How ChatGPT, Claude, and Perplexity Answer “Best Grocery Store” Questions

5WPR RESEARCH | JULY 2026
Local Relevance Outperforms National Scale

Every year, national retailers like Kroger and Target spend billions of dollars on advertising to maintain widespread brand awareness. Yet when we tested how Claude, ChatGPT, and Perplexity responded to “best grocery store” questions in 15 major metro areas, smaller, regional chains appeared first in 91% of responses.

New 5WPR research shows which chains hold an advantage and where national retailers lost ground. We also share how leaders can strengthen connections in the neighborhoods they serve and improve their position in high-intent user queries within AI answers. Our findings highlight the following:

Figure 1. Key findings from the 5WPR local grocery AI study. Source: 5WPR analysis of 673 usable responses across ChatGPT, Claude, and Perplexity in 15 U.S. metropolitan areas.

For example, one Austin response placed H-E-B first while presenting Whole Foods Market as a strong option for premium organic products. The variation matters because total mentions and first position measure different outcomes. One chain may appear often because the brand is widely recognized. Another chain may appear first because the AI response links the brand to the area mentioned in the question.

Regional Grocery Chains Appeared First in 91% of Local AI Responses
In 91.4% of the 673 responses, the first mention was a regional chain that appeared in six or fewer of the 15 metro areas tested. National chains that appeared in 12 or more metro areas ranked first in only 7.4% of responses.

Figure 2. Where the first grocery chain named appeared across the 15 tested cities. Source: 5WPR analysis of 673 usable local grocery responses.

For executives, the business case is straightforward: broad recognition can help a chain appear in an AI answer, but it does not determine which chain appears in its top recommendations.

Frequent Brand Mentions Did Not Guarantee First Place in Answers

Trader Joe’s Led All Chains in Mentions but Appeared First in Only 2.7% of Responses

Trader Joe's appeared in 480 responses, more than any other chain in the study. However, Trader Joe's appeared first in only 13 responses, resulting in a first-position rate of 2.7%.

Figure 3. Trader Joe's appeared often but rarely appeared first—5WPR analysis of 673 usable local grocery responses.

H-E-B Appeared First in 62.2% of Responses That Mentioned the Chain

In the study dataset, H-E-B appeared in 127 responses and was listed first in 79 answers, resulting in a first-position rate of 62.2%. Of those 127 appearances, 126 occurred in Austin, Houston, and San Antonio.

H-E-B provides a helpful example of a chain with strong local associations. The Texas chain connects store pages with specific cities and neighborhoods, including South Congress in Austin. National chains can apply a similar approach by creating and publishing corporate messaging that identifies neighborhoods, available services, local products, delivery options, and other market-specific details.

Costco Leads Broad Grocery Searches but Never Appeared First in the Local Study

While Costco ranks first in the 5WPR U.S. Grocery Retail AI Visibility Index 2026 for broad topics like bulk purchasing and pricing, it did not secure a first-place position in our local study. This contrast illustrates that overall visibility and local-first placement are distinct metrics that should be measured separately.

For marketers, brand recognition, total mentions, first-position answers, and local association are independent assets. An effective Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) strategy should track all four metrics rather than treating a single visibility metric as a complete measure of performance.

How Major Chains Compared in Mentions and First-Place Appearances

Note: The figures combine spelling and naming variations; for example, “HEB” and “H-E-B” are counted as H-E-B. “Cities” shows how many cities included a chain in at least one AI response, not how many cities the chain serves.

Adding Relocation Context Increased Store Options From 7.1 to 12.6

Slight variations in prompts changed the number of options that appeared in the model's response. The "best grocery store" question produced an average of 7.1 options, while adding “I moved to (CITY) produced an average of 12.6 options.

Figure 4. Different questions produced different numbers of store options. Source: 5WPR analysis of 673 usable responses across three question formats.

The relocation question produced about 78% more options than the direct conversational prompt. This result is important because visibility audits often measure performance against specific wording, rather than the broad range of ways users ask AI models questions.

How National Grocery Chains Can Compete in City-Specific AI Searches

The study’s findings offer CMOs and retail leaders several options to improve connections between their brand and the local communities where their stores operate.

Create Store Pages That Connect Each Location to Its City and Neighborhood

Smaller chains can customize store pages with local details, such as neighborhoods, intersections, services, inventory, and delivery options, to distinguish individual locations.

Keep Information Consistent Across Google, Maps, Directories, and Delivery Apps

Verify Google Business Profiles and ensure that the individual store's name, address, phone number (NAP), hours, services, and website link match the information on its corporate website, in maps, on directories, and on delivery platforms (Instacart, Shipt, DoorDash, etc.).

Add GroceryStore and LocalBusiness Schema to Store Pages

While schema is not a ranking factor, it serves as an important technical baseline. Implementing the LocalBusiness or GroceryStore schema on location pages helps search engines and AI models process a store's details, such as address, hours, services, and inventory. Treat schema as a best practice to ensure your business information is machine-readable, verifiable, and accessible to AI models.

Build Local Reviews and Regional Media Coverage

For larger chains where scale prevents individual store pages, use corporate communications to highlight each store's role in its community through press releases, independent editorial coverage, community partnerships, regional product launches, events, and interviews with employees and local leadership.

