AI SEO Statistics: Clothing 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 clothing 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
24-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 clothing store buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 27% | 20% | 20% | 93% |
| Suggests DIY first | 13% | 7% | 7% | 87% |
| Names specific providers | 27% | 47% | 80% | 40% |
| Gives price or cost info | 7% | 20% | 40% | 60% |
| Tells to check reviews | 40% | 60% | 0% | 27% |
| Tells to verify credentials | 13% | 33% | 0% | 60% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 100% |
| Mentions local proximity | 20% | 13% | 7% | 80% |
| Gives selection criteria | 40% | 73% | 40% | 33% |
| Warns about red flags | 7% | 33% | 7% | 60% |
| Asks a clarifying question | 33% | 73% | 0% | 20% |
| Recommends multiple quotes | 0% | 0% | 0% | 100% |
By model
How each assistant handled Clothing Store questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same clothing store questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 26.7% (ChatGPT) down to 20% (Claude), a 7-point gap on an identical question set.
Across the 15 clothing store answers it produced, ChatGPT recommended hiring a professional in 26.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 26.7% of answers (about 2.7 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 509 words per answer. On the remaining cues it told the buyer to check reviews in 40%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 clothing store answers it produced, Claude recommended hiring a professional in 20% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 46.7% of answers (about 3.8 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 33.3%, averaging 275 words per answer. On the remaining cues it told the buyer to check reviews in 60%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 clothing store answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 80% of answers (about 2.7 distinct providers per answer) and included price or cost information 40% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 241 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a clothing store buyer to a professional (26.7%) and Claude the least (20%). ChatGPT produced the longest answers, at 509 words on average. Specific providers were named most often by Gemini (80%) — even there, roughly one answer in 1 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 24.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a clothing store buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 73.3% (Claude) — a 73-point spread.
- Tells the buyer to check reviews: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
- Names a specific provider: from 26.7% (ChatGPT) to 80% (Gemini) — a 53-point spread.
- Gives price or cost information: from 6.7% (ChatGPT) to 40% (Gemini) — a 33-point spread.
- Tells the buyer to verify credentials: from 0% (Gemini) to 33.3% (Claude) — a 33-point spread.
The widest single gap — asks a clarifying question, 73 points — means a clothing 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 clothing store market.
Where they agree
The points of near-consensus in Clothing Store.
On other behaviors the three models move almost in lockstep — the points of near-consensus for clothing store, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0% across all three models.
- Recommends multiple quotes: 0% across all three models.
- Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
- Recommends hiring a professional: 20%–26.7% across all three (a 7-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).
Every behavior, measured
All twelve coded behaviors for Clothing Store, averaged across the three models.
The behaviors AI models reproduce most often for clothing store are names a specific provider (51.1% on average), gives selection criteria (51.1%) and asks a clarifying question (35.5%); the rarest are recommends multiple quotes (0%), mentions case studies or portfolio (0%) and suggests a DIY approach first (8.9%). 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:
- Names a specific provider: 51.1% on average (ChatGPT 26.7%, Claude 46.7%, Gemini 80%) — a 53-point spread.
- Gives selection criteria: 51.1% on average (ChatGPT 40%, Claude 73.3%, Gemini 40%) — a 33-point spread.
- Asks a clarifying question: 35.5% on average (ChatGPT 33.3%, Claude 73.3%, Gemini 0%) — a 73-point spread.
- Tells the buyer to check reviews: 33.3% on average (ChatGPT 40%, Claude 60%, Gemini 0%) — a 60-point spread.
- Recommends hiring a professional: 22.2% on average (ChatGPT 26.7%, Claude 20%, Gemini 20%) — a 7-point spread.
- Gives price or cost information: 22.2% on average (ChatGPT 6.7%, Claude 20%, Gemini 40%) — a 33-point spread.
- Warns about red flags or scams: 15.6% on average (ChatGPT 6.7%, Claude 33.3%, Gemini 6.7%) — a 27-point spread.
- Tells the buyer to verify credentials: 15.5% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 0%) — a 33-point spread.
- Mentions local proximity: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
- Suggests a DIY approach first: 8.9% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
- Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the clothing store buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the clothing store buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 33.3% of answers on average. Verifying credentials or certifications appeared in 15.5%. Warning about red flags or scams appeared in 15.6%.
On structuring the decision, a selection-criteria checklist showed up in 51.1% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for clothing store is "recommends multiple quotes" at 0% 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 Clothing Store providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 clothing store answers, a specific provider was named in 51.1% of responses on average — roughly 3.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for clothing store: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Clothing Store questions cover.
The 15 questions behind every percentage on this page were drawn from real clothing 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 clothing 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 clothing 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 →