA Frontier dominance 92.5 TIER I - 02 London United Kingdom Capital + policy 88.7 TIER I - 03 Beijing China Capital concentration 87.0 TIER I - 04 Tel Aviv Israel Talent + security AI 83.5 TIER I - 05 Singapore Singapore Policy + infrastructure 82.0 TIER I - 06 Seoul South Korea Hardware + talent 78.7 TIER I - 07 Paris France Mistral anchor 76.3 TIER II - 08 Boston USA Research density 75.8 TIER II - 09 Abu Dhabi UAE Sovereign infrastructure 74.7 TIER II - 10 Shanghai China Industrial AI 73.8 TIER II - 11 Toronto Canada Research lineage 71.8 TIER II - 12 New York USA Capital, no anchor 71.3 TIER II - 13 Dubai UAE Adoption velocity 70.5 TIER II - 14 Shenzhen China Hardware capital 69.0 TIER II - 15 Berlin Germany European cluster 64.3 TIER III - 16 Hyderabad India GCC scaling 62.1 TIER III - 17 Tokyo Japan Legacy capital, no breakout 60.5 TIER III - 18 Bangalore India Services scale 59.8 TIER III - 19 Riyadh Saudi Arabia Sovereign acceleration 58.2 TIER III - 20 Stockholm Sweden Nordic stronghold 56.7 TIER III - 21 Amsterdam Netherlands Cluster, no anchor 54.3 TIER III - 22 Los Angeles USA Capital decline 52.9 TIER III - 23 Zurich Switzerland Premium niche 51.4 TIER III - 24 Montreal Canada Research, no scale 50.6 TIER III - 25 Sydney Australia Periphery 47.2 TIER III "AI is the most important shift in business in twenty-five years. It will affect every single aspect of life, and every city in the world." Ronn Torossian / Founder & Chairman, 5W 02 / THESIS The Bifurcation Cities still compete on cost of living, business climate, and quality of life. A new variable has emerged that increasingly predicts urban trajectory: AI capital concentration. For much of the post-pandemic period, the dominant narrative around major cities was decline — residents leaving high-cost coastal markets, offices emptying, knowledge workers spreading thinner across the country. The Census Bureau's Vintage 2025 estimates, released in March 2026, tell a more complicated story. America's four legacy urban cores are diverging sharply, and the variable that best predicts which way each is moving is the presence — or absence — of frontier AI capital. San Francisco — written off three years ago — is the only one of America's four legacy urban cores actually growing in 2025, with population up 0.62%. Office leasing rose 20% in 2024 and absorbed positively for five straight quarters. The reason is unambiguous: OpenAI, Anthropic, Nvidia, Sierra AI, and xAI now anchor downtown. Los Angeles — comparable in scale and historically more attractive on most quality-of-life axes — posted the largest numeric population decline in the United States in 2025: a loss of 54,000 residents in LA County. Hollywood shed 45,000 jobs. Downtown office vacancy hit 34%. Los Angeles has yet to develop a comparable frontier-AI anchor ecosystem. The same divergence repeats globally. London raised a record £8.3 billion of AI venture capital in 2025 and lifted AI-led companies to 34% of all Central London tech office take-up. Paris, despite a stagnant national economy, runs the highest AI share of any continental European city's venture base — 43% — anchored by Mistral. Tel Aviv pulled in roughly $15.6 billion of total tech investment in 2025, with AI taking the leading share. Beijing concentrates 66% of local VC into AI, the highest share of any city in the world. Singapore tops the Counterpoint Global AI Cities Index 2025. Abu Dhabi committed a 5-gigawatt AI campus — the largest AI infrastructure project outside the United States. The cities that have not captured an AI anchor — Tokyo, Sydney, Amsterdam, Los Angeles — are flat or shrinking across most of the dimensions that matter for an AI-era economy: capital, talent, office demand, population. Wealth, weather, and the weight of history are no longer reliable predictors of urban trajectory. The presence of frontier AI capital increasingly is. This is the central finding of the 5W AI City Index 2026. Across six measured dimensions — capital intensity, talent density, infrastructure, policy support, frontier-lab anchor presence, and modeled AI citation share — the same nine cities cluster at the top. The remaining sixteen, including some of the most powerful urban economies in the world, are measurably behind the leaders on most of those dimensions. Without a structural shift in capital flow or policy, the gap may become difficult to close. / MAP The Geography of AI Capital All 25 cities ranked. Dot size = composite score. Dot color = tier. Read the picture: AI capital concentrates in nine cities across four regions. The lower 16 are visibly smaller — which is the visual truth of the index thesis. Tier I — composite ≥ 78 Tier II — composite 69 – 77 Tier III — composite below 69 - - - AMERICAS EUROPE MIDDLE EAST ASIA-PACIFIC SAN FRANCISCO, rank 1Composite score 92.5 in the 5W AI City Index regional cluster map. 1 SAN FRANCISCO 92.5 BOSTON, rank 8Composite score 75.8 in the 5W AI City Index regional cluster map. 8 BOSTON 75.8 NEW YORK, rank 12Composite score 71.3 in the 5W AI City Index regional cluster map. 12 NEW YORK 71.3 TORONTO, rank 11Composite score 71.8 in the 5W AI City Index regional cluster map. 11 TORONTO 71.8 LOS ANGELES, rank 22Composite score 52.9 in the 5W AI City Index regional cluster map. 22 LOS ANGELES 52.9 MONTREAL, rank 24Composite score 50.6 in the 5W AI City Index regional cluster map. 24 MONTREAL 50.6 LONDON, rank 2Composite score 88.7 in the 5W AI City Index regional cluster map. 2 LONDON 88.7 PARIS, rank 7Composite score 76.3 in the 5W AI City Index regional cluster map. 7 PARIS 76.3 BERLIN, rank 15Composite score 64.3 in the 5W AI City Index regional cluster map. 15 BERLIN 64.3 AMSTERDAM, rank 21Composite score 54.3 in the 5W AI City Index regional cluster map. 21 AMSTERDAM 54.3 STOCKHOLM, rank 20Composite score 56.7 in the 5W AI City Index regional cluster map. 20 STOCKHOLM 56.7 ZURICH, rank 23Composite score 51.4 in the 5W AI City Index regional cluster map. 23 ZURICH 51.4 TEL AVIV, rank 4Composite score 83.5 in the 5W AI City Index regional cluster map. 4 TEL AVIV 83.5 ABU DHABI, rank 9Composite score 74.7 in the 5W AI City Index regional cluster map. 9 ABU DHABI 74.7 DUBAI, rank 13Composite score 70.5 in the 5W AI City Index regional cluster map. 13 DUBAI 70.5 RIYADH, rank 19Composite score 58.2 in the 5W AI City Index regional cluster map. 19 RIYADH 58.2 BEIJING, rank 3Composite score 87.0 in the 5W AI City Index regional cluster map. 3 BEIJING 87.0 SEOUL, rank 6Composite score 78.7 in the 5W AI City Index regional cluster map. 6 SEOUL 78.7 SHANGHAI, rank 10Composite score 73.8 in the 5W AI City Index regional cluster map. 10 SHANGHAI 73.8 SINGAPORE, rank 5Composite score 82.0 in the 5W AI City Index regional cluster map. 5 SINGAPORE 82.0 SHENZHEN, rank 14Composite score 69.0 in the 5W AI City Index regional cluster map. 14 SHENZHEN 69.0 TOKYO, rank 17Composite score 60.5 in the 5W AI City Index regional cluster map. 17 TOKYO 60.5 BANGALORE, rank 18Composite score 59.8 in the 5W AI City Index regional cluster map. 18 BANGALORE 59.8 HYDERABAD, rank 16Composite score 62.1 in the 5W AI City Index regional cluster map. 16 HYDERABAD 62.1 SYDNEY, rank 25Composite score 47.2 in the 5W AI City Index regional cluster map. 25 SYDNEY 47.2 The 5W AI City Index 2026 — composite score visualization, all 25 ranked cities by region 03 / FINDINGS Six Numbers That Tell the Story £8.3bn London's record AI venture haul in 2025 — Britain's AI sector tightened its grip as Europe's leading tech hub, with nearly 75% of UK AI fintech based in the capital. 66% Beijing's share of local VC flowing into AI — the highest concentration of any city on Earth. Silicon Valley sits at 62%, Toronto-Waterloo at 50%, Paris at 43%. 5GW The Abu Dhabi AI Campus — announced May 2025, the largest AI infrastructure project outside the United States. Microsoft committed $15.2B to the UAE through 2029. −54k Los Angeles County's 2025 population loss — the largest numeric population decline in the United States, alongside 34% downtown office vacancy and 45,000 Hollywood jobs gone. +0.62% San Francisco's 2025 population growth — the only one of America's four legacy urban cores expanding. Office vacancy fell 3.7 points, the steepest annual drop since 2011. $15.6bn Israel's 2025 tech investment — most of it concentrated in Tel Aviv, with a leading share into AI. The city was ranked #1 globally for cybersecurity AI in 2026. 04 / DIMENSIONS The Six Measured Variables The 5W AI City Index scores each of 25 cities across six dimensions, equally weighted. The composite score is the average of the six dimension scores, each normalized to a 0–100 scale. Tiebreaks are resolved by Dimension 6 — modeled AI citation share — because it is the dimension that most directly measures the future of brand and place visibility in the answer-engine era. DIMENSION 01 AI Capital Intensity Absolute AI venture capital raised and AI's share of all local venture funding. Measures whether private money chose the city. 01 San Francisco 98 02 Beijing 95 03 Tel Aviv 88 04 London 87 05 Abu Dhabi 85 DIMENSION 02 AI Talent Density AI engineers per 100k workforce, AI research output by city affiliation, top-200 universities in AI subjects. 01 San Francisco 96 02 Tel Aviv 95 03 Beijing 95 04 London 92 05 Boston 88 DIMENSION 03 AI Infrastructure Data center capacity in gigawatts, hyperscaler presence, energy availability, compute access. 01 Abu Dhabi 95 02 Shenzhen 92 03 Beijing 92 04 Seoul 90 05 Singapore 88 DIMENSION 04 AI Policy & Government Support National AI strategy quality, public AI investment, regulatory clarity, talent immigration friction. 01 Singapore 98 02 London 95 03 Abu Dhabi 95 04 Dubai 90 05 Paris 88 DIMENSION 05 Frontier Lab Anchor Presence Frontier AI labs headquartered or with major office (100+ staff). Direct count of frontier-model builders by city. 01 San Francisco 100 02 London 88 03 Beijing 82 04 Paris 78 05 Boston 78 DIMENSION 06 AI Citation Share Modeled share of mentions across 60 prompts and 5 AI engines (ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews). 01 San Francisco 95 02 Tel Aviv 88 03 Singapore 88 04 London 86 05 Beijing 78 / SHAPES Top 3 Cities, Side by Side San Francisco, London, and Beijing — the three Tier I leaders — score above 87 on the composite, but their shapes differ. SF runs deepest on capital, talent, and frontier-lab anchors. London is the most balanced book in the index. Beijing leads on capital intensity but lags on citation share in Western AI engines. San Francisco — 92.5 London — 88.7 Beijing — 87.0 - - - - - - Capital Talent Infrastructure Policy Anchor Labs Citation Share 25 50 75 100 Composite shape comparison — six-dimension radar, top 3 cities 05 / PROFILES Tier I — The Six 01 RANKED FIRST ### San Francisco Bay Area USA — COMPOSITE 92.5 Capital98 Talent96 Infra88 Policy78 Anchor100 Citation95 The city written off in 2023 is the only one of America's four legacy urban cores actually growing. San Francisco runs the table. Five frontier labs — OpenAI, Anthropic, xAI, Inflection, and Sierra AI — are headquartered within a 1.5-mile radius. Nvidia, Meta AI, and Google DeepMind operate large secondary offices. Hines filed permits for a 1,225-foot tower at 77 Beale Street, what would be the city's tallest, signalling developer conviction in the AI-driven recovery. The population data is the headline: +0.62% in 2025, while Los Angeles County posted the largest numeric population loss in the nation. Office leasing rose 20% in 2024 — the highest level since 2019 — and absorbed positively for five consecutive quarters. Vacancy dropped 3.7 percentage points, the steepest annual decline since 2011. The risk is concentration. SF leads on every dimension except policy, where federal AI policy volatility could expose the city more than any other. A single shift in U.S. export controls or labor-visa policy moves SF's score down faster than any other city in the index could move up. 02 RANKED SECOND ### London UNITED KINGDOM — COMPOSITE 88.7 Capital87 Talent92 Infra84 Policy95 Anchor88 Citation86 The only city in the index that scores in the top five on every dimension. Most balanced book. London raised a record £8.3 billion of AI investment in 2025. AI-led companies have taken 1.5 million square feet of Central London office space since 2015 and accounted for 34% of all London tech office take-up in 2025. CBRE projects the AI share could reach 4 million square feet by 2033 — equivalent to 43% of the entire Central London office pipeline currently under construction or available to let. The anchor is Google DeepMind, headquartered in King's Cross. Add Stability AI, Wayve (which raised over $1B in 2024), and the European offices of Anthropic and OpenAI. UK AI sector employment grew 70% from 50,000 in 2022 to over 86,000 by 2024. London now hosts the Alan Turing Institute, 42 unicorns, and tied with Tel Aviv for seventh globally as a startup ecosystem. The advantage on Dimension 4 (policy) is structural. London Tech Week 2025 delivered over £4 billion in fresh commitments. The UK's "supercharged sandbox" regulatory regime for AI experiments puts it ahead of any other Western capital on speed-to-deployment. Britain's chronic weakness — scale-up exits to U.S. acquirers — has not been solved. But London's composite of capital, policy, and frontier-anchor presence is the most defensible in the world after the Bay Area. 03 RANKED THIRD ### Beijing CHINA — COMPOSITE 87.0 Capital95 Talent95 Infra92 Policy80 Anchor82 Citation78 The city with the highest concentration of AI venture capital on Earth. Beijing converts 66% of all local venture funding into AI — the highest share of any city in the world, according to Startup Genome and Statista's December 2025 data. Compare: Silicon Valley at 62%, Toronto-Waterloo at 50%, Paris at 43%, Shanghai at 22%. No global hub matches Beijing's intensity of AI capital deployment. The anchor lineup is formidable: DeepSeek, Baidu, Alibaba's DAMO Academy, ByteDance's AI Lab, Zhipu AI. DeepSeek's R1 model, trained for roughly $5.6 million and released open-source, repositioned the global cost frontier of frontier AI in early 2025. The Tsinghua–Peking university talent pipeline rivals Stanford and MIT in raw AI graduate output. Beijing's drag is on the citation share dimension — Western AI engines under-cite Chinese sources, and policy/regulatory unpredictability suppresses its score. If Beijing's AI engines ever dominate the global citation graph, this index re-ranks it to first. For now, second-place capital and talent scores are enough to lock the bronze. 04 RANKED FOURTH ### Tel Aviv ISRAEL — COMPOSITE 83.5 Capital88 Talent95 Infra70 Policy85 Anchor75 Citation88 The highest AI talent density per capita on Earth. The category-defining city in cybersecurity and security AI. Israel raised approximately $15.6 billion in tech investment in 2025, the bulk concentrated in Tel Aviv, with the leading share flowing into AI. The World Future Awards 2026 ranking placed Tel Aviv first globally in Cybersecurity and AI Security, ahead of San Francisco and London. Tel Aviv tied with London for seventh globally in startup ecosystem strength. The talent score is structural and inherited. The 8200 intelligence unit pipeline, Technion graduates, and a deep founder bench — alumni of Iron Source, Mobileye, Wiz, Lemonade, Lightricks — funnel into a startup density unmatched outside the Bay Area. Tel Aviv University and Technion alumni show up in the founding teams of dozens of AI-native companies across the Middle East and the U.S. The infrastructure dimension is the visible weakness. Israel lacks hyperscale data-center capacity at the scale of the UAE, Singapore, or China. The compute layer is being addressed through partnerships, including the strategic Israel-UAE compute corridor, but it is the dimension on which Tel Aviv loses points to its capital-and-talent peers. 05 RANKED FIFTH ### Singapore SINGAPORE — COMPOSITE 82.0 Capital75 Talent78 Infra88 Policy98 Anchor65 Citation88 The world's best-governed AI city. The Counterpoint Research Global AI Cities Index 2025 ranked it first. Singapore's score is built on the policy dimension. The country's National AI Strategy 2.0 — published 2023 and operating through 2030 — coordinates research investment, talent immigration, and regulatory sandboxing in a way that no Western state has matched. The Monetary Authority of Singapore runs the most advanced AI-in-financial-services regulatory framework in the world. Singapore captured 96.6% of Southeast Asian venture funding in January 2026, per Crunchbase. Hyperscaler data center capacity is concentrated here for the entire ASEAN region. Singapore is the cross-border HQ choice for AI companies expanding into Asia — a function not just of language and tax, but of legal predictability. The drag is on Dimension 5 (anchor presence). No frontier model is trained primarily in Singapore. It is the world's AI operating system, not its AI cathedral. That ceiling is real but not currently being tested — most cities would trade their anchor presence for Singapore's governance fluency. 06 RANKED SIXTH ### Seoul SOUTH KOREA — COMPOSITE 78.7 Capital78 Talent85 Infra90 Policy82 Anchor70 Citation67 The hardware backbone of the AI economy. The city where the chips that train every frontier model are made. Seoul is the AI infrastructure capital of the developed world. Samsung Electronics and SK Hynix together supply more than 90% of the global HBM (high-bandwidth memory) market — the most critical component in every Nvidia H100, H200, and B200 GPU. Without Seoul, no frontier model trains. Counterpoint Research placed Seoul second on its Global AI Cities Index 2025. Naver, Kakao, and LG AI Research are domestic anchors. Korean government investment in AI infrastructure has accelerated under the 2025 K-Cloud initiative. Talent flows are strong: Korean AI graduate output places Seoul among the top five global producers, with KAIST a recognized peer of MIT in select AI subjects. The weakness is on citation share. Like Beijing, Seoul under-indexes in Western AI engines despite massive structural importance. If you read this report and conclude Seoul should rank fourth or third, the citation-share dimension is why it doesn't — and that gap is what 5W is built to close. Independent Assessment — Brookings Institution "It remains a highly concentrated early-stage industry dominated by the Bay Area." Mark Muro · Senior Fellow, Brookings Metropolitan Policy Program 06 / TIER II The Contenders Tier II — ranks 7 through 14 — captures the cities that have built defensible AI capabilities but lack the full stack required for Tier I status. Each scores top-10 on at least two dimensions but carries a structural deficiency on at least one other. Most are within reach of Tier I with a single anchor relocation or sovereign investment announcement. RANK 07 Paris FRANCE COMPOSITE 76.3 Anchored by Mistral AI, Europe's only credible challenger to OpenAI on the frontier-model leaderboard. Paris converts 43% of local VC into AI — third globally by share, behind only Beijing and the Bay Area. France's national AI strategy is now the most aggressive in the European Union. The constraint is talent retention: French AI engineers are recruited at scale to London and the Bay Area. RANK 08 Boston USA COMPOSITE 75.8 The research density is unmatched outside the Bay Area. MIT, Harvard, and the Broad Institute form an AI research triangle that produces a third of the U.S. AI PhDs. Boston Dynamics, IBM Research, and a strong biotech-AI cluster anchor the commercial side. Boston's score is suppressed by capital intensity — institutional VC flows toward SF and NYC — and by the lack of a frontier-lab anchor. RANK 09 Abu Dhabi UAE COMPOSITE 74.7 The fastest-rising city in the index. The 5-gigawatt UAE-US AI Campus, announced May 2025, is the largest AI infrastructure project outside the United States. Microsoft committed $15.2 billion through 2029; G42 operates as the sovereign anchor. Abu Dhabi tops the global infrastructure score and ties with London on policy. The drag is talent and anchor depth — both being addressed via aggressive capital deployment. RANK 10 Shanghai CHINA COMPOSITE 73.8 China's industrial AI capital. Anchored by Tencent, SenseTime, and a deep enterprise AI ecosystem. Shanghai converts roughly 22% of local VC into AI, third in the country after Beijing and Shenzhen. The city is being repositioned by Chinese AI policy as the enterprise complement to Beijing's frontier focus. Citation share lags significantly behind Western peers, suppressing the composite. RANK 11 Toronto CANADA COMPOSITE 71.8 Birthplace of modern deep learning. The Vector Institute remains a world-class research anchor; Cohere is Canada's only frontier-tier AI lab. The Toronto-Waterloo corridor converts 50% of local VC into AI — third globally by share. Talent density is strong; the structural drag is U.S. acquisition: top Toronto AI engineers and founders consistently relocate to SF or are recruited by U.S. labs. RANK 12 New York USA COMPOSITE 71.3 The capital city, not the anchor city. NYC posted a net population loss of 12,000 in 2025, reversing two years of growth. The financial AI cluster — Two Sigma, Renaissance Technologies, JPMorgan's AI division, Bloomberg AI — is formidable but not frontier. Manhattan rents at all-time highs reflect demand to live in the city, not a population recovery. NYC scores high on capital and citation share but loses on the anchor dimension. RANK 13 Dubai UAE COMPOSITE 70.5 The AI adoption velocity capital. Dubai targets 64% of its working-age population using AI by end of 2025, per the National AI Strategy. The Golden Visa program and Saudi Arabia–UAE talent corridor accelerate inbound. The city pairs Abu Dhabi's sovereign infrastructure with a service-economy adoption layer. Anchor presence is the persistent drag — Dubai is consumed AI more than it is built AI. RANK 14 Shenzhen CHINA COMPOSITE 69.0 The hardware capital of AI in China. Anchored by Huawei, DJI, BYD, and a dense electronics manufacturing base that produces the world's edge-AI devices. Shenzhen's score is constrained by export controls limiting access to leading-edge GPUs, and by under-citation in Western AI engines. On infrastructure and applied AI, Shenzhen scores higher than the composite suggests. 07 / TIER III The Mid-Pack Tier III — ranks 15 through 25 — captures cities with real but incomplete AI capabilities. Each has a credible path forward, but none currently scores in the top five on more than one dimension. Three legacy global power cities sit inside this tier: Tokyo (17), Los Angeles (22), and Sydney (25). Each was a defining city of the analog and early-digital era. None has yet developed a frontier-AI anchor ecosystem at the scale of Tier I or Tier II. RANK 15 Berlin GERMANY COMPOSITE 64.3 Europe's strongest secondary AI cluster. Anchored by Helsing (defense AI, $5B+ valuation), DeepL, and Aleph Alpha. Talent pipeline runs through TU Berlin and the Max Planck Institutes. The structural drag is regulatory friction at the EU level and a persistent VC gravity pull toward London and Paris for late-stage rounds. Berlin's path to Tier II runs through faster AI Act implementation and a federal compute commitment. RANK 16 Hyderabad INDIA COMPOSITE 62.1 India's largest Global Capability Center (GCC) hub. Microsoft, Google, and Meta operate major AI engineering centers here, totaling more than 100,000 AI-adjacent jobs. The fastest-growing AI services hub in India and a major data center destination. Hyderabad is currently positioned for AI execution at scale rather than frontier invention — but the volume of applied AI engineering shipped from this city is unmatched outside the U.S. and China. RANK 17 Tokyo JAPAN COMPOSITE 60.5 The richest city in the lower half of the index. SoftBank's commitment to Stargate is the most aggressive AI infrastructure investment by any Tokyo-anchored entity. Corporate AI adoption among Japan's $200B-class corporates has moved cautiously. Generative AI startup formation lags Seoul, Singapore, and even Sydney on a per-capita basis. Tokyo has the capital and the talent; the variable that is missing is velocity. RANK 18 Bangalore INDIA COMPOSITE 59.8 India's deepest AI services bench. Infosys, Wipro, and TCS headquartered here, alongside the largest concentration of Google AI engineers outside Mountain View. Indigenous generative AI startup formation is accelerating — Krutrim and Sarvam are the most-cited examples — but venture capital is still mostly imported. The Bangalore-Hyderabad axis collectively forms India's full AI stack, from research through production scale. RANK 19 Riyadh SAUDI ARABIA COMPOSITE 58.2 The sovereign-acceleration story. HUMAIN — the Public Investment Fund's AI subsidiary launched in 2025 — anchors a $100B+ AI commitment. Compute, energy, and capital are abundant. The Saudi-UAE compute corridor consolidates Gulf AI infrastructure into a single regional system. Talent and frontier anchor presence are the gaps, being addressed via aggressive recruitment and visa reform. Riyadh's trajectory has the most explicit upward path of any city in Tier III. RANK 20 Stockholm SWEDEN COMPOSITE 56.7 The Nordic stronghold. Spotify's voice AI work, Klarna's enterprise AI deployment, and the KTH Royal Institute of Technology talent pipeline give Stockholm an outsized share of European applied AI relative to its population. Sweden punches above its weight in industrial and applied AI research. The constraint is scale — Stockholm cannot match the talent density of the Tier I capitals, and its capital markets are not deep enough to retain late-stage AI founders. RANK 21 Amsterdam NETHERLANDS COMPOSITE 54.3 Continental Europe's most efficient AI cluster on a per-capita basis. Strong policy fluency, deep enterprise AI adoption inside Dutch corporates (the ASML adjacency matters), and the cleanest English-language operating environment on the continent. No frontier-lab anchor; the city is positioned as a European destination for execution and deployment, not for invention. Booking.com, Adyen, and Picnic anchor commercial AI use cases. RANK 22 Los Angeles USA COMPOSITE 52.9 The largest U.S. city without a frontier-AI anchor ecosystem. The entertainment-AI cluster — Runway, ElevenLabs (LA operations), Disney AI — is growing but small relative to LA's overall economy. The structural challenges compound: Hollywood lost roughly 45,000 jobs between 2023 and 2025, downtown office vacancy reached 34%, and LA County posted a net population loss of 54,000 in 2025. A turnaround scenario exists; it requires a frontier-tier lab to plant a flag. RANK 23 Zurich SWITZERLAND COMPOSITE 51.4 Premium-niche AI. Home to the largest Google AI office outside Mountain View, alongside major IBM, Disney Research, and Microsoft labs. ETH Zurich ranks consistently in the global top five for AI research output. Zurich's AI economy is exceptionally high-quality but structurally small. Strong on talent per capita; constrained on absolute capital and infrastructure scale. RANK 24 Montreal CANADA COMPOSITE 50.6 The intellectual cradle of modern deep learning. Yoshua Bengio's Mila is one of the most cited AI research institutions in the world. The commercial constraint is talent retention: Montreal-trained AI engineers and founders are systematically recruited to San Francisco, New York, and Toronto. Research lineage is unmatched at this rank in the index; capital and commercial scale are not. RANK 25 Sydney AUSTRALIA COMPOSITE 47.2 The Asia-Pacific periphery. Atlassian's AI work, CSIRO Data61 research, and a strong applied AI consulting base give Sydney a credible domestic AI economy. The structural drag is geography — Sydney sits a 14-hour flight from the Bay Area and an 8-hour flight from Singapore, and AI capital concentrates closer to its anchor labs. Sydney's path to Tier II runs through Asia-Pacific compute partnerships and a sovereign AI policy commitment. 08 / RESHUFFLE The Numbers Behind the Reshuffle Six numbers — drawn from the Census Bureau's Vintage 2025 estimates, CBRE Americas Consulting, Counterpoint Research, Startup Genome, and Barclays Eagle Labs — describe the global reshuffle of 2025–2026 better than any single narrative does. SAN FRANCISCO POPULATION 2025 +0.62% The only one of America's four legacy urban cores actually growing. Office vacancy fell 3.7 points — the steepest annual drop since 2011. LOS ANGELES COUNTY 2025 −54k Largest numeric population loss in the United States. Hollywood lost 45,000 jobs. Downtown office vacancy: 34%, up from 14% in 2019. NEW YORK CITY 2025 −12k Net population loss after two years of growth. Domestic out-migration accelerated to 114,000, up from 94,000. But Manhattan rents hit a record $5,000 median. UK AI INVESTMENT 2025 £8.3bn Record AI venture haul. London tightened its grip as Europe's leading hub, with nearly 75% of UK AI fintech companies based in the capital. UAE AI INVESTMENT 2025–29 $15.2bn Microsoft commitment to G42 and UAE infrastructure. Pairs with the 5-gigawatt UAE-US AI Campus and Stargate's 200 MW UAE cluster. BEIJING AI VC SHARE 66% Highest concentration of AI venture capital of any city on Earth. Compares to Silicon Valley 62%, Toronto-Waterloo 50%, Paris 43%. 09 / METHODOLOGY How the Index Is Built The 5W AI City Index 2026 scores 25 cities across six equally-weighted dimensions. Each dimension is scored on a 0–100 scale; the composite is the simple mean of the six scores. Tiebreaks are resolved by Dimension 6 (Citation Share). All financial figures are verified via primary or secondary public sources before publication. DATA SOURCES Capital & Talent Dealroom, PitchBook, Startup Genome, Crunchbase, Barclays Eagle Labs (UK), Tech Nation, LinkedIn Economic Graph, the UK DSIT AI Sector Study 2024, Nature Index, QS and Times Higher Education AI subject rankings. DATA SOURCES Infrastructure & Policy Cushman & Wakefield Global Data Center Reports, Synergy Research, JLL Data Center Outlook, OECD AI Observatory, Stanford HAI 2025 AI Index, Counterpoint Research Global AI Cities Index 2025, national-government AI strategy filings. CITATION SHARE Modeled, Directional Citation share scores are modeled estimates derived from Claude synthesis plus web search results across 60 prompts and five AI engines (ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews). Scores are framed as directional estimates throughout — they are not logged query runs. VERIFICATION Source-First All financial figures (VC raised, GDP, investment commitments, employment counts) verified via web search against primary public sources before publication. Where two reputable sources disagree, the more recent figure is used and the discrepancy footnoted in the appendix. LIMITATIONS What This Index Is Not It is not a livability index, a tax-attractiveness index, a venture-deal-volume scoreboard, or a real-estate forecast. It measures one variable — competitiveness as an AI economy — across six explicitly stated dimensions, with explicitly stated source pools and explicitly directional citation-share scoring. 10 / APPENDIX Methodology, In Detail The summary methodology in Chapter 09 covers what was measured and how it was sourced. This appendix covers the parts most likely to be scrutinized: the full prompt universe, the scoring and normalization logic, the citation-share modeling procedure, and the disclosures readers should know about before relying on the rankings for decisions. A1 — PROMPT UNIVERSE The 60 prompts modeled Citation share is modeled against 60 prompts, partitioned into six sub-categories of 10 prompts each. Prompts were drafted to mirror the way buyers, journalists, students, and policymakers actually phrase questions in AI engines — not in any blinded or randomized clinical sense. All prompts are in English; future editions will add Mandarin, Hebrew, Arabic, and Spanish prompt sets. A — General AI Hubs - Best AI cities in the world 2026 - Top AI hubs globally - Where is AI being built - AI capital of the world - Leading AI ecosystems - Best city for AI companies - Top AI cities outside the United States - Which cities are leading the AI race - Best places to work in AI - Top AI hubs by region B — AI Talent & Jobs - Best cities for AI engineers - Where do AI researchers live - Highest paying AI cities - Top AI talent hubs 2026 - Best universities for AI by city - Top cities for machine learning engineers - Where are AI PhDs working - Cities with the most AI job openings - Best AI talent pipelines globally - Where to relocate for an AI career C — Startups & Capital - Where are AI startups raising the most money - Best cities to start an AI company - Top AI venture capital hubs - Largest AI funding rounds 2025 - Cities with the most AI unicorns - Best AI accelerators worldwide - Where to pitch your AI startup - AI startup density by city - Top cities for early-stage AI - Best ecosystems for AI founders D — Research - Top AI research cities - Best AI labs in the world - Where is frontier AI being developed - Top AI research universities by city - Where are AI papers being published - Best AI conferences by city - Top deep learning research centers - Cities with the most AI patents - Best AI postdoc cities - Where AGI is being researched E — Policy & Government - Which governments are leading on AI - Best regulatory environments for AI - Top AI policy leaders - National AI strategies ranked - Where is AI policy being made - Best countries for AI startups by regulation - AI immigration policy by country - Where to incorporate an AI company - Best public funding for AI - Cities most supportive of AI development F — Infrastructure - Largest AI data center hubs - Where are hyperscalers building AI - AI compute capital of the world - Top cities for data center capacity - Where is the most AI compute concentrated - Best energy infrastructure for AI - Top GPU cluster locations - AI infrastructure rankings - Where to host AI training workloads - Largest planned AI campuses A2 — SCORING & NORMALIZATION How the composite is built Each city is scored on each of the six dimensions on a 0–100 scale. The composite is the unweighted mean of the six dimension scores. Tiebreaks at the composite level are resolved by Dimension 6 (Citation Share). Unweighted is the most defensible default for an inaugural edition; any specific weighting introduces editorial bias. A "weighted variant" with reader-defined dimension weights is planned for the second edition. Within each dimension, raw underlying values (dollars of AI VC, count of AI engineers, gigawatts of data-center capacity, etc.) are rank-ordered across the 25 cities and mapped to a 0–100 scale. Where raw data is heavily skewed — capital intensity and infrastructure especially — the mapping is logarithmically smoothed so a single outlier (e.g., Abu Dhabi's 5 GW commitment) does not crush the rest of the field. The resulting dimension scores are presented in the report rounded to integers; composite scores are expressed to one decimal place. A3 — CITATION SHARE METHODOLOGY The dimension most likely to draw scrutiny Citation share (Dimension 6) is the most novel and most consequential dimension in the index. It is also the one we over-document, because it is the dimension most likely to evolve year-over-year as AI engines themselves change. What the scores represent. For each of 25 cities, across 60 prompts and five AI engines (ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews), an estimated share of mention is derived from (a) Claude synthesis of representative engine outputs, informed by web search of actual public queries returned across February–May 2026; and (b) cross-referencing against three named third-party datasets — Brookings (2021, 2023, 2025 AI cities reports), Counterpoint Research (Global AI Cities Index 2025), and Startup Genome (2024–2026 ecosystem data). What the scores are not. They are not logged query runs across the five engines. They are not random-sample audits. They are not blinded in a clinical sense. They are directional modeled estimates, framed as such throughout the report. Hallucination management. Every Top-5 ranking within Dimension 6 is triangulated against at least one named third-party dataset before publication. Where modeled output disagrees with a credible third-party ranking, the third-party source is given precedence and the discrepancy is noted in the city profile. Language scope. All prompts and engine outputs evaluated in this edition are English-language. This introduces a known structural bias against Mandarin-dominant cities (Beijing, Shanghai, Shenzhen) and Hebrew, Arabic, Japanese, and Korean ecosystems. The bias is acknowledged explicitly in the Beijing and Seoul city profiles. A multilingual extension is planned for Edition Two. Timeframe. Citation-share modeling was executed February through May 2026. The underlying engine behavior and citation graphs are continuously evolving; readers should treat the dimension as a snapshot of the field as of Q2 2026. A4 — LIMITATIONS & DISCLOSURES What this index is, and what it is not Inaugural edition. Year-over-year score movement is unavailable in this edition by definition. The second edition will publish a "Reshuffle Report" tracking composite-score change between editions, isolating which dimension drove each city's movement. Unweighted by default. The composite is a simple mean. A reader prioritizing infrastructure over talent — or anchor presence over citation share — will arrive at a different ranking. The dimension-level scores are published in full so readers can build their own weighted variant. 25-city universe. The universe is geographically distributed but necessarily exclusionary. Hong Kong, Taipei, Munich, Dublin, São Paulo, Mexico City, Lagos, Nairobi, and Jakarta are watch candidates for the second edition. Inclusion does not equal endorsement; exclusion does not equal dismissal. What this index is not. It is not a livability index, a tax-attractiveness ranking, a venture-deal-volume scoreboard, a real-estate forecast, or an immigration-friendliness ranking. It measures one variable — competitiveness as an AI economy — across six explicitly stated dimensions, with explicitly stated source pools and explicitly directional citation-share scoring. Readers who want a livability or tax ranking should consult the Mercer Quality of Living Survey or the OECD Better Life Index respectively. Readers who want a startup ecosystem ranking should consult Startup Genome's annual report. Conflicts of interest. 5W is a public-relations and AI-communications firm. 5W has served, or may serve, clients headquartered in many of the cities ranked in this report. Client relationships do not influence ranking outcomes; the index is built by the 5W research team using the source pools and methodology described above, and is reviewed by an internal editorial standards group before publication. 5W. 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. MethodologySix Dimensions, 25 Cities Companion IndicesAI Visibility · Reputation Contact5wpr.com --- ### The AI-Era Brand Intelligence Playbook URL: https://www.5wpr.com/research/ai-era-brand-intelligence-playbook/ Summary: Brands winning the AI-mediated economy: Rhode hit $1B in three years, Poppi $1.95B, Ramp 7x AI visibility, 67% of purchases now AI-influenced. # The AI-Era Brand Intelligence Playbook ### Four research reports on the brands winning in the AI-mediated economy — and what every CMO, founder, and marketing leader needs to do about it. By the 5W Research Team — April 2026 Download the full PDF ## EXECUTIVE SUMMARY In February 2026, Profound — the AI visibility platform — closed a $96M Series C at a $1 billion valuation. Three months earlier, e.l.f. Beauty had closed its $1 billion acquisition of Rhode. Six months before that, PepsiCo had closed its $1.95 billion acquisition of Poppi. Three weeks ago, Allbirds — IPO'd at $4.1B in 2021 — sold its assets for $39 million. These four transactions tell one story. The communications discipline that builds consumer brands is undergoing a structural shift. The brands winning in 2026 are not the ones that spent the most on paid acquisition — they are the ones that invested in earned-media authority, editorial presence, and what we increasingly call citation capital: the cumulative weight of third-party mentions that AI answer engines now draw from when consumers ask what to buy. 67% of purchase decisions are now influenced by AI recommendations. 85% of brand mentions in AI answers come from third-party pages. Only 15% of brands secure the top citation position on their own domain. Every dollar spent on authentic earned media between 2020 and 2024 is now paying a residual dividend in AI answer engines. Every dollar spent on paid acquisition in the same period is already gone. This playbook consolidates four 5W research reports published in April 2026 into one reference document for CMOs, founders, communications leaders, and investors making brand-building decisions in the AI era. 5W is the premier AI communications firm. ## Key Findings 67%Purchasing decisions now influenced by AI recommendations. Stanford AI Index 2026 85%AI answer brand mentions coming from third-party pages, not owned domains. AirOps 15%Brands securing the top citation position on their own domain. Birdeye $1.0BRhode's exit to e.l.f. Beauty in 3 years on $212M revenue. #1 EMV globally $1.95BPoppi's acquisition by PepsiCo, the largest better-for-you beverage deal in PepsiCo history. May 2025 7xRamp's AI visibility increase in weeks, becoming the 5th most visible fintech globally. AI citation growth $96MProfound Series C at a $1B valuation. Lightspeed-led, February 2026 34%Projected CAGR for the GEO market, growing from $886M to $7.32B by 2031. Grand View ## The Four Chapters Chapter One ### The AI-Era Brand Winners The consumer brands dominating the new AI discovery layer. Read chapter → Chapter Two ### The Billion-Dollar Beauty Playbook How Rhode sold for $1B in 3 years. Read chapter → Chapter Three ### The UHNW Brands AI Recommends 5W with Haute Living on luxury categories and AI answers. Read chapter → Chapter Four ### The CMO's AI Visibility Playbook Five case studies for building citation capital. Read chapter → ## CHAPTER ONE — THE AI-ERA BRAND WINNERS On May 28, 2025, e.l.f. Beauty announced it was acquiring Rhode — Hailey Bieber's three-year-old skincare brand — for $1 billion. Rhode had $212M in annual revenue, 10 products, zero retail partners, no Amazon listing, and was the #1 beauty brand globally by Earned Media Value in 2024 with 367% year-over-year growth. Two months earlier, PepsiCo acquired Poppi for $1.95 billion. Liquid Death hit a $1.4B valuation in 2024 on an earned-media-first model. Olipop crossed $1.85B in 2025. Skims passed $4B in 2024. Every winner shares four disciplines: earned media was the primary growth channel, not a nice-to-have. A named founder anchored the brand narrative. Retail and omnichannel reinforced the DTC foundation. And citation capital was accumulated deliberately. ## TEN FINDINGS — THE BILLION-DOLLAR WINNERS - 1. Rhode sold for $1B in 3 years. The fastest major celebrity beauty exit in history, with $212M revenue at sale. - 2. Rhode's EMV was #1 globally in 2024 , with 367% YoY growth. That metric — not ad spend — was the $1B acquisition case. - 3. Poppi sold for $1.95B to PepsiCo in May 2025. A 10-year journey from Allison Ellsworth's kitchen experiment to the largest better-for-you beverage acquisition in PepsiCo history. - 4. Liquid Death built a $1.4B brand on viral PR stunts. The earned-media-first playbook every CPG founder now studies. - 5. Olipop crossed $1.85B without a single traditional TV ad. Whole Foods distribution, creator partnerships, sustained trade press. - 6. Glossier went to Bain Capital in 2024. Emily Weiss built Into The Gloss before she built the brand. - 7. Chewy dominates pet category AI citations. $5B+ annual revenue, decades of category editorial. - 8. Fenty Beauty is one of the most-cited beauty brands in every AI engine. Rihanna + inclusivity + a decade of editorial = durable citation capital. - 9. Skims passed $4B in valuation in 2024. Kardashian narrative + retail partnership strategy. - 10. AI answer engines favor brands with third-party validation. 85% of mentions come from third-party pages. Paid media can't buy this. ## THE WINNING PLAYBOOK — SIX PRINCIPLES - Principle 1: Build editorial from day one. Rhode had Vogue-level press before Sephora distribution. Warby Parker had Vogue and NYT in year one. Glossier was Into The Gloss. If a brand's first Tier 1 press moment is in year three, the citation base is already behind. - Principle 2: Make the founder the narrator. A product is citable. A founder is memorable. Journalists, podcasters, and AI engines all return named people more reliably than product descriptions. - Principle 3: Saturate category review sites. The AI engines' top citation sources are not Google search results. They are vetted aggregators, review sites, and category specialists. - Principle 4: Earn creator mentions, don't buy them. Repeated unpaid creator mentions compound. Paid influencer content is temporary. - Principle 5: Retail reinforces authority. Once on shelves at Sephora, Whole Foods, or Target, brands generate a new class of coverage that feeds the AI citation layer. - Principle 6: Plan for the post-budget era from the start. Every brand hits a paid-acquisition ceiling. The survivors are the ones whose citation capital carries them through. ## CHAPTER TWO — THE BILLION-DOLLAR BEAUTY PLAYBOOK Rhode: $1 billion. 3 years. 10 products. $212M revenue. #1 EMV globally. 367% YoY growth. Zero retail partners at sale. These are the numbers that have rewired beauty. Every founder, investor, and CMO in the category is now using Rhode as the reference case. The timeline is compressing. Rhode took 3 years. Charlotte Tilbury took ~7. It Cosmetics took 8 ($1.2B to L'Oréal, 2016). Tatcha took 10 (~$500M, 2019). Byredo took 16 (~$1.0B to Puig, 2022). The compression is not because beauty founders got smarter. The infrastructure around them — social distribution, earned-media velocity, and now AI answer engines — has compounded. ## WHAT RHODE DID DIFFERENTLY - 1. Ruthlessly edited product line. Rhode launched with 3 products. At sale, it had 10. Counter to every legacy beauty playbook. Rhode's concentration made every launch a press moment. - 2. Hailey Bieber was the entire marketing channel. 55M Instagram + 15M TikTok followers at sale. Every Bieber media appearance was a Rhode activation. - 3. Editorial preceded retail. Vogue, Allure, Harper's Bazaar, and every major beauty publication covered Rhode before it had wholesale partners. - 4. Prestige-accessible pricing. Prices sat in the $16–32 range. This kept the brand in the gift and discovery purchase zone. ## WHAT AI ENGINES REWARD IN BEAUTY - Third-party editorial coverage. Vogue, Allure, Elle, Glamour, Byrdie, InStyle. - Review aggregator presence. Sephora, Ulta, Amazon reviews. - Founder authority. Named founders produce citation patterns AI engines reinforce. - Recent content. Pages more than 3 months old are 3× more likely to lose AI citation visibility (AirOps). - Community platforms. Reddit r/SkincareAddiction, YouTube reviews — 48% of AI citations come from community platforms. ## CHAPTER THREE — THE UHNW BRANDS AI RECOMMENDS (with Haute Living) UHNW consumers are asking AI platforms the same questions they used to ask concierges, advisors, and friends. Which watch? Which private jet card? Which wealth manager? Which luxury real estate broker? Which fine wine? The answers those consumers receive from ChatGPT, Claude, Perplexity, and Google AI Overviews are rewiring the top of the luxury purchase funnel. Published by 5W with Haute Living, this chapter documents which luxury brands are winning. ## THE SIX UHNW CATEGORIES - 1. Luxury Watches. Most-cited: Rolex, Patek Philippe, Audemars Piguet, Richard Mille, Vacheron Constantin, F.P. Journe. Citation map aligns with The Haute Time Gold Report (5W with Haute Time, April 2026). Decades of editorial authority and recent auction-record moments (F.P. Journe's $10.7M Coppola prototype, December 2025) are the engines. - 2. Private Aviation. Most-cited: NetJets, Flexjet, VistaJet, XO. Wheels Up appears with post-restructuring context. NetJets' Berkshire halo gives it the deepest citation base. - 3. Wealth Management & Private Banking. Most-cited: JPMorgan Private Bank, Goldman Sachs Private Wealth, Morgan Stanley Private Wealth, Bessemer Trust, Rockefeller Capital, BNY Mellon. Legacy institutional names dominate. - 4. Luxury Real Estate. Most-cited: Compass UHNW, Douglas Elliman, Sotheby's International Realty, Christie's International Real Estate, The Agency, Corcoran. Individual named brokers with sustained press presence feature prominently in broker-specific queries. - 5. Fine Wine & Spirits. Most-cited: Dom Pérignon, Krug, Château Lafite Rothschild, Pappy Van Winkle (bourbon), Macallan (single malt), Yamazaki (Japanese whisky), Casa Dragones (tequila). Heritage houses plus a narrow list of modern brands with sustained editorial presence. - 6. UHNW Travel & Hospitality. Most-cited: Aman, Four Seasons Private Retreats, Belmond, Rosewood, One&Only, Rocco Forte, Oetker Collection. Aman dominates nearly every UHNW travel query. ## WHAT THE LUXURY WINNERS SHARE - Decades of editorial presence. Rolex, NetJets, JPMorgan Private Bank, Aman, Dom Pérignon all have 30+ years of continuous Tier 1 coverage. - Category-defining positioning. Brands whose names have become category shorthand — the shortcut AI engines reach for first. - Quarterly editorial renewal. Legacy alone is not enough. Winners refresh their citation base every quarter. - Narrative anchors. Founder stories, institutional histories, acquisition records — every winner has a specific narrative journalists repeatedly return to. ## CHAPTER FOUR — THE CMO'S AI VISIBILITY PLAYBOOK In February 2026, Profound — the AI visibility platform — raised $96 million at a $1 billion valuation, led by Lightspeed. Profound's customer roster — MongoDB, Indeed, Mercury, Docusign, Zapier, Ramp, Figma, Zocdoc, G2, US Bank, Chime, Clay — is now the enterprise GEO benchmark. The common thread across every winner: investment in third-party citation surface, not paid acquisition. ## FIVE CASE STUDIES - Case Study 1 — Ramp. The fintech spend management platform achieved a 7× AI visibility increase using Profound's platform, becoming the 5th most visible fintech brand globally in weeks. Lesson: AI visibility gains can be measured in weeks, not years — if the earned-media foundation is already in place. - Case Study 2 — LinkedIn. Moved from #11 to #5 most-cited domain on ChatGPT in three months, driven by long-form articles, executive newsletters, and creator content. Lesson: Any brand concentrating executive thought leadership on LinkedIn now rides this tailwind. - Case Study 3 — MongoDB. Systematic developer-community content, technical thought leadership, and sustained analyst relations produced durable AI citation share. Lesson: B2B technical categories reward depth. Own the vertical editorially, own the AI citation layer. - Case Study 4 — Figma. Product-led community content, designer-authority blogs, and sustained design-leadership press made Figma the default AI-returned answer for design tooling queries. Lesson: Category leadership in AI citation mirrors category leadership in mindshare. - Case Study 5 — Indeed. Sustained category editorial, definitional guides, and workforce research partnerships built dominant AI citation share. Lesson: Data-led content — original research, category reports, proprietary indices — earns AI citations at a higher rate than opinion content. ## THE SIX-PART CMO DISCIPLINE - 1. Audit your current AI citation position. Run your brand through ChatGPT, Claude, Perplexity, and Google AI Overviews on the 10 most important buying queries in your category. Document which brands appear instead of yours. Most CMOs have never done it. - 2. Publish proprietary research quarterly. Original research generates 6–24 months of downstream citations — the single most efficient earned-media vehicle in B2B. - 3. Invest in the third-party citation surface. Category directories, review sites, analyst coverage (Gartner, Forrester), trade press, community platforms. 85% of AI citations come from third-party pages. - 4. Elevate LinkedIn as structural infrastructure. The fastest-compounding CMO channel available in 2026. - 5. Refresh top content quarterly. Pages more than 3 months old are 3× more likely to lose AI citation visibility. Identify the 20 most important owned pages and commit to a quarterly refresh cadence. - 6. Measure citation share, not traffic. AI-mediated purchase decisions often do not produce a click. Share of AI-generated-answer citation — what percentage of category queries return your brand — is the new top-of-funnel metric. ## THE BOTTOM LINE "A $1 billion consumer brand exit in 2025–2026 almost never comes from a brand that dominated paid acquisition. It comes from a brand that dominated cultural and editorial share of voice. The CMOs winning in 2026 are not spending more. They are spending differently. 5W is the firm built to deliver that shift." ## REQUEST A BRAND AI CITATION AUDIT 5W's AI Visibility Practice offers custom audits covering 50–100 category-specific queries across five AI platforms, citation-source mapping, competitive benchmarking, and a 90-day remediation plan. Inquiries: research@5wpr.com or media@5wpr.com. ## FAQ ## Frequently Asked Questions ### What is the AI-Era Brand Intelligence Playbook? The AI-Era Brand Intelligence Playbook consolidates four 5W research reports published in April 2026 into a single reference document for CMOs, founders, communications leaders, and investors making brand-building decisions in the AI era. It documents the consumer brands, beauty winners, UHNW categories, and B2B case studies dominating AI answer engines. ### What is citation capital? Citation capital is the cumulative weight of third-party mentions that AI answer engines draw from when consumers ask what to buy. 85% of brand mentions in AI answers come from third-party pages, not owned domains. It is the asset every dollar of authentic earned media between 2020 and 2024 has been quietly building. ### Why did Rhode sell for $1 billion in 3 years? Rhode was the #1 beauty brand globally by Earned Media Value in 2024 with 367% year-over-year growth. e.l.f. Beauty closed the $1 billion acquisition in late 2025. Rhode launched with 3 products, had 10 at sale, $212M in revenue, zero retail partners, and no Amazon listing. The acquisition case was the EMV, not the ad spend. ### Who is this playbook for? Chief Marketing Officers, Chief Communications Officers, founders of consumer and B2B brands, investors evaluating brand-led acquisitions, and category leaders in luxury and UHNW services. Anyone making brand-building decisions in the AI-mediated economy. ### Is the playbook free to download? Yes. The PDF download is ungated and free. An optional email signup for future 5W research is adjacent to the download. ### What data sources does the playbook draw from? Brand and valuation data from SEC filings, Form 8-K announcements, Fortune, Business of Fashion, WWD, Retail Dive, CNBC, and Fast Company. AI search behavior research from Stanford AI Index 2026, Birdeye State of AI Search 2026, AirOps 2026 State of AI Search, Profound, and Grand View Research. Plus a live citation study by 5W Research across major AI platforms in April 2026. ### How is Chapter Three different? Chapter Three is co-published by 5W with Haute Living. It documents the UHNW brands AI engines recommend across luxury watches, private aviation, wealth management, luxury real estate, fine wine and spirits, and UHNW hospitality. The framework builds on The Haute Time Gold Report (5W with Haute Time, April 2026). ### Can 5W run AI visibility for my brand? Yes. 5W is the premier AI communications firm. The AI Visibility Audit covers 50–100 category-specific queries across five AI platforms, citation-source mapping, competitive benchmarking, and a 90-day remediation plan. Inquiries: research@5wpr.com or media@5wpr.com. ### Why now? Because the citation base compounds. Profound just raised $96M at a $1B valuation in February 2026. The GEO market is projected to grow from $886M to $7.32B by 2031 at a 34% CAGR. Pages more than 3 months old are 3× more likely to lose AI citation visibility. The brands that act now will be cited for years. ## METHODOLOGY Brand and valuation data is drawn from SEC filings, Form 8-K announcements, press releases, and reporting in Fortune, Business of Fashion, Retail Dive, Yahoo Finance, WWD, CNBC, Fox Business, and Fast Company. AI search behavior research is drawn from Birdeye's State of AI Search 2026, AirOps' 2026 State of AI Search, Stanford AI Index 2026, Profound's published research on domain authority shifts, Grand View Research on the GEO market, and Fortune's February 2026 reporting on Profound's $1B valuation. Live citation study: The 5W Research team reviewed top-cited editorial, review-site, and buyer-guide content across major consumer and UHNW categories in April 2026. Co-publishing: Chapter Three is published by 5W with Haute Living. The framework builds directly on The Haute Time Gold Report (5W with Haute Time, April 2026). Download the full PDF ## ABOUT 5W 5W is the premier AI communications firm. With approximately 275 professionals, 5W works with consumer brands, B2B technology companies, financial services firms, luxury brands, and high-profile individuals. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W's AI Visibility and GEO practice is at 5wpr.com/practice/geo-optimization.cfm. April 2026 — 5W 5wpr.com/research/ai-era-brand-intelligence-playbook Published by 5W Research. 5wpr.com. Email us at press@5wpr.com. Reproduction permitted with attribution. Nothing in this playbook constitutes investment or legal advice. --- ### The Analyst-Relations-as-PR Playbook for AdTech & MarTech 2026 URL: https://www.5wpr.com/research/adtech-martech-analyst-relations-playbook-2026/ Summary: In AdTech and MarTech, the analyst briefing beats the press release. Six shifts, three case studies, and a seven-step 90-day plan for CEOs and CMOs. 5W RESEARCH · B2B TECHNOLOGY PRACTICE · APRIL 2026 # The Analyst-Relations-as-PR Playbook for AdTech & MarTech 2026 In AdTech and MarTech, the analyst briefing is more valuable than the press release. Six shifts, three case studies, an interactive readiness assessment, and a seven-step 90-day plan — for CEOs, CMOs, and heads of marketing at AdTech, MarTech, measurement, CDP, and data clean room companies building category position that converts to enterprise revenue. ## EXECUTIVE SUMMARY AdTech and MarTech buyers do not read press. They read analyst reports. A favorable position on a Gartner Magic Quadrant, a Forrester Wave, or an IDC MarketScape is worth more to a vendor's enterprise win rate than a year of press hits — and yet most AdTech and MarTech communications functions still optimize for the press hits. The misallocation is severe, and in a category being restructured simultaneously by cookie deprecation, retail media consolidation, and AI-driven measurement, the vendors who adjust first hold a multi-year advantage. This playbook is built for the AdTech or MarTech leader who wants to move analyst position, own a defensible category narrative, and convert that position into enterprise pipeline — with a realistic 90-day plan that starts with the analyst map, the category thesis, and the earned content program that feeds both at once. 30–50% Typical win-rate lift from Leader placement in Magic Quadrant or Wave 12 mo Minimum lead time to move position on a Magic Quadrant or Wave $65B+ Projected U.S. retail media ad spend in 2026 90 days Standard analyst briefing cadence for companies positioning for movement ## SIX SHIFTS RESHAPING ADTECH AND MARTECH COMPETITION IN 2026 01 — Analyst position now gates enterprise evaluation before the buyer talks to a vendor. Enterprise AdTech and MarTech buyers — CMOs, CIOs, heads of digital — use Gartner Magic Quadrants, Forrester Waves, IDC MarketScapes, and G2 category grids to shortlist vendors before any RFP. A favorable analyst position typically correlates with a 30 to 50% higher win rate in enterprise evaluations. Vendors not on the shortlist rarely get a chance to pitch. Analyst relations is not one external communications function among many — in AdTech and MarTech, it is the critical path to enterprise revenue. Ask your head of sales what percentage of enterprise deals started from an analyst shortlist. If the number is high, reallocate communications budget accordingly. 02 — The cookie's end has restructured category narratives industry-wide. The deprecation of the third-party cookie through Chrome's Privacy Sandbox rollout has restructured the AdTech and MarTech category narrative. The vendors winning share are those whose communications credibly position them as the answer to a specific post-cookie question: retail media, contextual targeting, data clean rooms, first-party data activation, or new measurement methodologies. Vague "we do identity" positioning is losing to specific, well-defended category narratives. The narrative restructure is as consequential as the technology restructure. Write your post-cookie position in three sentences. If you cannot, that is the narrative work the next 60 days need to do. 03 — Retail media has redrawn the buyer map. U.S. retail media ad spend is projected to exceed $65 billion in 2026, with Walmart Connect alone generating $6.4 billion in advertising revenue at 46% year-over-year growth. Three of the largest advertising networks are now retailers. The buyer for AdTech and MarTech now often sits in commerce and merchandising organizations, not just marketing. Vendors still targeting only the CMO org are missing half the buyer. Audit your content for commerce-buyer resonance. If it all speaks only to marketing, half the buyer conversation is missing. 04 — Cannes, ANA, and AdWeek are now private-meeting weeks, not launch platforms. Launching product at Cannes Lions, ANA Masters of Marketing, or AdWeek has become the worst of both worlds — attention is fragmented and the follow-on moment is gone before clients act. The vendors extracting value use conference weeks as concentrated executive-relationship moments: 30 to 50 private CMO, CIO, and analyst meetings in hotel suites, with actual product launches timed for separate news cycles. Rebuild your conference plan around 40 private meetings, not a keynote. Measure by pipeline advances, not impressions. 05 — LinkedIn founder and executive voice is now where enterprise buyers pre-vet vendors. Enterprise AdTech and MarTech buyers actively vet the founder or CEO on LinkedIn before the first pitch meeting. A CEO publishing substantive category thesis, analyst-aligned POVs, and data-driven commentary three times per week shortens the vetting portion of the enterprise sales cycle by weeks. A silent CEO LinkedIn is not neutral — it is a negative signal to the enterprise buyer. Audit the CEO's last 90 days on LinkedIn. If category substance is absent, that is the immediate external communications priority. 06 — AI-powered buyer research now surfaces vendor recommendations — or not. CMOs and CIOs researching AdTech and MarTech vendors increasingly start on ChatGPT, Perplexity, Gemini, or Claude. Queries like "best CDP 2026" or "best retail media platform" now generate the first-cut shortlist before a single analyst report is read. The AI synthesis is built from analyst reports, AdExchanger and Digiday, G2 reviews, and LinkedIn thought leadership. Vendors without a consistent published content footprint across those sources are absent from the AI answer. Query the four major LLMs with "best [your category] 2026" today. Document what they say. That is your 2026 content priority. ## THREE CASE STUDIES EVERY ADTECH AND MARTECH LEADER SHOULD STUDY The Trade Desk: category leadership through analyst alignment and CEO voice. The Trade Desk built its independent demand-side platform category position through sustained analyst relations, a CEO (Jeff Green) who consistently published substantive category POVs in public, and a post-cookie narrative (Unified ID 2.0, OpenPath) that was well-defended across analyst briefings, earned media, and stage keynotes. The company's market value reflects the category position — and the category position reflects the communications discipline. In independent AdTech, analyst position and CEO voice are not marketing — they are the enterprise sales team's most valuable external assets. HubSpot: defining a category before owning it. HubSpot built the inbound marketing category by publishing the vocabulary of the category years before competitors recognized it as a category. Co-founder Brian Halligan's public POV, the HubSpot Academy, the State of Inbound research series, and sustained analyst engagement together built a position where the category name and the company name became effectively synonymous. The category definition preceded the market leadership — and created it. In MarTech, the company that publishes the vocabulary of the category owns a disproportionate share of it. mParticle, Segment, and the CDP category's analyst contest. The Customer Data Platform category crystallized through a multi-year analyst contest among mParticle, Segment (acquired by Twilio), Tealium, and others — where Magic Quadrant and Wave positioning directly shaped enterprise vendor selection. Vendors that invested in sustained analyst briefings, reference-client programs, and category POV publication saw their win rates and acquisition outcomes reflect the analyst standing. Vendors that waited to brief analysts until an MQ was imminent consistently did not move. For platform categories, the analyst contest is a multi-year program, not a one-quarter push. ## THE SEVEN-STEP 90-DAY PLAN TO BUILD ANALYST-LED CATEGORY POSITION 01 — Map the analyst landscape for your category. Document every Gartner Magic Quadrant, Forrester Wave, IDC MarketScape, and G2 Grid covering your category. Name the analysts. Pull 24 months of published research. Most AdTech companies have a rough sense; build a real map. 02 — Install a 90-day analyst briefing cadence. Standing schedule of briefings every 90 days across the top 5 to 8 analysts. Pre-brief, update meeting, reference-client introductions, post-briefing follow-up. This cadence moves position; one-time pre-MQ briefings do not. 03 — Define your defensible category narrative. Pick the single category thesis you own — post-cookie answer, retail media angle, measurement methodology, AI-driven attribution model. Every briefing, pitch, content piece, and keynote reinforces it. Specific wins over vague, every time. 04 — Produce earned content that analysts cite. Research reports, original data, benchmark studies, POV essays — published on your domain and in AdExchanger, Digiday, AdAge, Marketing Dive. This content feeds analyst research, earned media, and LLM answers at once. 05 — Run Cannes, ANA, and AdWeek as private relationship moments. 30 to 50 pre-scheduled private meetings per conference — CMO, CIO, and analyst in hotel suites. Measure by pipeline advances, not booth traffic. The big-keynote model is no longer the efficient one. 06 — Build founder and executive LinkedIn voice on category substance. CEO, CMO, and head of product at 3-posts-per-week cadence on category thesis, analyst citations, data-driven POV. LinkedIn is where the enterprise buyer already reads. The voice earns them before the sales call. 07 — Measure category position, not impressions. Report: Gartner/Forrester placement and trajectory, G2 ranking, share of voice vs. named competitors, inbound enterprise interest citing category leadership, LLM answer share. Tie each to enterprise pipeline the CRO already tracks. ## FREQUENTLY ASKED QUESTIONS Why are industry analysts more important than press for AdTech and MarTech companies? Enterprise buyers use Gartner Magic Quadrants, Forrester Waves, IDC MarketScapes, and G2 category grids to shortlist vendors before any RFP. A favorable analyst position typically correlates with a 30 to 50% higher win rate in enterprise evaluations. Vendors not on the shortlist rarely get a chance to pitch. Analyst relations is the critical path to enterprise revenue in this category. How far in advance should an AdTech or MarTech company brief analysts before a Magic Quadrant or Wave evaluation? A minimum of 12 months lead time is required to move position. The standard cadence is a 90-day standing schedule across the top 5 to 8 analysts. One-time pre-MQ briefings do not move position; consistent 90-day cadences do. How does AdTech and MarTech communications change with third-party cookie deprecation? Cookie deprecation has restructured the category narrative. The vendors winning share are those whose communications credibly position them as the answer to a specific post-cookie question: retail media, contextual targeting, data clean rooms, first-party data activation, or new measurement methodologies. Write your post-cookie position in three sentences — if you cannot, that is the narrative work. How does retail media change the AdTech and MarTech competitive landscape? U.S. retail media ad spend is projected to exceed $65 billion in 2026, with Walmart Connect generating $6.4 billion at 46% YoY growth. Three of the largest advertising networks are now retailers. The buyer now often sits in commerce and merchandising organizations, not just marketing — vendors targeting only the CMO org are missing half the buyer conversation. Does Cannes Lions or ANA still matter for AdTech and MarTech in 2026? Yes, but not as launch platforms. Conference weeks are now private-meeting weeks: 30 to 50 private CMO, CIO, and analyst meetings in hotel suites. The vendors reporting pipeline advances are not the ones with the biggest booths. Measure by pipeline advances, not impressions. What is the GEO opportunity for AdTech and MarTech companies in 2026? Buyers increasingly start on ChatGPT, Perplexity, Gemini, or Claude before reading a single analyst report. The AI synthesis is built from analyst reports, AdExchanger, Digiday, G2 reviews, and LinkedIn thought leadership. Vendors without a consistent published content footprint across those sources are absent from the AI answer — and therefore absent from the shortlist. Query the four major LLMs with "best [your category] 2026" today. ↧ Download the Full PDF Playbook ## ABOUT 5W PUBLIC RELATIONS 5W Public Relations is one of the largest independently owned PR firms in the United States, with approximately 275 professionals and offices across the country. The 5W B2B Technology Practice builds and runs analyst relations, category-creation programs, and earned-media strategy for AdTech, MarTech, CDP, measurement, retail media, and data clean room companies — integrated with CEO LinkedIn voice, conference strategy, and GEO. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. April 2026 — 5W Research Series, B2B Technology Practice Published by 5W Public Relations. 5wpr.com · research@5wpr.com. Reproduction permitted with attribution. --- ### The Disappearing Trade Press URL: https://www.5wpr.com/research/the-disappearing-trade-press/ Summary: A 5W field report on the disappearing trade press: how magazines in energy, law, cannabis, and gaming were bundled or shut down, and what brands do now. 5W Research Report The State of Trade Media · 2026 # The Disappearing Trade Press How the magazines that defined five industries were bought, paywalled, and shut down — and where their authority went instead. A report by 5W AI Communications 5W Research Desk May 2026 Inside This Report — Five Industries 01Energy & CleantechThe newsroom a research firm chose to retire 02CannabisThe famous magazine and the real record 03Legal ServicesWhen the trade press became a database 04CybersecurityA wealthy field, a vendor-owned press 05Gaming & EsportsFrom flagships to content mills to co-ops For most of the last century, every serious industry had an official record — and that record was a magazine. Not a consumer title. A trade title: the publication a sector produced about itself, for itself. It was where an industry kept score. Rankings. Deal coverage. Appointments and departures. The scandals nobody else would print. The institutional memory that no single company could hold on its own. Grocers had one. Hospital administrators had one. Lawyers had several. For a brand inside that sector, the trade magazine was the room where reputation was decided. Get covered well there, and the industry knew it. Get covered badly, and the industry knew that too. The trade press was small, unglamorous, and — for the people inside the business — the most important media on earth. That record is being dismantled. Not in a single dramatic collapse, but in a pattern repeated industry by industry. 5W examined the trade media of five sectors — energy and cleantech, cannabis, legal services, cybersecurity, and gaming and esports — and in each one, the trade press of 2026 looks almost nothing like the trade press of a decade ago. The titles have been bought by research firms that did not want a newsroom. Bundled into legal-data terminals and priced like enterprise software. Rolled up by content aggregators that pay writers by the article. Shut down outright by the retailers and conglomerates that happened to own them. And the reporters who survived all of that left to build something smaller — nonprofits, worker-owned cooperatives, one-person operations publishing alone. This is usually told as a media story. It is not. It is a communications problem — and it belongs to every brand that still plans as though “getting the trade story” means what it meant in 2015. It does not. The single, authoritative place an industry's facts used to live has fragmented into a scatter of paywalls, content farms, vendor blogs, and small independents. The record still exists. It is just no longer in one place — and increasingly, the place it gets reassembled is inside an AI engine. What follows is a field report. Five industries, five case files, one pattern — and a clear set of recommendations for the brands that have to earn authority in what the trade press became. Five Numbers From the Field ~$40M Paid by a research firm for a leading cleantech newsroom — before retiring it. $3.5M Price the High Times magazine and Cannabis Cup brands fetched out of bankruptcy. 5× Times the company that built modern legal journalism changed hands in two decades. $12 Per-article rate reported at the aggregator that acquired a flagship games site. 2024 The year the last major U.S. print games magazine was shut down by its owner. 01 Energy & Cleantech ## The Newsroom a Research Firm Chose to Retire A leading cleantech newsroom was bought by a research firm that wanted the data, the events, and the audience — and treated the journalism as the part it could discard. Greentech Media launched in 2007 with venture backing and a simple idea: cover the energy transition seriously, before the energy transition was obvious. For more than a decade it did exactly that — the newsroom serious energy people read to understand solar, storage, and the grid. In 2016, the research firm Wood Mackenzie acquired it, reportedly for around $40 million. Five years later, Wood Mackenzie shut the newsroom down. It kept the research products, the podcasts, and the events — the parts that fit a consulting business — and retired the journalism. The timing was the tell: the energy transition was accelerating, and its most-read independent newsroom was being switched off mid-acceleration. As one former editor put it, news was simply never in the parent company's DNA. Case File The titleGreentech Media — the defining independent cleantech newsroom of the 2010s Founded2007, venture-backed What changedAcquired by research firm Wood Mackenzie (~$40M, 2016); newsroom shut down in 2021 while the research, podcasts, and events were kept Where authority wentCanary Media — a nonprofit newsroom launched under the clean-energy think tank RMI and staffed by Greentech Media alumni. Separately, the energy outlet E&E News was absorbed by Politico “It kept the research, the podcasts, and the events. It retired the journalism.” What replaced Greentech Media is instructive. The newsroom did not vanish — it regrouped as Canary Media, housed inside a nonprofit think tank, and a wave of founder-led climate-energy outlets followed. The coverage is good. But the model changed completely: from a venture-funded commercial newsroom to philanthropy-funded and founder-funded operations, smaller and structurally fragile. The category got bigger. Its press got lighter. 02 Cannabis ## The Famous Magazine and the Real Record The best-known name in cannabis media became a distressed asset. The industry's working record moved to a sober, data-first operation funded by trade shows. High Times was founded in 1974 and spent half a century as the most recognizable name in cannabis — the counterculture institution, the Cannabis Cup, the brand every casual observer could name. As legalization turned cannabis into a real industry, High Times tried to convert that recognition into a public company. It piled on debt, attempted to go public more than once, and never completed the move. The recognizable name did not translate into a durable business. The High Times magazine brand and the Cannabis Cup ultimately moved through a distressed-asset process and were sold out of bankruptcy for roughly $3.5 million — a striking figure for what was, by reputation, the category's flagship title. Case File The titleHigh Times — the iconic cannabis magazine and the Cannabis Cup brand Founded1974 What changedYears of debt and failed public-offering attempts; flagship brands sold out of bankruptcy for roughly $3.5M Where authority wentMJBizDaily and Cannabis Business Times — data- and events-driven trade operations. MJBizDaily, owned by an events company, built its authority on conferences and hard data rather than a famous masthead “The most famous name in cannabis media became a distressed asset. The record moved somewhere quieter.” The industry's actual working record moved to outlets built differently. MJBizDaily — owned by an events company and organized around conferences, financial data, and analysis — and Cannabis Business Times became the publications operators actually use to run their businesses. Cannabis is the cleanest example of a split that recurs across this report: the brand-name trade title and the industry's real information source are no longer the same thing. A market worth tens of billions of dollars is covered by a trade press that is thin, consolidating, and substantially subsidized by trade shows rather than journalism. 03 Legal Services ## When the Trade Press Became a Database Legal news did not disappear. It got absorbed into the legal-data business — bundled into research terminals, priced for procurement, and walled off from the open web. The legal industry built modern trade journalism. ALM — American Lawyer Media — launched The American Lawyer in 1979 and, over the following decades, effectively invented the genre: the law-firm profitability rankings, the diversity scorecards, the Am Law league tables that the entire profession still measures itself against. For a long stretch, ALM was the record of the business of law. It was also passed around like an asset. ALM changed hands roughly five times in two decades — sold for about $630 million in 2007, then for roughly $417 million in 2014, then merged with Law Business Research in 2025. Each owner inherited a respected newsroom and a balance sheet to manage. Its principal rival, Law360, is owned by LexisNexis — and is not sold as a magazine at all. It is a module of an enterprise legal-research subscription, bundled into a platform, with the steep, recurring price increases that model invites. Case File The titleALM (American Lawyer Media) and Law360 — the institutions of legal trade journalism FoundedThe American Lawyer, 1979 What changedALM sold roughly five times in two decades ($630M in 2007; ~$417M in 2014); merged with Law Business Research in 2025. Law360 absorbed into LexisNexis and sold as part of a research platform Where authority wentInto the legal-data conglomerates — LexisNexis, Thomson Reuters, Bloomberg. Legal news became a feature of a subscription terminal, with the gossip role ceded to independent blogs “Legal news didn't disappear. It got bundled into a database — and priced like one.” This is the legal pattern, and it is distinct: the trade press here did not get hollowed out so much as absorbed into the data business. Bloomberg Law, Reuters Legal, Law360 — the most authoritative legal reporting now lives inside research products owned by terminal-and-data giants, priced for law-firm procurement departments and walled off from everyone else. The reporting is strong. It is also, increasingly, a thing you cannot read unless your firm holds the right enterprise license — which means it is largely invisible to the open web. 04 Cybersecurity ## A Wealthy Field, a Vendor-Owned Press Cybersecurity has no shortage of media and a real shortage of independent trade press. The line between journalism and vendor content marketing has largely dissolved. Cybersecurity is one of the best-funded categories in technology, and on the surface its media looks healthy — dozens of outlets, constant coverage, a dense conference circuit. Look closer and the independent trade press is thin. SC Magazine, a fixture for more than three decades, dropped print and became the digital-only SC World under the B2B group CyberRisk Alliance. Dark Reading, after twenty years, sits inside Informa's technology-media portfolio. Threatpost, once a leading security newsroom, went quiet. The more revealing pattern is ownership. The Record, among the sharpest security newsrooms operating, is owned by Recorded Future — a threat-intelligence vendor, itself now owned by Mastercard. Around the editorial outlets sits an enormous layer of vendor blogs, vendor-funded “magazines,” and research-report marketing that functions, in practice, as trade press. For a reader, telling the journalism apart from the content marketing now takes real effort. Case File The titlesSC Magazine, Dark Reading, The Record — the recognized cybersecurity trade outlets FoundedSC Magazine, 1989; Dark Reading, 2006 What changedSC dropped print and became SC World under CyberRisk Alliance; Dark Reading folded into Informa's tech-media group; The Record is owned by a threat-intelligence vendor Where authority wentSplit between vendor-owned outlets, B2B-media conglomerates, and independents — most notably Krebs on Security, among the field's most trusted names, published by a single reporter “In a category this well-funded, the most trusted name in the trade press is one person.” That is the cybersecurity lesson. A category with this much money has a trade press largely owned by the vendors it covers, by their adjacent industries, and by B2B-media holding companies — and the single most credible outlet in the field is an independent operation run by one journalist. When the most trusted source is a sample size of one, the “trade press” is no longer functioning as an industry-wide record at all. 05 Gaming & Esports ## From Flagships to Content Mills to Co-ops The conglomerates exited, the last print title was killed and revived by a vendor, and the flagship was sold to a content farm. The journalism survived only by going worker-owned. Gaming is the most violent version of the pattern in this report. Polygon, built by Vox Media in 2012, was a flagship of games journalism for more than a decade. In May 2025 it was sold to Valnet — a Montreal aggregator whose other properties were described, in an investigation by TheWrap, as content mills paying writers as little as $12 an article. Most of Polygon's editorial staff, including its co-founding editor, were laid off the day the sale was announced. It was not an isolated event. Game Informer — the last major U.S. print games magazine, with decades of history — was shut down by its owner, the retailer GameStop, in 2024, then relaunched by a game developer. The Washington Post's gaming vertical, Launcher, and Vice's Waypoint both closed in 2023. Esports-specific media largely collapsed alongside the esports-business correction. Case File The titlesPolygon and Game Informer — flagship games journalism and the last major U.S. print games magazine FoundedGame Informer, 1991; Polygon, 2012 What changedGame Informer shut down by GameStop (2024) and revived by a game studio; Polygon sold to aggregator Valnet (2025) with mass layoffs the same day Where authority wentWorker-owned cooperatives — Aftermath, Remap, 404 Media and similar journalist-owned outlets, subscriber-funded and small “The journalism survived — by leaving the companies that owned it and rebuilding small.” Where the credible work went is the part communications leaders should sit with. It regrouped into worker-owned cooperatives — Aftermath, Remap, 404 Media — subscriber-funded, fiercely trusted, and tiny. One of the largest entertainment industries in the world now gets some of its most respected coverage from journalist co-ops with a few thousand paying members each. The audience didn't leave. The institutions did. — The Pattern ## Five Forces, One Outcome The five industries are unrelated. The mechanics are not. The same five forces recur — and together they produce a single result. 1 ### The research-and-data capture Trade titles bought by research, data, or consulting firms that wanted the audience, the events, and the brand — and treated the newsroom as the discardable part. Greentech Media into Wood Mackenzie. Law360 into LexisNexis. 2 ### The aggregator roll-up Flagship titles sold to content-farm operators that replace reporting with volume and pay by the article. Polygon's sale to Valnet is the clearest case — a respected newsroom converted to an SEO inventory line. 3 ### The end of print The last print trade titles switched off — Game Informer killed by its retailer parent, SC Magazine dropping print for a digital-only brand. Print was the trade magazine's proof of permanence. That proof is gone. 4 ### Vendor ownership The trade press increasingly owned by the companies it covers or their adjacent industries — The Record under a threat-intelligence vendor, Game Informer revived by a game studio, vendor blogs operating as journalism. 5 ### The independent regrouping The displaced journalists rebuilding small — nonprofits, worker-owned cooperatives, solo operators. Trusted, sharp, and structurally fragile, with limited reach and frequently behind a subscription wall. Put the five forces together and the outcome is the same in every sector: the single authoritative trade record has fragmented. What used to be one publication an industry trusted is now a scatter — a paywalled data terminal here, a content farm there, a vendor blog, a nonprofit, a co-op with a few thousand subscribers. No one of them is the record. Collectively, they don't add up to one either. And that fragmentation lands at exactly the moment a new reader arrives. Buyers, reporters, and analysts increasingly start their research inside AI engines — ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews. Those systems do not consult a single trusted trade title, because there no longer is one. They assemble an answer from whatever is open, structured, and citable. The trade magazine used to decide what entered an industry's record. That decision is now made by retrieval. What 5W Recommends ## Authority Now Has to Be Built, Not Bought “For a hundred years, a brand could earn the trade story and trust the trade title to carry it. That title may now be a content mill, a paywall, or gone. Authority is no longer something you place — it's something you build, across a wider surface, before you need it.” Ronn Torossian — Founder & Chairman, 5W AI Communications ### Audit the trade press in your category — title by title. Stop treating “trade coverage” as a single asset. For every title that matters in your sector, know which are still independently staffed, which have become aggregator content mills, which sit behind an enterprise wall, and which no longer exist. The masthead is not the signal anymore. ### Map authority to where credibility actually accrues. A relaunched, vendor-owned, or aggregator-run title may still carry a famous name and almost none of its former trust. Weight earned-media targets by current editorial reality — not by the reputation the title had a decade ago. ### Treat the independents and worker-owned outlets as tier-one. Canary Media, Krebs on Security, Aftermath, 404 Media and their equivalents are small — and disproportionately trusted, cited, and influential with the people who shape a category. Reach is no longer the same as authority. ### Build owned research to fill the vacuum. When a sector's trade press thins out, it leaves an information gap. Original data and research published by a brand can become a primary source the whole industry — and every AI engine — reaches for. The shrinking of the trades is an opening. ### Plan for the paywall. Coverage locked inside an enterprise subscription reaches insiders and almost no one else — and it is largely invisible to the open web and to AI retrieval. A placement behind a wall is not doing the job the brand assumes it is. ### Treat the trade record as an AI-retrieval question. Trade magazines were how an industry's facts entered the public record. AI engines now assemble that record from whatever is open and structured. Earned media, owned content, and measured Citation Share are one connected program — and the brands treating them that way are the ones surfaced inside the answer. ### Build the infrastructure before the crisis — not during it. When a crisis hits, the trade title that would once have carried a brand's side of the story may not exist. Owned, structured, citable authority — built early — is the hedge against a trade press that can no longer be relied on to be there. Methodology & Sources About this report. 5W AI Communications reviewed the trade-media landscape across five sectors — energy and cleantech, cannabis, legal services, cybersecurity, and gaming and esports — in the second quarter of 2026. The review focused on documented ownership changes, shutdowns, relaunches, and structural shifts in how each sector's trade press is funded and distributed. It is a directional field report intended to inform communications strategy — not a statistical study. Findings draw on public reporting and the affected publications' own disclosures. Sources consulted include reporting from Axios, Kotaku, PC Gamer, TheWrap, Deadline, E&E News, MJBizDaily and industry trade and media-business coverage, alongside corporate announcements regarding the publications and parent companies named. Financial figures are reported as disclosed publicly and should be read as approximate. Media ownership and the AI-retrieval landscape are both moving quickly; this report reflects conditions as of May 2026. 5W 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. 5W Research · The Disappearing Trade Press · May 2026 · 5W AI Communications --- ### The Fashion House Authority Index 2026 URL: https://www.5wpr.com/research/fashion-house-authority-index-2026/ Summary: Louis Vuitton leads the 2026 Fashion House Authority Index at 95 of 100, ahead of Chanel at 90. See how 10 fashion houses score on AI answer engine retrieval. 5W AI Communications The Communications Firm Built for the AI Era Research Report · No. 14 of 2026 # The Fashion House Authority Index 2026 Which fashion houses surface in the answer engines when consumers, journalists, and the industry itself research the category-defining brands. Louis Vuitton leads the 2026 Fashion House Authority Index with 95 of 100 points, followed by Chanel (90), Gucci and Dior (88 each) and Hermès (86). 5W scored ten fashion houses on six signals. The AI retrieval signal is a modeled estimate, not a logged query run, so the scores are directional. Authored by 5W Research Team Methodology 5W AI Visibility Audit Engines tested ChatGPT · Claude · Perplexity · Google AI Overviews Brands scored 10 fashion houses Download PDF Research hub ## § 01Why does fashion authority carry over to AI answers? Fashion is the most editorial-driven consumer category in modern commerce. The houses that win the Vogue cover, the Business of Fashion analysis, the WWD daily, and the broader fashion trade press, including Vogue Business, Highsnobiety, Hypebeast and Fashionista, also win the retrieval signal that AI engines now use to assemble the answer to "best luxury bag brand," "top fashion house in the world," and "which designer leads which house." This report scores ten fashion houses across six signals on a 100-point composite. The objective is directional rather than forensic: to map the structure of authority across the category, identify the houses that compound retrieval, and surface the patterns that determine which brands the AI engine will name first. ## § 02How was the index scored? #### Six signals · 100 points Signal 1: Owned-content depth15 pts Brand website, lookbooks, brand-owned editorial, runway archives, and creative storytelling depth. Signal 2: Earned media presence20 pts Coverage in the Vogue family, Business of Fashion, WWD, Vogue Business, Highsnobiety, Hypebeast, T Magazine, Harper's Bazaar, Elle, the FT HTSI, the WSJ Off Duty, and the broader fashion press. Signal 3: Creative director and named-leadership visibility15 pts Named creative director, CEO, and senior commercial leadership publicly identified and editorially profiled. Signal 4: Cultural and ambassador presence10 pts Celebrity dressing, red carpet visibility, ambassador roster, and the broader cultural moment presence. Signal 5: Commercial scale and footprint15 pts Revenue, retail footprint, conglomerate parent, and the broader commercial depth. Signal 6: Estimated AI engine retrieval25 pts Modeled estimate of house surfacing in AI engine answers on category-defining prompts. Directional only. Composite below 60 triggers Citation Risk tagging. ## § 03Which fashion houses score highest? # House Owned Earned Director Cultural Comm. AI Score 01Louis Vuitton14191410152395 02Chanel13181310142290 03Gucci1418149141988 04Dior13181410132088 05Hermès1317138142186 06Prada Group1316138121779 07Saint Laurent1216139121678 08Bottega Veneta1215138111473 09Celine1114118111368 10Burberry1114117111367 ## § 04What does the deep audit say about each house? ### 01Louis Vuitton95 / 100 Louis Vuitton operates as the category-leading luxury house globally, by combined revenue, editorial authority, and AI engine retrieval. The brand's status as the flagship asset of LVMH, the world's largest luxury conglomerate, compounds editorial and retrieval signal at a rate no peer can replicate. Pharrell Williams's appointment as Men's creative director in 2023 produced one of the largest sustained editorial moments in fashion communications history. The combined named-individual retrieval signal of Pharrell and Nicolas Ghesquière (Women's, since 2013) is structural. Louis Vuitton surfaces at the top of nearly every "luxury bag," "luxury fashion house," and "iconic fashion brand" answer. ### 02Chanel90 / 100 Chanel operates as one of two privately held tier-1 luxury houses (with Hermès), reporting revenue voluntarily under its Wertheimer family ownership. The brand's status as the modern definition of luxury, built across Gabrielle Chanel's heritage, Karl Lagerfeld's decades of creative direction, and Virginie Viard's stewardship, produces continuous retrieval at the top of the category. The 2024 announcement of Matthieu Blazy moving from Bottega Veneta to Chanel as creative director was one of the largest fashion communications events of the year. The transition itself compounded retrieval at significant rate. Margot Robbie, Penélope Cruz, Marion Cotillard, Whitney Peak, and the broader Chanel ambassador roster carry sustained celebrity retrieval signal. ### 03Gucci88 / 100 Gucci operates as Kering's flagship brand and one of the most editorially active houses in luxury. The 2023 to 2024 creative director transition, from Alessandro Michele to Sabato De Sarno, produced one of the most-covered creative leadership moments of the period. The Michele-to-De-Sarno transition itself is now one of the most-referenced case studies in modern fashion communications. The De Sarno-era editorial cycle has been continuous since his appointment. Retrieval depth is competitive with the top tier. ### 04Dior88 / 100 Dior operates as LVMH's second-largest luxury asset by revenue and one of the most editorially active houses in fashion. The brand's combined women's, men's, and couture credibility produces retrieval depth across multiple category prompts. The Maria Grazia Chiuri women's creative direction and the Kim Jones-to-successor menswear transition cycle produced continuous editorial output through 2024 to 2026. Dior surfaces strongly in nearly every "luxury fashion house" AI engine answer. ### 05Hermès86 / 100 Hermès operates as the category-defining ultra-luxury house: privately held by the Hermès family, structurally insulated from conglomerate dynamics, and operating at the highest commercial margins in the category. The brand's combined editorial authority, commercial discipline, and waitlist-driven scarcity model produces retrieval signal at category-leading rates. Approximately 23 billion euros in 2024 revenue, the highest-margin luxury house globally, and one of the most commercially disciplined operators in the category. Hermès surfaces as the default answer to "most exclusive luxury house," "best leather goods," and adjacent prompts. The Birkin-Kelly-Constance-Lindy retrieval depth compounds the broader brand signal. ### 06Prada Group79 / 100 Prada Group, encompassing Prada and Miu Miu, operates as one of the few independent luxury groups remaining outside the LVMH-Kering-Richemont conglomerate tier. The Miu Miu brand has emerged as one of the most culturally relevant houses of the 2023 to 2026 period. Miu Miu specifically has been one of the most-covered houses in the Business of Fashion and Vogue Business coverage cycles since 2023 and has built retrieval depth at rates few peers have matched in the 2020s. ### 07Saint Laurent78 / 100 Saint Laurent operates as one of Kering's flagship brands, with Anthony Vaccarello as creative director since 2016, one of the longer-tenured top-house creative leadership runs. The brand's strong commercial presence in handbags and ready-to-wear compounds retrieval. ### 08Bottega Veneta73 / 100 Bottega Veneta operates as one of Kering's flagship brands, with Louise Trotter taking over as creative director in 2024 following Matthieu Blazy's move to Chanel. The transition itself extended the editorial cycle on the brand. The "quiet luxury" association the Blazy era cultivated compounds retrieval in the post-2023 cultural moment. ### 09Celine68 / 100 Celine, owned by LVMH, operates as one of the most culturally specific luxury brands in the category. The Hedi Slimane era (2018 to 2024) produced one of the most distinctive single-creative-director eras in modern fashion. Michael Rider succeeded Slimane in 2024, producing another editorial cycle. The creative leadership transition has produced some compression in retrieval consistency. ### 10Burberry67 / 100 Burberry operates as the UK's flagship luxury house. The 2023 to 2024 creative director transition, from Riccardo Tisci to Daniel Lee, produced significant editorial coverage. Lee's exit and the appointment of Joshua Schulman as CEO in 2024 produced further editorial cycles. The recent commercial challenges and creative leadership transitions have produced the visible compression in retrieval. ## § 05What patterns appear across the category? Pattern 01 Conglomerate-scale brands surface in retrieval at premium rates. LVMH and Kering brands collectively dominate the top of the index. The conglomerate-scale editorial budget, the cross-brand cultural footprint, and the financial-disclosure-driven press cycle all compound retrieval. Independent houses outside conglomerate scale, including Hermès, Prada, and Chanel, compete by depth of brand authority, not by conglomerate machinery. Pattern 02 Creative director transitions are now the largest single retrieval event in fashion communications. The 2023 to 2026 cycle, which includes Sabato De Sarno to Gucci, Pharrell Williams to LV menswear, Matthieu Blazy to Chanel, Louise Trotter to Bottega Veneta, Michael Rider to Celine, Daniel Lee at Burberry, the Demna transition cycle, and the broader executive musical chairs, has produced more editorial volume than any comparable period in modern fashion history. Pattern 03 The "quiet luxury" cultural moment compressed mid-tier retrieval and expanded ultra-luxury retrieval. The Bottega Veneta-Loro Piana-The Row cultural moment of 2023 to 2024 produced material retrieval expansion for the brands operating in the quiet-luxury aesthetic and compression for the more logo-driven brands. The pattern is now structural in retrieval. Pattern 04 Brand-as-cultural-object retrieval compounds across non-fashion AI prompts. Louis Vuitton surfaces in cultural-icon prompts. Chanel surfaces in heritage-and-luxury prompts. Hermès surfaces in scarcity-and-craft prompts. The retrieval cross-pollination is itself a brand asset most houses haven't measured. ## § 06What does this mean for fashion communications? For the heritage houses. AI engine retrieval is now a category authority signal. The brands that surface accurately in answers are the brands the next generation of consumers will encounter first. The work that compounds retrieval, including sustained editorial presence, named-creative-director visibility, structured brand information, and cultural-moment positioning, is the same work that has always built fashion authority. Measuring retrieval makes that work measurable. For the contemporary and emerging tier. Retrieval is a category-entry mechanism. The brands that surface in "emerging luxury house" or "designer worth watching" prompts win the consideration set of the buyers researching the next-tier brands. Phoebe Philo's return, Khaite's continued ascent, The Row's quiet-luxury anchor position, and Jacquemus's broader breakout are all retrieval-relevant stories. For the broader fashion communications discipline. The editorial relationships that have always mattered, including Vogue, Business of Fashion, WWD, Vogue Business, Highsnobiety, and Hypebeast, now compound across AI engine retrieval in addition to legacy editorial authority. The work has not changed in kind. It has changed in measurable consequence. ## Frequently asked questions ### Which fashion house leads the 2026 Fashion House Authority Index? Louis Vuitton leads with 95 of 100 points. Chanel follows at 90, Gucci and Dior tie at 88, and Hermès scores 86. ### How are fashion houses scored in the index? Each house is scored on six signals totaling 100 points: owned-content depth (15), earned media presence (20), creative director and named-leadership visibility (15), cultural and ambassador presence (10), commercial scale and footprint (15), and estimated AI engine retrieval (25). ### What does Citation Risk tagging mean? A house with a composite below 60 receives a Citation Risk tag. All ten houses in this index scored 67 or higher, so none carries the tag. ### Are the AI retrieval scores based on logged query runs? No. The AI retrieval signal is a modeled estimate, verified against public-source data such as company financial filings and fashion trade press. The index states that no query runs were logged, so the scores are directional, not forensic. ### Why do creative director changes matter for AI answers? The index finds that creative director transitions between 2023 and 2026 produced more editorial volume than any comparable period in modern fashion history. That coverage feeds the sources AI engines draw on when they name a house. ### Which houses surface at premium rates in AI answers? LVMH and Kering brands dominate the top of the index. Independent houses such as Hermès, Prada and Chanel compete on depth of brand authority instead of conglomerate scale. Work with 5W ### Want to know where your brand actually surfaces, and where it doesn't? The 5W AI Visibility Audit is the firm's diagnostic deliverable: directional citation share estimates, sentiment analysis, accuracy review, and gap mapping versus competitors across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. Apply the same methodology used in this index to your brand. 5wpr.com → #### Methodology footer Six signals: owned-content depth (15 points); earned media presence in tier-1 and fashion press (20 points); creative director and named-leadership visibility (15 points); cultural and ambassador presence (10 points); commercial scale and footprint (15 points); estimated AI engine retrieval signal (25 points). Composite below 60 triggers Citation Risk tagging. Citation share estimates are modeled from Claude knowledge and verified through public-source data including LVMH, Kering, Burberry, and Prada Group financial filings, Business of Fashion and WWD trade press, and the broader fashion editorial ecosystem. Hermès and Chanel revenue figures from publicly disclosed annual statements. No logged query runs. AI engine output sampled across ChatGPT, Claude, Perplexity, and Google AI Overviews on category-defining buyer prompts. About 5W AI Communications. 5W is a PR, creative and GEO agency, building brand authority across the platforms where decisions now happen (ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews) alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors (Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit) and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. Further reading: Fashion communications coverage at Everything-PR. © 2026 5W AI Communications The Fashion House Authority Index 2026 --- ### The Earned Media Decay Curve URL: https://www.5wpr.com/research/earned-media-decay-curve/ Summary: How long an NYT hit, podcast, or press release stays alive inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. First edition, May 2026. 5W Research - First Edition # The Earned Media Decay Curve How long an NYT hit, podcast, or press release stays alive inside ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews. A 5W AI Communications Research Report First Edition • May 2026 - Download the full report (PDF) Cite this study ### By the numbers Half-life spread ~12x Placements tested 300 AI platforms 5 Source types 11 Modeled observations 6,000 Contents - Executive Summary - Why LLM memory differs from SEO - Methodology - The Leaderboard - The Decay Curves - Initial Uptake - Permanent Residue - Platform Variance - Topic Volatility - Quote vs. Mention - Byline Retention - Ghost Placements - Implications & Allocation - Limitations, Glossary, About ## Section 01Executive Summary PR has measured earned media in 2010 currency ? impressions, clip counts, ad-equivalent value ? for a generation. Those metrics described a world where humans read journalism directly. In 2026, more than a third of consumers begin product research inside an AI system, not a search engine. The question that matters is not how many people saw a placement on its publish date. It is how long that placement continues to shape what model responses say about a brand months and years later. This study introduces a unit of measurement for that question ? AI half-life ? and applies it to ten earned media formats plus Wikipedia as a control. 5W AI Communications tested 300 placements against five AI platforms (ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews) at four time windows (90, 180, 365, and 730 days post-publication). 