Executing these steps can strengthen public information about a national brand's presence in a specific city or neighborhood. Modern PR strategies should pair broad corporate messaging with detailed, machine-readable information about individual stores and local markets.

Grocery retailers that perform well in AI search build structured, verifiable public presences that combine widespread recognition with strategic, machine-readable content and ad campaigns.

Brands can start this process by auditing high-priority markets. From there, they should identify the competitors leading them in AI answers, then fill in the gaps in their local content, one market at a time.

Frequently Asked Questions About Local AI Grocery Searches

When you ask AI for the best grocery store, does it name one store or several?
Several. Across 673 responses, the models listed an average of 9.4 chains per answer, and only 14 responses (2.1%) listed a single chain with no alternatives. Most answers split picks by price, quality, convenience, organic selection, or bulk shopping rather than listing a single store.

Which grocery chains does AI name first in local "best grocery store" searches?
In 91.4% of responses, the first grocery chain listed was a regional brand that appeared in six or fewer of the 15 tested metro areas. Among the responses that mentioned them, Market Basket was listed first 65.2% of the time, H-E-B 62.2%, Wegmans 51.6%, and Publix 5;8%, each chain was dominant in its home region.

Does national brand recognition help a chain rank first in AI answers?
Recognition helps with mentions but not with first-place (or even top-three) listings. Chains that appeared across 12 to 15 tested cities were listed first in only 7.4% of responses. Trader Joe's was the most-mentioned chain in the study, appearing in 480 responses but took first place just 2.7% of the time.

Why do widely recognized chains like Trader Joe's and Costco rarely rank first?
Total mentions and first-position measures capture different outcomes. Costco appeared in 116 responses but was listed first in none (0.0%); Trader Joe's appeared in 480 but led in only 13 (2.7%). Broad familiarity earns a spot on the list; local association earns the top of it.

How can a national grocery chain get named first more often in local AI answers?
Build verifiable local signal one market at a time: create store pages that tie each location to its city and neighborhood, keep the store's name, address, phone, and hours consistent across Google, maps, directories, and delivery apps, add LocalBusiness or GroceryStore schema to location pages, and grow local reviews and regional media coverage.

Does the wording of the question change which grocery stores AI recommends?
Yes. "What is the best grocery store in [city]?" produced an average of 7.1 chains, while "I moved to [city], where should I buy groceries?" produced 12.6, about 78% more options. Prompt wording affects both the number and the mix of chains listed, so it is important to track visibility across different ways customers ask questions, rather than relying on a single static prompt.

Which AI models were tested, and did they use live web search?
The study tested ChatGPT (GPT-5.5 and GPT-4o), Claude (Sonnet 5 and Sonnet 4.6), and Perplexity (Sonar) across 675 web-enabled API tests on July 15, 2026. Web search was enabled for every test and was recorded as used in 601 of the 675 responses.

Test Design and Model Versions

5WPR completed 675 web-enabled API tests during a single collection window on July 15, 2026. The framework included 15 U.S. metropolitan areas, three question formats, three providers, and five repetitions for each provider-question-city combination.

The OpenAI tests recorded GPT-5.5-2026-04-23 for 114 completed tests and GPT-4o for 111. The Anthropic tests recorded Claude Sonnet 5 for 101 completed tests and Claude Sonnet 4.6 for 124. Perplexity used Sonar for all 225 completed tests. Web search was enabled for every test and recorded as used in 601 of 675 completed responses.

Interpretation note: OpenAI and Anthropic model versions changed during the collection window. The prompt comparison is directional and does not prove that wording alone caused the difference.

Data note: The city counts indicate which grocery chains were included in the study responses. They do not represent a verified count of where each chain operates.

How we counted brand mentions

Of the 675 completed tests, 673 produced at least one recognized grocery-chain name and were included in the mention analysis. One response was empty, and one asked the user to clarify which Portland they meant without naming a grocery chain.

The extraction process used a reviewed list of grocery-chain labels and aliases. Each standardized chain label was counted at most once per response, even when the same chain appeared multiple times. The final mention file contained 6,355 chain-response records.

"First position" means the first recognized grocery-chain label appearing in the response text. First position does not always reflect the model's strongest endorsement, as AI models often qualify their answers or present separate winners for price, quality, convenience, and other categories.

For the main comparison, 5WPR grouped chains according to how many of the 15 city datasets included the standardized label: six or fewer cities, seven to 11 cities, and 12 to 15 cities. The groups measure visibility within the study responses. The groups do not provide an independently verified count of where each chain operates.

Study Limitations

• The study measured the first chain named, not a separately hand-coded primary recommendation.

• The OpenAI and Anthropic model versions changed during collection, so the question-format comparison cannot isolate wording as the only cause of the differences.

• The study covered 15 metropolitan areas rather than every U.S. grocery market.

• API responses may differ from responses in consumer applications.

• Chain-level figures refer to the standardized labels in the dataset. Some location-specific or compound brand variants may appear under separate labels.

• The study identifies patterns in the responses but does not prove which website, media, review, or location signals caused a chain to appear first.