AI SEO Statistics: Fashion Brand (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
Apply these findings to your fashion brand SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for fashion brand.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
23.3% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 15 fashion brand benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 33.3% | 26.7% | 20% | 26.7% |
| Suggests DIY first | 6.7% | 6.7% | 6.7% | 6.7% |
| Names specific providers | 46.7% | 53.3% | 53.3% | 51.1% |
| Gives price or cost info | 20% | 26.7% | 53.3% | 33.3% |
| Tells to check reviews | 46.7% | 46.7% | 0% | 31.1% |
| Tells to verify credentials | 40% | 33.3% | 6.7% | 26.7% |
| Mentions case studies / portfolio | 13.3% | 0% | 0% | 4.4% |
| Mentions local proximity | 53.3% | 33.3% | 13.3% | 33.3% |
| Gives selection criteria | 66.7% | 93.3% | 66.7% | 75.6% |
| Warns about red flags | 33.3% | 33.3% | 26.7% | 31.1% |
| Asks a clarifying question | 66.7% | 80% | 0% | 48.9% |
| Recommends multiple quotes | 0% | 6.7% | 0% | 2.2% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 80% |
| Suggests DIY first | 100% |
| Names specific providers | 73.3% |
| Gives price or cost info | 66.7% |
| Tells to check reviews | 46.7% |
| Tells to verify credentials | 46.7% |
| Mentions case studies / portfolio | 86.7% |
| Mentions local proximity | 53.3% |
| Gives selection criteria | 46.7% |
| Warns about red flags | 80% |
| Asks a clarifying question | 6.7% |
| Recommends multiple quotes | 93.3% |
By model
How each assistant handled Fashion Brand questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same fashion brand questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 33.3% (ChatGPT) down to 20% (Gemini), a 13-point gap on an identical question set.
Across the 15 fashion brand answers it produced, ChatGPT recommended hiring a professional in 33.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 46.7% of answers (about 3.5 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 40%, averaging 490 words per answer. On the remaining cues it told the buyer to check reviews in 46.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 53.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 fashion brand answers it produced, Claude recommended hiring a professional in 26.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 53.3% of answers (about 4.4 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 33.3%, averaging 276 words per answer. On the remaining cues it told the buyer to check reviews in 46.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 93.3% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 fashion brand 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 53.3% of answers (about 1.3 distinct providers per answer) and included price or cost information 53.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 6.7%, averaging 232 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 13.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching fashion brand toward professional help (33.3%) and Gemini the least (20%). ChatGPT produced the longest answers, at 490 words on average. Specific providers were named most often by Claude (53.3%). Even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 23.3%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where the choice of assistant matters most for a buyer researching fashion brand:
- Asks a clarifying question: from 0% (Gemini) to 80% (Claude). The spread is 80 points.
- Tells the buyer to check reviews: from 0% (Gemini) to 46.7% (ChatGPT). The spread is 47 points.
- Mentions local proximity: from 13.3% (Gemini) to 53.3% (ChatGPT). The spread is 40 points.
- Gives price or cost information: from 20% (ChatGPT) to 53.3% (Gemini). The spread is 33 points.
- Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT). The spread is 33 points.
The widest single gap concerns asks a clarifying question at 80 points. This means a buyer researching fashion brand 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 fashion brand market.
Where they agree
The points of near-consensus in Fashion Brand.
On other behaviors the three models move almost in lockstep. The points of near-consensus for fashion brand, where all three landed within a few points of each other:
- Suggests a DIY approach first: 6.7% across all three models.
- Names a specific provider: 46.7%–53.3% across all three (a 7-point spread).
- Warns about red flags or scams: 26.7%–33.3% across all three (a 7-point spread).
- Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (6.7%).
Every behavior, measured
All twelve coded behaviors for Fashion Brand, averaged across the three models.
The behaviors AI models reproduce most often for fashion brand are gives selection criteria (75.6% on average), names a specific provider (51.1%) and asks a clarifying question (48.9%); the rarest are recommends multiple quotes (2.2%), mentions case studies or portfolio (4.4%) and suggests a DIY approach first (6.7%). 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:
- Gives selection criteria: 75.6% on average (ChatGPT 66.7%, Claude 93.3%, Gemini 66.7%). The spread is 27 points.
- Names a specific provider: 51.1% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 53.3%). The spread is 7 points.
- Asks a clarifying question: 48.9% on average (ChatGPT 66.7%, Claude 80%, Gemini 0%). The spread is 80 points.
- Gives price or cost information: 33.3% on average (ChatGPT 20%, Claude 26.7%, Gemini 53.3%). The spread is 33 points.
- Mentions local proximity: 33.3% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 13.3%). The spread is 40 points.
- Tells the buyer to check reviews: 31.1% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 0%). The spread is 47 points.
- Warns about red flags or scams: 31.1% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 26.7%). The spread is 7 points.
- Recommends hiring a professional: 26.7% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 20%). The spread is 13 points.
- Tells the buyer to verify credentials: 26.7% on average (ChatGPT 40%, Claude 33.3%, Gemini 6.7%). The spread is 33 points.
- Suggests a DIY approach first: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
- Mentions case studies or portfolio: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%). The spread is 13 points.
- Recommends multiple quotes: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%). The spread is 7 points.
Trust signals
How well the models protect the fashion brand buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the fashion brand buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 31.1% of answers on average. Verifying credentials or certifications appeared in 26.7%. Warning about red flags or scams appeared in 31.1%.
On structuring the decision, a selection-criteria checklist showed up in 75.6% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for fashion brand is "recommends multiple quotes" at 2.2% on average. This is 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 Fashion Brand providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 fashion brand answers, a specific provider was named in 51.1% of responses on average, or roughly 3.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for fashion brand: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Fashion Brand questions cover.
The 15 questions behind every percentage on this page form a frozen fashion brand (ecommerce / online retail; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact fashion brand question set rather than 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. It is not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific fashion brand 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-04 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Fashion Brand (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/ecommerce/fashion-brand