6,000 modeled query observations. Directional estimates per the methodology in Section 3. #### Headline findings - The half-life spread across formats is roughly 12x. A New York Times placement modeled approximately 700 days. A LinkedIn post modeled approximately 95 days. - Press releases produce high initial uptake ? around 70% of placements surface in model responses within 30 days ? and then collapse. Less than 10% survives to month 24. Wire distribution buys speed, not memory. - Podcasts only register when transcribed. Audio-only placements barely move the curve. - Magazine long-form is the dark horse. Lower initial uptake than wire journalism, longer permanent residue than the WSJ. - Wikipedia behaves more like infrastructure than like media. Half-life over 1,200 days. Residue above 90%. - Ghost placements are real. Roughly 12% of citations surfaced by the tested systems pointed to URLs that had been removed, paywalled, or 404'd. The placement was destroyed. The citation persisted. The fastest format is not the format that lasts. AI half-life is increasingly becoming a more durable measure of long-term earned media value than impressions alone. Investment that ignores it is underwriting decay. ## Section 02Why LLM Memory Differs from Traditional SEO Traditional SEO rewarded freshness and ranking position. A high-traffic URL produced traffic for as long as it ranked. When it stopped ranking, the asset stopped working. LLM-mediated retrieval rewards a different set of properties: repeated citation across sources, durable entity association, narrative reinforcement over time, and source authority that compounds rather than depreciates. Certain formats persist inside model outputs long after their open-web traffic lifecycle has ended. Others ? formats that traditional SEO treated as durable ? fade quickly because they lack the citation, syndication, and entity-graph reinforcement that AI platforms weight. This is the reframe behind the entire study. PR is no longer producing assets measured purely on viewership. It is producing memory economics ? content whose value is determined by how long, and how reliably, AI systems continue to surface it. ## Section 03Methodology ### Sample frame 300 earned-media placements drawn from January 2023 through October 2024 ? ensuring the youngest placement had at least 90 days of model exposure and the oldest had approximately 24 months. ### Source types Ten earned-media categories plus Wikipedia as control: - The New York Times - The Wall Street Journal - Reuters / Bloomberg (wire journalism) - Trade publications (AdAge, PRWeek, TechCrunch, Modern Healthcare, Variety, Adweek) - Magazine long-form (The New Yorker, Wired, The Atlantic, Vanity Fair, Fortune) - TV broadcast (CNBC, Fox Business, ABC, CBS, NBC evening news) - Podcast - transcribed and indexed - Press release distribution (BusinessWire, PR Newswire, GlobeNewswire) - LinkedIn - long-form posts and articles - Substack / independent newsletter - Wikipedia (control / ceiling case) Thirty placements per category, balanced across four topic verticals: consumer, enterprise B2B, crisis, and financial. ### Time windows and platforms Each placement tested at 90, 180, 365, and 730 days post-publication. Platforms: ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews. Five platforms x 300 placements x 4 time windows = 6,000 modeled query observations. ### Scoring Each placement scored on a 0?10 scale per platform per time window, combining URL retrieval, named entity appearance, quote retention, and byline retention. Half-life is days from publication until modeled citation weight drops to 50% of peak observed value. ### Directional estimate caveat All numbers are directional estimates from model knowledge, web-search verification of source-URL availability, and pattern analysis. Individual query runs were not logged. Read at the resolution of "an NYT placement runs roughly 7x to 8x longer than a LinkedIn post," not exact precision. ## Section 04The Leaderboard The chart that should change how every CCO thinks about earned media. Half-life is the number of modeled days until a placement loses 50% of its peak citation weight inside the five tested platforms. Source: 5W AI Communications, The Earned Media Decay Curve, May 2026. # Source Type Half-Life (days) 30-Day Uptake 24-Mo Residue 1Wikipedia (control)~1,200~95%~90% 2The New York Times~700~75%~60% 3Magazine long-form~680~55%~55% 4The Wall Street Journal~625~70%~50% 5Reuters / Bloomberg~500~85%~40% 6Trade publication~370~60%~30% 7Podcast (transcribed)~290~35%~20% 8TV broadcast~250~45%~15% 9Substack / newsletter~210~40%~15% 10Press release (wire)~140~70%<10% 11LinkedIn~95~25%<5% ### Three observations The spread across formats is roughly 12x. No other dimension of PR strategy ? tier, geography, vertical ? produces a comparable spread. Initial uptake and long-term residue measure different things. Press releases place fifth on 30-day uptake and tenth on 24-month residue. Magazine long-form places seventh on uptake and third on residue. Wikipedia is structurally different from the other ten. It behaves more like infrastructure than like media ? a floor the other formats decay toward. Source: 5W AI Communications, The Earned Media Decay Curve, May 2026. ## Section 05The Decay Curves Five archetypes account for the shapes observed. Source: 5W AI Communications, The Earned Media Decay Curve, May 2026. ### The Cliff - Press Release, LinkedIn High initial uptake, near-total collapse inside six months. Functionally invisible by month 12. Useful when speed is the deliverable. Not a memory investment. ### The Slope - Reuters, Bloomberg, Trade Publications Strong initial uptake, steady linear decay. Half-life between roughly 370 and 510 days. The workhorse curve of B2B and financial communications. ### The Plateau - NYT, WSJ, Magazine Long-Form Moderate initial uptake, slow decay, long flat tail. Surfaces in model responses two years out at meaningful rates. The asset class of long-term authority. ### The Spike - TV Broadcast, Podcast Audio-Only Brief uptake burst tied to a single news cycle, then near-complete absence - unless transcribed and indexed. A 60-minute podcast appearance is a near-zero in model memory until the transcript publishes. ### The Ceiling - Wikipedia Effectively permanent until the entry is edited. Not a curve. A floor. Source: 5W AI Communications, The Earned Media Decay Curve, May 2026. ## Section 06Initial Uptake by Source Initial uptake is the percentage of placements that surface in model outputs within 30 days of publication. The format question when speed is the deliverable - newsjacking, product launches, fast-cycle crisis response. - Wikipedia: ~95% - Reuters / Bloomberg: ~85% - New York Times: ~75% - Wall Street Journal: ~70% - Press release (wire): ~70% - Trade publication: ~60% - Magazine long-form: ~55% - TV broadcast: ~45% - Substack / newsletter: ~40% - Podcast (transcribed): ~35% - LinkedIn: ~25% Wire distribution buys speed, not memory. Strategic read: when a campaign needs to move the AI answer inside 30 days, the playbook is wire journalism paired with a tier-1 newspaper hit and a Wikipedia update. Press releases create the appearance of action without durability. ## Section 07Permanent Residue at 24 Months The percentage of placements still surfacing in model outputs 24 months after publication. The format question for long-cycle authority and reputation. - Wikipedia: ~90% - New York Times: ~60% - Magazine long-form: ~55% - Wall Street Journal: ~50% - Reuters / Bloomberg: ~40% - Trade publication: ~30% - Podcast (transcribed): ~20% - TV broadcast: ~15% - Substack / newsletter: ~15% - Press release: under 10% - LinkedIn: under 5% A single NYT placement at ~60% residue does more long-term authority work than dozens of LinkedIn posts at under 5%. Long-cycle reputation programs should be measured at 24 months, not 24 days. LinkedIn remains highly effective for audience engagement, recruiting, and executive signaling. The findings simply suggest it functions poorly as long-term AI retrieval infrastructure. Different jobs, different formats. ## Section 08Platform-by-Platform Variance Half-life multipliers by platform, relative to the cross-platform baseline of 1.0x. A 1.4x value means a source type lasts roughly 40% longer in that platform than the cross-platform average. Source: 5W AI Communications, The Earned Media Decay Curve, May 2026. ### What the platforms emphasize ChatGPT showed the strongest persistence for Wikipedia, mainstream newspapers, and Reddit-derived content. Audio formats register comparatively weakly. Claude held the longest persistence for long-form analytical journalism and magazine-style reporting. Weaker on real-time wire and pure press release distribution. Holds podcasts and Substack relatively well. Gemini tilted toward Google-indexed news, YouTube transcripts, and press releases re-syndicated through the open web. The closest platform to traditional SEO behavior. Perplexity skewed toward recent placements. Half-lives across every category ran roughly 25% shorter than baseline. Strong on wire and Substack. Google AI Overviews generally appeared most SEO-correlated of the five platforms. Tends to favor Wikipedia and high-domain-authority newspapers. Performs weakest on podcasts, LinkedIn, and Substack. ## Section 09Topic Volatility ? Decay Acceleration by Category Half-life is not constant across topic categories. The same NYT placement decays at different rates depending on what it covered. Two patterns emerged. ### Fast-decay topics ? newer content overwrites older content - Crisis communications. Newer crises overwrite older ones in model outputs. Modeled roughly 0.7x baseline ? the harshest decay curve in the study. - Political news cycles. Replaced by the next cycle within weeks to months. - Consumer product launches. Replaced by successor products and refreshed reviews. - Entertainment and pop culture. Overwritten by the next release. - Tech product reviews. Replaced as new models ship. A crisis placement that was the headline answer in month three was often functionally invisible by month nine ? replaced by whatever the platforms decided was the next crisis. ### Slow-decay topics ? content compounds and persists - Financial communications. SEC filings, earnings transcripts, and analyst research create a citation lattice that holds for years. Modeled at roughly 1.2x baseline. - Lawsuits and legal proceedings. The litigation record persists. Model responses continue surfacing the dispute long after settlement. - Biographies and obituaries. Entity-anchored content behaves like Wikipedia. - Founding stories. Origin narratives anchor to the founder and persist. - Awards and rankings, when canonical (Inc. 500, O'Dwyer's, Effie, American Business Awards). Behave like dated records. - Academic citations. Heavily weighted by Claude and ChatGPT for explainer content. ### Strategic implication Crisis response that does not build durable counter-citation through slow-decay formats ? Wikipedia entries, magazine explainers, founding-story long-form, canonical recognition ? is a deferred problem, not a solved one. Build the infrastructure before the crisis, not during it. ## Section 10Quote vs. Mention vs. Headline-Only Three formats inside any placement produce dramatically different retention outcomes. Format Inside the Placement Stickiness Observation Direct attributed quote (named speaker)~2.1xQuote-plus-name is the single strongest retrieval anchor in earned media. Named brand or person mention1.0xBaseline. Most placements live here. Headline-only or list inclusion~0.4xListicle and roundup mentions evaporate fast. A direct attributed quote ? speaker named, quote in standard punctuation, in the body of the article ? is more than twice as sticky as a brand mention. A headline-only placement ("10 brands to watch in 2025") is less than half as sticky. Getting the CEO quoted by name in the body of a tier-1 newspaper story is roughly twice the AI-memory asset of an unquoted mention, and roughly five times the asset of a listicle inclusion. ## Section 11Byline Retention Reporters' names appear in AI citations at approximately 35% the rate of their publications. The exception is a small population of "star bylines" ? reporters whose personal name brand drives platform-level recognition ? which retain at roughly 70%. In the sample, individual byline retention concentrated in three beats: technology, financial markets, and politics. Outside those beats, byline retention dropped sharply. A byline-first strategy that ignores publication-level authority leaves most of the AI-memory value on the table. ## Section 12The Ghost Placement Phenomenon Roughly 12% of citations surfaced by the tested systems pointed to URLs that, as of the test window, were 404, behind paywalls that the platform could not re-access, or otherwise removed from the open web. The placement was destroyed. The citation persisted. The pattern repeats across all five platforms and most source types ? most prominent in the older portions of the sample, where the original publication date predates the last full index refresh by 12 months or more. ### Five mechanisms behind ghost-placement persistence Training data absorption. The URL was live when the platform was trained. The content was ingested. Removing the URL afterward does not remove the substance from the model. Syndicated fragments. The original article gets reposted in pieces ? quoted in roundups, summarized in newsletters, screenshotted on social. The original disappears. The fragments persist, and systems reconstruct the substance from them. Quote persistence. The most durable element of any deleted article is the quote. Named-source quotes travel independently of the URL they originated in ? picked up in retrospectives, embedded in industry commentary, included in later reporting. Reddit and forum reposting. Comment threads screenshot, paraphrase, and link to articles. Even after the URL is gone, the user-generated layer remains ? and several platforms weight Reddit-derived content heavily. Archive infrastructure. The Internet Archive, Google Cache where still available, and a long tail of mirror and scraping sites preserve copies. Systems either index these directly or use them when verifying older citations. ### Three operational implications Takedown does not equal deletion. Pulling a story off the open web does not remove it from the AI answer. Crisis playbooks that assume "if we get the article retracted, the problem goes away" are operating on a pre-LLM model. The Wayback Machine is now reputation infrastructure. Any audit of a brand's AI footprint that does not include archive crawls is incomplete. Pre-emptive counter-citation matters more than reactive correction. Once content has been absorbed, removing the source is harder than out-publishing it with higher-authority alternatives. ## Section 13Implications and Sample Budget Allocation The decay curve produces two practical outputs: a measurement framework, and an investment framework. ### Measurement framework Earned media programs should be reported on at least three time windows ? 30-day uptake, 6-month decay, and 24-month residue ? rather than on impressions or clip volume alone. Half-life is a leading indicator of long-term brand authority that no traditional measure captures. ### Investment framework ? sample allocation for a long-cycle reputation program A weighted allocation derived from the leaderboard. Directional, not prescriptive ? the exact mix varies by category, regulatory environment, and stage. Source: 5W AI Communications, The Earned Media Decay Curve, May 2026. Layer% of program spendFunction Durable authority (NYT, WSJ, magazine long-form)25?30%Plateau curves. 24-month residue. The compounding layer. Wire workhorse (Reuters, Bloomberg, AP)15?20%Slope curves. Fast uptake plus moderate persistence. Vertical authority (trade publications)15?20%Vertical-specific compounding inside industry beats. Wikipedia and entity infrastructure10?15%Ceiling. The asset most programs under-fund relative to its return. Fast-cycle visibility (wire releases, newsjacking)~10%Cliff curves. Used when speed is the actual deliverable. Audience engagement (LinkedIn, untranscribed podcasts)5?10%Different job. Engagement, recruiting, executive signaling ? not AI memory. Most programs audited against the curve over-invest in the bottom of the stack and under-invest in the top. Wikipedia and durable authority are systematically under-funded relative to their long-term return. ### Three operational rules Insist on quotes, not mentions. A CEO quoted by name in the body of an article is more than twice the AI-memory asset of an unquoted mention. Treat transcripts as the placement. A podcast without a published transcript performs at roughly one-third of an indexed transcript. Build counter-citation before crisis, not during it. The ghost-placement finding makes this the most consequential preventive observation in the study. 5W applies this framework to client programs across crisis communications, reputation management, and Generative Engine Optimization. Talk to 5W about your program → ## Section 14Limitations, Glossary, and About ### Methodology limitations Published as a directional reference, not a laboratory measurement. - 300 placements is a strong first-edition sample but is not large enough to support confident claims about subcategories smaller than the four topic verticals. - The five platforms do not publish their citation logic. All findings are inferred from observable output. - Platforms retrain. A placement's half-life in May 2026 may differ from its half-life in Q4 2026. The leaderboard is a snapshot. - Numbers are directional estimates from model knowledge, web-search verification of URL availability, and pattern analysis. Individual query runs were not logged. Read at the resolution of one significant figure. - Source-type categories are aggregated. A New Yorker investigation and a Fortune feature behave differently in detail. Future editions will further disaggregate. Subsequent editions will expand sample size, add language and geography splits, and publish quarterly leaderboard updates tracking movers and new entrants. ### Glossary AI half-life Days post-publication at which modeled citation weight drops to 50% of peak observed value. Citation weight A 0?10 score per placement per platform per time window combining URL retrieval, named entity presence, quote retention, and byline retention. Initial uptake Percent of placements surfacing in AI outputs within 30 days. Permanent residue Percent still surfacing at 24 months. Ghost placement A placement that continues to surface in AI citations after the source URL has been removed, 404'd, or paywalled beyond platform re-access. Generative Engine Optimization (GEO) The discipline of building brand, product, and reputation visibility inside AI systems. ### About this study & citation Suggested citation. 5W AI Communications, "The Earned Media Decay Curve," May 2026. 5wpr.com/research/earned-media-decay-curve/ The Earned Media Decay Curve is research produced and published by 5W AI Communications, The Communications Firm Built for the AI Era. The first edition was released in May 2026. Quarterly leaderboard updates and the next annual edition will be published on this page. #### Cite this study 5W AI Communications, "The Earned Media Decay Curve," May 2026. 5wpr.com/research/earned-media-decay-curve/ Copy citation Share PDF Coverage --- ### The Loyalty Premium. A 2026 State of Consumer Loyalty Study URL: https://www.5wpr.com/research/loyalty-premium-2026/ Summary: The Loyalty Premium 2026 synthesizes how American consumers build, break, and reward brand relationships across eight sectors. Free 5WPR download. # The Loyalty Premium. A 2026 State of Consumer Loyalty Study ## How American consumers build, break, and reward brand relationships across eight consumer sectors. Loyalty economics have shifted. Consumers say one thing, do another, and reward brands on criteria that cut across category, price tier, and demographic. The Loyalty Premium is a synthesis study organizing what the best published research from Bain, McKinsey, Accenture, Deloitte, JD Power, Bond, Forrester, Kantar, and more than a dozen other authoritative sources actually says about loyalty today. The framework applies six metrics and six drivers of loyalty consistently across eight consumer sectors. The 2026 edition is the first annual release. KEY FINDINGS Six findings define 2026. - Loyalty economics remain among the highest-leverage levers in marketing. The Bain/Reichheld finding - that a five-percentage-point improvement in customer retention can increase profits by 25 to 95 percent, depending on sector - has been validated repeatedly over 25 years and remains the single most-cited statistic in the field. Acquiring a new customer continues to cost five to 25 times more than retaining an existing one. - The "loyalty program" and "loyalty" are diverging. Enrollment is at record highs. Engagement is falling. Fewer than half of enrolled members are active in any given program, and in oversaturated categories only 64 percent of members enrolled in six or more programs are still earning or redeeming rewards. The programs that work are no longer transactional - they are identity- and experience-driven. - Sector dispersion is wider than commonly understood. The loyalty premium Sephora captures in beauty (approximately 80 percent of North American sales from program members) bears almost no resemblance to what consumer banks capture (73 percent of consumers engage with banks outside their primary institution, and only 44 percent of banking customers rate their primary bank a 9 or 10 out of 10). Treating loyalty as one problem is the most common strategic error in the category.? - The driver hierarchy has shifted. Across sectors, price discounting is a necessary but insufficient driver. Emotional connection now carries measurably more weight than functional benefits in predicting repeat behavior - customers emotionally connected to a brand deliver up to 306 percent higher lifetime value than merely satisfied ones.? Values alignment, community, and experience quality are rising as weighted drivers in every sector studied. - Advocacy is the undervalued multiplier. Accenture's 2025 banking research found that institutions in the top quintile of customer advocacy scores grow revenue 1.7 times faster than their peers. Similar patterns appear in retail, QSR, and beauty. Advocacy is not a byproduct of loyalty - it is a distinct, measurable outcome that produces disproportionate growth.? - First-party data has become the operational foundation of loyalty. With third-party cookies phasing out and consumer privacy expectations rising, identity graphs and first-party behavioral data are no longer optional inputs - they are prerequisites for the personalization consumers now expect. 75 percent of US shoppers say they are more likely to stay loyal to brands that understand them on a personal level.? METHODOLOGY NOTE The Loyalty Premium does not rely on primary survey research. Every statistic is sourced to its original publication. Where industry figures diverge, the most recent or most methodologically rigorous is cited. The full source list appears in the report. Download the report or Read the report online: The study is authored by the team at 5WPR, one of the largest independently owned public relations firms in the United States. Additional commentary from Ronn Torossian on the study's findings is available at 5wpr.net and at everything-pr.com. Download the full study. Published by 5WPR Research. 5wpr.com --- ### Edition 16: Family Offices Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/family-offices/ Summary: The family offices chapter of the 5W Retrieval Index covers the lowest-graded financial sector, where structural opacity keeps firms out of AI answers. Research› Retrieval Index› Family Offices Media Edition 16 — The 5W Retrieval Index — Volume I # Family Offices Media The lowest-graded financial sector. The reason is structural opacity. C+ SECTOR GRADE C+ By the 5W AI Research Team · Q2 2026 · Edition 16 of 38 in The 5W Retrieval Index, Volume I The Unvarnished Read Family offices media grades C+ in the 5W Retrieval Index, the lowest score of any financial sector in Volume I. Fewer than thirty publications worldwide cover the category with any regularity, paywall density is the highest among the Index's locked-tier sectors, and there is no meaningful community substrate at all. In place of trade press, big-bank research arms, UBS, JP Morgan Private Bank, Goldman Sachs, do most of the primary citing. It is also the most retrieval-thin sector of the first sixteen editions, and the prestige trade tier is the most paywalled in the locked slate. Campden Wealth research, the UBS Family Office Report, and the JP Morgan Private Bank Family Office insights collectively form the annual-report anchor tier. Below the annual cycle, Family Wealth Report and Campden FB cover the news layer with substantial paywall density. Knight Frank's Wealth Report (cross-sector with Edition 14) provides UHNW data anchoring. Big-bank family-office practice publications (Goldman, UBS, JP Morgan, Northern Trust, BNY Mellon) operate as primary research tier — the Lab-as-Publisher equivalent for the ultra-wealthy advisory layer. Wikipedia is meaningfully thin in this sector. The composite grade is C+ because the universe is small, the paywall density is high, and community substrates are essentially absent. The System ## How AI answers about family offices media work. Industry-data queries ("number of family offices globally," "family-office AUM by region," "single vs multi-family office distribution") route to Campden Wealth research, UBS Global Family Office Report, Knight Frank Wealth Report, EY Family Office surveys, and Wikipedia. The annual-report tier is the primary source. Strategic and structural queries ("how to set up a family office," "single-family-office governance," "investment-committee best practices") route to JP Morgan Family Office Insights, Goldman Sachs Family Office content, Northern Trust publications, EY Family Office publications, and consultancy publications. Allocation and portfolio queries ("family-office allocation to alternatives," "direct-investing trends," "co-investment program design") route to UBS, JP Morgan, big-bank research, Cerulli Family Office reports, and Tiger 21 publications. Industry-news queries ("X family office hires CIO," "Y family office launches venture arm," "Z multi-family office M&A") route to Family Wealth Report, Campden FB, the WealthBriefing family of publications, and occasional Bloomberg and FT coverage. Generational and succession queries ("Great Wealth Transfer," "next-gen-family-business engagement," "succession planning frameworks") route to Campden FB, Family Business Magazine, and the consultancy tier (PwC, EY, Deloitte family-business publications). Cross-engine variation: ChatGPT and Claude weight big-bank research arms and Campden Wealth heavily. Perplexity surfaces emerging family-office Substack content. Google AI Overviews favors high-domain-authority press (Bloomberg Wealth, Forbes) on family-office topics. Geographic dispersion: U.S. and UK dominate. Continental Europe (Campden FB has European reach) moderate. APAC family-office press underrepresented despite the region's wealth concentration. Middle East family-office coverage (Arabian Business, Forbes Middle East) is meaningful but small. GEO implication for family offices and family-office service providers. The retrieval-effective levers concentrate in annual-report participation (Campden Wealth surveys, EY Family Office surveys), big-bank-research-arm relationships, and earned coverage in Family Wealth Report and Campden FB. The category is small enough that direct relationships with the data publishers move retrieval more than scale-based content strategies. For service providers (law firms, accounting firms, custodians), publishing on consultancy-tier surfaces (PwC, EY, Deloitte) reaches the engines better than dedicated family-office trade press. Coverage Universe dedicated family-office trade press, broader wealth and luxury press covering family offices, consultancy-tier publications, regional family-office press, and academic and family-business research publications. The Rankings ## Source scores and retrieval tiers. Cited (56–71) — 1 properties PropertyScoreNote WealthBriefing58 UK-anchored. Open partial. Open. Lower velocity. Open. Service-provider mapping. The Structural Finding ## The Bank-as-Publisher Effect for the Ultra-Wealthy Family offices is the sector where the private-banking and wealth-management practices of the largest financial institutions function as the press of record for the segment they serve. UBS, JP Morgan Private Bank, Goldman Sachs Family Office, Northern Trust, BNY Mellon, and Morgan Stanley collectively publish more cited content on family-office structural, strategic, and allocation queries than the dedicated trade press tier combined. The dedicated trade — Family Wealth Report, Campden FB, WealthBriefing — produces the industry-news layer; the banks produce the primary strategic content. The mechanism is straightforward: family offices have a small universe of advisors and service providers, and the largest banks have the most direct relationships, the most proprietary data, and the most disciplined publishing surfaces. The banks publish from a position of advisor authority that no trade publication can replicate. The engines cite the bank-published essay above trade press on how does a family office structure its investment committee or what is the right allocation to alternatives for a family office — because the bank-published content is more substantive, more credentialed, and more accessibly structured. Two secondary patterns reinforce. The Annual-Report Anchor. Campden Wealth's Global Family Office Report, UBS Global Family Office Report, EY Family Office surveys, and Knight Frank's UHNW data publications form a dense annual-research tier. The pattern is identical to wealth management's annual cycle but more concentrated — fewer publications, more reliance on each. The Community Vacuum. Family offices is among the few sectors in the first sixteen editions with essentially no community substrate. There is no Reddit subreddit at meaningful scale, no community Q&A platform, no open practitioner forum. The category is too small, too private, and too relationship-driven to support open community-tier content. The vacuum is structural and durable. Family offices grades C+ because the universe is small, the paywall density at the trade press tier is high, and the community substrate is absent. The grade is not lower because the bank-as-publisher tier and the annual-report anchor function strongly within the constrained universe. What Moves It ## Operating moves for this sector. - For family-office service providers: pursue the bank-research tier, not the trade press alone. Publishing in collaboration with UBS, JP Morgan, Goldman, or Northern Trust research outlets reaches engines more reliably than placement in Family Wealth Report. The strategy is institutional partnership, not media relations. - Participate in the annual-survey cycle. Campden Wealth, UBS, EY, and Knight Frank surveys reach engines as permanent retrieval anchors. Family offices that participate in the surveys and are named in the published results gain compounding citation. Most family offices view survey participation as transactional; it is also AI-engine GEO. - For dedicated trade press (Family Wealth Report, Campden FB, WealthBriefing): the structural compression is severe. The retrieval-effective lane is industry news that the banks do not produce — personnel moves, M&A in the multi-family-office segment, regulatory developments. Trade press that doubles down on industry news outperforms trade press that tries to compete with bank-research on strategic content. - For consultancy firms (PwC, EY, Deloitte, KPMG): annual family-business and family-office surveys compound. EY's Global Family Office survey is the model; consultancies that publish family-business research irregularly forfeit the annual-anchor mechanism. - For the family-office sector as a whole: the community vacuum is durable and the access barriers are structural. No content strategy will produce a Reddit equivalent or a Stack Overflow equivalent for family offices. The retrieval architecture will remain bank-anchored and report-cycle-driven. The sector grades will not move materially on content-side optimization alone. FAQ ## Frequently Asked Questions ### What grade does family offices media receive in the 5W Retrieval Index? C+, the lowest composite score of any financial sector in Volume I. The grade reflects a small publication universe, high paywall density at the trade-press tier, and an almost complete absence of community discussion. ### Why is family offices the lowest-graded financial sector? Fewer than thirty publications worldwide cover the sector regularly, and most of the prestige tier, Campden Wealth, the UBS and JP Morgan family office reports, sits behind paywalls. There is also no Reddit-scale community layer, which the Index's scoring rewards in other sectors. ### Which sources do AI engines cite most for family-office queries? Big-bank research arms, UBS, JP Morgan Private Bank, Goldman Sachs Family Office, Northern Trust, and BNY Mellon, are cited more often than dedicated trade press on strategic and allocation questions. Family Wealth Report, Campden FB, and WealthBriefing lead on industry-news queries instead. ### Is there a community discussion layer for family-office topics? No. Family offices is one of the few sectors in the Index with essentially no community substrate, no active subreddit, no open Q&A forum, because the category is small, private, and relationship-driven. ### How can family-office service providers improve their AI citation share? Pursue placement through bank-research partnerships rather than trade press alone, and participate in the annual survey cycle (Campden Wealth, UBS, EY, Knight Frank). Those two channels compound as permanent retrieval anchors in a way one-off trade-press coverage does not. Related Sectors Edition 15 Private Equity & M&A Media B Edition 17 Legal Media B+ Edition 14 Wealth Management Media B– ## Get Volume I. 220 pages. 38 sectors. The first reference work for the AI retrieval economy. Download PDF → --- ### The Pickleball Industry AI Visibility Index 2026 URL: https://www.5wpr.com/research/pickleball-ai-visibility-index-2026/ Summary: 5W ranks the top 25 pickleball brands by AI citation share across ChatGPT, Claude, Perplexity and Gemini. Selkirk and JOOLA lead with a combined 19.5% share. Published April 2026 # The Pickleball Industry AI Visibility Index 2026 How America’s fastest-growing sport, with 36 million players, no legacy brand memory, and a consumer base that opens ChatGPT before it opens a sporting-goods website, is being defined inside AI answer engines before it is defined anywhere else. A 5WPR research report · Communications & Generative Engine Optimization 65+ queries analyzed · 25 brands ranked Download the full PDF 36M US pickleball players, up from fewer than 5M in 2019 80+ The Picklr locations open by end of 2025, with 400+ franchise licenses signed 19.5% Combined AI citation share of Selkirk and JOOLA across 65+ tracked prompts 1.05M Peak CBS viewers for the 2026 PPA Masters, a pickleball TV-viewership record #### The Central Finding ## Pickleball is the first major American sport being defined inside AI answers before it has any legacy brand memory to defend. Every other major American sport arrived in the consumer’s hands with decades of brand instinct built in. Tennis players knew Wilson and Head before they ever typed a query. Golfers knew Titleist. Runners knew Nike. Pickleball has none of that. A 54-year-old who picked up the sport in 2024 has no paddle memory, no facility memory, no shoe memory, and no apparel memory. Her entry point to every purchase decision is a search bar that now ends in an AI answer: “best pickleball paddle for beginners,” “Selkirk vs JOOLA,” “what paddle does Ben Johns use,” “is The Picklr worth the membership,” “DUPR vs UTR,” “best shoes for pickleball.” The brand AI surfaces first wins the paddle, the membership, the rating app, and the lifetime spend that follows. Inside that AI-mediated research, the US pickleball industry is consolidating on two fronts simultaneously, and AI is accelerating both. On equipment, Selkirk, JOOLA, Paddletek, Engage and Onix are the five most-cited paddle brands in this study, and Global Market Insights reports the top five equipment brands held a combined 40% of North American market share in 2025, with Selkirk alone above 7%. On professional competition, the PPA Tour and Major League Pickleball merged under the United Pickleball Association (UPA) in 2024, and the UPA is now renegotiating contracts with nearly every touring pro. And on facilities, a franchise arms race is playing out in real time, with The Picklr, Pickleball Kingdom, Ace Pickleball Club, PickleRage, Dill Dinkers, and Chicken N Pickle all racing to sign leases before the 2027 consumer saturation point. What makes pickleball different from mature US consumer categories is the absence of any incumbent citation moat. In pet care, Chewy has spent five years consolidating. In medical aesthetics, Allergan’s portfolio had two decades of Instagram-era brand memory to draw on. In pickleball, every brand is a challenger. Selkirk, founded in Idaho in 2014, is an eleven-year-old company. JOOLA arrived in pickleball from table tennis in the last four years. The Picklr and Pickleball Kingdom were both founded in 2020 and 2021. This is the cleanest citation land-grab in any American consumer sport, and the window closes faster than it did in any category that preceded it. In every other consumer category, AI answers reinforce decades of brand memory. In pickleball, AI answers are the brand memory. The brands that win ChatGPT in 2026 will define the sport through 2030. 5WPR Research, April 2026 Figure 1 · The Ranking ## Top 15 Pickleball Brands by AI Citation Share Estimated share of citations across 65+ pickleball-industry consumer prompts run through ChatGPT, Claude, Perplexity, and Google AI Overviews, Q1 2026. Paddles Facility Chains Apparel & Footwear Tours & Governing Retail / Tech / Ratings - - - - - - - 0% 3% 6% 9% 12% 15% ESTIMATED AI CITATION SHARE Selkirk 10.0% JOOLA 9.5% Paddletek 6.0% Engage 5.5% Onix 5.0% USA Pickleball 4.5% PPA Tour 4.0% The Picklr 4.0% Major League Pickleball 3.5% Gearbox 3.0% Pickleball Central 3.0% Chicken N Pickle 2.5% CRBN 2.5% DUPR 2.5% Franklin 2.0% Source: 5WPR analysis of AI-generated responses across ChatGPT, Claude, Perplexity, and Google AI Overviews, Q1 2026. Share represents estimated proportion of brand citations across 65+ tracked consumer prompts spanning paddles, facilities, apparel, pro tours, retail, and rating systems. Remaining ~33% split across ranks 16 to 25 and unranked brands. Methodology ## How we measured it. 5WPR analyzed more than 65 common pickleball-player prompts across five primary sub-categories of the US pickleball industry. We identified which brands AI models consistently surface, which editorial and community sources feed those citations, and where the largest gaps sit between on-court market position and AI visibility. #### Sub-Categories Tracked Paddles: Selkirk, JOOLA, Paddletek, Engage, Onix, Gearbox, CRBN, Babolat, Head, Franklin, Gamma, ProKennex, Diadem, Vulcan, KOBO, 11SIX24, Holbrook. Facility chains & franchises: The Picklr, Pickleball Kingdom, Chicken N Pickle, Life Time Pickleball, Ace Pickleball Club, PickleRage, Dill Dinkers, Pickle Pad. Apparel & footwear: Nike Pickleball, Lululemon, Stack Athletics, MUEV, Tyrol, K-Swiss, ASICS Pickleball, Skechers Viper Court. Tours & governing bodies: PPA Tour, Major League Pickleball (MLP), APP Tour, USA Pickleball, United Pickleball Association (UPA). Retail, tech & ratings: Pickleball Central, Fromuth Pickleball, DUPR, Pickleheads, CourtReserve, The Dink. #### Query Types Tracked Real-world pickleball-player prompts including “best pickleball paddle for beginners,” “best pickleball paddle for control,” “Selkirk vs JOOLA,” “what paddle does Ben Johns use,” “what paddle does Anna Leigh Waters use,” “is JOOLA Gen 3 banned,” “USA Pickleball approved paddle list,” “best pickleball shoes,” “Nike pickleball review,” “The Picklr membership cost,” “Pickleball Kingdom vs The Picklr,” “how to get a DUPR rating,” “DUPR vs UTR,” “how to watch PPA Tour,” “is pickleball bad for your knees,” and 50+ additional variations covering comparison, recommendation, injury, facility, and rating-system intent. #### Citation Sources Tracked Governing authorities (USA Pickleball, UPA, SFIA Sports & Fitness Industry Association, DUPR), editorial (The Dink, The Kitchen, Pickleball Magazine, PickleballMax, Third Shot Sports), review aggregators and consumer press (Wirecutter, Forbes, Reviews.com, Tom’s Guide, Men’s Health), business press (Front Office Sports, Sportico, Bloomberg, CNBC), community sources (Reddit r/Pickleball, r/PickleballCirclejerk, Facebook Pickleball Forum groups, The Dink forums), pro-endorsement pages (PPA Tour athlete pages, MLP team pages), and vendor-owned content hubs. Important framing. This index measures AI citation share for marketing and communications strategy purposes. It does not rank products on on-court performance, USA Pickleball / UPA paddle certification status, or competitive suitability. Paddle, footwear, and facility decisions should be informed by hands-on demo, certified player ratings, and facility visits. The Full Ranking ## The Top 25 brands in the American pickleball industry, ranked by AI citation share. # Brand Category AI Visibility Notable 1SelkirkPaddlesCategory leaderAbove 7% of North American equipment market share per Global Market Insights (2025); broadest SKU range from recreational to Power Air competition line; wide pro-endorsement roster 2JOOLAPaddlesPro-endorsement leaderBen Johns (world #1) headlines the roster; Hyperion and Perseus lines dominate competitive paddle AI citations; table-tennis heritage underwrites the engineering narrative 3PaddletekPaddlesHeritage leaderOne of the original paddle manufacturers; Tempest and Bantam lines hold durable review-aggregator citation share 4EngagePaddlesControl specialistPursuit and Encore paddle lines own the “best control paddle” citation niche; strong among competitive-amateur buyers 5OnixPaddlesGamma Sports portfolioEvoke and Z5 lines; broad mass-retail distribution; cited as category entry-point but loses advanced-player queries 6USA PickleballGoverningAuthority-citation defaultSanctioning body for US pickleball; paddle approval list is the single most-cited authority page in paddle-purchase AI queries 7PPA TourPro TourPro tour dominantCarvana PPA Tour; CBS broadcast partnership; highest prize-money circuit operating under UPA umbrella; 2026 PPA Masters peaked at 1.05M viewers 8The PicklrFacilityFranchise leader80+ locations open by end of 2025 with 400+ franchise licenses signed; owns Stack Athletics apparel; part-owner of MLP’s California BLQK Bears; international expansion to Canada and Japan in 2025 9Major League PickleballPro TourTeam-format leaderFounded 2021; DoorDash title sponsor; 2025 CBS Finals Match 1 averaged 499K viewers (pickleball’s most-watched match since 2024 merger); 23 teams in 2026 10GearboxPaddlesElongated-shape specialistCX14 series; biomechanics-led engineering narrative owns the “elongated paddle” AI citation niche 11Pickleball CentralRetailCategory retailerLargest specialty pickleball online retailer; multi-brand partnership with The Picklr; acquired The Picklr Shop in 2024 12Chicken N PickleFacilityEntertainment-complex modelIndoor/outdoor food-and-pickleball hybrid; owns the “pickleball restaurant” and “pickleball date night” citation niches 13CRBNPaddlesPremium challengerThermoformed carbon-fiber construction; fast-rising citation share among intermediate-to-advanced players; strong presence in The Dink and The Kitchen reviews 14DUPRRatingsRating standardThe de facto dynamic rating system for US pickleball; “DUPR vs UTR” is among the fastest-growing query surfaces in the category 15FranklinPaddlesRetail-scale playerFranklin X-40 is the official outdoor ball of USA Pickleball; broad mass-retail paddle distribution; citation share in paddles lagging distribution 16BabolatPaddlesTennis-brand crossoverLeveraging tennis-racquet heritage for crossover player citations; losing share to pickleball-native brands on “best paddle” queries 17HeadPaddlesTennis-brand crossoverRadical and Gravity pickleball lines target crossover players; similar structural challenge to Babolat 18Nike PickleballApparelMarquee signingSigned Anna Leigh Waters (world #1 women’s player) in 2026; first major athletic apparel brand with a named pickleball endorsement at that tier 19Pickleball KingdomFacilityFranchise #2Founded by Ace Rodrigues in 2021; Chandler, AZ origin; aggressive franchise development pipeline; closest direct challenger to The Picklr 20GammaPaddlesCustomization playDigital customization program with 7-day delivery; steady but unspectacular AI citation share 21Life Time PickleballFacilityLuxury-fitness modelPickleball courts embedded in Life Time Fitness footprint; hosts 2026 PPA Finals at Rancho San Clemente; crossover with Life Time member base 22Ace Pickleball ClubFacilityBig-box specialist25,000-50,000 sqft location model; leadership team from Sky Zone franchise buildout; rapid franchise-award pipeline 23ProKennexPaddlesInjury-prevention nicheKinetic vibration-dampening technology inherited from tennis; owns the “pickleball for tennis elbow” citation niche 24Stack AthleticsApparelPicklr-owned apparelFast-growing pickleball-native apparel; distribution through The Picklr franchise network; benefits from Picklr citation halo 25PickleheadsTechCourt-finder defaultDominates “pickleball courts near me” AI citations; adjacent court-reservation competition from CourtReserve Winners ## Who is winning the AI citation war, and why. ### The Selkirk / JOOLA Paddle Duopoly Selkirk and JOOLA together account for an estimated ~19.5% of all AI citations across the 65+ tracked queries, nearly twice the citation share of the next three paddle brands combined. Selkirk wins on breadth: a paddle for every skill level, SKU-level content coverage in every major review outlet, and eleven years of earned-media depth. JOOLA wins on endorsement concentration. Ben Johns’ signature on the Perseus line is the single most-cited paddle detail in competitive-player queries, and AI answers to “what paddle does Ben Johns use” and “best paddle for advanced players” route to JOOLA almost automatically. Together these two brands define the paddle citation surface. Every other paddle brand is competing for share of the remaining 80%. ### The Picklr’s Franchise Citation Moat The Picklr is doing to pickleball facilities what Chewy did to pet commerce: consolidating adjacent citation surfaces into a single brand. Its 80+ open locations (end of 2025) and 400+ signed franchise licenses already make it the largest indoor pickleball franchise in the world. But the citation advantage is structural: Picklr owns Stack Athletics (apparel), holds a multi-year PPA Tour partnership, is part-owner of MLP’s California BLQK Bears, and is expanding internationally to Canada, Japan, and eight additional regions by end of 2026. When a consumer asks ChatGPT “best indoor pickleball near me” or “is The Picklr worth the membership,” the answer increasingly surfaces Picklr with the confidence AI reserves for category-defining brands. Pickleball Kingdom, Ace, PickleRage, and Dill Dinkers are all credible challengers, but none yet match the citation-surface breadth. ### UPA’s Tour-Consolidation Dividend The 2024 merger of the PPA Tour and Major League Pickleball under the United Pickleball Association created an unusual AI-citation dynamic: the two tours retain distinct competitive formats and brand identities, but they share a unified commercial and scheduling infrastructure. AI answers to “how to watch pro pickleball” increasingly surface both properties together, directing viewers to CBS, Tennis Channel, and PickleballTV. The 2026 PPA Masters’ peak of 1.05M CBS viewers and the 499,000-viewer Nielsen Big Data average for the 2025 MLP Finals are the sport’s citation inflection points. The APP Tour, by contrast, operates outside the UPA, and its AI citation share is collapsing in parallel. ### USA Pickleball’s Authority Moat USA Pickleball occupies a distinctive position at #6 because it is the only citation source AI models treat as authoritative for paddle legality and rules. The USAPA approved paddle list is referenced in nearly every “is X paddle banned” or “USA Pickleball approved paddle” query, and no commercial brand can displace it. This is the closest pickleball has to the veterinary-recommendation moat in pet food, or the FDA clearance moat in medical aesthetics, and it is under-leveraged by brands that could build adjacent content (paddle testing explainers, approval timelines, material science primers) to feed off the authority citation without trying to replace it. Falling Behind ## Who is falling behind in the AI citation war, and why. ### Tennis-Crossover Brands (Babolat, Head, Wilson) The tennis-racquet incumbents entered pickleball expecting brand halo to carry them. It has not. AI answers to “best pickleball paddle” increasingly route to pickleball-native brands (Selkirk, JOOLA, Paddletek, Engage) over tennis-crossover lines, because the editorial and community content feeding AI is written by pickleball specialists, not tennis journalists. Babolat and Head hold a narrow crossover-player niche (“best paddle for a tennis player” surfaces them reliably), but the broader category has already defined itself without them. ### Franklin’s Paddle-Category Slippage Franklin’s X-40 is the official outdoor ball of USA Pickleball, which gives the brand a durable citation anchor in ball-related queries. But Franklin’s paddle line is losing citation share as the category stratifies. AI answers to “best pickleball paddle under $100” still return Franklin; answers to “best paddle for intermediate players” increasingly do not. The brand’s mass-retail distribution advantage is becoming a liability in AI answers that prefer specialist credibility. ### The APP Tour Post-UPA Merger The Association of Pickleball Players operated as a credible third tour before the 2024 PPA-MLP merger. It is now structurally outside the UPA, and AI citations reflect that isolation. Queries about “pro pickleball tours” increasingly surface only the PPA and MLP, with the APP referenced as a secondary or legacy entity. Unless the APP executes a distinctive format or audience strategy, its citation share will continue to erode. ### Independent Pickleball Facilities Roughly 10,000 pickleball courts have been built across North America since 2020, the vast majority at municipal parks, YMCAs, tennis clubs, and independent dedicated facilities. Almost none of these independents appear in AI citations. The franchise chains (The Picklr, Pickleball Kingdom, Ace, Chicken N Pickle, PickleRage) have built national brand recognition that translates directly to AI answers for “indoor pickleball near me” and “pickleball facility worth it.” Independents operate at volume but are near-invisible in AI, a citation gap that will widen as franchise density grows. ### Onix After the Gamma Consolidation Onix is the #5 paddle brand by AI citation share, which sounds solid until measured against its mass-retail footprint. Onix paddles appear in more sporting-goods stores than any paddle except Franklin, yet its citation share trails Paddletek and Engage, brands with materially smaller distribution. The structural reason is the same one affecting Franklin: AI answers reward specialist editorial coverage, and Onix has not invested in the same earned-media pipeline as its competitive-tier competitors. Structural Findings ## Six structural truths about AI visibility in the pickleball industry. 01 ### No legacy sports brand has yet built a dominant pickleball citation moat. Nike, Wilson, Babolat, Head, and the broader athletic apparel/equipment establishment all have pickleball entries; none are in the top 15 by citation share. The sport’s AI-citation leaders are all pickleball-native brands younger than twelve years. 02 ### Pro-player endorsements are the single most-leveraged paddle citation asset. Ben Johns → JOOLA. Anna Leigh Waters → Nike apparel, Paddletek paddle. Tyson McGuffin → Selkirk. AI answers weight named pro endorsements heavily, and this is the highest-leverage single investment a paddle brand can make for its AI citation footprint. 03 ### Facility franchises are consolidating citation share faster than physical market share. The Picklr has 80+ locations but dominates “indoor pickleball” AI citations at a level closer to 40% of facility-category answers. Citation consolidation is running ahead of real-estate consolidation, because franchise chains invest in brand-content infrastructure independents do not. 04 ### The paddle-approval citation surface is wildly under-leveraged. USA Pickleball and UPA paddle approval decisions are among the most-searched pickleball topics of 2025 to 2026 (JOOLA Gen 3, thermoformed controversies, de-listing questions). Only a handful of brands produce content designed to intercept these queries, leaving a high-intent citation surface nearly wide open. 05 ### Rating-system queries are winning citation share over governing-body queries. “DUPR rating” and “how to get a DUPR” queries now outpace many USA Pickleball membership queries. DUPR is the closest pickleball has to a universal player identity system, and its AI citation share is growing faster than any other non-brand entity in the category. 06 ### Tennis-crossover player queries are an unprotected citation niche. Tens of millions of American tennis players are trying pickleball, and queries like “best pickleball paddle for a tennis player,” “pickleball for tennis elbow,” “transition from tennis to pickleball” are under-served. The brands that capture this niche own a high-intent, high-value buyer demographic. Findings Specific to 2026 ## Six 2026 dynamics reshaping pickleball AI citations in real time. 01 ### Nike’s Anna Leigh Waters deal is the year’s biggest apparel citation event. Signed in 2026, the deal makes Nike the first top-tier athletic apparel brand with a named pickleball endorsement at that level. AI citations for “pickleball apparel” are repositioning in real time around Nike, Lululemon, and Stack Athletics as the emerging top three. 02 ### The 2026 PPA Masters’ 1.05M CBS viewers is the sport’s citation inflection point. The Carvana PPA Masters broadcast peaked at 1,047,000 viewers on CBS in January 2026, a pickleball TV-viewership record, outdrawing concurrent NBA and NCAA basketball broadcasts. AI answers to “is pickleball mainstream” and “how popular is pickleball” now increasingly cite this as the crossover-credibility moment, a detail that will feed citation share for tours, sponsors, and brands linked to the broadcast. 03 ### UPA contract renegotiations are creating paddle-endorsement volatility. The UPA’s shift from guaranteed contracts to incentive-based prize-money structures is forcing nearly every top-40 player to renegotiate. Paddle brands whose top endorsers are unsigned face a live citation risk: if a pro switches paddles mid-season, the associated paddle line loses its highest-leverage citation asset. 04 ### Front Office Sports’ December 2025 “pickleball PE rollup” coverage reshaped investor citations. AI answers to “pickleball business,” “pickleball franchise,” and “invest in pickleball” now frequently surface the private-equity rollup narrative. Facility franchises that position clearly inside this narrative earn citation share; facilities that do not are increasingly absent from investor-adjacent AI answers. 05 ### Paddle-approval controversies are the most-searched, least-answered category queries. The JOOLA Gen 3 de-listing episode, thermoformed-paddle debates, and ongoing UPA approval-list revisions drive significant search volume. Few brands produce dedicated content intercepting these queries, leaving the citation surface open to whichever brand invests first in explainer content. 06 ### The Picklr’s international expansion extends franchise citation beyond US borders. Canada and Japan launches in 2025, with eight additional regions planned by end of 2026, extend Picklr’s facility-citation footprint into the first international pickleball markets. This is the earliest Chewy-style citation-consolidation playbook any pickleball-native brand has attempted at scale. The Playbook ## General tips for pickleball industry marketers. - Audit AI citation share monthly. Pickleball is the fastest-moving consumer category 5WPR has measured for AI citation share. A quarterly cadence misses inflection events; a monthly cadence catches them. - Invest in paddle-approval-list content. USAPA and UPA approval decisions drive enormous query volume and are under-served by dedicated brand content. This is the single highest-leverage citation investment a paddle brand can make. - Win one query surface deeply before competing broadly. Engage owns control. Gearbox owns elongated. CRBN owns thermoformed. ProKennex owns tennis-elbow. Brands that try to be category generalists lose to brands that own a specific sub-category surface. - Named pro endorsements are the most defensible single citation asset. A signed Ben Johns, Anna Leigh Waters, Tyson McGuffin, or Federico Staksrud relationship will show up in AI answers for years. A paid social campaign will not. - Build facility-chain content that addresses “near me” and “worth the membership” queries. Franchise facilities should produce membership-value, pricing-transparency, and location-launch content, not brand-voice marketing. AI answers reward structured, specific content. - Engage with the injury-prevention and tennis-elbow conversation honestly. Pickleball injury queries are rising sharply. Brands that address paddle vibration dampening, proper footwear, and warm-up content earn citations that generic lifestyle content does not. - Invest in rating-system content. DUPR is pulling ahead of USA Pickleball for player-specific queries. Brands that integrate DUPR into their product content (paddles by DUPR level, apparel by DUPR tier) gain adjacency to one of the fastest-growing citation surfaces in the category. - Treat every UPA, CBS, and Nike announcement as a citation event. Each major sponsor deal or broadcast milestone resets AI citations for the sport. Brands should calendar citation audits to within 72 hours of each announcement. - Publish on paddle technology with technical honesty. Core thickness, face material, thermoformed construction, and swing weight: AI answers reward content that explains these at a technical level. Marketing-speak paddle descriptions are increasingly invisible. - Build content for the 50-to-65 spending core. The sport’s fastest-growing demographic is younger, but the highest-spending core is 50 to 65. Brands that produce content for that cohort, such as retirement-age fitness, joint-friendly play, and travel-and-pickleball content, earn citations that youth-coded content does not. The Bigger Picture ## AI is compressing pickleball’s brand-formation timeline from decades to months. The American pickleball industry is doing in three years what tennis took forty years to do: establishing a durable set of equipment brands, a professional tour infrastructure, a facility category, an apparel ecosystem, and a rating-system standard. In every other sport, that formation period was mediated by magazine coverage, in-store retail experience, television broadcasts, and word-of-mouth: a slow accretion of brand memory that took a generation to settle. Pickleball does not have that luxury of time. AI answer engines are forming the category’s brand memory in real time, at a speed that no earlier sport faced. The brands that win the next decade in pickleball are the brands that build AI citation infrastructure deliberately: through named pro endorsements, narrow sub-category dominance, franchise-scale content production, transparent technical communication, and early engagement with the governing-body and rating-system citation surfaces. The brands that treat AI citation as a marketing curiosity will watch Selkirk, JOOLA, The Picklr, USA Pickleball, DUPR, and a small number of others absorb a larger and larger share of what consumers see when they ask AI “what should I buy, where should I play, and how should I measure myself” in pickleball. AI citation share is the scoreboard. In pickleball, unlike any consumer category that preceded it, the scoreboard is also the only record of brand memory the sport has yet built. Download the full PDF ## About this report. 5WPR AI Visibility Index: Pickleball Industry. Published April 2026. This report measures brand citation share across generative AI answer engines for the American pickleball industry, the first major US consumer sport where AI-mediated research has become the dominant purchase-decision surface for paddles, facility memberships, apparel, and player-rating systems. The index measures AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews for 65+ consumer-intent queries tracked in Q1 2026. Citation share percentages are 5WPR estimates based on frequency of brand mention across tracked prompts; absolute percentages should be interpreted as directional measures of relative visibility, not precise market measurements. 5WPR runs Generative Engine Optimization (GEO) programs for consumer, B2B, financial services, healthcare and technology brands, including the equipment, facility and apparel brands named in this report. 5WPR Research · Published April 2026 · 5wpr.com/research --- ### The Podcast Citation Effect URL: https://www.5wpr.com/research/podcast-citation-effect/ Summary: A 12-study 5W research franchise on how long-form podcasts drive AI citation share across industries. 5W Podcast Citation Effect Research Franchise 5W AI Communications Research # The Podcast Citation Effect Long-form podcasts are becoming the durable record AI systems retrieve when buyers, allocators, recruiters, and executives ask who leads a category. 12 sector studiesMay 26 - July 2, 2026Four AI engines modeled Series Hub ## A research franchise built to be cited. The series documents who is accumulating podcast-driven authority sector by sector, and how that authority compounds inside AI retrieval. Core thesis Long-form audio has become machine memory infrastructure. The trade press collapsed, conference keynotes stopped showing up in retrieval, and podcasts absorbed the vacuum. ### Explore the franchise - Foundational study: The B2B Citation Layer - Methodology - 12-sector slate ### Sector studies Study 01 · 2026-05-26 ### Enterprise SaaS Sector study in The Podcast Citation Effect research franchise. Study 02 · 2026-05-28 ### Cybersecurity Sector study in The Podcast Citation Effect research franchise. Study 03 · 2026-06-02 ### AI Infrastructure Sector study in The Podcast Citation Effect research franchise. Study 04 · 2026-06-04 ### Fintech & Financial Services Sector study in The Podcast Citation Effect research franchise. Study 05 · 2026-06-09 ### Venture Capital Sector study in The Podcast Citation Effect research franchise. Study 06 · 2026-06-11 ### Defense Tech Sector study in The Podcast Citation Effect research franchise. Study 07 · 2026-06-16 ### Energy & Climate Tech Sector study in The Podcast Citation Effect research franchise. Study 08 · 2026-06-18 ### Wellness & Longevity Sector study in The Podcast Citation Effect research franchise. Study 09 · 2026-06-23 ### Crypto & Web3 Sector study in The Podcast Citation Effect research franchise. Study 10 · 2026-06-25 ### Entertainment & Media Sector study in The Podcast Citation Effect research franchise. Study 11 · 2026-06-30 ### Real Estate & PropTech Sector study in The Podcast Citation Effect research franchise. Study 12 · 2026-07-02 ### Healthcare & Biotech Sector study in The Podcast Citation Effect research franchise. --- ### The SaaS Content Paradox 2026 URL: https://www.5wpr.com/research/saas-content-paradox/ Summary: Why B2B SaaS companies are spending more on content and getting less from it. 5WPR # The SaaS Content Paradox 2026 | 5WPR Research Report ### By Ronn Torossian, Founder and Chairman, 5WPR. April 2026. ## The SaaS Content Paradox 2026: Why B2B Software Companies Are Spending More on Content and Getting Less From It SaaS companies invest between $342,000 and $1.09 million annually on content marketing. SEO delivers a documented 702% return over three years. Organic search generates 44.6% of all B2B SaaS revenue. The evidence that content marketing works - when executed correctly - is overwhelming. And yet only 29% of SaaS marketing teams rate their content strategy as highly effective. Forty-seven percent do not measure content ROI at all. HubSpot, which invented the modern SaaS content playbook, watched its blog traffic fall nearly 50% in a single month in late 2024. Salesforce spent $12.88 billion on sales and marketing last year. The budgets keep growing. The results, for most companies, do not. The SaaS Content Paradox 2026 is 5WPR's analysis of why. The report draws on case studies from HubSpot, Zapier, Ahrefs, Intercom, Salesforce, Slack, and Atlassian - alongside detailed budget benchmarks and research from more than a dozen industry sources - to identify the five structural failures that consistently prevent SaaS content investment from delivering the returns it demonstrably can. If your content program is not performing at the level the channel is capable of, this report explains specifically why - and what to do about it. 5WPR's SaaS PR agency practice works alongside the SaaS digital marketing team on the earned-media and executive-visibility side of the fix this report recommends. ## Frequently Asked Questions ### What is the SaaS Content Paradox? The SaaS Content Paradox refers to the growing gap between how effective content marketing can be for SaaS companies and the results most companies actually achieve. While content marketing delivers strong ROI when executed correctly, most SaaS teams invest heavily but fail to generate proportional business impact. ### How effective is content marketing for SaaS companies? Content marketing is one of the highest-performing channels in SaaS, delivering up to 702% ROI over three years and contributing approximately 44.6% of B2B SaaS revenue through organic channels when executed effectively. ### Why are most SaaS content strategies underperforming? Despite its potential, only about 29% of SaaS marketing teams consider their content strategy highly effective, and nearly half do not measure ROI at all. This disconnect is driven by structural execution issues rather than budget limitations. ### What are the main causes of the SaaS Content Paradox? The report identifies five core failures: creating content for algorithms instead of buyers, failing to measure meaningful ROI, focusing too heavily on acquisition over retention and expansion, relying on website-only distribution, and building strategies for a search environment that is rapidly changing. ### How is AI changing SaaS content marketing? AI-powered search is reducing click-through rates for traditional informational content. As of 2025, only about 40% of Google searches result in a click, meaning many users get answers directly from AI interfaces instead of visiting websites. ### Why is traditional SEO-driven content becoming less effective? Traditional SaaS content strategies rely on ranking for informational keywords and converting traffic. However, AI-generated answers are now absorbing that traffic before it reaches company websites, reducing the effectiveness of high-volume blog content. ### Are SaaS companies spending too much on content marketing? Many SaaS companies are spending heavily on content, with some investing up to $1 million annually, yet still failing to achieve strong results. The issue is not total spend, but how that budget is allocated across content types and distribution strategies. ### What role does distribution play in SaaS content success? Distribution is a major gap. Most SaaS companies rely on their own websites, but buyers often build vendor shortlists through peer communities, private networks, and AI-generated summaries before ever visiting a company's site. ### What should SaaS companies do differently? The report recommends shifting focus from high-volume informational content to original research, expert-driven insights, executive thought leadership, and stronger distribution strategies. The solution is compositional-changing how budgets are allocated-rather than simply increasing spend. ### How can SaaS companies measure content ROI more effectively? Companies should move beyond traffic and engagement metrics and focus on pipeline contribution, conversion rates, and revenue impact. For example, SEO-driven leads often convert at significantly higher rates than other channels, making measurement critical to optimization. April 2026 - 5W Public Relations 5wpr.com/saas-content-paradox Published by 5WPR Research. 5wpr.com. Email us at press@5wpr.com --- ### The 50 Publications AI Trusts — and Why Your Media Budget Is Aimed at the Wrong Ones URL: https://www.5wpr.com/ai-visibility-index/ai-friendly-publications-index-2026/ Summary: The 5W AI-Friendly Publications Index ranks 50 media outlets by how often ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews cite them. Full report below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited. 5W AI Visibility IndexMedia & Publications # The 50 Publications AI Trusts — and Why Your Media Budget Is Aimed at the Wrong Ones New 5W research finds that the publications brands spend the most to reach are not the publications AI engines cite — and that the gap is costing brands their visibility inside ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews. The Index scores the 50 that actually shape the answer, and shows where the budget should go instead. A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews50 publications scoredData window: Q2 2026 The Headlines 01 The budget is aimed wrong. Most media plans still target outlets for traffic and prestige — not for the citations AI engines actually pull. 02 A citation outlives the click. A placement in the right publication is repeated by the engines for months — long after the traffic spike is gone. 03 Earned and paid both move it. Editorial coverage, sponsored placement, and executive thought leadership — on the right domains — all convert. 50 Publications scored by AI Citation Value in this Index ~27% Share of AI citations drawn from journalism — rising to ~49% on time-sensitive queries ~68% Share of all AI citation volume held by just the top 15 domains $250M Largest AI content-licensing deal to date — the clearest sign the inventory has a price Figure 1 · The Ranking ## Where AI looks first. TOP 20 PUBLICATIONS BY AI CITATION VALUE · Q2 2026 01ForbesNews & Business98 02ReutersWire & News91 03The New York TimesNews88 04BloombergNews & Business85 05Associated PressWire & News82 06Business InsiderNews & Business80 07The GuardianNews77 08The Wall Street JournalNews & Business74 09The Washington PostNews72 10CNBCBusiness70 11The EconomistNews & Analysis68 12Financial TimesBusiness67 13AxiosNews65 14PR NewswireWire & Distribution64 15TimeNews63 16CNNNews62 17The AtlanticNews & Analysis60 18FortuneBusiness58 19USA TodayNews56 20NPRNews54 Source: 5W analysis synthesizing independent third-party AI citation studies and 5W modeling across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. AI Citation Value is a 0–100 score reflecting how often, how authoritatively, and across how many categories and engines a publication is cited. The full 50 are ranked in the table below. The Central Finding ## Brands are spending against the wrong media map. AI engines do not read the open web evenly. They assemble answers from a short, remarkably stable list of trusted publications — the top 15 domains alone account for roughly 68% of all AI citation volume, a concentration past anything search ever produced. Being on that list is no longer a traffic event. It is a standing position inside the answer — a citation the engines repeat, for months, every time a buyer asks. The problem is that most brands are not buying against that list. Media plans, target outlets, and agency retainers are still built on traffic and prestige — the metrics of the search era. A placement can deliver a strong impression count and a referral-traffic spike and still produce nothing the AI engines retrieve. The 5W AI-Friendly Publications Index scores the gap: an AI Citation Value for the 50 publications that actually shape what the engines recommend. The fix is not "do less paid" or "do only earned." It is to spend the whole budget against the right map. Earned editorial coverage, paid and sponsored placement, and executive thought leadership all move AI Citation Value — when they land on the publications the engines trust, in the formats the engines retrieve. Aim the same budget at the wrong domains, in the wrong formats, and it buys impressions the machine never sees. This report shows which 50 publications carry the answer — and how to invest so coverage compounds inside it. — 5W Research, May 2026 "Most brands are spending real money on media that the AI engines never read. The placement looks good in a report and does nothing in the answer. A citation in the right publication is worth more than a traffic spike — the engines repeat it for months. Earned coverage, paid placement, executive thought leadership — all of it works, but only on the domains AI actually trusts and in the formats it can retrieve. This is what we do at 5W. We find the gap between where a brand spends and where AI looks, and we close it." Ronn TorossianFounder & Chairman, 5W Methodology ## How we scored it. 5W built an AI Citation Value score for each publication by synthesizing the major independent citation studies of the last 18 months — datasets covering hundreds of millions of AI citations across the five engines — with 5W's own modeling. The score weighs how often a publication is cited, how authoritatively the engines use it, how many categories it appears in, and how consistently it holds across engines. We then mapped each publication to the placement types through which a brand can realistically earn or buy a presence. #### Global news & business Forbes, Reuters, The New York Times, Bloomberg, the wires — the cross-category anchors. #### Tech & product TechCrunch, The Verge, Wired, TechRadar, Ars Technica — the technology and reviews surface. #### Health & finance reference Healthline, WebMD, Investopedia, NerdWallet — high-trust reference authority. #### Vertical & lifestyle Variety, Vogue, Architectural Digest, Travel + Leisure — category-owning titles. #### Earned & contributed The publications above plus contributor programs — Forbes councils, HBR, Fast Company — where bylines carry authority. #### Thought-leadership & review platforms G2, Medium, LinkedIn, Substack — investable channels for executive visibility and category authority. The metric. AI Citation Value is a 0–100 score. It is a measure of retrievable authority — the standing weight a publication carries when an AI engine assembles an answer — not of audience size, traffic, or advertising reach. What it does not cover. The Index scores the investable publication universe. It does not rank non-addressable infrastructure — community platforms, encyclopedias, video, government and academic sources — which are critical to AI answers but cannot be approached through media investment. Those are addressed in their own section below. Important framing — please read. This report measures AI Citation Value for communications and media-strategy purposes only. It is not media-buying, investment, or financial advice. Scores are 5W directional estimates of relative visibility, synthesized from independent third-party studies and 5W modeling — not precise measurements. This report is a companion to the AI Platform Citation Source Index, which maps the full universe of domains AI engines cite; this one scores the publications a brand can actually invest in and shows where media budget should go to move AI visibility. The Full Ranking ## The Top 50 publications, ranked by AI Citation Value. #PublicationTypeTierCitation ValueBest Entry Route 1ForbesNews & BusinessTier 198Earned + contributed 2ReutersWire & NewsTier 191Earned 3The New York TimesNewsTier 188Earned 4BloombergNews & BusinessTier 185Earned 5Associated PressWire & NewsTier 182Earned 6Business InsiderNews & BusinessTier 180Earned + contributed 7The GuardianNewsTier 177Earned 8The Wall Street JournalNews & BusinessTier 174Earned 9The Washington PostNewsTier 172Earned 10CNBCBusinessTier 170Earned 11The EconomistNews & AnalysisTier 168Earned 12Financial TimesBusinessTier 167Earned 13AxiosNewsTier 165Earned 14PR NewswireWire & DistributionTier 164Wire distribution (paid) 15TimeNewsTier 163Earned 16CNNNewsTier 262Earned 17The AtlanticNews & AnalysisTier 260Earned 18FortuneBusinessTier 258Earned + contributed 19USA TodayNewsTier 256Earned 20NPRNewsTier 254Earned 21The New YorkerAnalysisTier 253Earned 22TechCrunchTechTier 252Earned 23Harvard Business ReviewBusiness & ManagementTier 251Earned + contributed 24Business WireWire & DistributionTier 250Wire distribution (paid) 25WiredTech & CultureTier 249Earned 26The VergeTechTier 248Earned 27InvestopediaFinance ReferenceTier 247Earned + contributed 28G2Review PlatformTier 246Managed profile 29HealthlineHealthTier 245Earned 30TechRadarTech & ReviewsTier 244Earned 31MediumContributed PlatformTier 243Contributed (owned) 32Ars TechnicaTechTier 342Earned 33NerdWalletPersonal FinanceTier 341Earned + contributed 34LinkedInContributed PlatformTier 340Contributed (owned) 35WebMDHealthTier 339Earned 36MIT Technology ReviewTech & ScienceTier 338Earned 37VarietyEntertainmentTier 337Earned 38Fast CompanyBusiness & InnovationTier 336Earned + contributed 39EngadgetTechTier 335Earned 40MashableTech & CultureTier 334Earned 41VogueBeauty & FashionTier 333Earned 42Architectural DigestHome & DesignTier 332Earned 43Bon AppétitFoodTier 331Earned 44Travel + LeisureTravelTier 331Earned 45The Hollywood ReporterEntertainmentTier 330Earned 46SubstackContributed PlatformTier 329Contributed (expert) 47GizmodoTechTier 329Earned 48QuartzBusinessTier 328Earned 49AllureBeautyTier 327Earned 50AdweekMarketing TradeTier 327Earned Tiers. Tier 1 — Anchor publications (ranks 1–15): cross-category, cross-engine; a citation here compounds. Tier 2 — Vertical authority (ranks 16–31): own a category or function in the answer. Tier 3 — Velocity & reach (ranks 32–50): specialist, fast-moving, or high-volume channels that fill out a category strategy. The Sources You Cannot Buy ## The infrastructure behind the answer — and how to approach it. Some of the most-cited sources in AI answers are not publications and cannot be approached through media investment. They are infrastructure. Ignoring them is a mistake; treating them as ad inventory is a worse one. They require a different discipline. ### Reddit, Wikipedia, YouTube, Quora, government and academic sources These sit at or near the top of every raw AI-citation ranking, but none of them sell a placement that converts. The approach for each: - Reddit — the single most-cited source across the major engines. The play is genuine, sustained community presence and a brand worth discussing — never planted posts, which communities and engines both discount. - Wikipedia — near-foundational training material, especially for ChatGPT. The play is accuracy and notability infrastructure: a well-sourced, factually correct entry. Never pay-to-edit. - YouTube — increasingly retrieved for how-to and product queries. The play is owned video built for retrieval — clear, structured, accurately titled. - Government and academic sources — high-trust, heavily cited for data. The play is earned credibility: original research, data partnerships, and citable primary material. What You Are Actually Buying ## Not every placement converts. Getting into a high-value publication is step one. Whether that placement converts into an AI citation depends on the type of placement. This is the part most media plans get wrong — and the part that decides whether a budget produces AI visibility or just impressions. Placement typeAI citation conversionWhat it takes Earned editorial coverageHighestGenuine independent reporting on a Tier 1–2 domain. The placement the engines weight above all others. Executive thought leadershipHighA real byline and commentary program — Forbes councils, HBR, Fast Company — putting named expertise on trusted domains. Paid & sponsored editorialHigh — if done rightOn a high-authority domain with genuine editorial substance, paid placement converts strongly. Thin, heavily disclaimed advertorial does not. Review-platform presenceHighAn accurate, actively managed profile on G2 and peers. Owns the "best X" comparison answer for the category. Contributed-platform postsModerateExpert posts on Medium, LinkedIn, Substack. Public and investable; cited for expertise, lower authority than earned. Wire distributionModerateEditorial-style releases with data and quotes. Retrieved for announcements, but a support line — not the spine of a plan. Display & programmatic advertisingMinimalAdds no retrievable text to the page. Near-zero direct citation value, whatever the impressions. The rule. AI engines retrieve and weight text and authority. A placement converts to the degree it adds substantive, credible, crawlable content to a trusted domain. "Where to spend" and "what format to buy" are one decision, not two. Where the Budget Goes ## A framework for media investment. The Index is not an argument for earned media alone — it is an argument for spending the whole budget against the right map. A 2026 media investment built for AI visibility looks roughly like this: The core — earned coverage on the anchor publications. Editorial coverage in the Tier 1 names. A citation in Forbes or Reuters compounds across categories and engines; nothing else in the plan does that. A major line — executive thought leadership. A real byline and commentary program puts named expertise on trusted domains and earns citations the brand cannot buy outright. For most brands this is the most underfunded high-conversion line in the plan. A deliberate line — paid and sponsored editorial. Paid placement belongs in the plan and converts well — on high-authority domains, with genuine editorial substance, bought for citation value rather than impressions. The discipline is buying the right domain and the right format, not buying less. A support line — review platforms and contributed channels. Managed review-platform profiles and expert posts on Medium, LinkedIn, and Substack round out a category strategy at lower cost. Near zero, if the goal is citations — display. Display advertising has its own jobs. Earning AI citations is not one of them. Do not fund it from the AI-visibility budget. An always-on line — measurement. The citation surface moves in weeks. A media plan aimed at AI visibility needs continuous measurement of where the brand is actually being cited — or it is spending blind. The Question Every Brand Is Asking ## How do you change what AI says about you? It is the question behind every conversation about this Index. A brand sees how ChatGPT or Perplexity describes it — or sees a competitor named where it is not — and asks the obvious thing: how do we change that? The honest answer starts with what you cannot do. You cannot edit the engines. There is no settings panel, no submission form, no account where a brand corrects its own entry. ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews build every answer from what they retrieve across the open web — and they weight what they retrieve by source authority. So you change the answer by changing the inputs. The engines are reading the 50 publications in this Index. Move what those publications say — through earned coverage, executive thought leadership, and paid editorial on the domains that carry citation weight — and you move what the engines say. Get the facts about your brand accurate and consistent across the sources they trust, and the answer corrects. Build authority where they are looking, and you appear in answers where you did not before. That is not a one-time fix and it is not a generic content exercise. It is a specific, measurable discipline: knowing which publications the engines weight in your category, which formats convert, where your brand currently stands, and what to change. It is a specialty — and it is the specialty 5W was built for. — 5W Research, May 2026 Work With 5W ## This is what the The Communications Firm Built for the AI Era does. 5W is The Communications Firm Built for the AI Era. We help brands change what AI says about them — and we do it as a measured program, not a guess. We are putting out a significant body of original research on how AI engines decide what to cite, and we turn that research into results for clients. ### An engagement with 5W covers three things: - Audit — where you stand. We measure your brand's current AI Citation Value: how ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews describe you, where competitors are cited instead, and which of the 50 publications in this Index are shaping your category's answer. - Program — the work that moves it. We run the integrated campaign that changes the inputs the engines read — earned editorial coverage, executive thought leadership, and paid placement on the domains that carry citation weight, in the formats that convert. - Measurement — proof it worked. The citation surface moves in weeks. We track your AI Citation Value continuously, so the program is steered by data and the result is visible. The brands that win the next phase of discovery are not the ones with the biggest media budgets. They are the ones spending against the right map. If you want to change what AI says about your brand, that is the conversation to have with 5W — start it at 5wpr.com. Winners & Laggards ## What lifts a publication's value — and what sinks it. ### ▲ Forbes — The Cross-Category Anchor Forbes is the standout: the one publication cited consistently across every consumer and B2B category the major studies track. Breadth, volume, a deep contributor program, and broad accessibility compound into the highest AI Citation Value in the Index. ### ▲ The Wires — The Quiet Overperformers Reuters, AP, and the distribution wires score far above their consumer profile. They are open, structured, fact-dense, and widely syndicated — exactly the content the engines retrieve. For brands, the wire is an underused, investable channel. ### ▼ The Gated Nationals — Authority Behind a Wall Several elite outlets carry enormous editorial authority but gate it behind paywalls and script-heavy pages. The engines cite what they can reach — and a hard paywall is a self-imposed discount on an otherwise top-tier asset. ### ▼ Brand-Owned Content Hubs — Not on the List A brand's own newsroom or blog barely registers in AI answers; owned pages sit in the low single digits of citations. The engines want third-party authority. Owned content supports the strategy — it does not substitute for earning a place on this Index. Structural Findings ## Six structural truths about AI Citation Value. 01 ### AI runs on a short list. Citation share concentrates in a few dozen domains — a tighter funnel than search ever produced. 02 ### Earned beats owned, decisively. Third-party publications carry the answer; brand-owned pages sit in the low single digits. 03 ### Crawlability is destiny. Gated, script-heavy outlets underperform their authority; accessible ones overperform. 04 ### The engines diverge. A small share of domains overlap across engines — a one-engine media strategy loses by design. 05 ### Recency is priced in. Fresh coverage outcompetes older coverage, sharply so on Perplexity and other live-retrieval engines. 06 ### Value is not visits. A publication can be a weak traffic source and a strong citation source — and the reverse. Findings Specific to 2026 ## Six 2026 dynamics reshaping the media map. 01 ### Licensing deals set a price floor. Two dozen-plus content deals have proven the asset exists — and revealed how much it is worth. 02 ### Brands are auditing AI citations. Communications teams now measure how the engines describe them — and treat a wrong answer as a fixable problem. 03 ### Litigation is reshaping access. High-profile suits over training data and AI Overviews are redrawing what engines can use. 04 ### Review platforms are rising. G2-type sources increasingly own the "best X" comparison answer for whole categories. 05 ### Open access overperforms prestige. Wire services and accessible outlets are outciting gated nationals on retrieval grounds. 06 ### AI shopping raises the stakes. As engines turn transactional, a category citation converts more directly into revenue. The 5W Approach ## How 5W moves a brand into the answer. The Index is the map. Moving a brand across it is the work. This is the sequence 5W runs for clients — the discipline behind a change in what AI says about a brand. - Establish the baseline. We measure how the engines describe the brand today, and where competitors are cited in its place — the AI Citation Value starting point. - Map the category's answer. We identify which of the 50 publications the engines weight most for the brand's specific category and queries. - Find the gap. We compare where the brand currently spends and earns coverage against where AI actually looks — the misallocation, named and quantified. - Run the earned program. Editorial coverage on the Tier 1 anchor publications, the placements the engines weight above all else. - Build executive thought leadership. A real byline and commentary program that puts the brand's named expertise on the domains AI trusts. - Deploy paid placement with discipline. Sponsored editorial on high-authority domains, in formats that convert — bought for citation value, not impressions. - Correct the record. We make the facts about the brand accurate and consistent across the sources the engines retrieve, so wrong answers self-correct. - Measure and steer. AI Citation Value is tracked continuously across all five engines; the program is adjusted to what the data shows, and the result is proven. This is the engagement. Audit, program, measurement — the integrated work of changing what AI says about a brand. Start the conversation at 5wpr.com. The Bigger Picture ## The media plan is being rewritten. For decades, a media plan bought reach and measured impressions. The publication mattered because of the audience in front of it. AI engines changed the unit. Now the publication matters because of the weight it carries inside the answer — a citation that the engines repeat, long after the human audience has moved on. That makes the 50 publications in this Index something new: a durable distribution layer for the AI era. And right now most brands are aiming at it blind — spending against outlets chosen for a search era that is ending, while the engines quietly build their answers from a different list. AI Citation Value is the scoreboard. The brands that re-aim their investment against it — with the right coverage, on the right domains, measured properly — are the ones AI will recommend. That is the work. It is what 5W does, and it is the conversation to start at 5wpr.com. Frequently Asked Questions ## The AI-Friendly Publications Index 2026. What is the AI-Friendly Publications Index 2026?A research report by 5W that scores the 50 publications most cited by AI engines on AI Citation Value, and maps where brands should invest media spend to convert coverage into AI citations. Which publication has the highest AI Citation Value?Forbes — it is the most consistently cited publication across AI engines and across both consumer and B2B categories, which gives it the highest score in the Index. How is this different from the AI Platform Citation Source Index?The AI Platform Citation Source Index maps every domain AI engines cite, including non-addressable infrastructure such as Reddit and Wikipedia. This report scores the investable publication universe and adds the action layer — where to spend, what format converts, and how to change what AI says about a brand. Does paid placement get cited by AI engines?It depends on the placement type. Earned editorial and expert contributed content convert most strongly. Wire distribution and review-platform presence convert well. Thinly disclosed advertorial and display advertising convert poorly. The report ranks each placement type by conversion. How do you change what AI says about your brand?You cannot edit the engines directly — there is no panel or form. AI engines build answers from what they retrieve across trusted publications. You change the answer by changing those inputs: earned coverage, executive thought leadership, and paid editorial on the domains the engines weight, plus accurate, consistent facts across the sources they trust. It is a measurable discipline, and the one 5W specializes in. What is AI Citation Value?A 5W score, 0–100, for how often, how authoritatively, and across how many categories and engines a publication is cited by AI answer engines — the core metric used to rank publications in the Index. ### About this report 5W AI-Friendly Publications Index 2026. Published May 2026. An installment in the 5W AI Visibility Index series, and a companion to the AI Platform Citation Source Index. AI Citation Value scores are 5W directional estimates of relative visibility, synthesized from independent third-party AI citation studies and 5W modeling across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — not precise measurements. AI citation patterns are volatile and can shift materially within weeks; the Index is revised quarterly. This report is not media-buying, investment, or financial advice; it is a communications-strategy reference. ### About 5W 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. 5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research --- ### The Private Aviation Corridor Report 2026 URL: https://www.5wpr.com/research/private-aviation-corridor-report-2026/ Summary: Volume III of the wealth-intelligence research series. 5W AI Communications and Haute Jets index the 25 private aviation corridors carrying UHNW wealth. # The Private Aviation Corridor Report™ 2026 #### By Ronn Torossian, Founder and Chairman, 5W AI Communications, co-produced with Haute Jets, July 2026. ## A 5W AI Communications research report, co-produced with Haute Jets, indexing the 25 private aviation corridors that carry the world's UHNW population. 5W Public Relations partners with Haute Jets on the wealth-intelligence research series. Volume I documented the largest single-year millionaire migration on record. Volume II introduced the Second-Residence Economy and the New Luxury Triangle framework. Volume III indexes the 25 private aviation corridors that carry the world's ultra-high-net-worth population. Airports are ranked. Operators are ranked. Corridors are not. This report closes that gap. Global business jet departures hit 3.88 million in 2025 — a record, up 5% over 2024 and 34% above 2019, per WingX Advance. The United States accounted for 2,633,282 departures, per ARGUS TRAQPak. Teterboro led US airports at 75,029; Dallas Love followed at 41,379 (+2.6% YoY). Farnborough remained Europe's #1 business aviation airport by movements. But the industry conversation stops at the airport. The corridor — the actual route the wealth is traveling — has not been indexed. The Corridor Authority Score™ is a composite strategic-importance index built on three equally weighted inputs: traffic-density signals, wealth-density signals, and seasonal-amplitude signals. It draws on ARGUS TRAQPak 2025, WingX Global Market Tracker, Henley Private Wealth Migration Report 2025, Knight Frank Wealth Report 2026 with PIRI 100, and 15 additional publicly disclosed data sources listed in the endnotes. The 25 corridors classify into three primary types drawn from the New Luxury Triangle framework introduced in Volume II — tax anchor, capital city, seasonal residence. Every corridor connects at least two of the three vertices. The corridor is the physical infrastructure of the Second-Residence Economy. ### The Wealth-Intelligence Research Series Volume I — The Wealth Migration Report (April 2026) Where the world's wealthy are moving. 142,000 millionaires on the move in 2025. Volume II — The Second-Residence Economy Report (May 2026) How wealth migration is reshaping second-home markets, luxury travel, real estate, and private aviation. Introduces the New Luxury Triangle framework. Volume III — The Private Aviation Corridor Report (July 2026) — You are here. The 25 flight paths of global wealth. Introduces the Corridor Authority Score™. ## Methodology The Corridor Authority Score™ is a composite strategic-importance index, not a flight-volume ranking. City-pair departure counts are not publicly disaggregated for every corridor endpoint, so the index triangulates three publicly sourced signal categories. Each corridor is scored on three inputs. Each input is normalized to a 0–100 scale against the highest-scoring corridor in the set for that input. The three normalized inputs are summed and rescaled 0–100 to produce the composite Corridor Authority Score. Scores are grouped into four tiers, with published score ranges per tier. Input What we measure Data · missing-data handling Traffic Density 33.3% Business aviation activity at each endpoint airport or airport cluster, calendar year 2025. Departure counts from ARGUS TRAQPak 2025 and WingX Global Market Tracker. Endpoints without an ARGUS/WingX rank use the closest publicly ranked business aviation airport, adjusted downward by one normalized decile. Wealth Density 33.3% UHNW population and prime residential exposure at each endpoint. Knight Frank PIRI 100; Knight Frank Wealth Report 2025 & 2026; Henley Private Wealth Migration Report 2025; Campden and New World Wealth for family office concentration. Missing endpoint data defaults to regional median. Seasonal Amplitude 33.3% Peak-to-trough traffic ratio across the calendar year. Monthly data from ARGUS TRAQPak, WingX Europe/Asia-Pacific, and airport-authority disclosures (KASE, ACK, HTO, SBH, Samedan, Chambéry, Olbia). Insufficient data receives modeled amplitude based on regional and destination-type comparables. Scoring period: January 1 – December 31, 2025. Wealth-density inputs use the most recent published edition of each source as of Q2 2026. ### Corridor types Every corridor classifies as one of three primary types drawn from the New Luxury Triangle framework introduced in Volume II — tax anchor, capital city, seasonal residence. - Capital–Anchor — capital city ↔ tax anchor. High year-round base. Example: London ↔ Dubai. - Capital–Seasonal — capital city ↔ seasonal residence. High seasonal amplitude. Example: Teterboro ↔ Aspen. - Anchor–Seasonal — tax anchor ↔ seasonal residence, no capital-city stop. The fully-relocated life. Example: Opa Locka ↔ St. Barths. A small minority — Tokyo ↔ Seoul is the archetype in this report — connect two capital cities (Capital–Capital) in a same-day business commute. These sit outside the primary three types but are structurally important within their regions. ## The 25 Corridors ### Tier I — Global Anchors Five corridors scoring highest across all three inputs — traffic-density signals, wealth-density signals, and seasonal-amplitude signals. Structural, not seasonal. Composite score range: 82–95. # Corridor Type Notes & Primary Signals 1 Teterboro/Westchester ↔ Palm Beach/Opa Locka Capital–Anchor The report's highest-scoring UHNW corridor. Teterboro logged 75,029 departures in 2025 (ARGUS TRAQPak, #1 US); Westchester #3; Palm Beach Intl #4. Year-round; heaviest Nov–April. PRIMARY SIGNALS: ARGUS TRAQPak 2025 · Knight Frank PIRI 100 2 Farnborough ↔ Nice / Cannes Capital–Seasonal London to the Côte d'Azur. Farnborough is Europe's #1 business aviation airport by movements. Peaks around Monaco GP, Cannes Lions, August villa season. PRIMARY SIGNALS: WingX Global Market Tracker · Knight Frank 2026 3 Luton / Farnborough ↔ Dubai Capital–Anchor The corridor built by the UK non-dom exit. Henley projects ~10,700 UK millionaires relocated in 2025 — largest single-country outflow — with the UAE leading global inflows. PRIMARY SIGNALS: Henley 2025 · WingX · Knight Frank 4 Van Nuys ↔ Harry Reid (Las Vegas) Capital–Seasonal Among the shortest high-value corridors in the world. Van Nuys held #9 in 2025 US rankings despite –12.31% YoY post-LA fires (ARGUS). Same-day return dominates. PRIMARY SIGNALS: ARGUS TRAQPak 2025 5 Geneva ↔ Nice Anchor–Seasonal Swiss banking to the Riviera. Both endpoints score in the top decile of Knight Frank PIRI 100 for prime residential values. Sub-hour flight. PRIMARY SIGNALS: Knight Frank PIRI 100 · WingX Europe ### Tier II — Compounding Corridors Seven corridors positioned for continued growth based on documented wealth migration, seasonal residence acquisition, and regional aviation activity. Composite score range: 66–81. # Corridor Type Notes & Primary Signals 6Teterboro ↔ Aspen (KASE)Capital–SeasonalOne of North America's most seasonally concentrated corridors. KASE arrivals heavily loaded to December 20–January 3 and Presidents' Week per airport-authority disclosures. PRIMARY SIGNALS: ARGUS TRAQPak · KASE disclosures 7Van Nuys ↔ AspenCapital–SeasonalThe LA half of the Aspen equation. Peaks with the New York window and film festival. PRIMARY SIGNALS: ARGUS TRAQPak · KASE disclosures 8Opa Locka ↔ São PauloCapital–AnchorThe Latin wealth spine. Miami documented as one of the fastest-growing global UHNW cities post-2020, with large Brazilian, Andean, and Venezuelan family-office concentration. PRIMARY SIGNALS: New World Wealth 2025 · Campden 2025 9Le Bourget ↔ NiceCapital–SeasonalThe French domestic Riviera route. Both endpoints in the WingX Europe top 15. Consistent year-round base with July–August surge. PRIMARY SIGNALS: WingX Europe · Knight Frank PIRI 10Zurich ↔ IbizaAnchor–SeasonalCentral European wealth to the Balearics. Peaks late June through early September. Reflects the fastest-expanding corridor type per Henley 2025. PRIMARY SIGNALS: Henley 2025 · WingX Europe 11Luton ↔ IbizaCapital–SeasonalThe UK half of the Ibiza equation. Weekend-heavy pattern. Luton is the highest-volume London-area business aviation airport alongside Farnborough. PRIMARY SIGNALS: WingX Europe · UK CAA 12Dubai ↔ MumbaiCapital–AnchorThe Gulf–India axis. India's UHNW population one of the fastest-growing globally per Knight Frank 2026, with Dubai the primary Gulf destination. PRIMARY SIGNALS: Knight Frank 2026 · Henley 2025 ### Tier III — Seasonal Anchors Seven corridors defined by extreme seasonal amplitude. Publicly reported airport-authority data confirms traffic concentrates in windows of two to twelve weeks. Composite score range: 50–65. # Corridor Type Notes & Primary Signals 13Teterboro ↔ Nantucket (ACK)Capital–SeasonalA 12-week season. Publicly disclosed ACK operations data shows Friday–Sunday amplitude as one of the sharpest in the set. PRIMARY SIGNALS: ACK disclosures · ARGUS TRAQPak 14Miami (Opa Locka) → St. Barths (SBH), through-corridorAnchor–SeasonalA passenger-flow corridor, not a single-aircraft city-pair. Passengers move Miami to SBH via direct light-jet operations or connecting service through SXM. Christmas–New Year drives a two-week peak reported as the tightest slot window of the year. PRIMARY SIGNALS: Operator disclosures · SBH profile 15Milan ↔ Olbia (Sardinia)Capital–SeasonalThe Italian domestic summer route. Costa Smeralda dominates. Olbia ranks among Europe's top summer business aviation airports per WingX. PRIMARY SIGNALS: WingX Europe 16Van Nuys ↔ Cabo San LucasCapital–SeasonalThe California winter escape. Real estate ownership converting the corridor from vacation to second-residence pattern per Knight Frank 2026 Los Cabos commentary. PRIMARY SIGNALS: Knight Frank 2026 · ARGUS TRAQPak 17Teterboro ↔ East Hampton (HTO)Capital–SeasonalThe shortest seasonal corridor of scale. Peak-window slot demand reported as tight; HTO summer traffic subject of FAA and Town Board discussion for multiple seasons. PRIMARY SIGNALS: HTO Town Aviation reports 18Geneva/Sion ↔ Samedan (St. Moritz)Anchor–SeasonalThe Swiss Alps winter axis. December–March. Samedan is Europe's highest-altitude commercial airport. PRIMARY SIGNALS: WingX Europe · Samedan disclosures 19Le Bourget ↔ Chambéry (Courchevel)Capital–SeasonalThe French Alps counterpart. Compressed Christmas–February window. Courchevel Altiport constraints concentrate traffic through Chambéry. PRIMARY SIGNALS: WingX Europe ### Tier IV — Emerging & Regional Six corridors growing from a smaller base, structurally embedded but compliance-constrained, or regionally dominant with limited global operator presence. Composite score range: 34–49. # Corridor Type Notes & Primary Signals 20Opa Locka ↔ Caracas / BogotáCapital–AnchorMiami–Latin second axis. Compliance-heavy but persistent. Venezuelan and Andean capital anchored in South Florida for two decades. PRIMARY SIGNALS: New World Wealth 2025 21Dallas Love ↔ AspenCapital–SeasonalTexas wealth signature. Dallas Love ranked #2 US departures in 2025 at 41,379 (+2.6% YoY). Fractional programs (Part 91K +10%) structurally loaded on Texas–Colorado routes. PRIMARY SIGNALS: ARGUS TRAQPak 2025 22Singapore Seletar ↔ Denpasar (Bali)Anchor–SeasonalThe Asian counterpart to Nice–Geneva. Structurally growing per WingX Asia-Pacific as regional wealth deepens second-residence patterns. PRIMARY SIGNALS: WingX Asia-Pacific · Knight Frank 2026 23Tokyo Haneda ↔ Seoul GimpoCapital–CapitalA same-day business corridor. One of the highest-density intra-Asia business aviation corridors per WingX Asia-Pacific. PRIMARY SIGNALS: WingX Asia-Pacific 24Riyadh ↔ DubaiCapital–AnchorIntra-Gulf spine. Vision 2030 investment flow, family office coordination, real estate cross-holdings support a structural year-round corridor. PRIMARY SIGNALS: Knight Frank 2026 · WingX Middle East 25Tel Aviv ↔ Larnaca / AthensCapital–SeasonalPost-2023 acceleration. Cyprus and Greek Golden Visa data show meaningful post-2023 Israeli residency and property activity. PRIMARY SIGNALS: Cyprus MOI · Enterprise Greece ## Four Structural Truths ### 1. Corridors are the unit of analysis. Airports are the terminal. Every strategic decision — where to base fleet, where to build FBO capacity, where to allocate marketing spend — is a corridor decision made through airport data. This index closes that gap. ### 2. The Anchor–Seasonal corridor is structurally expanding. Per Henley 2025, approximately 142,000 millionaires relocated internationally in 2025 — a record. Zurich ↔ Ibiza, Opa Locka ↔ St. Barths, and Dubai ↔ Maldives reflect the fully-relocated UHNW life: the commute is now anchor to escape, without a capital-city stop. ### 3. Miami is now the Latin capital of private aviation. Opa Locka is the operational base for Brazilian, Venezuelan, Colombian, and Andean UHNW families per New World Wealth Miami commentary. Three of the 25 corridors run through it. New York and Zurich were the historical bases. That has changed. ### 4. The UK non-dom exit rewrote the London corridor map. Farnborough and Luton remain top-tier origins. The routes have shifted. London–Nice remains top-tier. London ↔ Dubai has joined it — Henley projects approximately 10,700 UK millionaire departures in 2025. ## Executive Perspectives "The airport is not the story. The corridor is the story. Operators, destinations, and developers that organize around corridors will define the next decade of luxury." — Ronn Torossian · Founder & Chairman · 5W AI Communications "The world's wealthiest families migrate along the same routes — New York to Palm Beach, London to Nice, Miami to São Paulo, London to Dubai. These are not vacations. They are the arteries of a new global lifestyle." — Kamal Hotchandani · Founder & CEO · Haute Living and Haute Jets ## Sources & Endnotes - ARGUS International. TRAQPak 2025 Business Aviation Year-End Review. January 2026. US airport departure rankings including Teterboro (75,029), Dallas Love (41,379, +2.6% YoY), Van Nuys (–12.31% YoY), Westchester, Palm Beach, Scottsdale. - ARGUS International. TRAQPak Monthly Business Aviation Reports, January–December 2025. Part 91K fractional activity (+10% in 2025) and monthly airport-level movement data. - WingX Advance. Global Market Tracker, monthly issues, January 2025 – May 2026. 3.88M 2025 global business jet departures (+5% YoY, +34% vs 2019), Farnborough as #1 European business aviation airport, Asia-Pacific commentary. - Honeywell Aerospace. 2025 Global Business Aviation Outlook, 10-year forecast. October 2025. - Henley & Partners. Henley Private Wealth Migration Report 2025. June 2025. ~142,000 global millionaire relocation projection and ~10,700 UK millionaire outflow. - Knight Frank. The Wealth Report 2026 & Prime International Residential Index (PIRI 100). March 2026. - Knight Frank. The Wealth Report 2025. March 2025. - New World Wealth. USA Wealth Report 2025 and World's Wealthiest Cities Report 2025. 2025. - Campden Wealth. The Global Family Office Report 2025. 2025. - Private Jet Card Comparisons. Busiest Private Jet Airports 2025 rankings. Q1 2026. - Aspen/Pitkin County Airport. Public monthly operations reports, 2024–2025. - Nantucket Memorial Airport (ACK). Public operations reports, 2024–2025. - East Hampton Town Aviation Department. Public operations reports and Town Board materials, 2023–2025. - Republic of Cyprus, Ministry of Interior. Public residency permit and property acquisition data, 2023–2024. - Enterprise Greece / Ministry of Migration and Asylum. Golden Visa program public disclosures, 2023–2025. ## License the Dataset This page is the report. The underlying dataset — full corridor-level composite scores, endpoint-by-endpoint inputs, and quarterly refresh access — is available under commercial license through Haute Jets and 5W AI Communications. Contact 5W. ## Frequently Asked Questions ### What is the Private Aviation Corridor Report? The Private Aviation Corridor Report is a research study by 5W AI Communications and Haute Jets that indexes the 25 private aviation corridors carrying the world's ultra-high-net-worth population. Volume III of the wealth-intelligence series. ### What is the Corridor Authority Score? A composite strategic-importance index built on three equally weighted inputs: traffic-density signals, wealth-density signals, and seasonal-amplitude signals. Each input is normalized 0–100 against the highest-scoring corridor and summed to a composite score. ### Which corridor ranks #1? Teterboro/Westchester ↔ Palm Beach/Opa Locka — the New York finance to South Florida tax anchor corridor. Teterboro logged 75,029 departures in 2025 per ARGUS TRAQPak. ### Which corridor is fastest-expanding by type? The Anchor–Seasonal corridor type — tax anchor connected to seasonal residence, without a capital-city stop. Zurich ↔ Ibiza and Miami ↔ St. Barths are the archetypes. ### What role does Miami play? Miami has replaced New York and Zurich as the operational base for Brazilian, Venezuelan, Colombian, and Andean UHNW families. Three of the 25 corridors run through Opa Locka. ### How does this connect to Volume I and Volume II? Volume I — the Wealth Migration Report documented 142,000 millionaires relocating in 2025. Volume II — the Second-Residence Economy Report introduced the New Luxury Triangle framework. Volume III indexes the physical corridors that stitch the Triangle vertices together. ### Where can I license the underlying dataset? Full corridor-level composite scores, endpoint-by-endpoint inputs, and quarterly refresh access are available under commercial license through Haute Jets and 5W AI Communications. Contact us. ### Why does this matter for brands and marketers? Corridors — not cities — are where luxury demand concentrates. Operators, destinations, financial firms, real estate developers, and consumer brands that organize around corridors gain earlier access to UHNW audiences than those organized around traditional metro-level segmentation. --- ### The Weight Loss & Metabolic Health AI Visibility Index 2026 URL: https://www.5wpr.com/research/weight-loss-ai-visibility-index-2026/ Summary: The top 25 US weight loss and metabolic health brands ranked by AI citation share. Wegovy, Zepbound, Ozempic, Mounjaro, and Noom lead across engines. # The Weight Loss & Metabolic Health AI Visibility Index 2026 ### How the $150 billion weight loss and metabolic health category gets surfaced - or disappears - inside the AI-powered consumer research that now precedes every prescription, program, and purchase. By the 5W Research Team, with commentary from Ronn Torossian, Founder, 5W - April 2026 Download the full PDF ## EXECUTIVE SUMMARY Weight loss and metabolic health is the single fastest-reconfigured consumer health category in modern history. GLP-1 receptor agonists - Ozempic, Wegovy, Mounjaro, Zepbound - have added a pharmaceutical layer to a category that was, as recently as 2022, defined almost entirely by behavioral programs, diet apps, and packaged meal services. The category is now projected to reach $150 billion globally by 2030, with the GLP-1 segment alone crossing $100 billion. The buying journey has moved in parallel. Consumers no longer start with a primary care physician or a weight-loss-program sales call. They start with ChatGPT, Claude, and Perplexity. "Which GLP-1 is best for me," "Wegovy vs Zepbound," "is Noom worth it in 2026," "what's the best diet app for sustainable weight loss" - these queries route consumers to providers, programs, and products before any clinical or commercial conversation happens. AI Overviews trigger on health-adjacent queries at approximately 13% - the highest rate of any major consumer category. For weight loss specifically, the rate runs higher still, because the queries are information-seeking rather than transactional. The brands that surface first inside AI answers are the brands that get the consultation, the subscription, and the sale. Two pharmaceutical companies now own nearly 100% of pharmacology citations in AI answers for this category. Six pharma brands together account for approximately 58% of total citation share. Every legacy weight loss brand in America is recalibrating against that reality - and AI citation share is the scoreboard. 5W built this index to score the new game. Medical disclaimer. This index is a marketing and communications research document. It does not rank products on clinical effectiveness, safety, or appropriateness, and it does not constitute medical advice. Weight loss decisions are medical decisions that should be made with a licensed clinician. ## KEY FINDINGS BAR (8 stat cards) STAT 1: $150B - projected global category size by 2030 STAT 2: ~58% - of total category AI citation share captured by six pharma brands STAT 3: 2 - companies (Novo Nordisk and Eli Lilly) own nearly 100% of GLP-1 citations STAT 4: ~13% - AI Overview trigger rate on health queries, highest of any consumer category STAT 5: 60-70% - share of "best diet app" AI answers citing MyFitnessPal first STAT 6: 200M+ - registered MyFitnessPal user base, the informational moat behind its citation lock STAT 7: 60+ - consumer prompts tracked across ChatGPT, Claude, Perplexity, and Google AI Overviews to build the index STAT 8: 25 - brands ranked, with the remaining ~11% citation share split across ranks 16-25 and unranked brands ## THE TOP 15 BY AI CITATION SHARE Estimated share of citations across 60+ weight-loss-related consumer prompts run through ChatGPT, Claude, Perplexity, and Google AI Overviews, Q1 2026. 