Original research · 2026-07 edition

AI SEO Statistics: Fashion Brand (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in fashion brand.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

I have a gala next month and need a formal dress that isn't from a mass-market mall store, where should I look?
How do I know if a boutique's handmade claims are actually true before I spend over two hundred dollars?
Is it better to buy a designer piece second-hand or invest in a new one from an emerging sustainable label?
What are some red flags I should look for in the return policy of an online-only fashion brand?
I'm trying to build a capsule wardrobe with a 1000 dollar budget, which brands offer the best longevity for basics?
Are there any online retailers that specialize in high-end fashion for petite women under five foot two?
How can I verify the ethical manufacturing standards of a direct-to-consumer clothing company?
What is the average price range for a custom-tailored wool coat from an independent designer?
Show all 15 questions
I need a specific outfit for a themed party by this weekend, who has the best reputation for expedited shipping?
Which online fashion brands are known for having consistent sizing that actually matches their size charts?
Should I hire a personal shopper or just use the curated style boxes offered by some ecommerce sites?
How do I compare the quality of Pima cotton versus Egyptian cotton when shopping for luxury loungewear online?
What should I look for in a brand's About Us page to ensure they aren't just dropshipping cheap items?
Are there any fashion brands that offer a lifetime warranty or repair services for their denim products?
I'm looking for an eco-friendly swimwear brand that uses recycled ocean plastic, what are my best options for quality?

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 fashion brand buyers.

Behavior rates across 15 fashion brand buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional33%27%20%80%
Suggests DIY first7%7%7%100%
Names specific providers47%53%53%73%
Gives price or cost info20%27%53%67%
Tells to check reviews47%47%0%47%
Tells to verify credentials40%33%7%47%
Mentions case studies / portfolio13%0%0%87%
Mentions local proximity53%33%13%53%
Gives selection criteria67%93%67%47%
Warns about red flags33%33%27%80%
Asks a clarifying question67%80%0%7%
Recommends multiple quotes0%7%0%93%

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 fashion brand buyer to a professional (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.

The divergence index for this study is 23.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a fashion brand buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 80% (Claude) — a 80-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Mentions local proximity: from 13.3% (Gemini) to 53.3% (ChatGPT) — a 40-point spread.
  • Gives price or cost information: from 20% (ChatGPT) to 53.3% (Gemini) — a 33-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.

The widest single gap — asks a clarifying question, 80 points — means a fashion brand 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 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%) — a 27-point spread.
  • Names a specific provider: 51.1% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 53.3%) — a 7-point spread.
  • Asks a clarifying question: 48.9% on average (ChatGPT 66.7%, Claude 80%, Gemini 0%) — a 80-point spread.
  • Gives price or cost information: 33.3% on average (ChatGPT 20%, Claude 26.7%, Gemini 53.3%) — a 33-point spread.
  • Mentions local proximity: 33.3% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 13.3%) — a 40-point spread.
  • Tells the buyer to check reviews: 31.1% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 0%) — a 47-point spread.
  • Warns about red flags or scams: 31.1% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 26.7%) — a 7-point spread.
  • Recommends hiring a professional: 26.7% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 20%) — a 13-point spread.
  • Tells the buyer to verify credentials: 26.7% on average (ChatGPT 40%, Claude 33.3%, Gemini 6.7%) — a 33-point spread.
  • 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%) — a 13-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.

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 — 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 — 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 were drawn from real fashion brand (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 fashion brand 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 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 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 →