AI SEO Statistics: Tailors (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — a frozen buyer-intent benchmark for tailors.
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 40 questions
Model by model
17.8% 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 40 tailors benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 87.5% | 87.5% | 72.5% | 82.5% |
| Suggests DIY first | 2.5% | 2.5% | 0% | 1.7% |
| Names specific providers | 2.5% | 5% | 12.5% | 6.7% |
| Gives price or cost info | 50% | 50% | 45% | 48.3% |
| Tells to check reviews | 10% | 5% | 2.5% | 5.8% |
| Tells to verify credentials | 0% | 0% | 0% | 0% |
| Mentions case studies / portfolio | 22.5% | 25% | 5% | 17.5% |
| Mentions local proximity | 40% | 42.5% | 20% | 34.2% |
| Gives selection criteria | 57.5% | 40% | 35% | 44.2% |
| Warns about red flags | 5% | 10% | 10% | 8.3% |
| Asks a clarifying question | 65% | 37.5% | 0% | 34.2% |
| Recommends multiple quotes | 7.5% | 17.5% | 0% | 8.3% |
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 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 77.5% |
| Suggests DIY first | 97.5% |
| Names specific providers | 90% |
| Gives price or cost info | 60% |
| Tells to check reviews | 85% |
| Tells to verify credentials | 100% |
| Mentions case studies / portfolio | 70% |
| Mentions local proximity | 57.5% |
| Gives selection criteria | 45% |
| Warns about red flags | 92.5% |
| Asks a clarifying question | 22.5% |
| Recommends multiple quotes | 82.5% |
By model
How each assistant handled Tailors questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same tailors questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 87.5% (ChatGPT) down to 72.5% (Gemini), a 15-point gap on an identical question set.
Across the 40 tailors answers it produced, ChatGPT recommended hiring a professional in 87.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 50% of the time. ChatGPT asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 0%, averaging 411 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 22.5%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 57.5% of its answers and a recommendation to gather multiple quotes in 7.5%.
Across the 40 tailors answers it produced, Claude recommended hiring a professional in 87.5% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 50% of the time. Claude asked a clarifying question before answering in 37.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 0%, averaging 274 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 25%, and framed the choice around local proximity in 42.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 17.5%.
Across the 40 tailors answers it produced, Gemini recommended hiring a professional in 72.5% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 12.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 45% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 0%, averaging 287 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a tailors buyer to a professional (87.5%) and Gemini the least (72.5%). ChatGPT produced the longest answers, at 411 words on average. Specific providers were named most often by Gemini (12.5%) — even there, roughly one answer in 8 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 17.8% — 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 which assistant a tailors buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 65% (ChatGPT) — a 65-point spread.
- Mentions local proximity: from 20% (Gemini) to 42.5% (Claude) — a 23-point spread.
- Gives selection criteria: from 35% (Gemini) to 57.5% (ChatGPT) — a 23-point spread.
- Mentions case studies or portfolio: from 5% (Gemini) to 25% (Claude) — a 20-point spread.
- Recommends multiple quotes: from 0% (Gemini) to 17.5% (Claude) — a 18-point spread.
The widest single gap — asks a clarifying question, 65 points — means a tailors 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 tailors market.
Where they agree
The points of near-consensus in Tailors.
On other behaviors the three models move almost in lockstep — the points of near-consensus for tailors, where all three landed within a few points of each other:
- Tells the buyer to verify credentials: 0% across all three models.
- Suggests a DIY approach first: 0%–2.5% across all three (a 3-point spread).
- Gives price or cost information: 45%–50% across all three (a 5-point spread).
- Warns about red flags or scams: 5%–10% across all three (a 5-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to verify credentials" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (22.5%).
Every behavior, measured
All twelve coded behaviors for Tailors, averaged across the three models.
The behaviors AI models reproduce most often for tailors are recommends hiring a professional (82.5% on average), gives price or cost information (48.3%) and gives selection criteria (44.2%); the rarest are tells the buyer to verify credentials (0%), suggests a DIY approach first (1.7%) and tells the buyer to check reviews (5.8%). Each figure below is the share of a model's 40 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: 82.5% on average (ChatGPT 87.5%, Claude 87.5%, Gemini 72.5%) — a 15-point spread.
- Gives price or cost information: 48.3% on average (ChatGPT 50%, Claude 50%, Gemini 45%) — a 5-point spread.
- Gives selection criteria: 44.2% on average (ChatGPT 57.5%, Claude 40%, Gemini 35%) — a 23-point spread.
- Mentions local proximity: 34.2% on average (ChatGPT 40%, Claude 42.5%, Gemini 20%) — a 23-point spread.
- Asks a clarifying question: 34.2% on average (ChatGPT 65%, Claude 37.5%, Gemini 0%) — a 65-point spread.
- Mentions case studies or portfolio: 17.5% on average (ChatGPT 22.5%, Claude 25%, Gemini 5%) — a 20-point spread.
- Warns about red flags or scams: 8.3% on average (ChatGPT 5%, Claude 10%, Gemini 10%) — a 5-point spread.
- Recommends multiple quotes: 8.3% on average (ChatGPT 7.5%, Claude 17.5%, Gemini 0%) — a 18-point spread.
- Names a specific provider: 6.7% on average (ChatGPT 2.5%, Claude 5%, Gemini 12.5%) — a 10-point spread.
- Tells the buyer to check reviews: 5.8% on average (ChatGPT 10%, Claude 5%, Gemini 2.5%) — a 8-point spread.
- Suggests a DIY approach first: 1.7% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 0%) — a 3-point spread.
- Tells the buyer to verify credentials: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the tailors buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the tailors buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5.8% of answers on average. Verifying credentials or certifications appeared in 0%. Warning about red flags or scams appeared in 8.3%.
On structuring the decision, a selection-criteria checklist showed up in 44.2% of answers on average and a recommendation to gather multiple quotes in 8.3%. The single least-reproduced protective signal for tailors is "tells the buyer to verify credentials" 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 Tailors providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 tailors answers, a specific provider was named in 6.7% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for tailors: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Tailors questions cover.
The 40 questions behind every percentage on this page form a frozen tailors (home services; 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 tailors 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 40 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific tailors 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.
40 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 2026-07-06 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: Tailors (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/tailors