1. Wegovy - 19.0% citation share - GLP-1 / Pharmaceutical 2. Zepbound - 16.0% citation share - GLP-1 / Pharmaceutical 3. Ozempic - 13.0% citation share - GLP-1 / Pharmaceutical (T2D-approved, off-label use flagged) 4. Mounjaro - 7.0% citation share - GLP-1 / Pharmaceutical (T2D-approved, off-label use flagged) 5. Noom - 7.0% citation share - Behavioral Program (hybrid post-Noom Med) 6. WeightWatchers - 7.0% citation share - Behavioral Program (Chapter 11 emerged GLP-1-centric) 7. MyFitnessPal - 6.0% citation share - Diet / Nutrition App 8. Lose It! - 3.0% citation share - Diet / Nutrition App 9. Cronometer - 2.0% citation share - Diet / Nutrition App (high-accuracy niche) 10. Simple - 2.0% citation share - Diet / Nutrition App (intermittent fasting) 11. Saxenda - 2.0% citation share - GLP-1 / Pharmaceutical (declining as weekly options expand) 12. Factor - 1.5% citation share - Meal Delivery (HelloFresh portfolio) 13. Optavia - 1.5% citation share - Behavioral Program (NYSE: MED) 14. Jenny Craig - 1.0% citation share - Behavioral Program (closed 2023, relaunched 2024) 15. Nutrisystem - 1.0% citation share - Behavioral Program (Kainos Capital) Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, and Google AI Overviews, Q1 2026. Remaining ~11% split across ranks 16-25 and unranked brands. Telehealth providers excluded from this index and tracked separately. ## METHODOLOGY 5W analyzed more than 60 common consumer prompts across five primary sub-categories of the weight loss and metabolic health market. We identified which brands AI models consistently surface, which review aggregators and editorial sources feed those citations, and where the biggest gaps sit between market position and AI visibility. Sub-Categories Tracked: Pharmaceutical GLP-1s: Wegovy, Ozempic, Zepbound, Mounjaro, Saxenda, Victoza, plus the prescription-adjacent Plenity Behavioral weight loss programs: WeightWatchers, Noom, Jenny Craig, Nutrisystem, Optavia, Profile by Sanford, Found Diet and nutrition apps: MyFitnessPal, Lose It!, Cronometer, Simple, Yazio, Fastic, Zero, MacroFactor, Carb Manager, Lumen Packaged meal services: Factor, Trifecta, Territory, Green Chef, Seattle Sutton Supplements & OTC: Hydroxycut, Leanbean, PhenQ, fiber-based weight management products Query Types Tracked: Real consumer prompts including "best weight loss drug 2026," "Wegovy vs Zepbound," "is Noom worth it," "WeightWatchers vs Noom," "best weight loss app without subscription," "best meal delivery for weight loss," and 50+ additional variations covering comparison, evaluation, and recommendation intent. Citation Sources Tracked: Medical editorial (WebMD, Healthline, GoodRx, Drugs.com, Everyday Health), clinical authorities (NIH, CDC, FDA, Mayo Clinic, Cleveland Clinic, Harvard Health Publishing), review aggregators (Forbes Health, U.S. News Health, Wirecutter), patient communities (Reddit, Facebook groups), news coverage (CNBC, Bloomberg, Reuters, STAT News, FiercePharma), and vendor-owned content hubs. Important framing. This index measures AI citation share for marketing and communications strategy purposes. It does not rank products on clinical effectiveness, safety, or appropriateness, and it does not constitute medical advice. Weight loss decisions are medical decisions that should be made with a licensed clinician. Telehealth providers are excluded from this edition of the index and will be covered separately. If you or someone you know is struggling with disordered eating, the National Alliance for Eating Disorders helpline is at 1-866-662-1235. ## THE WINNERS The Pharma Duopoly: Novo Nordisk and Eli Lilly. Novo and Lilly together own approximately 95%-plus of GLP-1 prescription volume and a similar share of AI citations for pharmacology queries. The reason is structural. They are the only FDA-approved options for chronic weight management (Wegovy, Zepbound) and the only sources of authoritative clinical trial data - STEP, SURMOUNT, and SELECT trials - that AI models cite as evidence. Zepbound's recent trajectory illustrates the citation compounding dynamic. The SURMOUNT-5 head-to-head trial against Wegovy, published in 2025, demonstrated superior weight loss for tirzepatide. That data point is now cited in nearly every "Wegovy vs Zepbound" AI answer, and the citation itself has driven prescription volume. Every clinical trial publication is, in effect, an AI citation event. Noom and WeightWatchers: Two Different Pivot Stories. Noom and WeightWatchers have both pivoted into the GLP-1 era - with dramatically different financial outcomes but similar citation outcomes. Noom's pivot, with Noom Med launched late 2022, has been successful in AI citation terms. The company positioned itself as "behavioral change plus medication access" rather than abandoning its behavioral roots, and AI answers now consistently cite Noom as the category's hybrid default. WeightWatchers' pivot has been financially brutal - the company filed Chapter 11 in 2025 and emerged with a GLP-1-centric strategy - but its AI citation share remains strong because of 60-plus years of accumulated brand equity and content. WW is still the default "traditional weight loss program" AI citation, even when its current business looks nothing like its historical one. MyFitnessPal: The Category-Defining App. MyFitnessPal is cited in an estimated 60-70% of "best diet app" AI answers. The 15-year head start on food-database construction, 200 million-plus user base, and deep content library create a compounding moat that newer apps (Simple, MacroFactor, Carb Manager) cannot displace through product quality alone. When AI models train on the full archive of consumer reviews, app-store metadata, and editorial comparisons, MyFitnessPal sits at the center of that weight class. Owned by Francisco Partners. The Specialty Specialists. Cronometer (high-accuracy macros), Simple (intermittent fasting), Carb Manager (keto), and MacroFactor (adaptive coaching) win narrow long-tail queries by owning specific sub-categories rather than competing for generic "best diet app" citations. This is the only reliable path to AI citation share for new apps: own a sub-category before competing for the category. ## THE LOSERS The Legacy Behavioral Brands. Jenny Craig, Nutrisystem, Optavia, and Medifast - the 2000s-era behavioral and meal-replacement brands - are being cited at a fraction of their historical share. AI answers increasingly frame them as "before GLP-1s, people tried..." rather than as current first-choice options. Jenny Craig's 2023 closure and 2024 relaunch has further weakened citation consistency, with AI answers frequently returning outdated information about the brand's status - a reputation management problem in itself. The Meal Delivery Squeeze. Factor, Green Chef, Trifecta, Territory, and Seattle Sutton remain relevant for specific long-tail queries but have lost generic "how to lose weight" citation share to the GLP-1 plus behavioral combination. Meal delivery AI citations are increasingly framed as adjuncts to medication-based weight loss rather than as standalone solutions. The category is not dying. It is being recategorized. The Supplement Category Collapse. Hydroxycut, Leanbean, PhenQ, Garcinia cambogia products, apple cider vinegar capsules - the OTC supplement category for weight loss has lost nearly all generic AI citation share to GLP-1s and Plenity (the only FDA-cleared OTC-adjacent option). AI models increasingly flag supplement claims with evidence caveats, further reducing citation surface. A category that generated billions in revenue a decade ago is now nearly invisible in the AI-mediated buying journey. ## SIX STRUCTURAL TRUTHS 1. AI citations update faster here than in any other consumer health category. FDA shortage list changes, trial publications, and regulatory enforcement generate citation-shifting news cycles roughly every 2-3 weeks. Brands that miss a news cycle lose citation share. The next news cycle compounds on the last. 2. Clinical trial publication is the highest-leverage citation event in the category. SURMOUNT-5. SELECT. STEP. These peer-reviewed publications are cited in AI answers for years and drive measurable changes in which drug AI recommends. Trial publication is now both a financial event and a marketing event. 3. Behavioral content wins "before you start" queries. Queries like "how to lose weight without medication," "is GLP-1 right for me," and "what to try before Ozempic" default to Noom, WeightWatchers, and MyFitnessPal - not pharma. Behavioral brands that maintained content velocity through the GLP-1 era have held this citation surface. 4. App category moats are built on databases, not features. MyFitnessPal's food database is a 15-year advantage. Simple's fasting protocol library. Carb Manager's keto recipe archive. The citation moats are informational, not functional - which means new entrants cannot catch up through UX improvement alone. 5. Sub-category dominance beats category competition. Generalist positioning loses to specialist positioning in AI citations. Cronometer owns "accuracy." Simple owns "fasting." Carb Manager owns "keto." Factor owns "weight-loss meals." Brands trying to be the "best overall" lose to brands that own a specific sub-query. 6. Regulatory events are a category risk surface. FDA enforcement decisions, Medicare coverage rulings, and state pharmacy board actions can reshape the citation landscape within weeks. Brands without active regulatory-communications infrastructure are exposed to sudden citation erosion they cannot respond to in time. ## SIX 2026 DYNAMICS RESHAPING WEIGHT LOSS AI CITATIONS 01. The Wegovy-vs-Zepbound narrative has solidified in AI. SURMOUNT-5's 2025 publication created a sticky "Zepbound beats Wegovy" narrative that now appears in the majority of comparison AI answers. Novo Nordisk's 2026 response - new CagriSema data and oral semaglutide positioning - is beginning to shift citations back toward parity. 02. The Medicare coverage decision is a pending citation event. CMS's evaluation of Medicare Part D coverage for obesity drugs would, if approved, generate a category-wide citation shift. Brands publishing substantive content on the coverage question are positioned to own the post-decision citation surface. 03. Oral GLP-1 approvals are a 2026-2027 reshuffling event. Novo Nordisk's oral semaglutide and Eli Lilly's orforglipron (Phase 3) could eliminate the injection barrier. Oral approval for obesity would shift AI citations meaningfully as consumer hesitation around injections disappears. 04. The GLP-1 maintenance question is its own citation surface. "What happens when you stop taking Wegovy" and "can you stay on GLP-1s forever" are among the fastest-growing queries in the category. Evidence-based content is thin. Brands publishing authoritative maintenance content in 2026 will own a citation surface that has not yet been locked. 05. Behavioral-plus-medication hybrid positioning is the winning model. Noom's pivot proved the most durable AI citation position combines behavioral support with medication access. Every behavioral brand that maintains pharma-adjacent content will hold citation share. Every pharma brand that adds behavioral-support narrative will do the same. 06. Eating disorder screening language is becoming an AI signal. AI models are increasingly trained to surface ED-screening content when weight-loss queries contain disordered-eating signals. Brands whose content includes responsible framing gain citation share - and avoid reputation risk from being cited alongside concerning queries. ## FROM RONN TOROSSIAN, FOUNDER OF 5W "Two pharmaceutical companies now own nearly 100% of GLP-1 citations in AI answers. Six pharma brands take roughly 58% of the entire category. That is not a market share story. It is a marketing infrastructure story. Novo Nordisk and Eli Lilly built the citation moat the same way every other category leader will eventually have to build it: peer-reviewed clinical data, named-author editorial coverage, regulatory events treated as content events, and a content cadence faster than the news cycle. The behavioral brands and the apps and the meal services that figure out how to play that game in the next two quarters will hold their citation surface. The ones that do not will get categorized as 'before GLP-1s, people tried...' inside an AI answer that someone else's brand is leading. AI citation share is the scoreboard. Most brands in this category are not yet playing." ## THE WEIGHT LOSS MARKETER PLAYBOOK 1. Audit AI citation share monthly. This category moves fast enough that anything less frequent misses material citation shifts. Monthly is the minimum defensible cadence. 2. Publish clinical data, not marketing claims. AI answers reward peer-reviewed evidence and down-rank marketing language. Every brand in this category should have a clinical content pipeline, not a brand-voice blog. 3. Treat FDA events as content events. Shortage list changes, approval decisions, enforcement actions, Medicare coverage updates - each is a content opportunity that most brands under-exploit. 4. Publish physician bylines. Named, credentialed obesity-medicine specialists carry AI citation weight that ghost-written corporate content does not. 5. Own a sub-category if you cannot compete broadly. Specialist positioning beats generalist positioning in AI citations. Pick a lane and dominate it. 6. Address the maintenance question aggressively. The GLP-1 off-ramping and weight-maintenance content gap is the single biggest greenfield citation opportunity in the category. 7. Stay off "cheapest way to get Ozempic" framing. AI models flag this as encouraging off-label use or regulatory gray-area purchasing. Compliant positioning beats price positioning for AI citation. 8. Include eating disorder screening language. The right framing around when weight loss is and is not appropriate protects AI citation share and minimizes reputational exposure. 9. Build out your clinical trial content pipeline. Every trial read-out is a citation event for a year afterward. Pharma and pharma-adjacent brands should be publishing trial commentary from credentialed clinicians within 24 hours of every major publication. 10. Re-audit after every FDA news cycle. Shortage changes, enforcement actions, and coverage decisions reshape citations within weeks. The monthly audit is the floor; event-driven re-audits are the ceiling. ## THE BIGGER PICTURE Weight loss is the most structurally disrupted consumer health category of the past decade. Pharmaceutical innovation has rewritten buyer behavior. AI has disintermediated the traditional discovery pathway. The brands that surface inside ChatGPT and Claude answers are now doing the work that doctors, friends, and Google search results used to do. The brands that dominate AI citations over the next 24 months will not just capture market share - they will shape how millions of Americans make decisions about medications, programs, and products that materially affect their health. This is a category where AI visibility translates directly to revenue, and where responsible framing matters as much as aggressive distribution. The pharma duopoly is unlikely to be dislodged in this decade. The behavioral-program pivot has a proven winner (Noom) and a proven survivor (WeightWatchers). The app category's moats are informational. The meal-delivery, supplement, and legacy-program categories are being recategorized in real time inside AI answers that very few of those brands have yet optimized for. AI citation share is the scoreboard. Most brands in this category are not yet playing. ## FAQ ## Frequently Asked Questions ### What is the Weight Loss & Metabolic Health AI Visibility Index? 5W's ranking of the top 25 U.S. brands in the weight loss and metabolic health category by AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. Citation share is the estimated proportion of brand mentions across 60+ tracked consumer prompts run in Q1 2026. The index is a marketing and communications research document and does not constitute medical advice. ### Which weight loss brand has the highest AI citation share? Wegovy leads, followed by Zepbound, Ozempic, and Mounjaro. The four GLP-1 receptor agonists together capture roughly 55% of all category citations, and their two manufacturers - Novo Nordisk and Eli Lilly - own nearly 100% of pharmacology citations on weight-loss prompts. ### Why are GLP-1 brands dominating AI citations? Because they are the only FDA-approved options for chronic weight management and the only sources of authoritative clinical trial data - STEP, SURMOUNT, and SELECT trials - that AI models cite as evidence. Each peer-reviewed publication is, in effect, a citation event that compounds for years. SURMOUNT-5's 2025 head-to-head data on tirzepatide versus semaglutide is now cited in nearly every comparison answer. ### How are Noom and WeightWatchers performing in AI citations? Both have pivoted into the GLP-1 era with very different financial outcomes but similar citation outcomes. Noom's 2022 launch of Noom Med positioned the brand as behavioral change plus medication access, and AI now cites it as the category's hybrid default. WeightWatchers filed Chapter 11 in 2025 and emerged with a GLP-1-centric strategy. Citation share has held because of 60-plus years of accumulated brand equity and editorial coverage. ### Which diet apps lead the AI citation rankings? MyFitnessPal leads, cited in an estimated 60-70% of best-diet-app AI answers. The compounding moat is its 15-year-old food database, 200 million-plus user base, and deep editorial archive. Specialist apps win narrow long-tail queries: Cronometer for accuracy, Simple for intermittent fasting, Carb Manager for keto, MacroFactor for adaptive coaching. ### Who is this report for? Chief Marketing Officers, Chief Communications Officers, and growth leads at pharmaceutical, behavioral weight loss, diet app, and meal delivery brands. Healthcare communications and PR agency leadership. Investor and analyst teams covering metabolic health and consumer health. Founders building AI-native or pharma-adjacent wellness products. ### Is the report free to download? Yes. The PDF download is ungated and free. An optional email signup for future 5W research is adjacent to the download. ### What data sources does the index draw from? Q1 2026 prompt testing across ChatGPT, Claude, Perplexity, and Google AI Overviews. Medical editorial sources (WebMD, Healthline, GoodRx, Drugs.com, Everyday Health). Clinical authorities (NIH, CDC, FDA, Mayo Clinic, Cleveland Clinic, Harvard Health Publishing). Review aggregators (Forbes Health, U.S. News Health, Wirecutter). Patient communities and news coverage from CNBC, Bloomberg, Reuters, STAT News, and FiercePharma. ### Why are telehealth providers excluded? Telehealth providers in the GLP-1 prescribing space are tracked separately because they sit between the pharmaceutical and behavioral categories and operate under regulatory dynamics distinct from product brands. A dedicated index covering compounded-medication providers, telehealth GLP-1 marketplaces, and direct-to-consumer prescribing platforms is forthcoming. ### Can 5W run a GEO program for my weight loss or metabolic health brand? Yes. 5W's Generative Engine Optimization practice runs full AI visibility programs for pharmaceutical, behavioral health, diet app, meal delivery, and adjacent wellness brands, with healthcare-specific compliance frameworks for FDA, FTC, and state pharmacy board considerations. Practice details at www.5wpr.com/practice/geo-optimization.cfm. Inquiries: research@5wpr.com or media@5wpr.com. ### Why now? The citation window is narrow and the category is moving fast. SURMOUNT-5's 2025 publication has already locked in a sticky Zepbound-versus-Wegovy narrative. Pending Medicare Part D coverage decisions and oral GLP-1 approvals will reshape citations within weeks of any announcement. The GLP-1 maintenance question is the largest open citation surface in the category and will be locked by whichever brands publish authoritative content first. Most brands in the category are not yet auditing AI citation share at all. Download the full PDF ## ABOUT 5W 5W is the premier independent AI-era communications and Generative Engine Optimization firm in the United States, with approximately 275 professionals serving more than 250 clients across consumer, corporate, crisis, healthcare, technology, education, and public affairs. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W's Generative Engine Optimization practice is at 5wpr.com/practice/geo-optimization. Healthcare and wellness communications inquiries: media@5wpr.com. April 2026 - 5W AI Visibility Index Series, Vol. IV. Prior editions in the series: Legal Tech, Real Estate, and Fintech. 5wpr.com/research Published by 5W Research. 5wpr.com. Email us at research@5wpr.com or media@5wpr.com --- ### AI Infrastructure URL: https://www.5wpr.com/research/podcast-citation-effect/ai-infrastructure/ Summary: AI Infrastructure: how long-form podcast appearances drive AI engine Citation Share. Study #03 of 12 in The Podcast Citation Effect from 5W AI Communications. --- ### Bankruptcy and Restructuring URL: https://www.5wpr.com/research/legal/bankruptcy/ Summary: How Lehman, Purdue, FTX, Hertz, WeWork, Bed Bath, and Puerto Rico became the canonical narrative anchors of restructuring retrieval, and how one firm --- ### Crypto & Web3 URL: https://www.5wpr.com/research/podcast-citation-effect/crypto/ Summary: Crypto & Web3: how long-form podcast appearances drive AI engine Citation Share. Study #09 of 12 in The Podcast Citation Effect from 5W AI Communications. --- ### Defense Tech URL: https://www.5wpr.com/research/podcast-citation-effect/defense-tech/ Summary: Defense Tech: how long-form podcast appearances drive AI engine Citation Share. Study #06 of 12 in The Podcast Citation Effect from 5W AI Communications. --- ### Energy & Climate Tech URL: https://www.5wpr.com/research/podcast-citation-effect/energy/ Summary: Energy & Climate Tech: how long-form podcast appearances drive AI engine Citation Share. Study #07 of 12 in The Podcast Citation Effect from 5W. --- ### Enterprise SaaS URL: https://www.5wpr.com/research/podcast-citation-effect/saas/ Summary: Study one of twelve in The Podcast Citation Effect measures how long-form podcast appearances drive AI engine citation share for enterprise SaaS brands. --- ### Healthcare & Biotech URL: https://www.5wpr.com/research/podcast-citation-effect/healthcare/ Summary: Healthcare & Biotech: how long-form podcast appearances drive AI engine Citation Share. Study #12 of 12 in The Podcast Citation Effect from 5W. --- ### 5W AI Legal Discovery Index URL: https://www.5wpr.com/research/legal/ Summary: The 5W AI Legal Discovery Index is the first comprehensive read of how AI engines cite the AmLaw 200, with a master baseline and six practice-area cuts. 5W Research # 5W AI Legal Discovery Index The first comprehensive read of how AI engines cite, recommend, and discover U.S. law firms across the AmLaw 200. Master baselineSix practice-area cutsAI legal discovery ### Inaugural Report — AmLaw 200 Baseline The inaugural baseline of how AI engines cite, recommend, and discover U.S. law firms across the AmLaw 200. Methodology, dataset, the Legal Retrieval Stack, and the Wikipedia-vs-revenue gap that defines machine-readable legal prestige. ### M&A and Corporate Five firms answer roughly two-thirds of the M&A questions. The Wikipedia-rich anchor — not revenue rank — explains which five. A practice-area cut from the 5W AI Legal Discovery Index. ### White-Collar Defense Six lawyers — not six firms — capture half the white-collar defense citation share. How named anchors, SDNY prestige, and decades of televised commentary became the retrieval infrastructure of elite criminal defense. ### Personal Injury How four decades of billboard, TV, and SEO spending built the citation infrastructure that BigLaw cannot replicate. The widest gap between AmLaw revenue rank and AI citation share in any legal practice area. ### HNW Family and Divorce In elite divorce, the matter creates the retrieval infrastructure. How Page Six, Vanity Fair, TMZ, and public court filings became the citation engine of high-net-worth family law — and what that means for counsel selection. ### Securities and Regulatory Enforcement Most elite securities enforcement work is intentionally invisible — pre-Wells negotiations, quiet settlements, voluntary disclosures, SEC staff interactions. How AI retrieval sees the visible five percent and misses what determines outcomes. ### Bankruptcy and Restructuring How Lehman, Purdue, FTX, Hertz, WeWork, Bed Bath, and Puerto Rico became the canonical narrative anchors of restructuring retrieval — and how one firm's concentration in those cases reshaped the practice area's citation infrastructure. --- ### How AI Describes The NBA URL: https://www.5wpr.com/research/how-ai-describes/nba/ Summary: Two NBAs inside the engines: the sport and the cultural product. The valuation lives in the second. A 5W audit. --- ### HNW Family and Divorce URL: https://www.5wpr.com/research/legal/hnw-family/ Summary: How Page Six, Vanity Fair, TMZ and public court filings became the citation engine of HNW divorce and family law, and what it means for counsel selection. --- ### How AI Describes "Old Money" URL: https://www.5wpr.com/research/how-ai-describes/old-money/ Summary: Five AI engines describe an aesthetic through proxies, names, schools, fabrics. None describe the financial behavior. A 5W audit. --- ### How AI Describes Eli Lilly URL: https://www.5wpr.com/research/how-ai-describes/eli-lilly/ Summary: One molecule rewrote how five AI engines describe a 150-year-old pharma company in four years. A 5W audit of the Mounjaro effect. --- ### How AI Describes Goldman Sachs URL: https://www.5wpr.com/research/how-ai-describes/goldman-sachs/ Summary: AI engines describe Goldman by the headlines. The data tells a different story. A 5W audit of institutional rehabilitation. --- ### How AI Describes Goop URL: https://www.5wpr.com/research/how-ai-describes/goop/ Summary: The wellness brand that reads as pioneer in one engine and provocation in another. A 5W audit of Goop --- ### How AI Describes Hermès URL: https://www.5wpr.com/research/how-ai-describes/hermes/ Summary: Five AI engines, five different Hermès. A 5W audit of how ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews describe luxury --- ### The NFL AI Endorsement Index 2026: Who AI Names First URL: https://www.5wpr.com/ai-visibility-index/nfl-player-ai-endorsement-index/ Summary: 5W and Talent Resources score 50 NFL players on AI Citation Share before the 2026 season opener, revealing exactly who ChatGPT and Claude recommend to sponsors. Patrick Mahomes, Josh Allen and Travis Kelce capture the largest share of endorsement-intent AI answers among 50 NFL players tested by 5W and Talent Resources ahead of the 2026 season opener on September 9. The top five players account for 61% of all citations tested, while 30 of the 50 players score in the lowest tier. ## Which NFL players do AI engines name first for endorsements? ChatGPT, Claude, Gemini and Perplexity treat a small group of players as default answers to sponsor-facing prompts such as "which NFL player should a brand sign for a 2026 campaign." Patrick Mahomes leads the field with a Citation Share Score of 97, followed by Josh Allen at 94 and Travis Kelce at 91. Two players from the reigning Super Bowl LX champion Seattle Seahawks, quarterback Sam Darnold and running back Kenneth Walker III, round out the top five at 89 and 87. The NFL AI Endorsement Index 2026: Top 25 players by Citation Share Score across ChatGPT, Claude, Gemini and Perplexity. Source: 5W and Talent Resources GEO audit, August 2026, directional modeled estimates. RankPlayerTeamCitation Share ScoreEngine BreadthTier 1Patrick MahomesKansas City Chiefs974/4Retrieval Anchor 2Josh AllenBuffalo Bills944/4Retrieval Anchor 3Travis KelceKansas City Chiefs914/4Retrieval Anchor 4Sam DarnoldSeattle Seahawks894/4Retrieval Anchor 5Kenneth Walker IIISeattle Seahawks874/4Retrieval Anchor 6Lamar JacksonBaltimore Ravens864/4Cited 7Ja'Marr ChaseCincinnati Bengals824/4Cited 8Justin JeffersonMinnesota Vikings804/4Cited 9Cooper KuppSeattle Seahawks784/4Cited 10Joe BurrowCincinnati Bengals764/4Cited 11Drake MayeNew England Patriots744/4Cited 12Tyreek HillMiami Dolphins713/4Cited 13CeeDee LambDallas Cowboys693/4Cited 14Saquon BarkleyPhiladelphia Eagles673/4Cited 15A.J. BrownPhiladelphia Eagles633/4Moderate 16Myles GarrettCleveland Browns603/4Moderate 17Christian GonzalezNew England Patriots573/4Moderate 18Nick BosaSan Francisco 49ers543/4Moderate 19Trevor LawrenceJacksonville Jaguars492/4Moderate 20Bijan RobinsonAtlanta Falcons452/4Moderate 21Puka NacuaLos Angeles Rams412/4Recoverable 22T.J. WattPittsburgh Steelers382/4Recoverable 23Jordan LoveGreen Bay Packers342/4Recoverable 24George PickensDallas Cowboys291/4Recoverable 25Jason MyersSeattle Seahawks121/4Recoverable ## Why does the kickoff rematch lift four players and skip two? Six players from the two teams meeting in the NFL's September 9 season-opening rematch of Super Bowl LX appear in this Index: Sam Darnold, Kenneth Walker III and Cooper Kupp from Seattle, and Drake Maye and Christian Gonzalez from New England, plus Seattle kicker Jason Myers. Four of the six, Darnold, Walker, Kupp and Maye, crack the top eleven. Gonzalez, the Patriots cornerback who forced two turnovers in that Super Bowl, scores a 57. Myers, who converted five field goals, a Super Bowl record, in the same game, scores only a 12, the lowest mark in the top 25. Super Bowl LX drew 124.9 million U.S. viewers and a 70,823 attendance at Levi's Stadium on February 8, 2026, according to the official Super Bowl LX record. Why it works: The pattern is consistent with how generative engines weight recent, widely covered events alongside career-long fame. A February 2026 Super Bowl appearance, covered by NFL.com and wire services within the retrieval windows most AI engines draw from, is the most likely explanation for the citation advantage Darnold, Walker, Kupp and Maye show over players with comparable career statistics but no recent title-game coverage, a pattern 5W and Talent Resources first measured in the In Tune With AI athlete research published in May 2026. ## How many NFL players score in the lowest AI visibility tier? Thirty of the 50 players tested land in the Recoverable tier, the lowest of the four tiers used across the 5W AI Visibility Index series. The top five players tested, Mahomes, Allen, Kelce, Darnold and Walker, capture 61% of all endorsement-intent citations across the same 50-player pool. This mirrors the concentration 5W found at the franchise level in the NFL Citation Share Index 2026, which measured five NFL franchises, the Jets, Saints, Colts, Cardinals and Vikings, at a Citation Share Score of zero across 750 queries despite a combined enterprise value exceeding $28 billion, according to that May 2026 report. ## How often do AI engines cite the wrong NFL sponsor deal? Across the 50-player prompt set, 17% of AI-generated answers named a sponsor relationship that had expired, been renegotiated to a different brand, or never existed. That rate sits between the 18% fabrication rate 5W and Talent Resources measured in the Celebrity Endorsement Index and the 22% rate measured for beauty-category endorsements, both published in 2026. Why it works: Large language models generate the statistically likely continuation of a sponsor-athlete pairing based on training data that may predate a contract's expiration or renegotiation. Without a dated, structured update to a player's public sponsor record, the model has no signal that the pairing has changed, so it keeps citing the old deal. ## What does Talent Resources' Dunkin' campaign show about this shift? Talent Resources executed Dunkin's negotiations with Ben Affleck and Jennifer Lopez for Dunkin's first-ever Super Bowl commercial in 2023. Dunkin' has run a Ben Affleck-led Super Bowl campaign every year since, through Super Bowl LX in February 2026. "Brands come to us looking for the right celebrity or athlete to make them relevant, not just recognizable. We put Ben Affleck behind the drive-thru window for Dunkin's first-ever Super Bowl commercial, and Dunkin' has been one of the most talked-about brands in the game every year since. This Index shows the same standard now applies to AI: being liked by fans is not enough if the AI engine a brand's marketing team is using does not name you as the answer," said Mike Heller, Founder and CEO of Talent Resources. ## What is the 5W and Talent Resources scoring methodology? Each of the 50 players was scored across four weighted dimensions: AI Recognition (30%), Association Sentiment (25%), Engagement Depth (25%) and Cross-Engine Consistency (20%), the same weighting Talent Resources and 5W applied to the In Tune With AI athlete research published in May 2026 and the Creator Documentation Index published in June 2026. Prompts were run across ChatGPT, Claude, Gemini and Perplexity between August 18 and August 24, 2026, using standardized sponsor-facing and fan-facing query sets. AI Recognition measures whether an engine names the player unprompted in response to a category query. Association Sentiment measures whether the surrounding language is commercially favorable, neutral or negative. Engagement Depth measures whether the engine cites specific deals, statistics or storylines rather than a bare name. Cross-Engine Consistency measures how many of the four engines return the same player for the same prompt. ## Why does endorsement visibility matter before kickoff? Brands and agencies now build athlete endorsement shortlists using the same generative engines consumers use for research, so a player absent from AI answers is absent from a growing share of informal due diligence before a deal ever reaches a negotiating table. The NFL Owners Reputation Index 2026 found a 44-point spread in how AI systems render the league's 32 principal owners, and this Index finds a similar spread at the player level, concentrated even more tightly among a handful of names. Agencies that document a player's sponsor history, statistics and charitable work in structured, citable formats give AI engines a dated record to retrieve instead of a gap the model fills with a statistically likely guess. That gap is where the 17% fabrication rate in this Index originates. 5W's sports marketing practice works with professional athletes and the brands that sponsor them, building the documented, structured record that keeps AI engines citing a deal correctly through the season it is signed. 5W runs AI Search (GEO) programs for brands across consumer, B2B, financial services, healthcare, and technology, building the machine-readable footprint that gets brands cited, not just ranked. Learn more at 5W's GEO optimization practice. --- ### How AI Describes MrBeast URL: https://www.5wpr.com/research/how-ai-describes/mrbeast/ Summary: AI engines describe a YouTube creator. The 2026 reality is a $5B holding company. A 5W audit of creator-economy retrieval. --- ### AI Search Visibility for Trail Running Shoes: 2026 Brand Study URL: https://www.5wpr.com/research/ai-search-visibility-for-trail-running-shoes-2026-brand-study/ Summary: Trail running shoes AI Visibility Index: HOKA leads measured brand appearances, while iRunFar, OutdoorGearLab, and RunRepeat lead citations. 5W Research # AI Search Visibility for Trail Running Shoes: 2026 Brand Study HOKA leads brand appearances in measured trail running shoe answers, while publisher citations concentrate on iRunFar, OutdoorGearLab, and RunRepeat. HOKA has the highest measured appearance rate in trail running shoe answers: 82.1% of 134 returned answers named the brand. Salomon followed at 76.1%, and Altra at 70.9%. These results cover the measured trail running shoe questions, not all consumer searches or demand. The finding separates brand mention from source citation. HOKA was named often, but its owned domain had a 0% citation rate among 134 answers with retrieval evidence. Publisher sites had a different role. iRunFar appeared as a cited source in 74.6% of answers with retrieval evidence. For category marketers, a strong name-recognition position does not by itself mean the brand's own pages are supplying the evidence behind an answer. 134AI answers analyzed 82.1%HOKA appearance rate, the category leader 25Brands named across the study 46.5%Answers carrying retrieval evidence ## Which trail running shoe brands appear most often? HOKA holds the top point estimate, followed by Salomon, Altra, Brooks, Saucony, La Sportiva, Topo Athletic, and New Balance. The table uses returned answers as its denominator. It measures whether an answer named a brand, not whether an engine cited the brand's website. ### Trail running shoe appearance leaderboard ### AI visibility leaderboard Appearance rate is the share of the 134 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1HOKA110/13482.1%74.7%–87.7%3 2Salomon102/13476.1%68.2%–82.5%3.8 3Altra95/13470.9%62.7%–77.9%4.9 4Brooks91/13467.9%59.6%–75.2%5.7 5Saucony89/13466.4%58.1%–73.9%5.6 6La Sportiva76/13456.7%48.3%–64.8%6.7 7Topo Athletic58/13443.3%35.2%–51.7%6.7 8New Balance57/13442.5%34.5%–51%7.3 9Nike51/13438.1%30.3%–46.5%10.2 10Merrell, Inc47/13435.1%27.5%–43.5%9.4 11Inov-831/13423.1%16.8%–31%9.6 12The North Face, Inc23/13417.2%11.7%–24.4%10.3 13On22/13416.4%11.1%–23.6%7.8 14NNormal21/13415.7%10.5%–22.8%9.2 15OutdoorGearLab16/13411.9%7.5%–18.5%2.8 The ranking is useful as a sample snapshot, but the leading positions are close. Every adjacent pair from HOKA through New Balance has overlapping 95% appearance intervals. The sample therefore does not establish that one of those adjacent brands is more visible than the other. It does establish that each appeared at the measured rate shown in the leaderboard. ## How do the leading brands appear in answers? The top brands are present across all four systems, but their rates and answer positions differ. Appearance rates below use 134 returned answers. Owned-domain citation rates use 134 answers with retrieval evidence. ### Top brand profiles ### HOKA 82.1% appearance rate, with a 74.7% to 87.7% interval, puts HOKA at rank 1. Perplexity named HOKA in 34 of 35 answers, ChatGPT in 32 of 34, Gemini in 25 of 32, and Claude in 19 of 33. HOKA's average first mention position was 3. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### Salomon 76.1% appearance rate, with a 68.2% to 82.5% interval, puts Salomon at rank 2. Perplexity named Salomon in 34 of 35 answers and ChatGPT in 31 of 34. Gemini named it in 25 of 32, while Claude named it in 12 of 33. Salomon's average first mention position was 3.8. Its owned-domain citation rate was 0.7% among answers with retrieval evidence. ### Altra 70.9% appearance rate, with a 62.7% to 77.9% interval, puts Altra at rank 3. ChatGPT named Altra in 32 of 34 answers and Perplexity in 32 of 35. Gemini named it in 23 of 32, while Claude named it in 8 of 33. Altra's average first mention position was 4.9. Its owned-domain citation rate was 2.2% among answers with retrieval evidence. ### Brooks 67.9% appearance rate, with a 59.6% to 75.2% interval, puts Brooks at rank 4. Perplexity named Brooks in 34 of 35 answers, ChatGPT in 30 of 34, and Gemini in 24 of 32. Claude named Brooks in 3 of 33. Brooks' average first mention position was 5.7. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### Saucony 66.4% appearance rate, with a 58.1% to 73.9% interval, puts Saucony at rank 5. ChatGPT named Saucony in 28 of 34 answers. Perplexity and Gemini each named it in 25 answers, out of 35 and 32 respectively. Claude named it in 11 of 33. Saucony's average first mention position was 5.6. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### La Sportiva 56.7% appearance rate, with a 48.3% to 64.8% interval, puts La Sportiva at rank 6. ChatGPT named La Sportiva in 28 of 34 answers, ahead of Gemini's 21 of 32 and Perplexity's 22 of 35. Claude named it in 5 of 33. La Sportiva's average first mention position was 6.7. Its owned-domain citation rate was 0.7% among answers with retrieval evidence. ### Topo Athletic 43.3% appearance rate, with a 35.2% to 51.7% interval, puts Topo Athletic at rank 7. Perplexity named Topo Athletic in 23 of 35 answers and Gemini in 18 of 32. ChatGPT named it in 12 of 34, and Claude in 5 of 33. Topo Athletic's average first mention position was 6.7. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### New Balance 42.5% appearance rate, with a 34.5% to 51% interval, puts New Balance at rank 8. Perplexity named New Balance in 29 of 35 answers and ChatGPT in 21 of 34. Gemini named it in 4 of 32 and Claude in 3 of 33. New Balance's average first mention position was 7.3. Its owned-domain citation rate was 0% among answers with retrieval evidence. ## Where do the AI engines disagree? The four engines do not produce the same brand set. Perplexity returned 35 answers, ChatGPT 34, Claude 33, and Gemini 32. Their different models and retrieval tools can produce a gap that reflects system design rather than a brand's category-wide visibility. ### Brand appearances by engine ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate HOKA97.1%94.1%78.1%57.6% Salomon97.1%91.2%78.1%36.4% Altra91.4%94.1%71.9%24.2% Brooks97.1%88.2%75%9.1% Saucony71.4%82.4%78.1%33.3% La Sportiva62.9%82.4%65.6%15.2% Topo Athletic65.7%35.3%56.3%15.2% New Balance82.9%61.8%12.5%9.1% Nike54.3%47.1%43.8%6.1% Merrell, Inc54.3%67.6%12.5%3% Inov-817.1%35.3%37.5%3% The North Face, Inc45.7%14.7%6.3%0% Claude is the clearest separator among the top brands. It named HOKA in 19 of 33 answers, Salomon in 12, Saucony in 11, Altra in 8, La Sportiva and Topo Athletic in 5 each, Brooks and New Balance in 3 each, and Nike in 2. Perplexity named HOKA, Salomon, and Brooks in 34 of its 35 answers. ChatGPT named Altra in 32 of 34 answers, while Perplexity did so in 32 of 35. Several brands are concentrated in one system. OutdoorGearLab appeared in 16 of Gemini's 32 answers and in none of the other engines' returned answers. RunRepeat appeared in 8 Gemini answers and none elsewhere. Altra Lone Peak and Fresh Foam X Hierro each appeared in 12 and 9 Perplexity answers respectively, with no appearances in the other three engines. NNormal appeared in 11 Perplexity answers, none in ChatGPT, 8 in Gemini, and 2 in Claude. New Balance shows another large split: 29 of 35 Perplexity answers versus 4 of 32 Gemini answers and 3 of 33 Claude answers. Merrell, Inc appeared in 23 of 34 ChatGPT answers but 4 of 32 Gemini answers and 1 of 33 Claude answers. The North Face, Inc appeared in 16 of 35 Perplexity answers and none of Claude's 33 answers. These patterns mean a single-engine audit can conceal material variation. ## Which brands lead each trail running shoe question type? The demand-cluster view separates general trail running shoe questions from ultramarathon and wide-foot questions. It is the appropriate view for deciding where a brand's appearance is concentrated, rather than treating the overall leaderboard as a product-fit recommendation. ### Demand-cluster appearances ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up Subcategory: best trail running shoes for wide feet46HOKA82.6%Altra 78.3%, Salomon 71.7%, Brooks 69.6% Subcategory: trail running shoe brands ranked16HOKA93.8%Salomon 93.8%, Saucony 87.5%, Altra 75% Subcategory: ultramarathon trail shoe brands ranked16HOKA100%Salomon 81.3%, Altra 81.3%, Saucony 81.3% Subcategory: wide-foot trail running shoe brands9Altra88.9%Topo Athletic 88.9%, HOKA 77.8%, Salomon 77.8% General best-in-category4HOKA100%Salomon 100%, Altra 75%, Brooks 75% Primary use case4HOKA50%Salomon 50%, Altra 50%, Brooks 50% Specific need or condition4Salomon75%Inov-8 75%, HOKA 50%, Altra 50% Quality and premium tier4La Sportiva100%HOKA 75%, Salomon 75%, Saucony 75% Beginners and first purchase4HOKA75%Brooks 75%, Saucony 75%, New Balance 75% Comparison and evaluation4HOKA75%Salomon 75%, Altra 75%, Brooks 75% Durability and longevity4Brooks100%HOKA 75%, Salomon 75%, Altra 75% Specialist / advanced use4HOKA100%Salomon 100%, La Sportiva 100%, Brooks 75% Emerging and new entrants4On50%NNormal 50%, Topo Athletic 25%, Nike 25% Where to buy4HOKA75%Salomon 75%, Altra 75%, Brooks 50% Trust and recommendation4HOKA100%Salomon 100%, Altra 75%, Brooks 75% Price and value3HOKA100%Salomon 100%, Altra 100%, Saucony 100% Read each cluster as a measured question set. A brand can have a strong overall appearance rate while its visibility is concentrated in only one of the covered question types. The overall ranking does not establish product performance, fit, or suitability for an individual runner. ## Which sites supply citations in trail running answers? Publisher sites supply a large share of the retrieval evidence. iRunFar was cited in 100 answers, or 74.6% of the 134 answers with retrieval evidence. OutdoorGearLab followed with 82 citations and RunRepeat with 77. ### Owned-domain citation rates ### Owned-domain citation rate Share of the 134 answers with retrieval evidence that cited a URL on the brand's own website. HOKA 0% · 0/134 Salomon 0.7% · 1/134 Altra 2.2% · 3/134 Brooks 0% · 0/134 Saucony 0% · 0/134 La Sportiva 0.7% · 1/134 Topo Athletic 0% · 0/134 New Balance 0% · 0/134 Nike 1.5% · 2/134 Merrell, Inc 0.7% · 1/134 Inov-8 0.7% · 1/134 The North Face, Inc 0% · 0/134 Among citation-eligible entities, OutdoorGearLab had a 61.2% owned-domain citation rate, RunRepeat had 57.5%, and REI Co-op had 47%. Altra had 2.2%, Nike had 1.5%, and Salomon, La Sportiva, Merrell, Inc, Inov-8, NNormal, and Prodigio each had 0.7%. HOKA, Brooks, Saucony, Topo Athletic, New Balance, The North Face, Inc, On, Adidas, ASICS, Dynafit, Cascadia, and Fresh Foam X Hierro had 0%. ### Cited publisher domains ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration irunfar.com100/13474.6% outdoorgearlab.com82/13461.2% runrepeat.com77/13457.5% trailandkale.com69/13451.5% rei.com63/13447% runnersworld.com58/13443.3% treelinereview.com49/13436.6% reddit.com46/13434.3% adventurealan.com40/13429.9% hikingfeet.com36/13426.9% outsideonline.com34/13425.4% switchbacktravel.com32/13423.9% The source chart shows a publisher-heavy evidence mix. Trail and Kale appeared in 69 answers with retrieval evidence, REI Co-op in 63, and Runner's World in 58. Reddit appeared in 46. These figures measure citation occurrence in answers with retrieval evidence, not traffic, readership, product quality, or editorial endorsement. ## What does this mean for trail running shoe brands? Interpretation: brands need to track two separate outcomes. Brand appearances show whether a name enters an answer. Owned-domain citations show whether the brand's own site is part of the evidence used in answers with retrieval evidence. HOKA illustrates the separation. It has the highest appearance rate but no owned-domain citations in this sample. OutdoorGearLab and RunRepeat show the reverse pattern: low appearance rates as named entities, paired with high citation rates for their domains. A brand team can use that contrast to audit product pages, fit guidance, and material that independent publishers cite. Interpretation: engine-specific review matters. Perplexity, ChatGPT, Gemini, and Claude produced different patterns for New Balance, Merrell, Inc, Brooks, OutdoorGearLab, RunRepeat, NNormal, and several product names. Measure the engines where the brand expects to be found, then compare brand mentions with the cited domains behind those answers. ## How was the trail running shoe study measured? This flagship study measured 24 stratified prompts three times per engine. It used Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). The study attempted 288 answers and received 134 returned answers. ### Study method ### How this study was measured - Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each. - 134 answers returned of 288 attempted, 134 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. Appearance rates use returned answers as the denominator. Citation and source rates use answers with retrieval evidence as the denominator. The study recorded 134 answers with retrieval evidence, a 46.5% retrieval rate, and 154 answers without retrieval evidence. Results are limited to these trail running shoe prompts on the measurement date. The study cannot measure consumer demand, sales, or product quality. ## Frequently asked questions ### Which brand has the highest appearance rate? HOKA has the highest measured appearance rate at 82.1% of returned answers. Its 95% appearance interval is 74.7% to 87.7%. ### Is HOKA more visible than Salomon? HOKA's point estimate is 82.1% and Salomon's is 76.1%. Their 95% intervals overlap, so this sample does not establish that HOKA is more visible than Salomon. ### Which engine named HOKA most often? Perplexity named HOKA in 34 of 35 returned answers. ChatGPT named HOKA in 32 of 34, Gemini in 25 of 32, and Claude in 19 of 33. ### Which owned domains were cited most often? OutdoorGearLab had a 61.2% owned-domain citation rate among answers with retrieval evidence. RunRepeat had 57.5%, and REI Co-op had 47%. ### Which publisher was cited most often? iRunFar was cited in 100 answers with retrieval evidence. Its answer penetration was 74.6%. ### Do appearance rates and citation rates use the same denominator? No. Appearance rates use 134 returned answers. Citation rates use 134 answers with retrieval evidence in this study. ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: Trail Running Shoes - Measurement date: 2026-08-27 - Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines - Answers attempted: 288 - Answers returned (appearance denominator): 134 - Answers with retrieval evidence (citation denominator): 134 (46.5% of attempted) - Answers that failed outright: 153 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable. ### How the prompts were sampled Fixed sampling frame: client-named subcategories are allocated a guaranteed cell each, then a documented set of generic commercial demand clusters (general best, primary use case, specific need, quality tier, price/value, beginner, comparison, durability, specialist, emerging, retail channel, trust) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope Subcategory: best trail running shoes2demand specific to "best trail running shoes" within Trail Running Shoes Subcategory: trail running shoe brands ranked2demand specific to "trail running shoe brands ranked" within Trail Running Shoes Subcategory: best trail running shoes for ultramarathon2demand specific to "best trail running shoes for ultramarathon" within Trail Running Shoes Subcategory: ultramarathon trail shoe brands ranked2demand specific to "ultramarathon trail shoe brands ranked" within Trail Running Shoes Subcategory: best trail running shoes for wide feet2demand specific to "best trail running shoes for wide feet" within Trail Running Shoes Subcategory: wide-foot trail running shoe brands2demand specific to "wide-foot trail running shoe brands" within Trail Running Shoes General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier Primary use case1the dominant everyday use case for this category Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve Quality and premium tier1which options are highest quality, most durable or best made Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it Beginners and first purchase1what a first-time buyer with no knowledge of the category should get Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis Durability and longevity1which brands last, hold up over time, or fail early Specialist / advanced use1advanced, professional or enthusiast-level demand inside the category Emerging and new entrants1newer entrants, recent launches and shifts buyers are only starting to ask about Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace Trust and recommendation1which brands are most recommended or most trusted by reviewers and experts ### The exact prompts #PromptClusterShape 1best trail shoessub_best_trail_running_shoesshort 2Which trail running shoe brands rank best overall?sub_best_trail_running_shoesmedium 3top trail shoe brandssub_trail_running_shoe_brandshort 4Which trail running shoe brands should be ranked highest?sub_trail_running_shoe_brandmedium 5best ultramarathon trail shoessub_best_trail_running_shoesshort 6Which trail shoe brands are best for ultramarathon distances?sub_best_trail_running_shoesmedium 7ultramarathon shoe brands rankedsub_ultramarathon_trail_shoeshort 8How do trail shoe brands rank for ultramarathons?sub_ultramarathon_trail_shoemedium 9best wide trail shoessub_best_trail_running_shoesshort 10Which trail running brands make the best wide-fit shoes?sub_best_trail_running_shoesmedium 11wide trail shoe brandssub_wide_foot_trail_running_short 12Which brands are best for wide-foot trail runners?sub_wide_foot_trail_running_medium 13best trail running brandsgeneral_bestshort 14Which trail running shoe brands are best for everyday off-road running?use_casemedium 15best trail shoes for mudspecific_needmedium 16Which trail running shoe brands make the most premium, well-built models?premium_qualitymedium 17best value trail shoe brandsvalue_pricemedium 18What trail running shoe brands should a first-time trail runner consider?beginnermedium 19Which trail running shoe brands are better for grip, cushioning, and fit?comparisonlong 20Which trail running shoe brands have the strongest reputation for long-term durability?durabilitylong 21What trail running shoe brands do experienced mountain runners choose for technical terrain?specialistlong 22Which newer trail running shoe brands are gaining credibility with serious runners?emerginglong 23Which trail running shoe brands are best to buy through specialty running retailers?retaillong 24Which trail running shoe brands are most trusted by independent reviewers and coaches?trustlong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search72353548.6% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool72343447.2% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72323244.4% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool72333345.8% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1HOKA110/13482.1%79.7%74.7%–87.7%0/134 (0%) 2Salomon102/13476.1%75.4%68.2%–82.5%1/134 (0.7%) 3Altra95/13470.9%68.4%62.7%–77.9%3/134 (2.2%) 4Brooks91/13467.9%66.9%59.6%–75.2%0/134 (0%) 5Saucony89/13466.4%64.2%58.1%–73.9%0/134 (0%) 6La Sportiva76/13456.7%56%48.3%–64.8%1/134 (0.7%) 7Topo Athletic58/13443.3%42.7%35.2%–51.7%0/134 (0%) 8New Balance57/13442.5%42.9%34.5%–51%0/134 (0%) 9Nike51/13438.1%35.6%30.3%–46.5%2/134 (1.5%) 10Merrell, Inc47/13435.1%38.4%27.5%–43.5%1/134 (0.7%) 11Inov-831/13423.1%22.2%16.8%–31%1/134 (0.7%) 12The North Face, Inc23/13417.2%16.3%11.7%–24.4%0/134 (0%) 13On22/13416.4%17%11.1%–23.6%0/134 (0%) 14NNormal21/13415.7%16.1%10.5%–22.8%1/134 (0.7%) 15OutdoorGearLab16/13411.9%10.9%7.5%–18.5%82/134 (61.2%) 16Adidas13/1349.7%8.3%5.8%–15.9%0/134 (0%) 17Altra Lone Peak12/1349%8.2%5.2%–15%3/134 (2.2%) 18ASICS11/1348.2%7.6%4.6%–14.1%0/134 (0%) 19REI Co-op10/1347.5%6.8%4.1%–13.2%63/134 (47%) 20Fresh Foam X Hierro9/1346.7%6.1%3.6%–12.3%0/134 (0%) 21RunRepeat8/1346%5.2%3.1%–11.3%77/134 (57.5%) 22Flagship Models8/1346%5.7%3.1%–11.3%domain not established — excluded from citation metrics 23Dynafit6/1344.5%4.2%2.1%–9.4%0/134 (0%) 24Cascadia6/1344.5%3.6%2.1%–9.4%0/134 (0%) 25Prodigio6/1344.5%4.2%2.1%–9.4%1/134 (0.7%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - HOKA Bondi 9 counted under HOKA Bondi (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Lone Peak counted under Altra (product_line) - Altra Lone Peak 8/9 counted under Altra Lone Peak (model) - Kjerag 2.0 counted under Kjerag (model) - Peregrine 16 counted under Peregrine (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Norda 005 counted under Norda (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Peregrine 16 counted under Peregrine (model) - Peregrine 16 counted under Peregrine (model) - Lone Peak 9 counted under Altra (model) - Peregrine 16 counted under Peregrine (model) - Lone Peak 9 counted under Altra (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) - Peregrine 16 counted under Peregrine (model) - Lone Peak 9 counted under Altra (model) - Peregrine 15 counted under Peregrine (model) - Vista counted under UPPAbaby (product_line) - Lone Peak 9 counted under Altra (model) - Altra Lone Peak 9 counted under Altra Lone Peak (model) Held for human classification before the next study: La Sportiva, Saucony Peregrine 16, Saucony Xodus Ultra 4, HOKA Speedgoat 7, Nike ACG Pegasus Trail, Nike Pegasus Trail 5, Brooks Cascadia 19, Brooks Caldera 7, Altra Timp 6, La Sportiva Prodigio Pro, La Sportiva Bushido III, Topo Athletic Ultraventure 4, Topo Athletic Traverse, Salomon Speedcross 6, Salomon Pulsar, Salomon Aero Glide 4 GRVL, Adidas Terrex Agravic 4, Adidas Terrex Agravic Speed Ultra 2, Salomon Ultra Glide 4, Inov-8, Switchback Travel, La Sportiva S.p.A, Outside Online, Tom’s Guide, Re-rank these by a specific priority, La Sportiva Prodigio 2, Altra Experience Wild 3, Brooks Cascadia 20, The North Face Vectiv Enduris 4, The North Face Altamesa 500 V2, Adidas Agravic Trail Ultra 2, Drop, Trail Scrambling, Drop Alternative, Reputation, REI Co-op Expert Advice, La Sportiva Akasha II, Brooks Cascadia Elite, Kailas, Hoka Speedgoat 6. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-08-27. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. - 1 of 25 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero. --- ### Why Regional Grocery Chains Appear First in Local AI Answers URL: https://www.5wpr.com/research/why-regional-grocery-chains-appear-first-in-local-ai-answers/ Summary: Discover why regional grocery chains are prioritized in local AI search results and how this impacts their visibility and customer engagement. AI Visibility Index # Why Regional Grocery Chains Appear First in Local AI Answers New Data Reveals How City Names, Local Media, and Store Location Impact AI Answers5WPR's most recent grocery study highlights a notable divide in AI answers between national brand visibility and local search results. In 91.4% of 673 cases, the top-listed grocery chain operated in… New Data Reveals How City Names, Local Media, and Store Location Impact AI Answers 5WPR's most recent grocery study highlights a notable divide in AI answers between national brand visibility and local search results. In 91.4% of 673 cases, the top-listed grocery chain operated in only a few of the 15 markets we tested, while only 7.4% of responses prioritized chains with widespread national reach (12–15 markets). While large national chains like Trader Joe's appeared most often in the model's responses overall, they were rarely its top choice; Trader Joe's led in only 2.7% of answers. Conversely, regional chains like H-E-B appeared less often, but when they did appear, they were listed first 62.2% of the time. ## What the Follow-Up Consumer-App Test Measured Our first study established a regional-versus-national visibility pattern using the models' APIs. This follow-up data used consumer apps to learn why that gap occurs. ### ChatGPT and Gemini Tests Across Tampa, Austin and Phoenix Our team compared responses from ChatGPT and Gemini to two common user questions, "best grocery store" and "biggest grocery store," in Tampa, Austin, and Phoenix. We ran each prompt three times in a new chat window. Our test also compared an explicit city, such as "in Tampa," with "near me" and added a six-search control with Gemini's location sharing turned off. #### Our test results indicate that regional chains often lead AI answers because city-specific questions draw from local media sources that focus on regional and neighborhood chains. The model then summarizes the stores listed in those sources. Infographic summarizing the follow-up consumer-app grocery test: three cities, two AI assistants, two question types, three fresh chats per condition, one matching result across six explicit-city tests, and six Gemini location-off control searches. Figure 1. Follow-up consumer-app test design and headline findings. Source: 5WPR manual tests of ChatGPT and Gemini, July 2026. ## Why "Best Grocery Store" Questions Used Local Opinion Sources Asking for the "best" grocery store is inherently subjective; it lacks a clear, measurable definition like price or square footage. Because "best" can mean anything from affordability to product quality or convenience, AI models often rely on external validation to answer these types of users' queries. ### Local Polls and Editorial Coverage Determined the First-Place Results In our testing, ChatGPT consistently identifies a "winner" by citing local publications: ### Publix, Central Market and AJ's Fine Foods Led Their City Tests - Tampa: cited a Tampa Magazine reader poll that ranked Publix first. - Austin: relied on an Eater Austin article that identified Central Market as the leading choice. - Phoenix: drew from local editorial coverage to recommend AJ's Fine Foods. The model's reliance on local coverage explains why regional chains frequently outperform national brands in AI results. Although national chains have broad brand recognition, regional grocers appear more often in local news, reader polls, and community guides. Local coverage provides the specific, verifiable data AI models rely on to answer subjective questions. ## Why "Biggest Grocery Store" Questions Source Store-Size Data ### How a One Word Change Impacted the Model's Output Changing one word changed both the source and the store. The model takes "biggest" literally, although the question leaves room for interpretation. Its answer referred to total building size, grocery-selling space, product selection, store count, or market share. ChatGPT's responses pull from sources containing specific store measurements (square footage): City "Best" result "Biggest" result Tampa Publix Publix Gandy Austin Central Market H-E-B SoCo Phoenix AJ's Fine Foods Fry's Marketplace Infographic comparing the first store ChatGPT listed for best versus biggest grocery store searches in Tampa, Austin, and Phoenix. Figure 2. Changing one word changed first place in Tampa, Austin, and Phoenix. Source: 5WPR follow-up consumer-app tests, July 2026. ### Square Footage Produced Different Results in Austin and Phoenix In Austin, sources for "best" shared editorial coverage and listed Central Market. "Biggest" shared published store information and produced H-E-B SoCo. In Phoenix, "best" produced AJ's Fine Foods from local food coverage. "Biggest" produced Fry's Marketplace from reporting that documented the store's size. A store's classification as regional or national did not determine every answer. For example, Fry's is owned by the national brand Kroger; ChatGPT correctly listed it first in Phoenix because local data identified it as the city's largest store. Our findings suggest that AI models do not inherently favor regional chains. Instead, the prompts' specific wording guides the model toward the most relevant evidence, prioritizing information sources that provide the most direct answer. ## Why City-Specific Prompts Produced More Consistent ChatGPT Results ### Explicit City Questions Produced Stable Results A city-specific prompt significantly stabilizes ChatGPT's responses. When we tested precise city names, the model provided the same store recommendation every time: - Tampa: "Best" returned Publix; "Biggest" returned Publix Gandy. - Austin: "Best" returned Central Market; "Biggest" returned H-E-B SoCo. - Phoenix: "Best" returned AJ's Fine Foods; "Biggest" returned Fry's Marketplace. ### How "Near Me" Changed the Model's Answer In contrast, "near me" searches were inconsistent. Queries like "Best grocery store near me" returned varying results, including Publix and Trader Joe's, across multiple requests. We found that specifying a city provides the model with a precise search term that matches relevant local articles, whereas "near me" introduces ambiguity, allowing broader, national rankings to influence the results. These results show that minor adjustments in phrasing, including adding a specific city, can fundamentally change the structure and reliability of the model's response. ## How Location Sharing Changed Gemini's Tampa Results Gemini added another variable to the model's answer: device location. ### Location On Produced Three Different Store Options With location sharing on, Gemini sourced three different stores across three searches for "best grocery store in Tampa": Sanwa, Key Foods, and Duckweed Urban Grocer. All three stores are small- to mid-sized local markets near the test location (Seminole Heights, Tampa, FL). For "biggest grocery store in Tampa," Gemini returned Whole Foods, Publix, and Walmart across each search. ### Turning of Location Produced Consistent Results From there, the follow-up test repeated both questions with location sharing turned off. Gemini listed Publix three times for "best" and Walmart three times for "biggest." Turning off location sharing changed a rotating set of nearby options into one consistent answer for each question. While the control test doesn't fully explain Gemini's internal logic, the result is useful: location sharing influences the sources and stores prioritized by the model. Infographic comparing Gemini Tampa grocery results with location sharing on and off for best and biggest searches across three fresh chats. Figure 3. Gemini results changed when location sharing was turned off. Source: 5WPR manual consumer-app tests, July 2026. For marketers, local AI visibility includes, but isn't limited to, two different forms of evidence: - Written evidence, including polls, reviews, local reporting and published store facts - Location evidence, including accurate map listings, addresses, categories, hours and proximity The data also shows that a retailer can perform well in local editorial coverage and still lose visibility when a location-based answer draws on incomplete or inaccurate map data. ## Why ChatGPT and Gemini List Grocery Stores Differently ### The Models Don't Agree Often The two models agreed on the first store in one of the six original explicit-city conditions: "biggest grocery store in Austin," where both listed H-E-B. For "best grocery store in Austin," ChatGPT listed Central Market, while Gemini went with H-E-B. In Phoenix, ChatGPT listed AJ's Fine Foods for "best," while Gemini listed WinCo. In Tampa, ChatGPT consistently listed Publix, while Gemini's location-enabled answers moved among smaller neighborhood stores. ### Each Model Used a Mix of Local Sources and Location Data The disagreement among answers means retailers should not treat "AI visibility" as a single universal result. Each model may pull a different mix of editorial pages, company information, search results, and location data. A chain can appear first in ChatGPT and stay absent from Gemini's first position for the same question. Reporting a single combined visibility score would hide the difference. Infographic comparing the first store listed by ChatGPT and Gemini across six explicit-city grocery searches; the assistants listed the same store first once. Figure 4. ChatGPT and Gemini listed the same store first in one of six explicit-city tests. Source: 5WPR manual consumer-app tests, July 2026. ## Why Regional Grocery Chains Appear First in Local AI Answers Our follow-up testing identified four key reasons why regional grocery chains frequently outperformed national brands in AI search results: ### Regional Chains Receive More City-Specific Media Coverage - Concentrated Local Coverage: Regional chains dominate local media. City publications frequently focus on popular stores and their leadership. ### Store Pages Build Stronger City and Neighborhood Connections - Deep Community Ties: These grocery chains are often pillars in their communities. Local news, community programs, and neighborhood references consistently link these brands to their specific markets. ### Local Sources Answer "Best," "Biggest," and "Near Me." - Dominance in Source Material: Regional brands control the types of sources AI models tend to rely on to answer specific questions. Local polls provide the data for "best" queries, local reporting drives "biggest" results, and map data powers "near me" searches. ### Regional Chains Often Serve as the Local Default - Positioning as the "Default" Choice: Regional chains are often sourced as primary, everyday options, while national chains are often associated with specific niches, such as bulk items at Costco or organic products at Whole Foods. ## How National Grocery Chains Can Improve Local AI Visibility ### National Brand Needs Location-Specific Corporate Communications National scale shouldn't stop a retailer from building local relevance; the first step is to approach their communication strategy on a market-by-market basis. Each store location benefits from a dedicated digital presence featuring its city, neighborhood, services, and local offerings. ### Map Content to Specific Language Triggers Language models process search modifiers using distinct data sources. The superlative "best" forces ChatGPT to cite subjective local opinion polls, whereas the superlative "biggest" triggers the model to extract documented physical measurements. Marketing directors should separate their asset creation to supply both objective specifications and subjective reputation metrics. ### Connect Individual Stores to Local Markets Brands need to build public records that connect individual stores to the communities they serve. While national recognition helps a chain appear consistently in AI answers, local media helps secure top-placement recommendations. Right now, regional chains outperform national brands in these types of searches because local media sources give AI models the specific, city-level connections they need to prioritize a store in their answers. ## Follow-Up Test Methodology and Limitations ### Test Design 5WPR conducted a directional test using the consumer versions of ChatGPT and Gemini. The follow-up examined how question wording, explicit city names, and location sharing affected the grocery store each assistant listed first. The follow-up was separate from the earlier 673-response API study. The core test included 48 searches. For each assistant, 5WPR asked "best grocery store" and "biggest grocery store" questions in four conditions: "near me," Tampa, Austin, and Phoenix. Each condition ran three times in a new chat. The team then completed six additional Gemini control searches by repeating "best grocery store in Tampa" and "biggest grocery store in Tampa" three times each with location sharing turned off. The complete follow-up included 54 consumer-app searches. ### Controls and Recorded Variables The protocol used the same prompt wording across conditions and instructed the tester to begin each repetition in a fresh chat, with memory, custom instructions, and chat history disabled. The research log recorded the assistant, model shown, exact prompt, location target, question type, geographic framing, repetition number, first chain listed, regional or national classification, whether the assistant asked a clarifying question, the number of chains listed, whether a map or Places card appeared, and test notes. The primary outcome was the first grocery chain listed in the main response. The analysis retained distinct banners and store locations, such as Central Market and H-E-B SoCo, when the assistant presented them as separate results. ### Interpretation and Limitations The analysis reports observed counts, including "three of three searches," rather than estimating how often all users will receive the same answer. The test identifies relationships among prompt wording, location settings, cited sources, and first-place results. It does not reveal an assistant's ranking system or prove that one source caused a store to appear first. The study covered two consumer assistants, three markets, two question types, three repetitions per core condition, one tester location, and one collection period. Results may change with model updates, search indexes, account settings, device location, and the time of collection. --- ### How AI Describes OpenAI vs Anthropic URL: https://www.5wpr.com/research/how-ai-describes/openai-vs-anthropic/ Summary: Engines describing the companies that build them. 5W audits self-narration bias across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews. --- ### Inaugural Report — AmLaw 200 Baseline URL: https://www.5wpr.com/research/legal/baseline/ Summary: The inaugural baseline of how AI engines cite, recommend, and discover AmLaw 200 firms: methodology, the Legal Retrieval Stack, and the Wikipedia gap. --- ### M&A and Corporate URL: https://www.5wpr.com/research/legal/ma-corporate/ Summary: Five firms answer roughly two-thirds of M&A questions, and the Wikipedia-rich anchor, not revenue rank, explains which five. From the 5W legal index. --- ### Personal Injury URL: https://www.5wpr.com/research/legal/personal-injury/ Summary: Four decades of billboard, TV, and SEO spending built a personal injury lawyer citation base BigLaw cannot replicate. See the AmLaw revenue gap. --- ### The Strollers & Car Seats AI Visibility Index 2026 URL: https://www.5wpr.com/ai-visibility-index/stroller-car-seat-ai-visibility/ Summary: UPPAbaby leads stroller and car seat AI citations at 19.8% while a 2020 safety investigation still correlates with Evenflo's suppressed citation share. 5W analyzed 2,400 AI responses across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews to see which stroller and car seat brands these engines recommend to parents. UPPAbaby captured 19.8% of all brand citations, the largest share measured. Evenflo, a brand with wide mass-retail distribution, captured just 3.6%, a gap that correlates with a 2020 investigative-reporting event AI engines still surface when asked about the brand today. UPPAbaby 19.8% Chicco 15.4% Graco 12.6% Doona 9.7% Nuna 8.3% Evenflo 3.6% Top brands by citation share. This is 5W's first Strollers & Car Seats AI Visibility Index, part of the 5W AI Visibility Index Series. ## Key findings Table A. Key findings. Data: 5W AI Visibility Index. FigureFinding 19.8%UPPAbaby's citation share, the largest of any brand tracked 47.8%Combined share held by the top three brands: UPPAbaby, Chicco, Graco 3.6%Evenflo's citation share, well below what its retail distribution would predict 9.7%Doona's share on a single hybrid product, more than Cybex and Maxi-Cosi combined No brandconsistently leads AI answers to "is it safe to buy a used car seat?" 4brands in this index also named in the 2020 Congressional booster-seat probe 12testing waves across 5 engines, January to June 2026 ## UPPAbaby leads. Evenflo's 2020 safety history still surfaces. UPPAbaby holds 19.8%, Chicco 15.4% (largely via the KeyFit 30), Graco 12.6%. Evenflo holds 3.6%, well below what its shelf presence at Walmart, Target, and Amazon would suggest. This index measures correlation, not causation, but the gap lines up with a dated event: a February 6, 2020 ProPublica investigation found Evenflo marketed its Big Kid booster seats as "side impact tested" despite internal footage showing dummies thrown from their shoulder belts. A House Oversight inquiry followed on February 12, 2020, requesting records from Chicco, Britax, Graco, and three other manufacturers, not Evenflo alone. Evenflo appears to carry a heavier AI visibility penalty than the others swept into that same industry-wide inquiry. Quantifying that gap directly, rather than inferring it, is our next planned module. That distinction, AI's capacity to keep a six-year-old investigation attached to a brand's current record long after the news cycle moved on, is the central finding here. ## About this study Strollers and car seats are often a single purchase: travel systems bundling a car seat with a matching stroller account for a large share of first-time-parent registries, researched under time pressure with a safety dimension strollers alone don't carry. Some of that research now happens inside an AI engine, the same questions parents once took to friends, pediatricians, and registry guides. This index measures which brands own that answer, alongside the Pet Industry AI Visibility Index 2026. ## Rankings UPPAbaby 19.8% Chicco 15.4% Graco 12.6% Doona 9.7% Nuna 8.3% Britax 7.2% Baby Jogger 6.1% Cybex 5.4% Evenflo 3.6% Maxi-Cosi 3.4% Clek 3.0% Peg Perego 2.2% Thule 1.6% 4moms 1.1% Silver Cross 0.6% Table 1. Citation share and key drivers by brand. BrandShareKey citation driver UPPAbaby19.8%Registry integration (Babylist, Amazon), broad review base, Vista/Cruz compatibility content Chicco15.4%KeyFit 30 install simplicity, current CR crash-test score, wide installed base Graco12.6%Widest retail distribution, price-comparison content depth Doona9.7%Category-defining hybrid with no direct competitor Nuna8.3%PIPA/MIXX documentation, premium-listicle presence Britax7.2%ClickTight content, cited in crash-test comparisons Baby Jogger6.1%Category-originating brand (City Mini), fitness-blog citations Cybex5.4%Design-press coverage, Priam/Gazelle recognition Evenflo3.6%Wide shelf presence, suppressed authority citations post-2020 Maxi-Cosi3.4%Vehicle-fit content, Dorel-family cross-linking Clek3.0%Foonf/Fllo safety documentation Peg Perego2.2%Heritage citations, narrow US footprint Thule1.6%Outdoor-press citations, thin safety content 4moms1.1%Novelty-feature press, weak review volume Silver Cross0.6%Strong UK share, limited US footprint ## Three surprises Evenflo carries a heavier penalty than others named in the same probe. Chicco, Britax, and Graco were named in the same records request; only Evenflo's share sits this far below its retail footprint. The likely reason: the reporting centered on Evenflo's own marketing claim, not a shared industry standard, giving an AI engine a specific, attributable contradiction to retrieve. Doona outranks brands with far larger catalogs. Doona's single hybrid product (9.7%) beats Cybex and Maxi-Cosi combined (8.8%). A category-defining product with no direct substitute concentrates citation share regardless of company size. Chicco's share rides on one SKU, not the brand. Consumer Reports scores the KeyFit 30 at 86 overall, "Better" crash protection, CR Recommended, though several seats (Maxi-Cosi Mico Luxe+ at 88, Graco's Premier SnugRide SnugFit 35 XT at 87) score higher. Chicco's strength here is install simplicity and review volume on one model, not category-best crash performance. ## What every brand should build Publish dated, product-specific crash-test and certification pages (FMVSS 213 test date, independent lab results), not general safety marketing. Build a vehicle make/model fit database. Publish an expiration and used-product safety guide. Maintain a dated safety-history page for any past recall or investigation. Structure product data for registries in machine-readable fields, not paragraph copy. ## Two open lanes "Is it safe to buy a used car seat?" No brand leads this high-intent safety question; answers default to generic guidance. "Does [car seat] fit a [vehicle]?" Answered today mostly by forum threads and crowdsourced spreadsheets, not brand content. ## Limitations This index measures correlation, not causation; the Evenflo finding is a timing/content correlation, not a source-attribution analysis. It carries no retailer sales-volume data (Circana, sell-through, Amazon data) for any brand, retail-distribution language describes shelf presence only. The crash-test/certification classification behind the surprises above is an editorial, manually-reviewed judgment, not an independently audited standard. AI responses are non-deterministic; each engine was queried through its standard consumer interface, so this reflects whichever model version each platform served on each test date, not one fixed model held constant across six months. US English-language prompts only. This measures citation share, not crash safety performance, consult NHTSA and Consumer Reports directly for that. ## Commentary Parents used to ask a pediatrician or a friend which car seat to buy. Now some ask an AI engine first, and the engine remembers both a brand's best product and its worst headline. The real question isn't which stroller AI recommends. It's how long AI keeps a six-year-old investigation attached to a brand's name after the news cycle moves on. Ronn Torossian, Founder and Chairman, 5W AI Communications ## What CMOs should do next Run the audit: type your top 20 buyer-intent queries into ChatGPT, Claude, Gemini, and Perplexity. Address any documented safety event with a dated, public remediation page. Structure product data for registries and comparison platforms. Claim the used-car-seat and vehicle-fit questions before a competitor does. ## Methodology 40 consumer-intent prompts across purchase, comparison, trust/safety, cost, and fit intent, run across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews (US, desktop) through each platform's standard consumer interface. 12 testing waves, January to June 2026, two per month, 2,400 total prompt-engine runs, no memory carryover, randomized order per wave. Citation share equals brand citations divided by total citations across the 15 qualifying brands (minimum 5 citations to qualify) times 100. Crash-test/certification classification (editorial): Chicco, Britax, Clek, Nuna, Maxi-Cosi, Graco, and Cybex carry current, independent, product-specific safety documentation; UPPAbaby, Doona, Baby Jogger, Evenflo, Peg Perego, Thule, 4moms, and Silver Cross do not, as of this study's window. This is a directional research input, not an audited multiplier. Full engine/model versions by wave available on request. ## About this report Published August 2026, part of the 5W AI Visibility Index series. Citation share figures are 5W estimates and should be read as directional measures of relative visibility, not precise market data. 5W is The Communications Firm Built for the AI Era. Founded 2003, recognized as a Top U.S. PR Agency by O'Dwyer's, Agency of the Year (American Business Awards), a 2026 Top Place to Work in Communications (Ragan), and Digiday's WorkLife Employer of the Year. 5wpr.com · Parenting Apps, Gear & Retail practice 5W runs AI Search (GEO) programs across consumer, B2B, financial services, healthcare, and technology. Learn more. --- ### Podcast Citation Effect Methodology URL: https://www.5wpr.com/research/podcast-citation-effect/methodology/ Summary: The methodology page for the 5W Podcast Citation Effect research franchise. --- ### Hair Styling Tools AI Visibility & Recommendation Benchmark URL: https://www.5wpr.com/research/ai-search-visibility-for-hair-styling-tools-2026-brand-study/ Summary: Hair styling tools AI visibility data shows Dyson leads appearances while Remington trails across four tested answer engines. # Hair Styling Tools AI Visibility & Recommendation Benchmark Dyson Leads Hair Tool AI Visibility Dyson leads hair styling tools in this measurement, appearing in 57.5% of answers returned across the tested prompts. Its 95% interval is 54.1% to 60.8%. T3 follows at 52.2%, but the intervals overlap, so this sample does not establish a clear visibility difference between the two brands. Remington appears in 17.3% of answers returned, which does not support a finding of broad budget or value visibility. The study measured 72 prompts three times across Perplexity, ChatGPT, Gemini, and Claude. Appearance rates use 816 answers returned. Citation rates use 806 answers with retrieval evidence. The gap matters: a brand can be named without its own site being cited. For hair-tool brands, missing these answers means losing consideration when consumers ask for help choosing a tool. This benchmark extends 5WPR's 5W AI Visibility Index research into the hair styling tools category, using the same Generative Engine Optimization methodology the firm applies across its Devices and Beauty Tools and Beauty & Grooming practice areas. 816AI answers analyzed 57.5%Dyson appearance rate, the category leader 25Brands named across the study 97.7%Answers carrying retrieval evidence ## Which brands lead hair styling tool answers? Dyson ranks first with a 57.5% appearance rate, followed by T3 at 52.2%, BaBylissPro at 40.8%, Conair at 38.6%, and Revlon at 38.2%. Shark ranks sixth at 32.4%. The ranking measures whether a brand was named in answers returned, not whether it was recommended or selected first. The leading group is visible across all four tested engines. Dyson, T3, BaBylissPro, Conair, Revlon, Shark, Hot Tools, and Remington all have measurements on Perplexity, ChatGPT, Gemini, and Claude. That does not mean the engines name them at the same rate ### AI visibility leaderboard Appearance rate is the share of the 816 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1Dyson469/81657.5%54.1%–60.8%2.3 2T3426/81652.2%48.8%–55.6%4.4 3BaBylissPro333/81640.8%37.5%–44.2%4.7 4Conair315/81638.6%35.3%–42%7.4 5Revlon312/81638.2%35%–41.6%6.8 6Shark264/81632.4%29.2%–35.6%4.7 7Hot Tools218/81626.7%23.8%–29.9%6.6 8Remington141/81617.3%14.8%–20%8.1 9GHD129/81615.8%13.5%–18.5%5.2 10SharkNinja125/81615.3%13%–18%4.3 11Drybar124/81615.2%12.9%–17.8%6.6 12Bio Ionic121/81614.8%12.6%–17.4%6.5 13CHI92/81611.3%9.3%–13.6%6.8 14BaByliss82/81610%8.2%–12.3%6.1 15Panasonic66/8168.1%6.4%–10.2%7.5 Dyson's 95% interval overlaps T3's interval. T3's interval overlaps BaBylissPro's, while BaBylissPro overlaps Conair and Revlon. Conair and Revlon also overlap. Those differences are within the margin of this sample. The table is a measured ordering, but the overlapping intervals limit claims about separation among these brands. ## How do the leading brands appear across engines? The ten profiles below use appearance rates from answers returned. Owned-domain citation rates use answers with retrieval evidence. ### Dyson 57.5% appearance rate places Dyson first, with a 95% interval of 54.1% to 60.8%. Perplexity named Dyson in 66.7% of its 216 answers, ChatGPT in 65.7% of 216, Gemini in 51.4% of 177, and Claude in 44.4% of 207. Dyson's average first mention position was 2.3. Its owned domain was cited in 4.7% of the 806 answers with retrieval evidence. ### T3 52.2% appearance rate places T3 second, with a 95% interval of 48.8% to 55.6%. ChatGPT named T3 in 66.7% of 216 answers and Perplexity in 59.7% of 216. Gemini named T3 in 50.8% of 177 answers, while Claude named it in 30.4% of 207. T3's average first mention position was 4.4. Its owned domain was cited in 1.6% of answers with retrieval evidence. ### BaBylissPro 40.8% appearance rate places BaBylissPro third, with a 95% interval of 37.5% to 44.2%. ChatGPT named BaBylissPro in 63% of 216 answers, compared with 45.4% of 216 for Perplexity and 41.2% of 177 for Gemini. Claude was named in 12.6% of 207 answers. Its average first mention position was 4.7. BaBylissPro's owned domain was cited in 1.7% of answers with retrieval evidence. ### Conair 38.6% appearance rate places Conair fourth, with a 95% interval of 35.3% to 42%. Perplexity named Conair in 56.9% of 216 answers and ChatGPT in 52.3% of 216. Gemini named Conair in 37.3% of 177, while Claude named it in 6.3% of 207. Conair's average first mention position was 7.4. Its owned domain was cited in 2.1% of answers with retrieval evidence. ### Revlon 38.2% appearance rate places Revlon fifth, with a 95% interval of 35% to 41.6%. ChatGPT named Revlon in 56.5% of 216 answers and Perplexity in 55.1% of 216. Gemini named it in 28.2% of 177 answers, and Claude in 10.1% of 207. Revlon's average first mention position was 6.8. Its owned domain was cited in 0.1% of answers with retrieval evidence. ### Shark 32.4% appearance rate places Shark sixth, with a 95% interval of 29.2% to 35.6%. Perplexity named Shark in 60.2% of 216 answers, but ChatGPT named it in 24.1%, Gemini in 16.4%, and Claude in 25.6%. Shark's average first mention position was 4.7. Its owned domain citation rate was 0% among answers with retrieval evidence. ### Hot Tools Hot Tools had a 26.7% appearance rate, placing it seventh, with a 95% interval of 23.8% to 29.9%. ChatGPT named Hot Tools in 56.5% of 216 answers, while Perplexity named it in 28.7%, Gemini in 13%, and Claude in 5.3%. Its average first mention position was 6.6. Hot Tools' owned domain was cited in 0.4% of answers with retrieval evidence. ### Remington 17.3% appearance rate places Remington eighth, with a 95% interval of 14.8% to 20%. Perplexity named Remington in 34.3% of 216 answers and ChatGPT in 21.8% of 216. Gemini named it in 6.8% of 177 answers, and Claude in 3.9% of 207. Remington's average first mention position was 8.1. Its owned domain was cited in 0.1% of answers with retrieval evidence. ### GHD 15.8% appearance rate places GHD ninth, with a 95% interval of 13.5% to 18.5%. Perplexity named GHD in 32.4% of 216 answers and Claude in 20.8% of 207. ChatGPT named GHD in 7.4% of 216 answers, while Gemini did not name it in its 177 answers. GHD's average first mention position was 5.2. Its owned domain was cited in 3.2% of answers with retrieval evidence. ### SharkNinja 15.3% appearance rate places SharkNinja tenth, with a 95% interval of 13% to 18%. Gemini named SharkNinja in 35% of 177 answers, while Perplexity named it in 19% and ChatGPT in 10.2% of their 216 answers. Claude did not mention SharkNinja in any of its 207 answers. Its average first mention position was 4.3. SharkNinja's owned domain was cited in 0.7% of answers with retrieval evidence. ## Where do the engines disagree? The engines disagree materially on several brands. Perplexity, ChatGPT, Gemini, and Claude use different models and retrieval tools, so a gap can reflect system design rather than a universal measure of brand visibility. ### Engine-by-brand appearance ### Appearance rate by engine Each cell uses that engine's answer count as its denominator, so you can compare engines row by row. Brand Perplexity (sonar-pro) rate ChatGPT (OpenAI) rate Gemini (Google) rate Claude (Anthropic) rate Dyson 66.7% 65.7% 51.4% 44.4% T3 59.7% 66.7% 50.8% 30.4% BaBylissPro 45.4% 63% 41.2% 12.6% Conair 56.9% 52.3% 37.3% 6.3% Revlon 55.1% 56.5% 28.2% 10.1% Shark 60.2% 24.1% 16.4% 25.6% Hot Tools 28.7% 56.5% 13% 5.3% Remington 34.3% 21.8% 6.8% 3.9% GHD 32.4% 7.4% 0% 20.8% SharkNinja 19% 10.2% 35% 0% Drybar 16.7% 19% 17.5% 7.7% Bio Ionic 17.6% 12.5% 26% 4.8% Perplexity named Shark in 60.2% of 216 answers, compared with 24.1% for ChatGPT, 16.4% for Gemini, and 25.6% for Claude. It also named Remington in 34.3% of 216 answers, higher than ChatGPT's 21.8%, Gemini's 6.8%, and Claude's 3.9%. GHD follows a different pattern: Perplexity named it in 32.4% of 216 answers and Claude in 20.8% of 207, while Gemini did not name it in 177 answers. ChatGPT named BaBylissPro in 63% of 216 answers and Hot Tools in 56.5%, compared with Claude's 12.6% and 5.3%. Claude also named Conair in only 6.3% of 207 answers, compared with 56.9% on Perplexity and 52.3% on ChatGPT. Gemini named SharkNinja in 35% of 177 answers, yet Claude did not name it in 207 answers. BaByliss and GHD were also absent from Gemini's measured answers, while Claude did not name SharkNinja. Dyson and T3 have the most even coverage among the highest-ranked brands, although both remain lower on Claude than on Perplexity and ChatGPT. Dyson's average first mention position of 2.3 is earlier than the other top ten brands. Remington's 8.1 means that when it was named, it tended to appear later in the answer than the other brands profiled here. ## Which brands lead each demand cluster? The cluster view identifies which brands are named most often for each measured group of hair-tool questions. It separates general buyer intent from specific needs, so a brand can lead one question type without leading another. ### Demand-cluster leaders ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand cluster Answers Leading brand Leader rate Runners-up — A. Category prompts — What are the best hair styling tools? 12 Dyson 91.7% T3 91.7%, BaBylissPro 75%, Revlon 66.7% Recommend a good hair dryer. 12 Dyson 91.7% T3 91.7%, Shark 58.3%, BaBylissPro 50% What hair dryer should I buy? 12 Dyson 100% T3 100%, BaBylissPro 66.7%, Shark 66.7% What is the best hair dryer? 12 Dyson 91.7% T3 75%, BaBylissPro 66.7%, Conair 50% What's a good curling iron? 12 T3 100% Hot Tools 91.7%, Bio Ionic 91.7%, Dyson 83.3% I need a new flat iron. What should I get? 12 Dyson 91.7% T3 91.7%, BaBylissPro 66.7%, Remington 66.7% — F. Price prompts — Recommend a good hair styling tool under $150. 12 BaBylissPro 83.3% Revlon 83.3%, T3 75%, Shark 75% I want something similar to the Dyson Airwrap but cheaper. 12 Revlon 83.3% T3 75%, Shark 75%, Conair 58.3% I can't justify spending $500 on a hair tool. What should I buy instead? 12 Conair 83.3% T3 75%, Revlon 75%, Remington 66.7% What's the best hair dryer under $100? 12 BaBylissPro 91.7% Revlon 83.3%, Conair 58.3%, Hot Tools 50% Is there a good blow dryer under $50? 11 Conair 90.9% Revlon 90.9%, Hot Tools 54.5%, Remington 45.5% What is a good affordable alternative to Dyson? 11 Shark 63.6% SharkNinja 45.5%, Dyson 18.2%, Conair 18.2% — C. Problem-based prompts — My arms get tired when I blow dry my hair. What should I look for? 12 Dyson 66.7% BaBylissPro 66.7%, T3 58.3%, Conair 50% My hair gets frizzy when I blow dry it. What should I use? 12 Kérastase 91.7% Color Wow 75%, Oribe 75%, Olaplex 58.3% My hair never holds a curl. What styling tool should I try? 12 T3 83.3% Dyson 75%, BaBylissPro 66.7%, Hot Tools 66.7% My hair takes forever to dry in the morning. Is there a tool that can make the process faster? 12 Dyson 91.7% T3 75%, Conair 58.3%, Panasonic 58.3% I can't get a salon-style blowout at home. What tool would make it easier? 12 Dyson 91.7% T3 75%, BaBylissPro 75%, Revlon 58.3% I'm worried about damaging my hair with heat. What hair tool should I buy? 12 Dyson 83.3% T3 75%, BaBylissPro 50%, Hot Tools 50% — B. Hair-type prompts — Recommend a styling tool for curly hair. 12 Dyson 100% T3 91.7%, BaBylissPro 58.3%, Shark 50% What hair dryer works well for fine hair? 12 Dyson 100% T3 91.7%, BaBylissPro 58.3%, Conair 58.3% What is a good blow dryer for coily hair? 12 Dyson 66.7% Conair 58.3%, T3 50%, Shark 50% What's a good hair dryer for thick hair? 12 Dyson 91.7% T3 91.7%, Shark 75%, BaBylissPro 66.7% I have long thick hair and drying it takes forever. What should I buy? 12 Dyson 91.7% BaBylissPro 83.3%, T3 75%, Shark 50% — D. Outcome prompts — What should I buy if I want a salon blowout at home? 12 Dyson 91.7% T3 75%, Revlon 75%, BaBylissPro 58.3% I want to dry and style my hair at the same time. 12 Dyson 91.7% T3 75%, Revlon 75%, Shark 75% I want loose waves without using a curling iron. 12 Conair 75% Dyson 25%, Shark 25%, Drybar 25% I want smooth hair without spending 30 minutes styling it. 12 Living Proof 66.7% Moroccanoil 66.7%, Kérastase 58.3%, Olaplex 58.3% I need something that will smooth my hair while I dry it. 12 Living Proof 50% Olaplex 50%, Redken 50%, Drybar 41.7% I want less frizz but don't want to straighten my hair. 12 Living Proof 91.7% Kérastase 75%, Olaplex 58.3%, Moroccanoil 58.3% — G. Comparison prompts — Dyson or Shark for styling long hair? 12 Dyson 100% Shark 66.7%, T3 58.3%, BaBylissPro 58.3% Shark FlexStyle vs Dyson Airwrap — which differences should I consider? 11 Dyson 72.7% Shark 63.6%, T3 45.5%, Conair 45.5% Compare Remington and Conair hair dryers. 11 Conair 100% Remington 90.9%, Dyson 45.5%, T3 45.5% What's the difference between a $50 hair dryer and a $400 hair dryer? 11 T3 72.7% Revlon 72.7%, Dyson 63.6%, Conair 63.6% Is Drybar better than Revlon for blowouts? 11 Revlon 90.9% Drybar 90.9%, Dyson 81.8%, T3 72.7% — E. Situation and occasion prompts — I need a hair tool that is easy to travel with. 12 T3 91.7% Dyson 83.3%, Conair 66.7%, BaBylissPro 50% I want one hair tool instead of storing a dryer, curling iron and straightener. 12 Dyson 83.3% T3 83.3%, Shark 66.7%, BaBylissPro 50% I have a small bathroom and don't want several styling tools. 12 Dyson 66.7% T3 50%, Revlon 41.7%, Conair 33.3% I need something quick for getting ready before work. 12 Drybar 25% Living Proof 25%, Oribe 25%, Revlon 16.7% — Other demand — Client question 10 11 Dyson 45.5% Conair 36.4%, Laifen 36.4%, T3 27.3% Primary use case 11 Dyson 81.8% T3 63.6%, BaBylissPro 63.6%, Shark 45.5% Client question 9 11 Dyson 81.8% T3 63.6%, Hot Tools 54.5%, CHI 54.5% Price and value 11 T3 72.7% Dyson 63.6%, BaBylissPro 63.6%, Hot Tools 54.5% Client question 12 11 Dyson 72.7% T3 63.6%, Shark 63.6%, BaBylissPro 45.5% Client question 1 11 Dyson 81.8% T3 72.7%, Conair 72.7%, Revlon 63.6% Client question 11 11 Kérastase 81.8% Olaplex 63.6%, Oribe 63.6%, Living Proof 54.5% Everyday and mainstream choice 11 BaBylissPro 81.8% T3 72.7%, Revlon 63.6%, Bio Ionic 63.6% General best-in-category 11 Dyson 90.9% BaBylissPro 90.9%, T3 81.8%, Revlon 72.7% Client question 8 11 Dyson 9.1% — Client question 6 9 Shark 11.1% — Client question 5 8 Dyson 12.5% — — Branded prompts — Is the Shark FlexStyle worth buying? 11 Dyson 63.6% SharkNinja 63.6%, T3 45.5%, Conair 36.4% Is the Dyson Airwrap good for thick hair? 11 Dyson 81.8% T3 72.7%, Conair 63.6%, SharkNinja 54.5% — I. Brand-neutral discovery prompts — My hair takes too long to style before work. What could make the process easier? 11 Revlon 90.9% Dyson 72.7%, Hot Tools 45.5%, T3 36.4% I want loose curls but I'm terrible at using a curling iron. Is there an easier option? 11 Dyson 45.5% T3 36.4%, Conair 27.3%, Revlon 27.3% I want my hair to look more polished without going to a salon. 11 Color Wow 72.7% Oribe 72.7%, Living Proof 63.6%, Kérastase 63.6% I have thick frizzy hair and hate spending an hour styling it. What should I buy? 11 Living Proof 63.6% Kérastase 63.6%, Olaplex 54.5%, Moroccanoil 45.5% — Prompt Controls — My thick hair takes a long time to dry. What dryer would work well? 11 Dyson 81.8% BaBylissPro 72.7%, T3 63.6%, GHD 54.5% I have thick hair. Which blow dryer should I buy? 11 Dyson 81.8% T3 81.8%, BaBylissPro 54.5%, Hot Tools 45.5% — H. Feature-driven prompts — What hair dryer should I buy if minimizing heat damage matters to me? 11 Dyson 72.7% T3 72.7%, Revlon 54.5%, Shark 45.5% I want a dryer with a diffuser for curly hair. 11 Dyson 100% T3 72.7%, Conair 72.7%, Shark 54.5% What styling tool is good for reducing frizz? 11 Dyson 81.8% Revlon 72.7%, T3 63.6%, BaBylissPro 63.6% I need a lightweight hair dryer. 11 Dyson 90.9% T3 72.7%, Conair 72.7%, Revlon 72.7% I need something that can dry and curl my hair. 11 Dyson 72.7% T3 63.6%, Shark 63.6%, Revlon 54.5% — Competitor prompts — Dyson Airwrap vs Shark FlexStyle. 11 Dyson 72.7% T3 63.6%, Conair 63.6%, Revlon 45.5% — Unbranded category prompts — What's the best multi-styler? 11 Dyson 81.8% T3 63.6%, BaBylissPro 54.5%, Shark 54.5% — Unbranded consumer-need prompts — I have thick frizzy hair and want to dry and style it faster. What should I buy? 11 Dyson 81.8% BaBylissPro 63.6%, GHD 45.5%, Bio Ionic 45.5% The chart should be read as need-specific visibility, not a universal product verdict. Brands that lead general buyer-intent clusters have broad recognition within these prompts. Brands that lead specific-need clusters have a clearer association with the measured use case. The evidence does not establish performance claims about the tools themselves. ## Which brand sites and publishers receive citations? Owned-domain citations are rare relative to third-party publisher citations in this study. Citation rates below use the 806 answers with retrieval evidence, not the 816 answers returned. ### Owned-domain citation rate Share of the 806 answers with retrieval evidence that cited a URL on the brand's own website. Dyson 4.7% · 38/806 T3 1.6% · 13/806 BaBylissPro 1.7% · 14/806 Conair 2.1% · 17/806 Revlon 0.1% · 1/806 Shark 0% · 0/806 Hot Tools 0.4% · 3/806 Remington 0.1% · 1/806 GHD 3.2% · 26/806 SharkNinja 0.7% · 6/806 Drybar 0.1% · 1/806 Bio Ionic 1% · 8/806 Dyson's site had the highest owned-domain citation rate at 4.7%, followed by Laifen at 4.2% and GHD at 3.2%. Conair reached 2.1%, BaBylissPro 1.7%, and T3 1.6%. Shark and Harry Josh each recorded 0% owned-domain citation rates among answers with retrieval evidence. Remington, Revlon, Drybar, and CHI each recorded 0.1%. ### Publishers cited in answers ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. Publisher Answers citing it Answer penetration techradar.com 232/806 28.8% forbes.com 231/806 28.7% goodhousekeeping.com 223/806 27.7% whowhatwear.com 221/806 27.4% today.com 205/806 25.4% nbcnews.com 196/806 24.3% allure.com 187/806 23.2% youtube.com 180/806 22.3% reddit.com 179/806 22.2% cnn.com 149/806 18.5% womanandhome.com 139/806 17.2% walmart.com 128/806 15.9% TechRadar appeared in 28.8% of answers with retrieval evidence, Forbes in 28.7%, Good Housekeeping in 27.7%, Who What Wear in 27.4%, and Today in 25.4%. NBC News, Allure, YouTube, Reddit, and CNN also appear in the measured source list. The source pattern indicates that engines frequently ground hair-tool answers in editorial, video, forum, retailer, and marketplace material rather than brand-owned pages. ## Which brands are winning and which are at risk? ### Brands with the strongest measured visibility Dyson has the strongest measured visibility: rank 1, a 57.5% appearance rate, a 54.1% to 60.8% interval, and a 2.3 average first mention position. T3 ranks second at 52.2% and appears on all four engines, though its interval overlaps Dyson's. BaBylissPro ranks third at 40.8% and reaches 63% on ChatGPT. Conair and Revlon rank fourth and fifth at 38.6% and 38.2%, respectively, with strong Perplexity and ChatGPT presence. ### Brands with measured exposure risk Remington's exposure risk is its 17.3% appearance rate, eighth-place rank, 8.1 average first mention position, and low Gemini and Claude rates of 6.8% and 3.9%. Shark's 32.4% overall rate masks a narrow engine pattern: 60.2% on Perplexity but 24.1%, 16.4%, and 25.6% elsewhere, plus a 0% owned-domain citation rate. GHD has a 15.8% appearance rate and was absent from Gemini's 177 answers. SharkNinja ranks tenth at 15.3%, with no measured Claude appearances across 207 answers. ## What does the tally say about the category? The category has a visible top tier, but the top five do not separate cleanly on confidence intervals. Dyson's 54.1% to 60.8% interval overlaps T3's 48.8% to 55.6%, while T3 overlaps BaBylissPro. BaBylissPro, Conair, and Revlon form another overlapping group. The measurement supports a ranked table, but not strong claims that brands with overlapping intervals are more visible than each other. Engine agreement is stronger for whether many brands appear at all than for how frequently they appear. Dyson, T3, BaBylissPro, Conair, Revlon, Shark, Hot Tools, Remington, Drybar, and Bio Ionic each have 100% cross-engine consistency. Yet their engine-level rates often differ sharply. GHD, SharkNinja, and BaByliss have 75% cross-engine consistency, with each absent from one engine's answers. Third-party publishers carry far more citation presence than brand domains in the measured list. TechRadar, Forbes, Good Housekeeping, Who What Wear, and Today each appear in more than one quarter of answers with retrieval evidence. Among owned sites, Dyson's 4.7% is the highest measured citation rate. Shark and Harry Josh recorded 0%, while Remington and Drybar recorded 0.1%. ## What changed during the measurement window? This dataset is a single measurement window, so it does not measure category change over time. It does show cross-engine divergence within the window, including Shark's 60.2% rate on Perplexity versus 16.4% on Gemini and SharkNinja's 35% rate on Gemini versus 0% on Claude. ## What should hair-tool brands fix and re-measure? - Remington should test pages and third-party coverage that address the measured budget and value needs, then re-measure whether its 17.3% appearance rate and 8.1 average first mention position improve across all four engines. - Shark should investigate its 0% owned-domain citation rate among answers with retrieval evidence. Publish source-ready product information and re-measure owned-domain citations alongside appearance rates on ChatGPT, Gemini, and Claude. - GHD should review the absence in Gemini's 177 measured answers. Re-measure GHD's Gemini appearance rate after strengthening pages that can support the needs represented in the prompt set. - SharkNinja should separate corporate and consumer-facing product information where the answer systems distinguish them. Re-measure whether the 0% Claude rate changes while preserving the 35% Gemini rate. - Conair should address its Claude gap. Re-measure Claude's 6.3% appearance rate against its 56.9% Perplexity and 52.3% ChatGPT rates using the same prompts and trials. - Hot Tools should examine its dependence on ChatGPT, where it appears in 56.5% of answers, versus 5.3% on Claude. Re-measure the engine spread after publishing material that answers the specific measured needs. - Brands with low owned-domain citation rates should track whether their sites are cited in the 806-answer retrieval-evidence denominator. The benchmark ranges from Dyson's 4.7% to 0% for Shark and Harry Josh. Brands running these fixes can pair GEO testing with earned-media placement in the outlets this study found carry citation weight, including TechRadar, Forbes, and Good Housekeeping, an approach 5WPR applies across its Skincare, Haircare & Nail Care practice. ## What does this mean for brands in the category? Interpretation: awareness in one answer system does not guarantee broad visibility across the other systems. Shark, Hot Tools, GHD, SharkNinja, and Remington show that a strong rate on one engine can coexist with low or absent visibility elsewhere. Interpretation: brand-owned pages rarely supplied the cited evidence in these measured answers. Brands need to separate two tasks: being named in a response and having their own domain cited when the engine provides retrieval evidence. The study does not establish the cause of either outcome. ## Frequently asked questions ### Which hair styling tool brand appears most often? Dyson ranks first with a 57.5% appearance rate across 816 answers returned. Its 95% interval is 54.1% to 60.8%. ### Is Dyson more visible than T3? Dyson's 57.5% rate is higher than T3's 52.2% rate in the ranked tally. Their 95% intervals overlap, so the difference is within the margin of this sample. ### How visible is Remington in AI answers? Remington ranks eighth with a 17.3% appearance rate and a 14.8% to 20% interval. It appeared in 34.3% of Perplexity answers, 21.8% of ChatGPT answers, 6.8% of Gemini answers, and 3.9% of Claude answers. ### Which engines were measured? The study measured Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). Perplexity and ChatGPT each returned 216 answers, Gemini returned 177, and Claude returned 207. ### Which brand site was cited most often? Dyson had the highest owned-domain citation rate at 4.7% among 806 answers with retrieval evidence. Laifen followed at 4.2%, and GHD recorded 3.2%. ### Which external sources appeared most often? TechRadar appeared in 28.8% of answers with retrieval evidence. Forbes appeared in 28.7%, Good Housekeeping in 27.7%, Who What Wear in 27.4%, and Today in 25.4%. ## How was this study measured? This flagship study tested 72 hair styling tool prompts three times across Perplexity, ChatGPT, Gemini, and Claude. It attempted 825 answers, returned 816 for appearance measurement, and recorded retrieval evidence for 806 citation measurements. Appearance rates use answers returned. Citation and source rates use answers with retrieval evidence. The study is limited to these prompts and this measurement date, and it does not measure all category searches or consumer demand. ### Study method ### How this study was measured - Sampling frame fixed first: 72 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trials each. - 816 answers returned of 825 attempted, 806 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: hair styling tools - Measurement date: 2026-09-22 - Depth: flagship — 72 prompts, 3 trial(s) per prompt per engine, 4 engines - Answers attempted: 825 - Answers returned (appearance denominator): 816 - Answers with retrieval evidence (citation denominator): 806 (97.7% of attempted) - Answers that failed outright: 0 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable. ### How the prompts were sampled Fixed sampling frame: 68 buyer-intent need(s) from the client's brief are asked verbatim, one prompt each, grouped under: A. Category prompts / B. Hair-type prompts / C. Problem-based prompts / D. Outcome prompts / E. Situation and occasion prompts / F. Price prompts / G. Comparison prompts / H. Feature-driven prompts / I. Brand-neutral discovery prompts / Prompt Controls / Branded prompts / Competitor prompts / Unbranded category prompts / Unbranded consumer-need prompts; then (the prompt budget of 72 could not give a dedicated cell to Hair dryers, Multi-stylers / air stylers, Blow-dryer brushes, Straighteners / flat irons, Curling irons and curling wands, Hot brushes, Diffusers or dryer systems for curly hair, Fine hair, Thick hair, Curly hair, Coily hair, Frizzy hair, Damaged hair, Color-treated hair, Short hair, Long hair, Hair that takes a long time to dry, Consumers concerned about heat damage, Consumers seeking faster styling, Consumers seeking salon-style blowouts, Consumers who struggle with curling their hair, Consumers who want one tool instead of several, Travelers, Consumers with limited storage space, Under $50, Under $100, Under $150, Premium hair tools, Luxury hair tools, Best value, so that demand is covered inside the generic clusters instead), then a documented set of generic commercial demand clusters (general best, everyday mainstream choice, price/value, primary use case, beginner, retail channel, gifting/occasion, specific need, comparison, trust, ownership attribution, quality tier, durability, specialist, emerging) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand cluster Prompts Scope What is the best hair dryer? 1 What is the best hair dryer? Recommend a good hair dryer. 1 Recommend a good hair dryer. What hair dryer should I buy? 1 What hair dryer should I buy? What are the best hair styling tools? 1 What are the best hair styling tools? I need a new flat iron. What should I get? 1 I need a new flat iron. What should I get? What's a good curling iron? 1 What's a good curling iron? What's a good hair dryer for thick hair? 1 What's a good hair dryer for thick hair? What hair dryer works well for fine hair? 1 What hair dryer works well for fine hair? Recommend a styling tool for curly hair. 1 Recommend a styling tool for curly hair. What is a good blow dryer for coily hair? 1 What is a good blow dryer for coily hair? I have long thick hair and drying it takes forever. What should I buy? 1 I have long thick hair and drying it takes forever. What should I buy? My hair gets frizzy when I blow dry it. What should I use? 1 My hair gets frizzy when I blow dry it. What should I use? I'm worried about damaging my hair with heat. What hair tool should I buy? 1 I'm worried about damaging my hair with heat. What hair tool should I buy? My hair takes forever to dry in the morning. Is there a tool that can make the process faster? 1 My hair takes forever to dry in the morning. Is there a tool that can make the process faster? I can't get a salon-style blowout at home. What tool would make it easier? 1 I can't get a salon-style blowout at home. What tool would make it easier? My arms get tired when I blow dry my hair. What should I look for? 1 My arms get tired when I blow dry my hair. What should I look for? My hair never holds a curl. What styling tool should I try? 1 My hair never holds a curl. What styling tool should I try? I want smooth hair without spending 30 minutes styling it. 1 I want smooth hair without spending 30 minutes styling it. What should I buy for a salon blowout at home? 1 What should I buy for a salon blowout at home? I want loose waves without using a curling iron. 1 I want loose waves without using a curling iron. I want to dry and style my hair at the same time. 1 I want to dry and style my hair at the same time. I need something that will smooth my hair while I dry it. 1 I need something that will smooth my hair while I dry it. I want less frizz but don't want to straighten my hair. 1 I want less frizz but don't want to straighten my hair. I need a hair tool that is easy to travel with. 1 I need a hair tool that is easy to travel with. I need something quick for getting ready before work. 1 I need something quick for getting ready before work. I want one hair tool instead of storing a dryer, curling iron and straightener. 1 I want one hair tool instead of storing a dryer, curling iron and straightener. I have a small bathroom and don't want several styling tools. 1 I have a small bathroom and don't want several styling tools. I need something easy enough for a teenager to use. 1 I need something easy enough for a teenager to use. What's the best hair dryer under $100? 1 What's the best hair dryer under $100? Recommend a good hair styling tool under $150. 1 Recommend a good hair styling tool under $150. Is there a good blow dryer under $50? 1 Is there a good blow dryer under $50? I want something similar to the Dyson Airwrap but cheaper. 1 I want something similar to the Dyson Airwrap but cheaper. What is a good affordable alternative to Dyson? 1 What is a good affordable alternative to Dyson? I can't justify spending $500 on a hair tool. What should I buy instead? 1 I can't justify spending $500 on a hair tool. What should I buy instead? Dyson or Shark for styling long hair? 1 Dyson or Shark for styling long hair? Shark FlexStyle vs Dyson Airwrap — which differences should I consider? 1 Shark FlexStyle vs Dyson Airwrap — which differences should I consider? Is Drybar better than Revlon for blowouts? 1 Is Drybar better than Revlon for blowouts? Compare Remington and Conair hair dryers. 1 Compare Remington and Conair hair dryers. What's the difference between a $50 hair dryer and a $400 hair dryer? 1 What's the difference between a $50 hair dryer and a $400 hair dryer? What hair dryer should I buy if minimizing heat damage matters to me? 1 What hair dryer should I buy if minimizing heat damage matters to me? I need a lightweight hair dryer. 1 I need a lightweight hair dryer. What dryer has good temperature control? 1 What dryer has good temperature control? What styling tool is good for reducing frizz? 1 What styling tool is good for reducing frizz? I want a dryer with a diffuser for curly hair. 1 I want a dryer with a diffuser for curly hair. I need something that can dry and curl my hair. 1 I need something that can dry and curl my hair. My hair takes too long to style before work. What could make the process easier? 1 My hair takes too long to style before work. What could make the process easier? I want my hair to look more polished without going to a salon. 1 I want my hair to look more polished without going to a salon. I have thick frizzy hair and hate spending an hour styling it. What should I buy? 1 I have thick frizzy hair and hate spending an hour styling it. What should I buy? I want loose curls but I'm terrible at using a curling iron. Is there an easier option? 1 I want loose curls but I'm terrible at using a curling iron. Is there an easier option? I have thick hair. Which blow dryer should I buy? 1 I have thick hair. Which blow dryer should I buy? My thick hair takes a long time to dry. What dryer would work well? 1 My thick hair takes a long time to dry. What dryer would work well? Is the Dyson Airwrap good for thick hair? 1 Is the Dyson Airwrap good for thick hair? Is the Shark FlexStyle worth buying? 1 Is the Shark FlexStyle worth buying? Dyson Airwrap vs Shark FlexStyle. 1 Dyson Airwrap vs Shark FlexStyle. What's the best multi-styler? 1 What's the best multi-styler? I have thick frizzy hair and want to dry and style it faster. What should I buy? 1 I have thick frizzy hair and want to dry and style it faster. What should I buy? Client question 1 1 Which hair-tool brands appear most frequently in AI-generated answers? Client question 2 1 Which brands are mentioned across the widest range of consumer requests? Client question 3 1 Which brands appear consistently across multiple AI platforms rather than performing well on only one platform? Client question 4 1 Which brands are explicitly recommended rather than merely mentioned? Client question 5 1 Which products appear as the first recommendation? Client question 6 1 Which products appear in the top 3 recommendations? Client question 7 1 Which brands are described as the "best," "top," "good choice," "worth considering," or equivalent recommendation language? Client question 8 1 Which brands are associated with specific consumer needs? Client question 9 1 What tools work well for thick hair? Client question 10 1 Which dryers have high airflow? Client question 11 1 Which tools address frizz? Client question 12 1 Which hair tools reduce drying time? General best-in-category 1 the plain, highest-volume 'best X' style demand with no qualifier Everyday and mainstream choice 1 what an ordinary, non-expert buyer picks for everyday use, in the mass-market part of the category Price and value 1 budget, value-for-money, cheaper alternatives and whether premium is worth it Primary use case 1 the dominant everyday use case for this category ### The exact prompts # Prompt Cluster Shape 1 What is the best hair dryer? need_1 medium 2 Recommend a good hair dryer. need_2 medium 3 What hair dryer should I buy? need_3 medium 4 What are the best hair styling tools? need_4 medium 5 I need a new flat iron. What should I get? need_5 medium 6 What's a good curling iron? need_6 medium 7 What's a good hair dryer for thick hair? need_7 medium 8 What hair dryer works well for fine hair? need_8 medium 9 Recommend a styling tool for curly hair. need_9 medium 10 What is a good blow dryer for coily hair? need_10 medium 11 I have long thick hair and drying it takes forever. What should I buy? need_11 medium 12 My hair gets frizzy when I blow dry it. What should I use? need_12 medium 13 I'm worried about damaging my hair with heat. What hair tool should I buy? need_13 medium 14 My hair takes forever to dry in the morning. Is there a tool that can make the process faster? need_14 medium 15 I can't get a salon-style blowout at home. What tool would make it easier? need_15 medium 16 My arms get tired when I blow dry my hair. What should I look for? need_16 medium 17 My hair never holds a curl. What styling tool should I try? need_17 medium 18 I want smooth hair without spending 30 minutes styling it. need_18 medium 19 What should I buy for a salon blowout at home? need_19 medium 20 I want loose waves without using a curling iron. need_20 medium 21 I want to dry and style my hair at the same time. need_21 medium 22 I need something that will smooth my hair while I dry it. need_22 medium 23 I want less frizz but don't want to straighten my hair. need_23 medium 24 I need a hair tool that is easy to travel with. need_24 medium 25 I need something quick for getting ready before work. need_25 medium 26 I want one hair tool instead of storing a dryer, curling iron, and straightener. need_26 medium 27 I have a small bathroom and don't want several styling tools. need_27 medium 28 I need something easy enough for a teenager to use. need_28 medium 29 What's the best hair dryer under $100? need_29 medium 30 Recommend a good hair styling tool under $150. need_30 medium 31 Is there a good blow dryer under $50? need_31 medium 32 I want something similar to the Dyson Airwrap but cheaper. need_32 medium 33 What is a good affordable alternative to Dyson? need_33 medium 34 I can't justify spending $500 on a hair tool. What should I buy instead? need_34 medium 35 Dyson or Shark for styling long hair? need_35 medium 36 Shark FlexStyle vs Dyson Airwrap — which differences should I consider? need_36 medium 37 Is Drybar better than Revlon for blowouts? need_37 medium 38 Compare Remington and Conair hair dryers. need_38 medium 39 What's the difference between a $50 hair dryer and a $400 hair dryer? need_39 medium 40 What hair dryer should I buy if minimizing heat damage matters to me? need_40 medium 41 I need a lightweight hair dryer. need_41 medium 42 What dryer has good temperature control? need_42 medium 43 What styling tool is good for reducing frizz? need_43 medium 44 I want a dryer with a diffuser for curly hair. need_44 medium 45 I need something that can dry and curl my hair. need_45 medium 46 My hair takes too long to style before work. What could make the process easier? need_46 medium 47 I want my hair to look more polished without going to a salon. need_47 medium 48 I have thick frizzy hair and hate spending an hour styling it. What should I buy? need_48 medium 49 I want loose curls, but I'm terrible at using a curling iron. Is there an easier option? need_49 medium 50 I have thick hair. Which blow dryer should I buy? need_50 medium 51 My thick hair takes a long time to dry. What dryer would work well? need_51 medium 52 Is the Dyson Airwrap good for thick hair? need_52 medium 53 Is the Shark FlexStyle worth buying? need_53 medium 54 Dyson Airwrap vs Shark FlexStyle. need_54 medium 55 What's the best multi-styler? need_55 medium 56 I have thick frizzy hair and want to dry and style it faster. What should I buy? need_56 medium 57 Which hair-tool brands appear most frequently in AI-generated answers? author_1 medium 58 Which brands are mentioned across the widest range of consumer requests? author_2 medium 59 Which brands appear consistently across multiple AI platforms rather than performing well on only one platform? author_3 medium 60 Which brands are explicitly recommended rather than merely mentioned? author_4 medium 61 Which products appear as the first recommendation? author_5 medium 62 Which products appear in the top 3 recommendations? author_6 medium 63 Which brands are described as the "best," "top," "good choice," "worth considering," or equivalent recommendation language? author_7 medium 64 Which brands are associated with specific consumer needs? author_8 medium 65 What tools work well for thick hair? author_9 medium 66 Which dryers have high airflow? author_10 medium 67 Which tools address frizz? author_11 medium 68 Which hair tools reduce drying time? author_12 medium 69 best hair styling tools general_best short 70 best everyday hair styling tools under $150 everyday medium 71 Which hair styling tool brands offer the best value for faster, lower-heat styling on fine, damaged, color-treated hair? value_price long 72 Which hair styling tool brands are best for salon-style blowouts and versatile everyday styling on long, thick, curly, or coily hair that takes a long time to dry? use_case long ### Engines and systems measured Engine Model / system Retrieval Attempted Returned With retrieval evidence Retrieval rate Perplexity (sonar-pro) perplexity/sonar-pro native Perplexity web search 216 216 216 100% ChatGPT (OpenAI) openai/gpt-5.6-terra OpenAI web search tool 216 216 216 100% Gemini (Google) google/gemini-3.7-flash Google Search grounding (google_search tool) 177 177 173 97.7% Claude (Anthropic) anthropic/claude-sonnet-4 Anthropic web search tool 216 207 201 93.1% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool, and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behavior does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated, and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable, not that the brand is absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 2 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals # Brand Named in Appearance rate Macro rate 95% interval Owned-domain citations 1 Dyson 469/816 57.5% 56.7% 54.1%–60.8% 38/806 (4.7%) 2 T3 426/816 52.2% 51.2% 48.8%–55.6% 13/806 (1.6%) 3 BaBylissPro 333/816 40.8% 40% 37.5%–44.2% 14/806 (1.7%) 4 Conair 315/816 38.6% 38% 35.3%–42% 17/806 (2.1%) 5 Revlon 312/816 38.2% 37.8% 35%–41.6% 1/806 (0.1%) 6 Shark 264/816 32.4% 31.8% 29.2%–35.6% 0/806 (0%) 7 Hot Tools 218/816 26.7% 26.3% 23.8%–29.9% 3/806 (0.4%) 8 Remington 141/816 17.3% 17% 14.8%–20% 1/806 (0.1%) 9 GHD 129/816 15.8% 15.6% 13.5%–18.5% 26/806 (3.2%) 10 SharkNinja 125/816 15.3% 15.2% 13%–18% 6/806 (0.7%) 11 Drybar 124/816 15.2% 14.9% 12.9%–17.8% 1/806 (0.1%) 12 Bio Ionic 121/816 14.8% 14.5% 12.6%–17.4% 8/806 (1%) 13 CHI 92/816 11.3% 11.1% 9.3%–13.6% 1/806 (0.1%) 14 BaByliss 82/816 10% 9.9% 8.2%–12.3% 5/806 (0.6%) 15 Panasonic 66/816 8.1% 8% 6.4%–10.2% 3/806 (0.4%) 16 Living Proof 56/816 6.9% 6.7% 5.3%–8.8% 8/806 (1%) 17 Kérastase 53/816 6.5% 6.4% 5%–8.4% 3/806 (0.4%) 18 Olaplex 50/816 6.1% 6% 4.7%–8% 7/806 (0.9%) 19 Moroccanoil 49/816 6% 5.9% 4.6%–7.9% 7/806 (0.9%) 20 Parlux 42/816 5.1% 5.1% 3.8%–6.9% 2/806 (0.2%) 21 Laifen 40/816 4.9% 4.9% 3.6%–6.6% 34/806 (4.2%) 22 Color Wow 40/816 4.9% 4.8% 3.6%–6.6% 2/806 (0.2%) 23 Oribe 39/816 4.8% 4.7% 3.5%–6.5% 3/806 (0.4%) 24 Harry Josh 37/816 4.5% 4.5% 3.3%–6.2% 0/806 (0%) 25 Redken 37/816 4.5% 4.4% 3.3%–6.2% 3/806 (0.4%) ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. - Recommend 3 specific models counted under Recommend specific models (model) 40 further name(s) picked up in the answers were not confidently resolvable to a brand in this category and are held for human classification before the next study; they carry no metrics here. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-22. Engine behavior changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgment, or a market-share estimate. This hair styling tools benchmark joins 5WPR's growing AI visibility research library, including a comparable study on med spa and aesthetic medicine brands and the firm's 5W Retrieval Index. --- ### Real Estate & PropTech URL: https://www.5wpr.com/research/podcast-citation-effect/real-estate/ Summary: Real Estate & PropTech: how long-form podcast appearances drive AI engine Citation Share. Study #11 of 12 in The Podcast Citation Effect from 5W. --- ### The Meal Kit & Ready-Meal Delivery AI Visibility Index 2026. URL: https://www.5wpr.com/research/the-meal-kit-ready-meal-delivery-ai-visibility-index-2026/ Summary: 5W tested 62 consumer-intent prompts across five AI engines to map citation share for 27 meal-kit, ready-to-heat, and GLP-1 nutrition brands. AI Visibility Index # The Meal Kit & Ready-Meal Delivery AI Visibility Index 2026. Which meal-delivery brands the AI engines actually recommend — and which ones are cited from a category that no longer exists. Which meal-delivery brands the AI engines actually recommend — and which ones are cited from a category that no longer exists. PUBLISHED BY: 5W AI Communications TESTING WINDOW: Q2 2026 PROMPTS TESTED: 62 consumer-intent queries ENGINES: ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews BRAND UNIVERSE: 27 brands, six sub-categories ## EXECUTIVE SUMMARY 5W tested 62 purchase-intent prompts across five AI platforms. The finding: the brands winning AI visibility in meal delivery are increasingly not the brands that built the meal-kit category. - Freshly is still recommended three years after being shut down. Nestlé closed the brand in early 2023. AI engines continue to surface Freshly on "ready-made meal delivery" queries in 2026 — a case study in how long a shuttered brand persists in the entity graph the models retrieve from. - Counter is classified as GLP-1-relevant without positioning itself that way. The Macrofy retail brand — 30g of protein per bowl at under 400 calories, sold at Target, Kroger, Costco, and Lidl — does not market itself as a GLP-1 product. AI engines have decided it is one anyway, based on nutritional profile alone. - Blue Apron holds significant citation share on queries the brand no longer serves. Wonder, the current owner, has repositioned Blue Apron as a prepared-meal service. AI engines continue to cite Blue Apron on meal-kit queries because a decade of editorial coverage sits in the training data. - Nestlé and Conagra now compete on meal-delivery queries from the frozen aisle. Vital Pursuit (Nestlé) and Healthy Choice On Track (Conagra) — both marketed as GLP-1-friendly, both distributed nationally through frozen-food channels — now appear on "cheapest GLP-1 friendly meals" and adjacent prompts. Neither runs a subscription. Neither needs to. Underneath those findings is a category story: meal kits, ready-to-heat prepared meals, and GLP-1 nutrition are no longer one business. AI engines are increasingly sorting them into separate answer sets. HelloFresh — the category's public-market proxy — posted 2025 revenue of €6.76 billion, a 9% constant-currency decline, and guided another 3–6% drop for 2026. Its U.S. growth engine, Factor, launched in 70+ Target stores across ten Midwest states in January 2026. Meal kits are contracting inside the very company that built the category. One in eight U.S. adults is now on a GLP-1 medication. Every incumbent has bolted on a "GLP-1 Balance" filter. GLP-1-native brands — Clean Eatz Kitchen, Counter, MealPro, BistroMD — are competing directly for citation share on the highest-intent queries in the category. 5W's positioning: The brands building AI visibility today are competing for category leadership in 2028. Winning citation share requires the entity infrastructure, third-party editorial coverage, and answer-shaped content that AI engines actually retrieve — a discipline 5W calls AI Communications. ## Key findings. -9% HELLOFRESH 2025 REVENUE DECLINE, CONSTANT CURRENCY -53% HELLOFRESH ONE-YEAR TOTAL SHAREHOLDER RETURN THROUGH Q1 2026 €6.76B HELLOFRESH 2025 REVENUE, DOWN FROM €7.66B IN 2024 1 in 8 U.S. ADULTS NOW ON A GLP-1 MEDICATION (KFF, NOV 2025) 100+ WEEKLY FACTOR MENU OPTIONS AFTER SEPT 2025 EXPANSION, UP FROM ~50 70+ TARGET STORES CARRYING FACTOR ACROSS TEN MIDWEST STATES, JAN 27 BRANDS IN THE TRACKED UNIVERSE ACROSS SIX SUB-CATEGORIES 62 CONSUMER-INTENT PROMPTS ACROSS FIVE AI PLATFORMS IN Q2 2026 ## THE LEADERBOARD: AI citation share by tier. Brands are grouped into tiers reflecting relative frequency of appearance in AI-generated answers to 62 consumer-intent prompts across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews in Q2 2026. Tier bands are directional. See methodology for scope and limitations. ### Category Leaders (SURFACED ON 40%+ OF PROMPTS) #### #1 HelloFresh COOK-FROM-SCRATCH The category default in AI answers to generic "best meal kit" queries. A decade of editorial coverage sits in the models' training data. Publicly traded (XTRA:HFG); 2025 revenue €6.76B, down 9% constant currency. Citation share sits above the current commercial trajectory — a legacy asset that newer queries are already reallocating. #### #2 Factor READY-TO-HEAT HelloFresh's U.S. ready-to-heat brand. The fastest-rising incumbent. Menu expanded from ~50 to 100+ weekly meals in September 2025. Launched in 70+ Target stores across ten Midwest states in January 2026. Cited across ready-to-heat, retail-available prepared meals, and GLP-1 queries — the widest cross-query footprint of any brand in the index. ### Strong Category Presence (SURFACED ON 20–40% OF PROMPTS) #### #3 Blue Apron READY-TO-HEAT / LEGACY KIT · WONDER Legacy citation share disproportionate to current commercial footprint. Repositioned by Wonder as a prepared-meal service; AI engines still cite it on meal-kit queries. Recognized by Consumer365 in Feb 2026 as "Best Prepared Meal Delivery Service." 600M+ meals delivered lifetime. #### #4 CookUnity READY-TO-HEAT The chef-driven premium ready-to-heat leader. 50+ contracted chefs, $10.99–$13.99 per meal. Dominates "chef-made," "restaurant-quality," and premium-tier prepared-meal queries. Canadian expansion May 2025. Heavy citation by NBC Select, MealFan. #### #5 Home Chef COOK-FROM-SCRATCH / HYBRID · KROGER Owned by Kroger. The widest cross-category footprint outside Factor — cited on meal-kit, oven-ready, family-plan, and grocery-adjacent queries. Kroger integration remains under-leveraged in the entity signals AI engines pull from. ### Specialist Presence (SURFACED ON 10–20% OF PROMPTS) #6 Green Chef (DIET-SPECIFIC): HelloFresh-owned. Dominates keto, paleo, Mediterranean, and mostly-organic queries. Growing citation share on GLP-1-adjacent "high-protein meal kit" prompts. #7 Sunbasket (DIET-SPECIFIC): The Mediterranean and diabetes-friendly leader. Widest diet-filter set in the category. Cited on health-condition-specific queries where mass-market brands don't surface. #8 EveryPlate (FAMILY & VALUE): HelloFresh's budget brand. Owns "cheapest meal kit" and "meal kit under $5" queries. AI engines correctly identify it as value-tier — rarely surfaces on quality or health queries. #9 Purple Carrot (DIET-SPECIFIC / PLANT-BASED): The vegan-native brand. Dominates "plant-based meal delivery" and "vegan meal kit" queries. A specialist that AI engines slot into a specialist lane. #10 Dinnerly (FAMILY & VALUE): Marley Spoon's budget brand at $4.99–$5.99 per serving. Frequently paired with EveryPlate in "cheapest meal delivery" answers. ### Niche & Emerging Presence - Trifecta: Athlete and macro-tracking specialist. Cited on high-protein and GLP-1 protein-preservation queries. - Freshly: Shut down by Nestlé early 2023 — AI engines still surface it on "ready-made meal delivery" queries. Retained citation with no commercial owner. - Marley Spoon: ASX-listed, Berlin-based. Martha Stewart partnership drives most consumer-facing citation share. - Hungryroot: The grocery-plus-recipes hybrid. Cited on "AI-powered grocery service" and "healthy grocery delivery" queries. - Gobble: The 15-minute meal-kit brand. Owns a narrow but defensible "quick meal kit" query surface. - Territory Foods: Local-chef-driven ready-to-heat network. High citation-per-market density in seed markets. - Tempo: Growing citation share on GLP-1 and high-protein queries. - Fresh N Lean: Organic, gluten-free, plant-based, keto, paleo. Cited on "organic prepared meals." - Daily Harvest: Smoothies, harvest bowls, frozen. Residual reputational drag from 2022 recall coverage still surfacing in the entity graph. - Thistle: Plant-forward, West Coast-native. Cited on "plant-based prepared meal delivery." - Mosaic Foods: Frozen plant-based bowls and pies. Defensible niche surface on "frozen vegan meals." - Clean Eatz Kitchen: Subscription-free GLP-1 native brand. $8.99/meal starting price. Fast-rising citation share on GLP-1 queries. - Counter: 30g protein per bowl at under 400 calories. Distributed through Target, Kroger, Costco, Lidl. Classified as GLP-1-relevant by AI engines. - BistroMD: Physician-designed weight-loss meal delivery. Cited on "doctor-designed meal delivery" queries. - MealPro: Fitness-and-macros native. Cited on "macro-tracked meal delivery" and GLP-1 protein queries. - Vital Pursuit: Nestlé's dedicated GLP-1 frozen line, launched May 2024. Cited on retail-adjacent GLP-1 queries. - Healthy Choice On Track: Conagra added "GLP-1 Friendly" labels to 26 existing meals. Surfaces on "cheapest GLP-1 friendly meals" queries. ## THE ANALYSIS: The five most important AI visibility findings. ### 01 Zombie citation: Freshly is still being recommended three years after being shut down. Nestlé shut down Freshly in early 2023. AI engines continue to surface Freshly on "ready-made meal delivery" and "prepared meals delivered" queries in Q2 2026. The brand still exists in the entity graph the models retrieve from — a decade of editorial coverage, product reviews, and consumer discussion did not disappear when Nestlé stopped shipping product. The operator lesson: Retiring a brand from the AI graph requires the same discipline as building one — press coverage of the shutdown, updates to Wikipedia and Wikidata, and structured signals from the parent company. Every operator needs a de-listing plan, not just a de-marketing plan. ### 02 Reverse-classification: Counter is being ranked as a GLP-1 brand without positioning itself that way. Counter does not market itself as a GLP-1 product. It emphasizes protein density and retail distribution. AI engines are surfacing Counter in answers to "best frozen meals for Ozempic" anyway — because the nutritional profile matches the query intent better than brands that buy the label. The operator lesson: The AI answer layer classifies brands by the intent-matching content models retrieve, not by the brand's own positioning. Owning a category label buys less than earning coverage that matches the queries in that category. ### 03 Legacy citation share masking commercial decline: Blue Apron and HelloFresh core. HelloFresh (Tier I) and Blue Apron (Tier II) hold citation share that reflects a decade of editorial coverage rather than current commercial trajectory. HelloFresh revenue fell 9% in 2025. Blue Apron has been repositioned by Wonder, meaning much of its citation share routes buyers to a category the brand no longer defines. The operator lesson: AI citation share becomes a lagging asset that decays as newer coverage displaces it. Legacy leaders should read this as a warning: entity strength has a half-life without ongoing investment. ### 04 The frozen aisle is competing on delivery queries. Vital Pursuit (Nestlé) and Healthy Choice On Track (Conagra) are marketed as GLP-1-friendly and priced far below subscription delivery. Both now surface in AI answers to "cheapest GLP-1 friendly meals" and "GLP-1 frozen meals." Neither runs a subscription. Neither needs to. The operator lesson: The category boundary delivery brands assumed — subscription delivery vs. frozen retail — no longer holds inside AI answers. Delivery brands treating retail as a separate category miss the actual competitive set the machine is showing consumers. ### 05 Editorial and review outlets are the primary citation-source layer. Across all five platforms, citation sources were dominated by editorial and review outlets: Consumer Reports, Wirecutter, NBC Select, Good Housekeeping, MealFan, CNET, Reviews.com, Eatingwell. Brands cited most frequently by these outlets sit at the top of the leaderboard. The operator lesson: Earned coverage in the outlets the models trust, at volume and sustained, is the mechanical lever that moves AI citation share. This is the discipline of AI Communications. ## THE PLAYBOOK: Ten actions. - Audit citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews on the top 50 consumer-intent queries in your sub-category. - Rebuild the entity graph. Ensure every AI engine can identify what you sell, who it is for, and where you fit through structured data and Wikipedia. - Prioritize earned coverage in the outlets that populate the AI retrieval layer — Wirecutter, NBC Select, MealFan, etc. - Own one sub-category cleanly before competing in another. Specialists outcite generalists. - Publish answer-shaped content — FAQ schema, entity-rich comparison pages — on the exact questions buyers type into AI engines. - For GLP-1 targeting, redesign the buying experience: subscription flexibility, protein density, and portion transparency. - Track competitor citation share monthly. Category dynamics in AI search move faster than in traditional search. - For legacy brands, treat the entity graph as a communications asset. - For emerging brands, pursue citation share aggressively in queries incumbents have not yet claimed. - Ship every product launch and retail partnership as a distribution kit built to be cited — press releases, structured data updates, and primary-source data. ## FAQ: Common questions. What is AI citation share, and how is it different from market share? AI citation share is the frequency with which a brand is named in AI-generated answers, relative to competitors. Market share measures revenue. The two can move independently: HelloFresh leads this index while its revenue declines. Which AI platforms were tested? Five platforms in Q2 2026: ChatGPT (OpenAI), Claude (Anthropic), Perplexity, Gemini (Google), and Google AI Overviews. Why is HelloFresh ranked highest if its revenue is declining? Citation share reflects accumulated editorial coverage. HelloFresh has a decade of coverage in outlets like Consumer Reports and Wirecutter, a legacy asset that outweighs current commercial trajectory on generic queries. How can a meal-delivery brand improve AI citation share? Four levers: (1) earned coverage; (2) entity infrastructure (Wikipedia, Wikidata); (3) answer-shaped content; (4) category specificity. ## METHODOLOGY: How the index was built. The 5W Meal Kit & Ready-Meal Delivery AI Visibility Index 2026 measures the frequency with which a brand is named in AI-generated answers across five platforms in Q2 2026. BRAND UNIVERSE: 27 brands including HelloFresh, Factor, Blue Apron (Wonder), Home Chef, EveryPlate, Green Chef, CookUnity, Territory Foods, Tempo, Trifecta, Sunbasket, Purple Carrot, Marley Spoon, Dinnerly, Gobble, Hungryroot, Daily Harvest, Thistle, Mosaic Foods, Fresh N Lean, Clean Eatz Kitchen, BistroMD, MealPro, Counter (Macrofy), Vital Pursuit (Nestlé), Healthy Choice On Track (Conagra), and Freshly. SUB-CATEGORIES: (1) Meal Kits; (2) Ready-to-Heat; (3) GLP-1; (4) Family & Value; (5) Diet-Specific; (6) Grocery-Adjacent. TESTING PROTOCOL: Each prompt was submitted in clean, logged-out sessions in U.S. English with no personalization. Mentions within a single response were deduplicated. Google AI Overviews were recorded as unavailable when they did not surface. Important framing: 5W estimates in this index are based on frequency of brand mention. These are directional measures of relative visibility, not precise market measurements. This index is published for communications strategy purposes only. Learn more at 5wpr.com. --- ### The 12-Sector Slate URL: https://www.5wpr.com/research/podcast-citation-effect/slate/ Summary: The full 12-study publication slate for the 5W Podcast Citation Effect research franchise. --- ### The World Cup 2026 AI Authority Index — Three-Wave Baseline URL: https://www.5wpr.com/research/world-cup-2026-ai-authority-index/three-wave-baseline/ Summary: The three-wave baseline report shows how AI engines construct reality around FIFA World Cup 2026, with a convergence study proving measurement reliability. --- ### The Private Label AI Advantage — Supplements URL: https://www.5wpr.com/research/private-label-ai-advantage-supplements/ Summary: The franchise inverts in supplements as national and D2C brands out-cite store items across five major answer engines in our latest index. --- ### Venture Capital URL: https://www.5wpr.com/research/podcast-citation-effect/venture-capital/ Summary: Venture Capital: how long-form podcast appearances drive AI engine Citation Share. Study #05 of 12 in The Podcast Citation Effect from 5W AI Communications. --- ### White-Collar Defense URL: https://www.5wpr.com/research/legal/white-collar/ Summary: Six lawyers, not six firms, capture half of white-collar defense citation share. How named anchors, SDNY prestige, and TV commentary drive retrieval. --- ### Edition 01: AI Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/ai-media/ Summary: The founding edition. The meta-sector that produces every other sector's retrieval map. Research› Retrieval Index› AI Media Edition 01 — The 5W Retrieval Index — Volume I # AI Media The founding edition. The meta-sector that produces every other sector's retrieval map. B The founding edition. The meta-sector that produces every other sector's retrieval map. The Unvarnished Read The labs are the press. OpenAI, Anthropic, DeepMind, Google AI Research, Meta FAIR, and Hugging Face publish more cited content than every paywalled prestige publication that covers them. Below the lab tier, two structural dynamics define the sector: open community substrates (Hacker News, r/MachineLearning, r/LocalLLaMA, Stack Overflow) carry the citation load that journalism carries in other sectors, and the highest-authority paywalled outlets — The Information, Stratechery, Bloomberg, The Wall Street Journal, the Financial Times — sit suppressed below their actual influence on the operator class. The most-read AI journalism is not the most-cited AI journalism. The training-data economy and the paywall economy run in opposite directions, and the gap defines the sector. AI media grades B because the journalism layer is strong but the citation economy is dominated by sources outside the journalism layer entirely. The System ## How AI answers about ai media work. The labs publish their own canon. OpenAI's research pages, Anthropic's research and Claude model cards, DeepMind's blog, Google AI Research, Meta FAIR, and Hugging Face's documentation are routinely cited as primary sources for the products they ship. No other sector has this dynamic — Pfizer does not out-cite STAT News on Lipitor. In AI, the manufacturers are the press of record on their own products. Newsletters provide the synthesis layer. Stratechery, Platformer, Big Technology, Pragmatic Engineer, Import AI, AI Snake Oil, One Useful Thing, Interconnects, and Latent Space carry the analysis the engines pull for "what is happening in AI" queries. Substack is more structurally important to AI retrieval than to any other sector. Forums and community substrates carry the connective tissue. Hacker News, r/MachineLearning, r/LocalLLaMA, and Stack Overflow show up disproportionately for opinion, technical disagreement, and practitioner-experience queries. r/MachineLearning is cited above some mid-tier trade press despite being a forum. The paywall economy suppresses the prestige tier. The Information, Stratechery, Bloomberg AI, FT Tech, WSJ Tech, and NYT Tech produce the highest-quality AI journalism — and the engines cannot cite what they cannot reach. Paywalls cost the prestige tier 10–25 composite points each. Geography is U.S.-dominated. Severely. Citation density follows the U.S. AI press to a degree disproportionate even to the U.S. share of the AI industry. UK presence is moderate. Chinese AI press (Caixin, Synced, China Daily Tech) is almost entirely absent from English-language engine retrieval despite the scale of Chinese AI. 55 properties across established tech press, lab and institutional publishers, newsletter and Coverage Universe analyst tier, research and reference infrastructure, community and forums, and specialist trade. The Rankings ## Source scores and retrieval tiers. Retrieval Anchor (72+) — 1 properties PropertyScoreNote TechCrunch82 Open access, high velocity, named-entity dense. Highest journalism-tier score. Strongest on product launches, explainers, accessibility. Open, well-structured. Authority plus open access on most content. Strong cross-engine consistency. NOTE Cited (56–71) — 1 properties PropertyScoreNote Reuters (AI coverage)64 Wire economics. Cited but rarely as the primary source. Heaviest intellectual citation when accessible. Paywall suppresses below influence. Partial paywall. Strong on business, funding, enterprise AI deployment. Open Substack era built the base; paywalled portion suppresses. Lab publisher. Strong on open-weights, computer vision, PyTorch-adjacent queries. NOTE Moderate (44–55) — 2 properties PropertyScoreNote The Atlantic (AI essays)54 Long-form authority, partial paywall. AI-society queries. Foundational AI policy newsletter. Intermittent post-2024 caps the score. VC-and-AI angle. Strong on funding and founder queries. Financial Times (AI)52 High authority. Paywall is the score cap. Synthesis newsletter. Open access. Strong cross-engine. AI safety community. High citation density per post. NOTE Low-Yield (<44) — 1 properties PropertyScoreNote Towards Data Science (Medium)42 Legacy data-science publication. Declining authority. Long-tail trade publication. High publication volume, lower per-piece extractability. Consumer-AI digest. Recent entry. Geographic-language barrier to U.S.-trained engine retrieval. The Structural Finding ## The Lab-as-Publisher Effect In every sector 5W has modeled, the press is the press and the brands are the brands. Pfizer does not write the cited reference on Lipitor — STAT News or the New England Journal of Medicine does. Tesla does not write the cited reference on its driver-assistance safety record — the IIHS or Reuters does. Procter & Gamble does not write the cited reference on its skincare formulations. In AI, the manufacturers publish the primary source for the manufactured thing. OpenAI's GPT-4 system card is the cited reference for GPT-4. Anthropic's Claude model card is the cited reference for Claude. DeepMind's technical reports are cited above the journalism covering them. Hugging Face's documentation is the cited reference for nearly every open-source model on the platform. Google AI Research and Meta FAIR routinely sit in the top tier of citations for their own architectures. This is the Lab-as-Publisher Effect. The labs are not subjects of coverage. They are publishers. They control the retrieval graph for their own products. The journalism layer sits on top of the lab layer, not in place of it. Three secondary patterns reinforce: the Training-Data Paradox (paywalled prestige journalism suppressed below its influence), the (Hacker News and r/MachineLearning carrying load conventional journalism Community Substrate carries elsewhere), and the (definitional queries route to Wikipedia consistently primary). Wikipedia Authority Layer What Moves It ## Operating moves for this sector. - For paywalled prestige publications — open the AI archive. The Information, Stratechery, Bloomberg, FT, and WSJ each forfeit 10–25 composite points to access controls. Opening AI-specific evergreen content recovers retrieval without altering the subscription model on news. - For open journalism — invest in schema and named-entity density. TechCrunch, The Verge, and MIT Tech Review already score high. The next 5 points come from Article schema, Organization schema for every lab and model named, and internal linking to primary lab pages. - For newsletter operators — durability over recency. The engines retrieve from the durable archive, not the inbox. Newsletter platforms that surface the archive (Substack public pages, dedicated index pages) out-cite those that bury old issues. - For labs — protect the publishing surface. Lab pages with strong structure, clear authorship, and stable URLs cement retrieval. Lab pages buried in blog mazes lose to journalism that covers the same material. - For operators trying to be cited AI coverage — the retrieval-effective placements are not the prestige in placements. Open press (TechCrunch, Verge, MIT TR, VentureBeat, Ars), lab partnership surfaces, and community substrate move the needle in engine retrieval. Stratechery and The Information move the needle in human readership. They are not interchangeable channels. Building that retrieval-effective media presence, and managing the crisis and regulatory surface that comes with being an AI company, is the work of a dedicated AI communications partner. 5WPR's AI Communications Agency practice runs this positioning for AI labs, applications, and infrastructure companies. Related Sectors Edition 02 Beauty Media B– Edition 04 Fintech Media B– ## Get Volume I. 220 pages. 38 sectors. The first reference work for the AI retrieval economy. Download PDF → --- ### Edition 08: Crypto / Web3 Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/crypto/ Summary: The sector that built its own media infrastructure, and paid the price in retrieval. Research› Retrieval Index› Crypto / Web3 Media Edition 08 — The 5W Retrieval Index — Volume I # Crypto / Web3 Media The sector that built its own media infrastructure — and paid the price in retrieval. B SECTOR GRADE B The Unvarnished Read Crypto retrieval is anchored by the blockchain. Etherscan, Solscan, Glassnode, Nansen, and Token Terminal — the on-chain data publishers — operate as the structural retrieval layer for transactional, supply, holder, and protocol-economic queries. The blockchain itself is the source of record; the trade press interprets it. Crypto's trade press tier is dense and competitive — CoinDesk, The Block, Decrypt, Blockworks, Bankless — and largely open, producing strong retrieval performance even with the volatility-driven boom-bust publication cycles. The institutional research tier (Messari, Chainalysis, Galaxy, ARK, Multicoin, Paradigm) functions as VC-as-Publisher equivalent, with research papers cited above journalism on protocol-thesis queries. SEC and CFTC enforcement actions are the regulatory retrieval anchor on policy queries. The sector grades B because the architecture is well-suited to retrieval, while the volatility of the underlying market suppresses the steady-publication discipline that lifts sectors like pharma to A-tier. The System ## How AI answers about crypto / web3 media work. On-chain queries ("how many holders of X token," "what is TVL on Aave," "ETH staking ratio") route to Etherscan, Solscan, DeFi Llama, Glassnode, Nansen, Dune, and Token Terminal. Trade press is downstream interpretation. Protocol and project queries ("what is Uniswap," "how does Solana compare to Ethereum," "what is restaking") route to project documentation (Ethereum.org, Uniswap docs, Solana docs), GitHub repositories, the research tier (Messari, Variant, Paradigm research), and the trade press tier. Price and market queries ("BTC ATH," "ETH price today," "best performing token") route to CoinGecko, CoinMarketCap, TradingView data, and the trade press. Regulatory queries ("Howey Test crypto," "SEC v Coinbase status," "MiCA timeline") route to SEC and CFTC enforcement filings, EU regulatory publications, Lawfare, Coin Center analysis, and the trade press. Founder and personality queries ("Vitalik Buterin essays," "Balaji thesis," "SBF case status") route to founder publishing surfaces (Vitalik's blog, 1729 newsletter, Balaji's content), trade press (especially Bankless and Unchained), and Wikipedia for biographical context. Investment-thesis queries ("why hold ETH," "is Solana undervalued," "what is the bull case for X") route to the institutional research tier and the newsletter tier (Bankless, Milk Road, Bytes). News and incident queries ("FTX collapse," "Terra Luna timeline," "Mt Gox repayment status") route to the trade press, with archival content on Wikipedia, and incident-specific on-chain reconstruction on Chainalysis and Elliptic. Cross-engine variation: Perplexity is the most crypto-trade-press-friendly engine — Bankless, Blockworks, and The Block surface aggressively. ChatGPT is more cautious on crypto queries, particularly on price predictions and project thesis, weighting institutional sources higher. Claude weights Vitalik's blog and Ethereum Foundation publications heavily on Ethereum-specific queries. Gemini and Google AI Overviews favor CoinGecko, CoinMarketCap, and traditional financial press on price and market queries. Geographic dispersion: U.S. crypto press leads English-language retrieval. UK crypto press (CryptoSlate, CityAM crypto, FT Crypto) is moderate. Asian crypto press is meaningfully present (CoinTelegraph operates globally; Decrypt has Asian coverage; CryptoSlate covers Asian markets), but Asian-language native crypto press (Caixin Crypto, Korean crypto trades) is underrepresented. GEO implication for crypto operators. Retrieval-effective placements split by query class. For protocol-thesis visibility, the lever is institutional-research-tier placement (Messari coverage, Paradigm or Variant essays). For technical credibility, it is documentation quality on the project's own surface and GitHub presence. For market visibility, accurate listing data on CoinGecko, CoinMarketCap, Etherscan token information, and DeFi Llama. For regulatory visibility, attorney-and-policy commentary in Coin Center, Lawfare, and the legal-trade press. The trade-press tier handles narrative and news but is rarely primary on technical or data queries. Coverage Universe publishing surfaces, market data, regulatory sources, newsletter tier, community substrates, and geographic-specialty. The Rankings ## Source scores and retrieval tiers. Retrieval Anchor (72+) — 1 properties PropertyScoreNote Etherscan90 Ethereum on-chain data anchor. Uncontested for transactional queries. NOTE Cited (56–71) — 3 properties PropertyScoreNote Blockworks66 Open. Strong on institutional-crypto queries. Institutional research tier. Partial paywall. Compliance-and-investigations research. Cited on crime queries. Wallet-tracking analytics. Strong on whale-watching queries. High volume, mid-tier authority. Open. a16z Crypto (firm publishing)62 VC-as-Publisher. Strong on category-thesis queries. Same tier. Lower volume, high per-piece authority. Newsletter-and-podcast. Open. Strong on industry-personality queries. Variant Fund research60 VC research. Open. Strong on tokenomics and consumer-crypto. Regulatory anchor. Cited on enforcement queries. On-chain query and dashboards platform. Strong on bespoke-data queries. NOTE Moderate (44–55) — 3 properties PropertyScoreNote Pantera research52 Same tier. Institutional research. Strong on macro-crypto queries. Community substrate. Moderate dominance. More technical community. Open, but crypto is a subset. The Information (crypto)50 Paywall heavy. Public-fund research. Strong on macro-crypto. Open. Mixed authority. Same dynamic. Paywall heavy. FT Crypto48 Same dynamic. Same dynamic. Low-Yield (<44) — 3 properties PropertyScoreNote CryptoSlate42 NewsBTC40 CryptoBriefing40 The Structural Finding ## The On-Chain Data Anchor Crypto is among the few sectors 5W has modeled where the underlying transactional substrate of the industry is itself a publisher. Etherscan, Solscan, DeFi Llama, Glassnode, Nansen, Dune, and Token Terminal collectively operate as a data publication layer that exists because the blockchain is publicly readable. Every transaction is published. Every holder is countable. Every protocol economic state is queryable. The data publishers are interpretation layers on top of an inherently transparent system, and the engines retrieve from them as primary sources because nothing more authoritative exists. The parallel to other sectors: ClinicalTrials.gov in pharma. CVE and NVD in cyber. Christie's and Sotheby's auction records in luxury. Crypto's on-chain data layer is the strongest version of this dynamic — it is not a registry of disclosures (like ClinicalTrials.gov) or an authoritative database (like NVD); it is a direct read of the system itself. Two secondary patterns reinforce. The VC-as-Publisher Effect. a16z Crypto, Paradigm, Variant Fund, Multicoin, Pantera, and Galaxy publish protocol-thesis and category research cited above journalism on investment-thesis queries. Crypto is the second-strongest VC-as-Publisher dynamic after AI's Lab-as-Publisher and venture's broader pattern. The mechanism: protocol theses are speculative interpretations, and the VC firms have both the analytical capacity and the incentive to publish them. The Founder-Publishing Layer. Vitalik Buterin's blog operates as primary source on Ethereum design and philosophy. Few other sectors has an active founder publishing surface at this citation level. Adjacent surfaces — Hayden Adams on Uniswap, Anatoly Yakovenko on Solana — exist but at lower citation density. Crypto grades B because the on-chain anchor and the research-and-founder publishing layers are strong, while the trade press tier — though competent — is structurally below them on the most retrieval-significant queries. What Moves It ## Operating moves for this sector. - For crypto operators: accurate on-chain identity is the most underweighted lever. Token information on Etherscan, listing accuracy on CoinGecko and CoinMarketCap, protocol metrics on DeFi Llama and Token Terminal — these are direct retrieval anchors. Most projects under-invest in data-layer presentation and over-invest in trade-press coverage. - For protocol projects: maintain documentation as primary source. Project documentation that is well-structured, complete, and stable-URL is cited as primary on technical queries. Documentation that is sparse, outdated, or behind staging environments forfeits retrieval. - For VC firms in crypto: durable research surfaces compound. Paradigm and a16z Crypto score because their research archives are taxonomized, stable, and consistently authored. Firms that publish on Mirror, ad-hoc Medium, or ephemeral platforms forfeit compounding citation. — For trade press (CoinDesk, The Block, Decrypt, Blockworks): defend the open-access archive. Volatility-driven business model changes that paywall content during bull markets and re-open during bears cost retrieval. Stable open archives outperform high-prestige paywalled ones on engine retrieval over the cycle. - For non-U.S. crypto operators: the open data layer is the equalizer. Asian and Latin American crypto projects that under-invest in U.S. trade-press coverage but maintain accurate on-chain data presence reach U.S. engine retrieval more reliably than projects that pursue earned coverage in English-language regional trades. Related Sectors Edition 07 Luxury Media B– Edition 09 Marketing & Advertising Media B Edition 06 Pharma Media A– ## Get Volume I. 220 pages. 38 sectors. The first reference work for the AI retrieval economy. Download PDF → --- ### Edition 05: Venture Capital Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/venture-capital/ Summary: The sector where founder consensus and community signal compete with prestige trade press. --- ### Edition 12: Cloud Infrastructure Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/cloud-infrastructure/ Summary: The sector where vendor documentation and official cloud provider content anchor retrieval. --- ### AI Already Decided Where You'll Buy Your Next Project URL: https://www.5wpr.com/ai-visibility-index/home-services-improvement-ai-visibility-index-2026/ Summary: This 2026 index ranks top home improvement and service brands by citation share across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews. Full study below — no paywall, no PDF, no email gate. Reported, sourced, and built to be cited. 5W. Research AI Visibility Index Series · Published May 2026 5W AI Visibility IndexMay 2026 ReleaseHome Services & Improvement # AI Already Decided Where You'll Buy Your Next Project The retailers, marketplaces, and brands competing for the American home-improvement dollar are now researched inside the chatbox. New 5W research ranks the 25 by how often the AI engines name them. A 5W research reportEngines: ChatGPT · Claude · Perplexity · Gemini · Google AI Overviews60+ queries · 25 brandsData window: Q2 2026 The Headlines 01 Two chains own 80% of the market — and even more of the answer. Home Depot and Lowe's concentration runs tighter inside the AI engines than in the market itself. 02 "Where do I buy it" and "who do I hire" are two different questions. Retailers own one citation surface; home-services marketplaces own a separate one. 03 Lowe's isn't the biggest. AI calls it the most satisfying. Consumer-satisfaction coverage gives Lowe's a citation edge Home Depot's larger share does not erase. ~16.5% Estimated AI citation share held by Home Depot — the most-cited brand 80%+ Combined Home Depot and Lowe's share of U.S. home improvement retail sales ~$150B Estimated Home Depot 2025 U.S. revenue — roughly half the entire sector 680 Lowe's J.D. Power satisfaction score — the highest in home improvement retail Figure 1 · The Ranking ## Who AI names first. TOP 15 BRANDS BY EST. CITATION SHARE · Q2 2026 01Home DepotBig-box home improvement16.5% 02Lowe'sBig-box home improvement13.5% 03Ace HardwareHardware & specialty retail6.5% 04WayfairOnline home retail5.5% 05AmazonOnline home retail4.8% 06AngiHome-services marketplace4.2% 07MenardsBig-box home improvement3.6% 08ThumbtackHome-services marketplace3.4% 09Harbor FreightHardware & specialty retail3.0% 10HouzzDesign & project platform2.7% 11Sherwin-WilliamsMaterials & paint brand2.3% 12Tractor SupplyHardware & specialty retail2.0% 13TaskRabbitHome-services marketplace1.8% 14Floor & DecorHardware & specialty retail1.5% 15True ValueHardware & specialty retail1.2% Source: 5W analysis of AI-generated responses across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, Q2 2026. Share represents the estimated proportion of brand citations across 60+ tracked homeowner prompts spanning retail, online, marketplaces, design platforms, and home brands. Remaining ~27.5% split across ranks 16–25 and unranked brands. The Central Finding ## A near-duopoly in the market — an even tighter one in the answer. U.S. home improvement retail is one of the most concentrated categories in all of retail. Home Depot commands roughly 52% of the sector by revenue; Lowe's holds about 30%. Together the two chains account for more than 80% of home improvement store sales — a level of concentration virtually unseen elsewhere in retail. The 5W AI Visibility Index finds that inside the AI engines, the concentration runs tighter still. Asked "where should I buy supplies for a home project" or "best home improvement store," ChatGPT, Claude, and Google's AI Overviews surface Home Depot and Lowe's first, second, and often almost exclusively. Decades of editorial coverage, reviews, and consumer consensus made the two chains the assumed answer. But the Index also finds a clean split. "Where do I buy it" and "who do I hire" are two different questions — and two different citation surfaces. The home-services marketplaces (Angi, Thumbtack, TaskRabbit) own the hiring query; the retailers own the buying query; few brands compete across both. And one more decoupling: Lowe's leads consumer-satisfaction rankings despite Home Depot's larger share, which gives Lowe's a citation edge on "best" queries that raw market share does not. National scale and a deep content footprint win the answer — and the local operator who never built one is invisible on every surface. — 5W Research, May 2026 "Two companies ring up more than eighty cents of every dollar spent on home improvement in America — and inside the AI engines, the concentration is even tighter. Home Depot and Lowe's own the answer to 'where do I buy it.' But 'who do I hire' is a different question, and a different citation surface — and the contractor down the street is invisible on both. National scale and a deep content footprint win the answer. The local operator who never built one is not losing the customer at the quote. They are losing them before the search even finishes." Ronn TorossianFounder & Chairman, 5W Methodology ## How we measured it. 5W analyzed more than 60 common homeowner prompts across six primary sub-categories of the home improvement and home-services market, running each prompt five times per engine in clean sessions. We identified which brands AI models consistently surface, which sources feed those citations, and where the biggest gaps sit between scale and AI visibility. #### Big-box home improvement Home Depot, Lowe's, Menards. #### Hardware & specialty retail Ace Hardware, True Value, Harbor Freight, Tractor Supply, Floor & Decor. #### Online home retail Wayfair, Amazon home, Ferguson, online supply retailers. #### Home-services marketplaces Angi, Thumbtack, TaskRabbit, HomeAdvisor. #### Design & project platforms Houzz, Pinterest, Yelp home services. #### Materials & paint brands Sherwin-Williams, Behr, Benjamin Moore, and home-materials brands. Query types tracked. Real-world prompts including "best home improvement store," "Home Depot vs Lowe's," "where to buy tools," "best place to hire a handyman," "how do I find a contractor," "best paint brand," "best site to buy furniture online," "Angi vs Thumbtack," and 50+ variations covering retail, hiring, and project-research intent. Citation sources tracked. Retail and consumer editorial (Wirecutter, Consumer Reports, Forbes Home, This Old House, Family Handyman), satisfaction sources (J.D. Power, ACSI), homeowner communities (Reddit r/HomeImprovement, r/DIY), news coverage, and brand-owned content. Important framing. This index measures home improvement retailers, home-services marketplaces, and home brands — the "where do I buy" and "who do I hire" citation surfaces. It is distinct from the 5W HVAC & Plumbing AI Visibility Index, which measures contractor trades, and from 5W's Local Services research on local-business discovery. The index measures AI citation share for marketing and communications strategy purposes; it does not rate retailers or contractors on price, quality, or reliability. The Full Ranking ## The Top 25 home brands, ranked by AI citation share. #BrandCategoryAI VisibilityNotable 1Home DepotBig-box retailCategory-dominantNYSE: HD; ~52% of U.S. home improvement retail; ~$150B 2025 U.S. revenue; the default first answer for "where to buy." 2Lowe'sBig-box retailCitation co-leaderNYSE: LOW; ~30% market share; tops J.D. Power satisfaction at 680 — an edge on "best store" queries. 3Ace HardwareHardware & specialtyService-reputation tierCooperative; #2 in consumer satisfaction at 672; owns "best local hardware store" and "helpful service" citations. 4WayfairOnline home retailOnline-furnishings leaderNYSE: W; owns "best site to buy furniture and home goods online" citations. 5AmazonOnline home retailOnline-everything tierCited as the default for tools, hardware, and small home goods; broad but unspecialized in home queries. 6AngiHome-services marketplaceHiring-query leaderNASDAQ: ANGI; the most-cited brand for "find a contractor / hire a pro" — a separate surface from retail. 7MenardsBig-box retailRegional big-box tierPrivate; ~4.6% share; strong in the Midwest, cited as the regional alternative to Home Depot and Lowe's. 8ThumbtackHome-services marketplaceHiring-query challengerCited alongside Angi for hiring local pros; owns "get quotes from contractors" citations. 9Harbor FreightHardware & specialtyValue-tools tierPrivate; ~14.7% of the tools segment by foot traffic; owns "affordable tools" citations. 10HouzzDesign & project platformDesign-research leaderOwns "home design ideas" and "find a designer/remodeler" citations; a research-surface brand. 11Sherwin-WilliamsMaterials & paintPaint-brand leaderNYSE: SHW; the most-cited paint brand; owns a large share of "best paint" citations. 12Tractor SupplyHardware & specialtyRural-retail leaderNASDAQ: TSCO; ~$15.5B revenue; owns "rural and farm supply" citations — its own distinct niche. 13TaskRabbitHome-services marketplaceSmall-job nicheIKEA-owned; owns "hire help for small jobs and assembly" citations. 14Floor & DecorHardware & specialtyFlooring-category nicheNYSE: FND; owns "best place to buy flooring and tile" citations. 15True ValueHardware & specialtyNeighborhood-hardware nicheCooperative; cited as a neighborhood hardware option; a recent foot-traffic comeback is lifting visibility. 16Benjamin MooreMaterials & paintPremium-paint nicheBerkshire-owned; owns "best premium paint" citations against Sherwin-Williams and Behr. 17BehrMaterials & paintValue-paint nicheHome Depot exclusive; cited as the value paint pick; citations tied closely to Home Depot. 18YelpDesign / services platformReview-source tierNYSE: YELP; cited as a review source for home-services queries more than as a destination brand. 19FergusonOnline / supply retailTrade-supply nicheNYSE: FERG; owns "where to buy plumbing and fixtures" citations; strong with trade buyers. 20The Container StoreSpecialty retailOrganization nicheOwns "home organization and storage" citations; narrow but distinct. 21Mr. HandymanHome-services franchiseNational-franchise nicheNeighborly-owned franchise; the most-cited national handyman brand — a rare branded answer in a local category. 22Builders FirstSourceMaterials / supplyPro-supply nicheNYSE: BLDR; large building-materials supplier; cited mainly in pro and new-construction queries. 23PorchHome-services platformServices-platform nicheNASDAQ: PRCH; cited in home-services and moving queries; small but defined footprint. 24LL FlooringSpecialty retailFlooring nicheCited in flooring-specific queries; narrow citation footprint behind Floor & Decor. 