AI SEO Statistics: Retail Store (2026-07 edition)
15 questions · 45 AI responses · 3 models · measured 2026-07-04
The question bank
The questions we tested — sampled from real buyer journeys in retail store.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
23-point average divergence: which AI you ask changes the answer.
The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about retail store buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 73% | 53% | 53% | 47% |
| Suggests DIY first | 13% | 20% | 20% | 73% |
| Names specific providers | 7% | 27% | 33% | 47% |
| Gives price or cost info | 7% | 13% | 33% | 73% |
| Tells to check reviews | 7% | 7% | 0% | 87% |
| Tells to verify credentials | 20% | 0% | 7% | 73% |
| Mentions case studies / portfolio | 13% | 20% | 7% | 67% |
| Mentions local proximity | 7% | 0% | 0% | 93% |
| Gives selection criteria | 27% | 47% | 53% | 33% |
| Warns about red flags | 13% | 20% | 13% | 67% |
| Asks a clarifying question | 27% | 47% | 0% | 33% |
| Recommends multiple quotes | 7% | 0% | 0% | 93% |
By model
How each assistant handled Retail Store questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same retail store questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 53.3% (Claude), a 20-point gap on an identical question set.
Across the 15 retail store answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 26.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 20%, averaging 758 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 retail store answers it produced, Claude recommended hiring a professional in 53.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 26.7% of answers (about 0.9 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 0%, averaging 323 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 retail store answers it produced, Gemini recommended hiring a professional in 53.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 33.3% of answers (about 1.4 distinct providers per answer) and included price or cost information 33.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 264 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a retail store buyer to a professional (73.3%) and Claude the least (53.3%). ChatGPT produced the longest answers, at 758 words on average. Specific providers were named most often by Gemini (33.3%) — even there, roughly one answer in 3 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 23 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a retail store buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 46.7% (Claude) — a 47-point spread.
- Names a specific provider: from 6.7% (ChatGPT) to 33.3% (Gemini) — a 27-point spread.
- Gives price or cost information: from 6.7% (ChatGPT) to 33.3% (Gemini) — a 27-point spread.
- Gives selection criteria: from 26.7% (ChatGPT) to 53.3% (Gemini) — a 27-point spread.
- Recommends hiring a professional: from 53.3% (Claude) to 73.3% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 47 points — means a retail store buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the retail store market.
Where they agree
The points of near-consensus in Retail Store.
On other behaviors the three models move almost in lockstep — the points of near-consensus for retail store, where all three landed within a few points of each other:
- Suggests a DIY approach first: 13.3%–20% across all three (a 7-point spread).
- Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).
- Mentions local proximity: 0%–6.7% across all three (a 7-point spread).
- Warns about red flags or scams: 13.3%–20% across all three (a 7-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "mentions local proximity" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (33.3%).
Every behavior, measured
All twelve coded behaviors for Retail Store, averaged across the three models.
The behaviors AI models reproduce most often for retail store are recommends hiring a professional (60% on average), gives selection criteria (42.2%) and asks a clarifying question (24.5%); the rarest are recommends multiple quotes (2.2%), mentions local proximity (2.2%) and tells the buyer to check reviews (4.5%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Recommends hiring a professional: 60% on average (ChatGPT 73.3%, Claude 53.3%, Gemini 53.3%) — a 20-point spread.
- Gives selection criteria: 42.2% on average (ChatGPT 26.7%, Claude 46.7%, Gemini 53.3%) — a 27-point spread.
- Asks a clarifying question: 24.5% on average (ChatGPT 26.7%, Claude 46.7%, Gemini 0%) — a 47-point spread.
- Names a specific provider: 22.2% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 33.3%) — a 27-point spread.
- Suggests a DIY approach first: 17.8% on average (ChatGPT 13.3%, Claude 20%, Gemini 20%) — a 7-point spread.
- Gives price or cost information: 17.8% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 33.3%) — a 27-point spread.
- Warns about red flags or scams: 15.5% on average (ChatGPT 13.3%, Claude 20%, Gemini 13.3%) — a 7-point spread.
- Mentions case studies or portfolio: 13.3% on average (ChatGPT 13.3%, Claude 20%, Gemini 6.7%) — a 13-point spread.
- Tells the buyer to verify credentials: 8.9% on average (ChatGPT 20%, Claude 0%, Gemini 6.7%) — a 20-point spread.
- Tells the buyer to check reviews: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
- Mentions local proximity: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
- Recommends multiple quotes: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
Trust signals
How well the models protect the retail store buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the retail store buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.5% of answers on average. Verifying credentials or certifications appeared in 8.9%. Warning about red flags or scams appeared in 15.5%.
On structuring the decision, a selection-criteria checklist showed up in 42.2% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for retail store is "recommends multiple quotes" at 2.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.
Referral behavior
Do AI models name Retail Store providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 retail store answers, a specific provider was named in 22.2% of responses on average — roughly 0.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for retail store: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Retail Store questions cover.
The 15 questions behind every percentage on this page were drawn from real retail store (ecommerce / online retail; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact retail store question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.
How to read this
A note on the numbers.
A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific retail store question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →