AI SEO Statistics: Fitness Club (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 fitness club.
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 fitness club buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 33% | 33% | 33% | 73% |
| Suggests DIY first | 33% | 27% | 7% | 67% |
| Names specific providers | 20% | 53% | 73% | 47% |
| Gives price or cost info | 13% | 40% | 53% | 53% |
| Tells to check reviews | 13% | 40% | 0% | 60% |
| Tells to verify credentials | 13% | 7% | 7% | 93% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 100% |
| Mentions local proximity | 27% | 60% | 27% | 47% |
| Gives selection criteria | 60% | 87% | 60% | 60% |
| Warns about red flags | 40% | 27% | 7% | 60% |
| Asks a clarifying question | 60% | 80% | 0% | 13% |
| Recommends multiple quotes | 0% | 7% | 0% | 93% |
By model
How each assistant handled Fitness Club questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same fitness club 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 33.3% (ChatGPT), a 0-point gap on an identical question set.
Across the 15 fitness club answers it produced, ChatGPT recommended hiring a professional in 33.3% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 20% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 13.3%, averaging 547 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 fitness club answers it produced, Claude recommended hiring a professional in 33.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 53.3% of answers (about 2 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 6.7%, averaging 273 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 60%; a selection-criteria checklist appeared in 86.7% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 fitness club answers it produced, Gemini 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 73.3% of answers (about 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 6.7%, and told the buyer to verify credentials in 6.7%, averaging 224 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 26.7%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a fitness club buyer to a professional (33.3%) and ChatGPT the least (33.3%). ChatGPT produced the longest answers, at 547 words on average. Specific providers were named most often by Gemini (73.3%) — 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 24.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a fitness club buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (Claude) — a 80-point spread.
- Names a specific provider: from 20% (ChatGPT) to 73.3% (Gemini) — a 53-point spread.
- Gives price or cost information: from 13.3% (ChatGPT) to 53.3% (Gemini) — a 40-point spread.
- Tells the buyer to check reviews: from 0% (Gemini) to 40% (Claude) — a 40-point spread.
- Mentions local proximity: from 26.7% (ChatGPT) to 60% (Claude) — a 33-point spread.
The widest single gap — asks a clarifying question, 80 points — means a fitness club 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 fitness club market.
Where they agree
The points of near-consensus in Fitness Club.
On other behaviors the three models move almost in lockstep — the points of near-consensus for fitness club, where all three landed within a few points of each other:
- Recommends hiring a professional: 33.3% across all three models.
- Mentions case studies or portfolio: 0% across all three models.
- Tells the buyer to verify credentials: 6.7%–13.3% 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 "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (13.3%).
Every behavior, measured
All twelve coded behaviors for Fitness Club, averaged across the three models.
The behaviors AI models reproduce most often for fitness club are gives selection criteria (68.9% on average), names a specific provider (48.9%) and asks a clarifying question (46.7%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (2.2%) and tells the buyer to verify credentials (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:
- Gives selection criteria: 68.9% on average (ChatGPT 60%, Claude 86.7%, Gemini 60%) — a 27-point spread.
- Names a specific provider: 48.9% on average (ChatGPT 20%, Claude 53.3%, Gemini 73.3%) — a 53-point spread.
- Asks a clarifying question: 46.7% on average (ChatGPT 60%, Claude 80%, Gemini 0%) — a 80-point spread.
- Mentions local proximity: 37.8% on average (ChatGPT 26.7%, Claude 60%, Gemini 26.7%) — a 33-point spread.
- Gives price or cost information: 35.5% on average (ChatGPT 13.3%, Claude 40%, Gemini 53.3%) — a 40-point spread.
- Recommends hiring a professional: 33.3% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 33.3%).
- Warns about red flags or scams: 24.5% on average (ChatGPT 40%, Claude 26.7%, Gemini 6.7%) — a 33-point spread.
- Suggests a DIY approach first: 22.2% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 6.7%) — a 27-point spread.
- Tells the buyer to check reviews: 17.8% on average (ChatGPT 13.3%, Claude 40%, Gemini 0%) — a 40-point spread.
- Tells the buyer to verify credentials: 8.9% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 6.7%) — a 7-point spread.
- Recommends multiple quotes: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
- Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the fitness club buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the fitness club buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 17.8% of answers on average. Verifying credentials or certifications appeared in 8.9%. Warning about red flags or scams appeared in 24.5%.
On structuring the decision, a selection-criteria checklist showed up in 68.9% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for fitness club 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 Fitness Club providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 fitness club answers, a specific provider was named in 48.9% of responses on average — roughly 1.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for fitness club: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Fitness Club questions cover.
The 15 questions behind every percentage on this page were drawn from real fitness club (fitness services; 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 fitness club 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 fitness club 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 →