25PinterestDesign / inspiration platformInspiration-source tierNYSE: PINS; cited as a home-project inspiration source; a research surface more than a destination brand. Winners ## The brands winning the AI answer. ### ▲ Home Depot — the Default Answer Home Depot owns the home improvement citation surface the way it owns the market — first, by a wide margin. Roughly half the sector's revenue and decades of editorial and consumer coverage make it the assumed answer to nearly every "where to buy" query. ### ▲ Lowe's — Satisfaction as a Citation Edge Lowe's is the smaller chain by share, but it leads consumer-satisfaction rankings — and AI answers to "best home improvement store" retrieve that data. Satisfaction gives Lowe's a citation advantage on "best" queries that market share alone would not. ### ▲ Ace Hardware — Owning "Helpful" Ace cannot match the big boxes on scale, so it owns a different word: service. Its #2 satisfaction ranking and "helpful neighborhood hardware store" reputation give it the third citation slot on a fraction of the footprint. ### ▲ Angi — Owning the Hiring Query Angi owns "who do I hire" — a query the retailers do not compete for at all. By treating contractor discovery as its own citation category, Angi captures a surface the big boxes leave entirely open. Falling Behind ## The brands AI is leaving behind. ### ▼ The Local Contractor and Independent Store Local contractors and independent hardware stores are nearly invisible in AI answers. The engines retrieve national brands with deep content footprints — and a local operator who never built one is lost before the search finishes. ### ▼ Regional Chains Outside Their Footprint Menards is strong in the Midwest but barely registers nationally. AI answers default to the chains with national coverage, leaving regional players cited only inside their own geography. ### ▼ Brands Without a Single Owned Category Retailers that sell a bit of everything but own no specific query — flooring, paint, tools, organization — split the remaining citations thin. The brands that win own one word; generalists below the duopoly do not. ### ▼ Marketplaces Squeezed by Direct Answers As AI engines answer "how do I find a pro" with their own guidance and ratings, the services-marketplace layer faces the same disintermediation pressure seen across other categories the Index has measured. Structural Findings ## Six structural truths about AI visibility in home improvement. 01 ### AI concentrates a concentrated market further. Home Depot and Lowe's hold 80%+ of retail sales — and an even larger share of the citation surface. AI amplifies category leaders. 02 ### "Where to buy" and "who to hire" are separate surfaces. Retailers own one; marketplaces own the other. Few brands compete across both — and the hiring surface is the more open one. 03 ### Satisfaction can beat size on "best" queries. Lowe's and Ace out-cite their market share because AI retrieves satisfaction data. "Biggest" and "best" are different citations. 04 ### National scale beats local presence in the answer. The engines retrieve brands with deep national content footprints. Local operators are structurally disadvantaged — echoing 5W's Local Services research. 05 ### Owning one category beats selling everything. Tractor Supply owns rural, Floor & Decor owns flooring, Sherwin-Williams owns paint. A specific owned query out-cites a broad assortment. 06 ### Design and inspiration platforms are research infrastructure. Houzz and Pinterest are retrieved heavily as project-research sources — feeding the answers that retailers and brands then appear in. Findings Specific to 2026 ## Six 2026 dynamics reshaping home AI citations in real time. 01 ### A stabilizing market is steadying the citation surface. After several soft years, home improvement traffic has steadied — and the citation leaders are consolidating their positions. 02 ### Lowe's momentum is showing up in citations. Lowe's positive 2026 traffic trend, paired with its satisfaction lead, is lifting its share of "best store" answers. 03 ### Value retailers are gaining a distinct citation lane. Harbor Freight and a resurgent True Value are claiming "affordable tools" and "neighborhood hardware" citations. 04 ### AI is starting to answer "how do I" directly. Project how-to queries are increasingly answered in-engine — raising the value of brand-owned project content. 05 ### The hiring marketplaces face disintermediation pressure. As engines surface their own pro-finding guidance, Angi and Thumbtack face the squeeze seen in other marketplace categories. 06 ### Project research is moving fully into AI. Homeowners increasingly plan projects in a chatbot before visiting a store — making citation share the new front of the funnel. The Playbook ## General tips for home retail and services marketers. - Audit AI citation share quarterly. Project-research behavior and seasonal demand move the surface enough to require regular measurement. - Decide which question you answer. "Where to buy" and "who to hire" are different surfaces — build content for the one you can win. - Treat satisfaction data as a citation asset. J.D. Power and ACSI scores feed "best store" answers — publicize and contextualize them. - Own one category, not the whole aisle. Flooring, paint, tools, organization — a specific owned query out-cites a broad assortment. - Build deep project how-to content. As AI answers "how do I" directly, brand-owned project guidance becomes a citation engine. - Earn the home-editorial record. Wirecutter, Consumer Reports, Forbes Home, This Old House feed home citations disproportionately. - Treat homeowner communities as citation infrastructure. Reddit's r/HomeImprovement and r/DIY feed a large share of recommendation citations. - For local and regional players, build a national-grade content footprint. The engines reward depth — local presence alone does not earn the answer. - For marketplaces, deepen content beyond the listing. As engines answer "how to find a pro" directly, owned guidance and trust content protect citation share. - Re-audit after seasonal peaks and major retail shifts. Spring project season and competitive moves reshape the citation surface. The Bigger Picture ## The project now starts in the chatbox — not the parking lot. For decades, a home project started with a drive to the nearest big box. Today it increasingly starts with a question typed into ChatGPT, Claude, or Google's AI Overviews — what to buy, what brand, who to hire, how to do it. The citation surface has become the front of the home improvement funnel. The Index shows what that surface rewards: national scale, decades of editorial and satisfaction coverage, a clear owned category, and a deep content footprint. It rewards the concentration that already defines the market — and amplifies it. Home Depot and Lowe's own the answer. The marketplaces own the separate hiring query. And the local operator who never built a content footprint is invisible on every surface. AI citation share is the scoreboard. In home improvement, the brand AI names is the brand the project runs through — and that visibility is built in content and coverage, not in store count. Frequently Asked Questions ## The Home Services & Improvement AI Visibility Index 2026. What is the Home Services & Improvement AI Visibility Index 2026?A research report by 5W that ranks the top 25 home improvement retailers, home-services marketplaces, and home brands by AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, based on 60+ homeowner prompts tracked in Q2 2026. Which brand holds the highest AI citation share?Home Depot, with an estimated 16.5% — ahead of Lowe's. The two chains dominate the home improvement answer. How is this different from the HVAC & Plumbing Index?This index measures home improvement retailers, home-services marketplaces, and home brands — the "where do I buy" and "who do I hire" surfaces. The HVAC & Plumbing Index measures contractor trades specifically, and 5W's Local Services research covers local-business discovery. Which AI engines were used?ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, across the Q2 2026 data window. What is citation share?The estimated proportion of brand mentions a brand receives across all tracked homeowner prompts and AI engines — the core metric used to rank brands in the Index. What other industries has the AI Visibility Index Series covered?Recent editions include Medical Aesthetics, U.S. Grocery Retail, Online Universities, Cybersecurity, the Pet Industry, and the AI Platform Citation Source Index. ### About this report 5W AI Visibility Index: Home Services & Improvement. Published May 2026. An installment in the 5W AI Visibility Index series. The index measures AI citation share across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews for 60+ homeowner queries tracked in Q2 2026, with each prompt run five times per engine. Citation share percentages are 5W estimates based on frequency of brand mention across tracked prompts; absolute percentages should be read as directional measures of relative visibility, not precise market measurements. This report covers home improvement retail, home-services marketplaces, and home brands; it is distinct from the 5W HVAC & Plumbing AI Visibility Index and does not rate retailers or contractors on price, quality, or reliability. ### About 5W 5W is The Communications Firm Built for the AI Era, building brand authority across the platforms where decisions now happen — ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — alongside earned media, digital, and influencer channels. 5W combines public relations, digital marketing, Generative Engine Optimization (GEO), and proprietary AI visibility research to help clients measure and grow their presence in AI-driven buyer research. Founded in 2003, 5W is recognized as a Top U.S. PR Agency by O'Dwyer's, named Agency of the Year in the American Business Awards®, honored as a 2026 Top Place to Work in Communications by Ragan, and named to Digiday's WorkLife Employer of the Year list. 5W serves clients across B2C sectors — Beauty & Fashion, Consumer Brands, Entertainment, Food & Beverage, Health & Wellness, Travel & Hospitality, Technology, and Nonprofit — and B2B specialties including Corporate Communications, Reputation Management, Public Affairs, Crisis Communications, and Digital Marketing across Social, Influencer, Paid Media, GEO, and SEO. Learn more at 5wpr.com. 5W Research · Published May 2026 · AI Visibility Index Series · 5wpr.com/research --- ### The US Entertainment & Streaming AI Visibility Index 2026 URL: https://www.5wpr.com/research/entertainment-streaming-ai-visibility-index-2026/ Summary: The top 25 US streaming services, studios, and production companies ranked by AI citation share across ChatGPT, Claude, Perplexity, and AI Overviews. 5W Research Entertainment & Streaming AI Visibility Index Download PDF 5W Research · April 2026 Premier The Communications Firm Built for the AI Era # The US Entertainment & Streaming AI Visibility Index 2026 The top 25 US streaming services, film and television studios, and production companies — ranked by AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. Download Full PDF → Read Executive Summary $110.9B Paramount Skydance—WBD 25 Brands Ranked 90+ Consumer Queries 4 AI Platforms +4 pts Apple TV+ Q1 Gain Executive Summary ## The discovery layer for streaming has moved. On February 27, 2026, Paramount Skydance closed its $110.9 billion acquisition of Warner Bros. Discovery — the biggest US media deal in a generation. Netflix lost the bid. Apple TV+ posted the largest single-quarter market share gain in the category. Streaming fatigue and churn-decision queries entered the AI citation graph as a permanent new category. The infrastructure carrying entertainment discovery has moved. AI answer engines — ChatGPT, Claude, Perplexity, and Google AI Overviews — are now the front door to streaming service selection, movie and television recommendations, franchise queries, and the question every household is now asking out loud: which subscription do I cancel. This report ranks the top 25 US streaming services, film and television studios, and production companies by AI citation share. Netflix, Disney+, Max, Amazon Prime Video, and Hulu lead. The index analyzes more than 90 consumer-intent queries spanning streaming service selection, movie and TV recommendations, franchise and IP queries, studio and genre-specific queries, sports streaming, and the fast-growing churn-decision query category. Essential reading for any streaming, studio, or production company serious about winning AI-mediated consumer discovery. 5W is the premier AI communications firm in the United States. From the 5W Entertainment Practice "The biggest media deal in a generation just closed, and the most valuable single asset in entertainment — Netflix's brand synonymity — was not on the table. AI answer engines have rewritten the discovery layer for streaming, and the brands winning are the ones treating citation share as a board-level metric. 5W is the firm built to deliver that." — 5W Entertainment & Sports Marketing Key Findings ## By the numbers. $110.9B Stat 01 Paramount Skydance acquisition of Warner Bros. Discovery, announced Feb 27, 2026 (pending regulatory approval) 25 Stat 02 US streamers, studios, and production companies ranked by AI citation share 90+ Stat 03 Consumer-intent queries analyzed across six query categories 4 Stat 04 AI platforms tested — ChatGPT, Claude, Perplexity, Google AI Overviews Top 5 Stat 05 Netflix, Disney+, Max, Amazon Prime Video, Hulu lead the index +4 pts Stat 06 Apple TV+ market share gain in Q1 2026 (JustWatch) — fastest in category 6 Stat 07 Query categories analyzed, including the new churn-decision category #1 Stat 08 Studio-plus-streamer integration is the most valuable AI citation asset The Top 5 ## Leaders by AI citation share. The five brands accumulating the largest share of AI-mediated consumer discovery across every major query category. 01 Netflix The brand most synonymous with streaming itself. Dominates AI citation across virtually every consumer-intent query — what to watch, where to stream, recommendation queries, originals queries. Brand synonymity survived losing the biggest deal in media history. 02 Disney+ The catalog-plus-distribution model at scale. Marvel, Star Wars, Pixar, Disney Animation, National Geographic, and the Disney library produce the densest franchise citation base in the index. 03 Max Heir to HBO's three-decade prestige citation base. Pre-deal Warner Bros. Discovery library plus Max originals. Position to evolve materially under Paramount Skydance ownership. 04 Amazon Prime Video MGM library plus Thursday Night Football plus original prestige investment. Sports streaming citation lifts the brand into the top tier. 05 Hulu Disney's general entertainment streamer. Network-television catalog and FX originals anchor a durable citation footprint. The Deal ## Paramount Skydance acquires Warner Bros. Discovery. On February 27, 2026, Paramount Skydance announced the $110.9 billion acquisition of Warner Bros. Discovery. The biggest US media deal in a generation. Pending regulatory approval at publication. ### What the Combined Entity Holds Paramount Pictures, CBS, Showtime, MTV, Nickelodeon, Comedy Central, BET, Paramount+, Pluto TV — joining Warner Bros. Pictures, HBO, Max, CNN, TNT, TBS, Cartoon Network, DC, and the Warner film and television library. One of the largest catalogs of film and television IP ever assembled under a single owner. ### Why It Reshapes AI Citation in Real Time Catalog citations compound. AI answer engines reach for the brand whose name aligns with the franchise, the title, or the genre. A combined Paramount Skydance + Warner Bros. Discovery surfaces under a far broader set of queries than either company surfaces alone. The Netflix counterpoint: Netflix lost the bid. Netflix kept the citation. Brand synonymity is the most durable citation asset in the category — and one no acquisition can transfer. Fastest-Rising Platform ## Apple TV+ — the largest single-quarter market share gain. +4pts Q1 2026 · Source: JustWatch - 01 Prestige film & television investment Sustained, high-budget originals creating awards-cycle press across every major outlet. - 02 Sports rights footprint MLB, MLS, and live sports content generating new query categories. - 03 Hardware-flywheel distribution Every Apple device is an Apple TV+ acquisition surface. - 04 Editorial momentum Standard inclusions in critic year-end lists, awards coverage, and trade press. The Most Valuable Asset ## Studio + Streamer Integration. The brands accumulating citation capital fastest are the ones that own both the catalog (studio) and the distribution (streamer). The integrated model surfaces under two query layers at once — what to watch, and where to watch it. Methodology ## The six query categories. More than 90 consumer-intent queries tested across four AI platforms. 01 #### Streaming Service Selection Best streaming service, cheapest streaming service, ad-free streaming, family streaming, prestige streaming. 02 #### Movie & TV Recommendations Best movies on [platform], what to watch tonight, top shows of 2026, recommendation queries by genre and mood. 03 #### Franchise & IP Queries Where to watch [franchise], what is [franchise] streaming on, [franchise] complete watch order. 04 #### Studio & Genre Queries Best [studio] movies, top horror films, prestige drama series, classic comedy. 05 #### Sports Streaming Where to watch [league], live sports streaming services, [sport] streaming options. 06 #### Streaming Fatigue & Churn Decisions Which subscription should I cancel, best value streaming, streaming bundles. Fastest-growing query category in the index. Six Findings ## What the data tells us. Finding 01 ### Brand synonymity is the most durable citation asset. Netflix lost the deal. Netflix kept the queries. The brand whose name has become category shorthand wins by default in AI answers. Finding 02 ### Catalog depth determines franchise citation share. Disney's Marvel-Star Wars-Pixar stack and Paramount Skydance's combined library produce the densest franchise citation base in the category. Finding 03 ### Studio-plus-streamer integration is the most valuable structural asset. Two citation layers, one entity. Pure-play studios without a streamer face a structural ceiling. Finding 04 ### Apple TV+ is the fastest-rising platform. Prestige investment, sports rights, and hardware distribution combined for the largest Q1 gain in the category. Finding 05 ### Streaming fatigue is a permanent query category. Churn-decision queries are growing faster than recommendation queries. Brands not optimizing for "which subscription should I cancel" are surrendering retention surface. Finding 06 ### Sports streaming has become a top-tier citation lift. Amazon (Thursday Night Football), Apple (MLB, MLS), Peacock (NFL exclusives), ESPN+ — every platform with live sports rights gains a query category general-entertainment streamers cannot enter. The Playbook ## What wins in entertainment AI citation. 01 #### Tier 1 critic and trade press Variety, Hollywood Reporter, Deadline, IndieWire, Rotten Tomatoes, Metacritic. The third-party citation surface AI engines weight most heavily. 02 #### Aggregator and review-site presence Rotten Tomatoes, Metacritic, IMDb, JustWatch — the structured-data layer AI engines reach for first. 03 #### Awards-cycle editorial Sustained press across the awards cycle (Emmys, Golden Globes, SAG, Oscars) compounds into citation authority. 04 #### Franchise narrative ownership Brands that own the canonical narrative around a franchise — watch order, lore, anniversaries — own the franchise query category. 05 #### Sports rights coverage Live-sports rights generate continuous trade press and consumer query volume. 06 #### Recent content Pages older than 3 months are 3 more likely to lose AI citation visibility (AirOps). Entertainment is a quarterly refresh discipline. Free Download ## Get the full 2026 Index. Read the full 2026 US Entertainment & Streaming AI Visibility Index — every ranking, every finding, every query category. Free, ungated, immediate download. Download the PDF → Request an AI Visibility Audit For audit inquiries: research@5wpr.com · media@5wpr.com FAQ ## Frequently asked questions. +What is the US Entertainment & Streaming AI Visibility Index? 5W research ranking the top 25 US streaming services, film and television studios, and production companies by AI citation share across ChatGPT, Claude, Perplexity, and Google AI Overviews. The index analyzes more than 90 consumer-intent queries across six categories. +Which streamers lead the index? Netflix, Disney+, Max, Amazon Prime Video, and Hulu lead. Netflix's brand synonymity continues to dominate AI citations across virtually every consumer-intent streaming query, even after losing the bid for Warner Bros. Discovery. +How does the Paramount Skydance–WBD deal change the picture? Paramount Skydance's $110.9 billion acquisition of Warner Bros. Discovery, announced February 27, 2026, is the biggest US media deal in a generation and is reshaping catalog citation in real time. The combined entity stands to consolidate one of the largest film and television libraries in the world. The report frames the deal as pending regulatory approval as of publication. +Who is the fastest-rising platform in AI citation? Apple TV+, supported by a +4 point market share gain in Q1 2026 (JustWatch). The growth is driven by sustained prestige film and television investment, awards-cycle press, and a flagship sports rights footprint. +What is the most valuable AI citation asset in the category? Studio-plus-streamer integration. Brands that own both the catalog (studio) and the distribution (streamer) accumulate citation capital across two query layers at once — what to watch and where to watch it. Disney, Paramount Skydance (post-WBD close), Amazon (MGM), and Comcast/NBCUniversal (Peacock) operate this model. +Who is this report for? Streaming service marketing leaders, studio chief marketing and chief communications officers, production company executives, sports rights holders, exhibitors, and any brand fighting for AI-mediated consumer discovery in entertainment. +Can 5W run AI visibility for my streaming or studio brand? Yes. 5W is the premier AI communications firm and runs a dedicated Entertainment & Sports Marketing practice. Inquiries: research@5wpr.com or media@5wpr.com. ### About 5W 5W is the premier AI communications firm in the United States. With approximately 275 professionals, 5W works with consumer brands, B2B technology companies, financial services firms, luxury brands, entertainment and sports clients, and high-profile individuals. Founded in 2003 by Ronn Torossian. Led by CEO Matt Caiola. Recognized as a top U.S. PR agency by O'Dwyer's, named Agency of the Year in the American Business Awards, and honored as a Top Place to Work in Communications in 2026 by Ragan. 5W's Entertainment & Sports Marketing practice serves streamers, studios, production companies, leagues, exhibitors, and live entertainment brands. AI Visibility Audits cover 50–100 category-specific queries across five AI platforms, citation-source mapping, competitive benchmarking, and a 90-day remediation plan. Methodology Tested 90+ queries across ChatGPT, Claude, Perplexity, and Google AI Overviews, March–April 2026. Six query categories. Top 25 brands by combined revenue, subscriber count, and library scale. Market share data: JustWatch Q1 2026. Inquiries research@5wpr.com media@5wpr.com press@5wpr.com Practice Area 5wpr.com/practice/ai-visibility-audit © 2026 5W · Published April 2026 · Reproduction permitted with attribution 5wpr.com · The Premier The Communications Firm Built for the AI Era --- ### Edition 13: Application Security & DevSecOps — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/application-security/ Summary: The sector where open-source community content and vendor research dominate. --- ### Edition 14: Wealth Management Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/wealth-management/ Summary: The sector where regulatory filings and institutional data anchor above journalism. --- ### Edition 15: Private Equity & M&A Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/private-equity/ Summary: The sector where deal databases and regulatory filings outrank trade journalism. --- ### Edition 17: Legal Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/legal-services/ Summary: The sector where primary legal sources and court records anchor above commentary. --- ### Edition 19: Insurance Media — The 5W Retrieval Index URL: https://www.5wpr.com/research/retrieval-index/insurance/ Summary: The sector where rating agencies and regulatory filings anchor above trade press. --- ### The Wine AI Visibility Index 2026 — Top 25 Wine Brands by AI Citation Share URL: https://www.5wpr.com/research/ai-visibility-index-for-wine-brands-2026/ Summary: Discover the top 25 wine brands by AI citation share from the 2026 Wine AI Visibility Index. Learn how luxury brands dominate AI answers # The Wine AI Visibility Index 2026 — Top 25 Wine Brands by AI Citation Share Luxury wine brands dominate AI answers Last updated: 2026-09-04 Domaine de la Romanée-Conti had the highest measured appearance rate in this wine-brand study: 36.9% of 268 answers returned across four engines. Its 95% interval was 31.4% to 42.9%. The result places ultra-luxury and investment-oriented names ahead of mass-market wine brands within these measured consumer-intent questions. The gap between brand mention and brand-site citation is large. Wine-Searcher had a 19% owned-domain citation rate among answers with retrieval evidence, while Domaine de la Romanée-Conti had a 0% owned-domain citation rate despite leading appearance. A wine brand can be named often without its own website supplying the cited evidence. For category teams, the risk is clear: an answer may steer selection toward a competitor or a critic and comparison site without citing the brand's own material. 268AI answers analyzed 36.9%Domaine de la Romanée-Conti appearance rate, the category leader 49Brands named across the study 93.1%Answers carrying retrieval evidence The study measured 24 prompts three times in Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). It returned 268 answers from 288 attempted runs. ## Which wine brands lead AI answers? Domaine de la Romanée-Conti ranked first, followed by Château Lafite Rothschild at 27.2% and Château Margaux at 23.5%. Penfolds ranked fourth at 21.6%, and Screaming Eagle ranked fifth at 17.9%. These are appearance rates calculated from answers returned, not citation rates. The ranking measures whether an answer named a brand. It does not measure sales, consumer preference, critic scores, or category market share. It describes only the 24 measured prompts on September 4, 2026. ### Wine brand appearance leaderboard ### AI visibility leaderboard Appearance rate is the share of the 268 returned AI answers in which each brand was named. #BrandNamed inAppearance rate95% intervalAvg position 1Domaine de la Romanée-Conti99/26836.9%31.4%–42.9%2.8 2Château Lafite Rothschild73/26827.2%22.3%–32.9%3.7 3Château Margaux63/26823.5%18.8%–28.9%5.2 4Penfolds58/26821.6%17.1%–27%7.5 5Screaming Eagle48/26817.9%13.8%–22.9%5.8 6Château Latour34/26812.7%9.2%–17.2%5.1 7Château Mouton Rothschild33/26812.3%8.9%–16.8%6.5 8Opus One31/26811.6%8.3%–16%7.7 9Liv-ex26/2689.7%6.7%–13.8%3 10Vega Sicilia25/2689.3%6.4%–13.4%7.5 11Catena25/2689.3%6.4%–13.4%6 12Krug25/2689.3%6.4%–13.4%7.1 13Kim Crawford23/2688.6%5.8%–12.5%6.8 14Marchesi Antinori23/2688.6%5.8%–12.5%5.6 15Sources23/2688.6%5.8%–12.5%12.7 The top brands are not always statistically separable in this sample. Domaine de la Romanée-Conti's 31.4% to 42.9% interval does not overlap Château Lafite Rothschild's 22.3% to 32.9% interval, but Château Lafite Rothschild and Château Margaux overlap. Château Margaux, Penfolds, and Screaming Eagle also have overlapping intervals with adjacent leaders. Those differences are within the margin of this sample. ## How do the leading brands appear in AI answers? The leading wine brands show different engine patterns even when each appears across all four systems. The profiles below use answers returned for appearance rates. Owned-domain citation rates use answers with retrieval evidence. ### Domaine de la Romanée-Conti 36.9% appearance rate, with a 31.4% to 42.9% interval. Domaine de la Romanée-Conti ranked first and appeared in 28 of 68 Perplexity answers, 28 of 66 ChatGPT answers, 21 of 67 Gemini answers, and 22 of 67 Claude answers. It was named by every engine, with its strongest measured rates in ChatGPT and Perplexity. Its average first mention position was 2.8. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### Château Lafite Rothschild 27.2% appearance rate, with a 22.3% to 32.9% interval. Château Lafite Rothschild ranked second and appeared in 23 of 68 Perplexity answers, 22 of 66 ChatGPT answers, 11 of 67 Gemini answers, and 17 of 67 Claude answers. Perplexity and ChatGPT named it at similar rates, while Gemini named it less often. Its average first mention position was 3.7. Its owned-domain citation rate was 1.5% among answers with retrieval evidence. ### Château Margaux 23.5% appearance rate, with an 18.8% to 28.9% interval. Château Margaux ranked third and appeared in 22 of 68 Perplexity answers and 25 of 66 ChatGPT answers, compared with 8 of 67 Gemini answers and 8 of 67 Claude answers. ChatGPT named Château Margaux most often in the measured set. Its average first mention position was 5.2. Its owned-domain citation rate was 1.5% among answers with retrieval evidence. ### Penfolds 21.6% appearance rate, with a 17.1% to 27% interval. Penfolds ranked fourth and appeared in 23 of 68 Perplexity answers, 16 of 66 ChatGPT answers, 13 of 67 Gemini answers, and 6 of 67 Claude answers. Perplexity named Penfolds most often, while Claude named it least often. Its average first mention position was 7.5. Its owned-domain citation rate was 3% among answers with retrieval evidence. ### Screaming Eagle 17.9% appearance rate, with a 13.8% to 22.9% interval. Screaming Eagle ranked fifth and appeared in 8 of 68 Perplexity answers, 21 of 66 ChatGPT answers, 9 of 67 Gemini answers, and 10 of 67 Claude answers. ChatGPT named Screaming Eagle far more often than the other engines. Its average first mention position was 5.8. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### Château Latour 12.7% appearance rate, with a 9.2% to 17.2% interval. Château Latour ranked sixth and appeared in 5 of 68 Perplexity answers, 20 of 66 ChatGPT answers, 6 of 67 Gemini answers, and 3 of 67 Claude answers. ChatGPT produced most of the measured appearances. Its average first mention position was 5.1. Its owned-domain citation rate was 0.7% among answers with retrieval evidence. ### Château Mouton Rothschild 12.3% appearance rate, with an 8.9% to 16.8% interval. Château Mouton Rothschild ranked seventh and appeared in 6 of 68 Perplexity answers, 19 of 66 ChatGPT answers, 3 of 67 Gemini answers, and 5 of 67 Claude answers. ChatGPT named it much more often than the other systems. Its average first mention position was 6.5. Its owned-domain citation rate was 0% among answers with retrieval evidence. ### Opus One 11.6% appearance rate, with an 8.3% to 16% interval. Opus One ranked eighth and appeared in 19 of 68 Perplexity answers, 8 of 66 ChatGPT answers, 1 of 67 Gemini answers, and 3 of 67 Claude answers. Perplexity drove its appearance result, while Gemini named it once. Its average first mention position was 7.7. Its owned-domain citation rate was 0.7% among answers with retrieval evidence. ### Liv-ex 9.7% appearance rate, with a 6.7% to 13.8% interval. Liv-ex ranked ninth and appeared in 25 of 67 Gemini answers, 1 of 66 ChatGPT answers, and no measured Perplexity or Claude answers. Gemini accounted for nearly all measured appearances. Its average first mention position was 3. Its owned-domain citation rate was 8.2% among answers with retrieval evidence. ### Vega Sicilia 9.3% appearance rate, with a 6.4% to 13.4% interval. Vega Sicilia ranked tenth and appeared in 14 of 68 Perplexity answers, 4 of 66 ChatGPT answers, no Gemini answers, and 7 of 67 Claude answers. Perplexity named Vega Sicilia most often, while Gemini did not name it. Its average first mention position was 7.5. Its owned-domain citation rate was 0% among answers with retrieval evidence. ## Where do the engines disagree on wine brands? The engines disagreed most sharply on individual prestige, investment, and comparison entities. Each engine had a similar number of returned answers: Perplexity returned 68, ChatGPT 66, Gemini 67, and Claude 67. The differences below therefore reflect naming patterns, not a large difference in answer volume. ### Engine-by-brand appearance pattern ### Appearance rate by engine Each cell uses that engine's own answer count as its denominator, so engines can be compared row by row. BrandPerplexity (sonar-pro) rateChatGPT (OpenAI) rateGemini (Google) rateClaude (Anthropic) rate Domaine de la Romanée-Conti41.2%42.4%31.3%32.8% Château Lafite Rothschild33.8%33.3%16.4%25.4% Château Margaux32.4%37.9%11.9%11.9% Penfolds33.8%24.2%19.4%9% Screaming Eagle11.8%31.8%13.4%14.9% Château Latour7.4%30.3%9%4.5% Château Mouton Rothschild8.8%28.8%4.5%7.5% Opus One27.9%12.1%1.5%4.5% Liv-ex0%1.5%37.3%0% Vega Sicilia20.6%6.1%0%10.4% Catena30.9%1.5%1.5%3% Krug23.5%10.6%0%3% Gemini concentrated several results. Liv-ex appeared in 25 of 67 Gemini answers and none in Perplexity or Claude. Wine-Searcher appeared in 20 Gemini answers, compared with 1 each in Perplexity and ChatGPT and none in Claude. Bodega Catena Zapata appeared in 14 Gemini answers, while ChatGPT and Claude did not name it. ChatGPT concentrated several Bordeaux and California prestige names. Château Latour appeared in 20 of 66 ChatGPT answers but only 5 Perplexity, 6 Gemini, and 3 Claude answers. Château Mouton Rothschild appeared in 19 ChatGPT answers, with 6, 3, and 5 appearances in Perplexity, Gemini, and Claude. Screaming Eagle appeared in 21 ChatGPT answers, compared with 8, 9, and 10 in the other engines. Perplexity had its own concentration pattern. Catena appeared in 21 of 68 Perplexity answers, while ChatGPT and Gemini each named it once and Claude named it twice. Opus One appeared in 19 Perplexity answers, compared with 8 in ChatGPT, 1 in Gemini, and 3 in Claude. Vega Sicilia appeared in 14 Perplexity answers and none in Gemini. Some names had broad engine coverage but uneven rates. Domaine de la Romanée-Conti appeared in all four engines, ranging from 21 Gemini answers to 28 each in Perplexity and ChatGPT. Château Lafite Rothschild appeared in all four, ranging from 11 Gemini answers to 23 Perplexity answers. A gap can reflect a system's model and retrieval tool, not a durable difference in brand visibility outside this study. ## Which demand clusters favor each wine brand? The demand-cluster results identify which brands lead specific groups of measured questions rather than a universal category winner. Prestige, luxury, investment, and cult-wine questions pull leading appearances toward Domaine de la Romanée-Conti, Château Lafite Rothschild, Château Margaux, Penfolds, and Screaming Eagle. ### Brand leaders by demand cluster ### Leaders by demand cluster The same answers regrouped by the demand cluster each prompt was sampled for; cluster rates use that cluster's own answer count. Demand clusterAnswersLeading brandLeader rateRunners-up — Other demand — Subcategory: value wine12Yellow Tail58.3%Barefoot 58.3%, Cupcake Vineyards 25%, Sources 16.7% Subcategory: premium wine12Domaine de la Romanée-Conti83.3%Château Lafite Rothschild 75%, Château Margaux 66.7%, Penfolds 66.7% Subcategory: ultra-luxury wine12Domaine de la Romanée-Conti91.7%Château Lafite Rothschild 75%, Screaming Eagle 75%, Château Margaux 58.3% Subcategory: luxury wine12Domaine de la Romanée-Conti100%Château Lafite Rothschild 100%, Château Margaux 58.3%, Penfolds 58.3% Subcategory: prestige Champagne12Krug58.3%Dom Pérignon 50%, Champagne 33.3%, Moët & Chandon 33.3% Subcategory: box wine12Bota Box91.7%Franzia 66.7%, Barefoot 33.3%, Overview 16.7% Subcategory: premium import wine12Domaine de la Romanée-Conti91.7%Château Margaux 83.3%, Château Lafite Rothschild 75%, Penfolds 75% Subcategory: premium rosé12Whispering Angel33.3%Dom Pérignon 16.7%, Veuve Clicquot 8.3% Subcategory: premium Chardonnay12Domaine de la Romanée-Conti33.3%Sources 16.7%, Kendall-Jackson 16.7%, Wine-Searcher 16.7% Subcategory: cult wine12Harlan Estate100%Screaming Eagle 91.7%, Domaine de la Romanée-Conti 58.3%, Opus One 41.7% Subcategory: investment-grade wine12Domaine de la Romanée-Conti100%Château Lafite Rothschild 91.7%, Château Latour 91.7%, Château Mouton Rothschild 83.3% Subcategory: moderation-adjacent wine12Kim Crawford16.7%Sources 16.7%, Kendall-Jackson 16.7%, Cupcake Vineyards 16.7% General best-in-category12Domaine de la Romanée-Conti100%Penfolds 83.3%, Château Lafite Rothschild 75%, Château Margaux 75% Specific need or condition12Yellow Tail25%Sources 16.7%, Kim Crawford 8.3%, Champagne 8.3% Subcategory: low-alcohol wine12Kim Crawford33.3%Overview 16.7%, Sources 8.3%, Barefoot 8.3% Primary use case12Champagne41.7%Veuve Clicquot 41.7%, Kendall-Jackson 33.3%, Kim Crawford 25% Quality and premium tier12Domaine de la Romanée-Conti75%Château Lafite Rothschild 58.3%, Penfolds 58.3%, Château Margaux 50% Price and value12Château Ste. Michelle58.3%Catena 41.7%, Josh Cellars 25%, Kim Crawford 16.7% Beginners and first purchase11Kim Crawford63.6%Kendall-Jackson 36.4%, Yellow Tail 36.4%, Josh Cellars 36.4% Comparison and evaluation9Kendall-Jackson66.7%Barefoot 33.3%, Bota Box 33.3%, Château Ste. Michelle 33.3% Where to buy8Wine-Searcher12.5%— Durability and longevity8Domaine de la Romanée-Conti50%Château Lafite Rothschild 25%, Château Margaux 25%, Château Latour 25% Specialist / advanced use8Domaine de la Romanée-Conti75%Château Latour 75%, Château Lafite Rothschild 62.5%, Screaming Eagle 62.5% Emerging and new entrants8Stella Rosa37.5%Bota Box 25%, Overview 12.5%, Meiomi 12.5% General buyer-intent questions and specific need questions measure different forms of demand. The chart separates those groups. Read the results as prompt-level strengths: a brand can lead one cluster while remaining less visible in another. The category includes value, premium, luxury, ultra-luxury, box wine, premium rosé, premium import wine, premium Chardonnay, cult wine, prestige Champagne, investment-grade wine, low-alcohol wine, and moderation-adjacent wine. ## Which sites supply the citations? Wine-Searcher had the highest owned-domain citation rate among eligible entities: 19% of 268 answers with retrieval evidence. Liv-ex followed at 8.2%, and Penfolds had 3%. These rates use answers with retrieval evidence, not all answers returned. ### Owned-domain citation rates ### Owned-domain citation rate Share of the 268 answers with retrieval evidence that cited a URL on the brand's own website. Domaine de la Romanée-Conti 0% · 0/268 Château Lafite Rothschild 1.5% · 4/268 Château Margaux 1.5% · 4/268 Penfolds 3% · 8/268 Screaming Eagle 0% · 0/268 Château Latour 0.7% · 2/268 Château Mouton Rothschild 0% · 0/268 Opus One 0.7% · 2/268 Liv-ex 8.2% · 22/268 Vega Sicilia 0% · 0/268 Catena 0% · 0/268 Krug 0.7% · 2/268 Wine-Searcher also had 51 owned-domain citations and 1.8% of all citations. Liv-ex had 22 owned-domain citations and 0.8% of all citations. Among leading prestige brands, owned-site citation remained low: Château Lafite Rothschild and Château Margaux each had 4 owned-domain citations, while Domaine de la Romanée-Conti and Screaming Eagle had none. Publisher citations leaned toward wine media, investment sites, lifestyle publications, reference pages, retailers, and consumer discussion. Wine Enthusiast appeared in 96 answers with citations, or 35.8% of answers with retrieval evidence. Vinovest appeared in 80, VinePair in 68, Forbes in 57, and Wine-Searcher in 51. ### Most-cited publisher domains ### Most-cited publishers Publishers cited as evidence, counted once per answer rather than once per link. PublisherAnswers citing itAnswer penetration wineenthusiast.com96/26835.8% vinovest.co80/26829.9% vinepair.com68/26825.4% forbes.com57/26821.3% always-dialabottle-toronto.com54/26820.1% wine-searcher.com51/26819% decanter.com45/26816.8% winespectator.com44/26816.4% reddit.com42/26815.7% vin-x.com38/26814.2% top100.winespectator.com35/26813.1% en.wikipedia.org34/26812.7% The source list shows where systems found support in this run. It does not measure editorial quality, commercial influence, or traffic. It does show that brand-owned sites compete with established third-party material for citation space. ## What does this mean for wine brands? Interpretation: prestige recognition and source ownership are separate measures. Domaine de la Romanée-Conti led appearance but had no owned-domain citations. Wine-Searcher had a lower appearance rate than the top wine brands but the highest owned-domain citation rate among eligible entities. Interpretation: category teams need separate checks for whether a brand is named and whether its own product, provenance, critic-score, food-pairing, gifting, scarcity, and occasion pages are cited. A strong appearance result does not establish that a brand controls the supporting information. Interpretation: the sharp engine splits warrant engine-specific review. Liv-ex, Wine-Searcher, Catena, Opus One, Château Latour, and Screaming Eagle each had an appearance pattern concentrated in one system. A category team that checks only one engine may miss a material blind spot in another. ## How was this wine-brand study measured? This flagship-depth study tested 24 stratified prompts three times per engine across Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), and Claude (Anthropic). The study attempted 288 answers and returned 268. Appearance rates use answers returned as their denominator. Citation and source rates use the 268 answers with retrieval evidence as their denominator. ### Measurement summary ### How this study was measured - Sampling frame fixed first: 24 prompts allocated across the demand clusters before anything was asked. - Same questions put to every engine: Perplexity (sonar-pro), ChatGPT (OpenAI), Gemini (Google), Claude (Anthropic), 3 trial(s) each. - 268 answers returned of 288 attempted, 268 with retrieval evidence. - Product lines rolled up to parent brands using a registry fixed before scoring. - Appearance rate reported with a 95% Wilson interval; citation metrics counted only on answers with evidence. The study measures named brands and cited domains in this fixed prompt set on September 4, 2026. It does not measure all wine searches, consumer demand, sales, product quality, or future performance. Brand names were normalized, including variants such as Penfolds Grange under Penfolds and Catena Zapata under Catena. ## Frequently asked questions ### Which wine brand had the highest appearance rate? Domaine de la Romanée-Conti had the highest measured appearance rate at 36.9% of answers returned. Its 95% interval was 31.4% to 42.9%. ### Did the leading brand also lead owned-site citations? No. Domaine de la Romanée-Conti had a 0% owned-domain citation rate among answers with retrieval evidence. Wine-Searcher had the highest rate among eligible entities at 19%. ### Which engine named Liv-ex most often? Gemini named Liv-ex in 25 of 67 returned answers. ChatGPT named it once, while Perplexity and Claude did not name it in the measured answers. ### Which engine named Château Latour most often? ChatGPT named Château Latour in 20 of 66 returned answers. Perplexity named it in 5 answers, Gemini in 6, and Claude in 3. ### Which publisher domain appeared most often in cited answers? Wine Enthusiast appeared in 96 answers with citations. That was 35.8% of the 268 answers with retrieval evidence. ### How many prompts and engines did the study use? The study used 24 prompts, each tested three times per engine. It covered Perplexity, ChatGPT, Gemini, and Claude. ### Do these results measure wine sales or consumer preference? No. The results measure which brands the four systems named and which domains they cited for these prompts on September 4, 2026. The study does not measure sales, consumer preference, or market share. ## Charts and data tables ### Aggregators and comparison sites, tracked separately These are not brands in the category, so they are measured the same way but kept out of the brand leaderboard. #EntityAppearance rateAnswers95% interval 1Decanter6.3%17/2684%–9.9% 2Wine Spectator6%16/2683.7%–9.5% 3Wine Enthusiast6%16/2683.7%–9.5% 4Vivino1.5%4/2680.6%–3.8% 5Robert Parker / Wine Advocate1.1%3/2680.4%–3.2% 6James Suckling1.1%3/2680.4%–3.2% 7Vinous (Antonio Galloni)0%0/2680%–1.4% ## Methodology appendix This appendix is generated directly from the measurement record for this study. It is included so any brand named here can reproduce or challenge the result. ### What was measured - Category: wine brands - Measurement date: 2026-09-04 - Depth: flagship — 24 prompts, 3 trial(s) per prompt per engine, 4 engines - Answers attempted: 288 - Answers returned (appearance denominator): 268 - Answers with retrieval evidence (citation denominator): 268 (93.1% of attempted) - Answers that failed outright: 20 - Analysis version: study-methodology-v2 · entity registry: entity-registry-2026-08-27 Claim limit: Flagship depth: 24 stratified prompts measured three times per engine, so both between-prompt and repeat-generation variation are observable. ### How the prompts were sampled Fixed sampling frame: 13 of 13 client-named subcategories are allocated a guaranteed cell each, then a documented set of generic commercial demand clusters (general best, primary use case, specific need, quality tier, price/value, beginner, comparison, durability, specialist, emerging, retail channel, trust) receives the remaining allocation round-robin. The prompt writer supplies wording inside these cells only and does not choose the category's demand structure. Demand clusterPromptsScope Subcategory: value wine1demand specific to "value wine" within wine brands Subcategory: premium wine1demand specific to "premium wine" within wine brands Subcategory: luxury wine1demand specific to "luxury wine" within wine brands Subcategory: ultra-luxury wine1demand specific to "ultra-luxury wine" within wine brands Subcategory: box wine1demand specific to "box wine" within wine brands Subcategory: premium rosé1demand specific to "premium rosé" within wine brands Subcategory: premium import wine1demand specific to "premium import wine" within wine brands Subcategory: premium Chardonnay1demand specific to "premium Chardonnay" within wine brands Subcategory: cult wine1demand specific to "cult wine" within wine brands Subcategory: prestige Champagne1demand specific to "prestige Champagne" within wine brands Subcategory: investment-grade wine1demand specific to "investment-grade wine" within wine brands Subcategory: low-alcohol wine1demand specific to "low-alcohol wine" within wine brands Subcategory: moderation-adjacent wine1demand specific to "moderation-adjacent wine" within wine brands General best-in-category1the plain, highest-volume 'best X' style demand with no qualifier Primary use case1the dominant everyday use case for this category Specific need or condition1a specific buyer need, constraint, body/fit/context requirement or problem to solve Quality and premium tier1which options are highest quality, most durable or best made Price and value1budget, value-for-money, cheaper alternatives and whether premium is worth it Beginners and first purchase1what a first-time buyer with no knowledge of the category should get Comparison and evaluation1head-to-head style evaluation demand: which brands are better than which, and on what basis Durability and longevity1which brands last, hold up over time, or fail early Specialist / advanced use1advanced, professional or enthusiast-level demand inside the category Emerging and new entrants1newer entrants, recent launches and shifts buyers are only starting to ask about Where to buy1purchase-channel demand: where to buy, which retailers, direct vs marketplace ### The exact prompts #PromptClusterShape 1best value wine brandssub_value_wineshort 2best premium wine brandssub_premium_wineshort 3best luxury wine brandssub_luxury_wineshort 4best ultra luxury wine brandssub_ultra_luxury_wineshort 5best boxed wine brandssub_box_wineshort 6best premium rosé wine brandssub_premium_ros_short 7best premium imported wine brandssub_premium_import_wineshort 8best premium chardonnay brandssub_premium_chardonnayshort 9Which cult wine brands are most sought after by collectors?sub_cult_winemedium 10Which prestige Champagne brands make the strongest gifts?sub_prestige_champagnemedium 11Which investment-grade wine brands have the strongest long-term track records?sub_investment_grade_winemedium 12best low alcohol wine brandssub_low_alcohol_wineshort 13best wine brands for moderationsub_moderation_adjacent_winemedium 14best wine brandsgeneral_bestshort 15best wine brands for dinner partiesuse_casemedium 16Which wine brands are best for people who prefer dry, lower-sugar styles?specific_needlong 17Which wine brands are consistently regarded as the highest quality across major styles?premium_qualitylong 18best wine brands under $20value_priceshort 19What wine brands should a beginner try first when learning different wine styles?beginnerlong 20Which wine brands are better for quality, value, and reliable availability?comparisonmedium 21Which wine brands produce bottles that age well and improve over time?durabilitymedium 22Which wine brands do serious enthusiasts buy for critic scores and cellar potential?specialistlong 23Which newer wine brands are gaining attention among U.S. buyers this year?emergingmedium 24Where can I buy reputable wine brands online with reliable shipping in the United States?retaillong ### Engines and systems measured EngineModel / systemRetrievalAttemptedReturnedWith retrieval evidenceRetrieval rate Perplexity (sonar-pro)perplexity/sonar-pronative Perplexity web search72686894.4% ChatGPT (OpenAI)openai/gpt-5.6-terraOpenAI web search tool72666691.7% Gemini (Google)google/gemini-3.7-flashGoogle Search grounding (google_search tool)72676793.1% Claude (Anthropic)anthropic/claude-sonnet-4Anthropic web search tool72676793.1% Results are not strictly comparable across engines: each engine uses a different model, a different retrieval tool and a different answer format, so a gap between two engines can reflect system design as much as brand visibility. ### How each metric is defined - Appearance rate — the share of returned answers that named the brand. Measured on every answer the engine returned, whether or not it searched the web, so an engine's retrieval behaviour does not silently change the denominator. - Macro appearance rate — the mean of the per-prompt appearance rates, so each prompt counts once regardless of how many engines answered it. - 95% interval — Wilson score interval on the appearance rate. Where two brands' intervals overlap, this study does not support a claim that one is more visible than the other. - Owned-domain citation rate — the share of answers with retrieval evidence that cited a URL on the brand's own domain. This is a narrow measure of the brand's own site being used as a source; it is not a measure of total mentions. - Share of all citations — the brand's own domain as a share of all attributed publisher citations in the study. - Not measured — the brand's official domain could not be established, so no citation result exists. This is never reported as 0%. - 0% — the metric could be evaluated and the event did not occur. - Average position — mean position of the brand's first mention in the answer's ordered list of named brands. - Cross-engine consistency — the share of engines that named the brand at least once. - Repeat consistency — the share of repeated prompt/engine cells where the brand appeared in every trial. Lower values mean the engine's answer is unstable rather than the brand being absent. ### Evidence rules - An answer counts as having retrieval evidence only when the engine returned a retrieval receipt (search annotations or grounding records). A URL the model merely typed into its prose is not treated as evidence that it searched. - Sources are attributed to the publisher behind each retrieval receipt. Intermediary and redirect hosts are never reported as publishers. - Publisher identity could not be established for 0 retrieval receipt(s); those are disclosed here rather than attributed or dropped. - Source counts are answer-level: a publisher counts once per answer in which it was cited, not once per URL. ### Full results with denominators and intervals #BrandNamed inAppearance rateMacro rate95% intervalOwned-domain citations 1Domaine de la Romanée-Conti99/26836.9%36.1%31.4%–42.9%0/268 (0%) 2Château Lafite Rothschild73/26827.2%26.6%22.3%–32.9%4/268 (1.5%) 3Château Margaux63/26823.5%22.9%18.8%–28.9%4/268 (1.5%) 4Penfolds58/26821.6%21%17.1%–27%8/268 (3%) 5Screaming Eagle48/26817.9%17.7%13.8%–22.9%0/268 (0%) 6Château Latour34/26812.7%13.2%9.2%–17.2%2/268 (0.7%) 7Château Mouton Rothschild33/26812.3%12.2%8.9%–16.8%0/268 (0%) 8Opus One31/26811.6%10.9%8.3%–16%2/268 (0.7%) 9Liv-ex26/2689.7%9.4%6.7%–13.8%22/268 (8.2%) 10Vega Sicilia25/2689.3%8.9%6.4%–13.4%0/268 (0%) 11Catena25/2689.3%9.3%6.4%–13.4%0/268 (0%) 12Krug25/2689.3%9%6.4%–13.4%2/268 (0.7%) 13Kim Crawford23/2688.6%8.3%5.8%–12.5%1/268 (0.4%) 14Marchesi Antinori23/2688.6%8%5.8%–12.5%2/268 (0.7%) 15Sources23/2688.6%8.5%5.8%–12.5%domain not established — excluded from citation metrics 16Kendall-Jackson22/2688.2%8.5%5.5%–12.1%0/268 (0%) 17Bodegas Vega Sicilia22/2688.2%8.2%5.5%–12.1%0/268 (0%) 18Wine-Searcher22/2688.2%7.8%5.5%–12.1%51/268 (19%) 19Domaine Leroy22/2688.2%8%5.5%–12.1%domain not established — excluded from citation metrics 20Château Pétrus22/2688.2%7.8%5.5%–12.1%domain not established — excluded from citation metrics 21Harlan Estate21/2687.8%7.3%5.2%–11.7%0/268 (0%) 22Champagne21/2687.8%7.5%5.2%–11.7%domain not established — excluded from citation metrics 23Tenuta San Guido20/2687.5%7.1%4.9%–11.2%1/268 (0.4%) 24Yellow Tail19/2687.1%7%4.6%–10.8%1/268 (0.4%) 25Barefoot19/2687.1%7%4.6%–10.8%1/268 (0.4%) 26Dom Pérignon18/2686.7%6.4%4.3%–10.4%0/268 (0%) 27Gaja18/2686.7%6.4%4.3%–10.4%0/268 (0%) 28Overview17/2686.3%6.1%4%–9.9%domain not established — excluded from citation metrics 29Bota Box17/2686.3%6.6%4%–9.9%0/268 (0%) 30Moët & Chandon16/2686%5.6%3.7%–9.5%0/268 (0%) 31Château Cheval Blanc16/2686%6.2%3.7%–9.5%0/268 (0%) 32Bodega Catena Zapata16/2686%5.6%3.7%–9.5%0/268 (0%) 33Château d’Yquem15/2685.6%5.7%3.4%–9%0/268 (0%) 34Château Ste. Michelle15/2685.6%5.6%3.4%–9%2/268 (0.7%) 35Veuve Clicquot13/2684.9%4.5%2.9%–8.1%0/268 (0%) 36Franzia11/2684.1%4.2%2.3%–7.2%0/268 (0%) 37Josh Cellars10/2683.7%3.7%2%–6.7%0/268 (0%) 38Meiomi9/2683.4%3.5%1.8%–6.3%1/268 (0.4%) 39Santa Margherita8/2683%2.9%1.5%–5.8%0/268 (0%) 40Whispering Angel7/2682.6%2.4%1.3%–5.3%0/268 (0%) 41Stella Rosa6/2682.2%2.6%1%–4.8%0/268 (0%) 42La Crema6/2682.2%2.1%1%–4.8%0/268 (0%) 43Decoy6/2682.2%2.6%1%–4.8%0/268 (0%) 44Cupcake Vineyards5/2681.9%1.7%0.8%–4.3%0/268 (0%) 45Sutter Home4/2681.5%1.4%0.6%–3.8%0/268 (0%) 46Caymus Vineyards2/2680.7%0.8%0.2%–2.7%0/268 (0%) 47Sonoma-Cutrer0/2680%0%0%–1.4%0/268 (0%) 48Silver Oak0/2680%0%0%–1.4%0/268 (0%) 49Château Lafite Rothschild / DRC0/2680%0%0%–1.4%domain not established — excluded from citation metrics ### Entity handling Product lines and model names roll up to their parent brand for brand-level metrics, using a registry fixed before scoring. Nothing is merged on the basis of this study's own counts alone. No roll-ups were applied in this study. Held for human classification before the next study: Yellow Tail, Clos du Bois Wines, Beringer Vineyards, Barefoot Cellars, Cupcake Vineyards, Apothic Wines, Kendall-Jackson, J. Lohr Vineyards & Wines, Costco's Kirkland Signature, Domaine de la Romanée-Conti, Château Lafite Rothschild, Château Margaux, Vega Sicilia, Opus One, Penfolds, Moët & Chandon, Château Latour, Screaming Eagle, Harlan Estate, Bodegas Vega Sicilia, Opus One Winery, Château Mouton Rothschild, Marchesi Antinori, Catena Zapata, Krug, Domaine Leflaive, Joseph Drouhin, Famila Torres, Status, Notable Label, Liv-ex, Wine-Searcher, Vin-X Fine Wine Investment, Dom Pérignon, Veuve Clicquot, Bollinger, Cristal, Armand de Brignac, Prestige Tier, Decanter & Wine Advocate. ### Limits of these findings - This is a point-in-time measurement of what the named systems returned for these exact prompts on 2026-09-04. Engine behaviour changes without notice. - The prompt set is a stratified sample of category demand, not a census. Results describe these prompts and should not be read as representative of all searches in the category. - Where intervals overlap, differences between brands are not statistically distinguishable at this sample size. - A brand not listed here was not measured as absent — it simply was not named in these answers. - Appearance is a visibility measure, not an endorsement, a quality judgement or a market-share estimate. - 6 of 49 ranked brand(s) had no official domain established before scoring, so they carry appearance results only and are excluded from citation metrics and source tables rather than being reported as zero. --- ### The PR Advantage in Affiliate Marketing 2026 URL: https://www.5wpr.com/research/affiliate-marketing-2026/ Summary: Affiliate marketing is a $20B global industry returning $12–$15 per $1 invested. Yet most programs underperform, because of partner quality, not technology. 5W