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.
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 |
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.
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.
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.
