AI SEO Statistics: Health Wellness 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 health wellness 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
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 health wellness store buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 67% | 60% | 33% | 60% |
| Suggests DIY first | 13% | 27% | 7% | 80% |
| Names specific providers | 7% | 7% | 33% | 60% |
| Gives price or cost info | 13% | 20% | 20% | 80% |
| Tells to check reviews | 20% | 13% | 0% | 80% |
| Tells to verify credentials | 47% | 27% | 20% | 53% |
| Mentions case studies / portfolio | 27% | 0% | 0% | 73% |
| Mentions local proximity | 20% | 20% | 13% | 87% |
| Gives selection criteria | 60% | 73% | 53% | 47% |
| Warns about red flags | 27% | 27% | 20% | 67% |
| Asks a clarifying question | 60% | 60% | 0% | 13% |
| Recommends multiple quotes | 0% | 7% | 0% | 93% |
By model
How each assistant handled Health Wellness Store questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same health wellness store questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 66.7% (ChatGPT) down to 33.3% (Gemini), a 33-point gap on an identical question set.
Across the 15 health wellness store answers it produced, ChatGPT recommended hiring a professional in 66.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.6 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 26.7%, and told the buyer to verify credentials in 46.7%, averaging 496 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 26.7%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 15 health wellness store answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 26.7%, averaging 284 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 20%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 6.7%.
Across the 15 health wellness store 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 33.3% of answers (about 0.9 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 248 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 53.3% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a health wellness store buyer to a professional (66.7%) and Gemini the least (33.3%). ChatGPT produced the longest answers, at 496 words on average. Specific providers were named most often by Gemini (33.3%) — even there, roughly one answer in 3 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 22.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a health wellness store buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 60% (ChatGPT) — a 60-point spread.
- Recommends hiring a professional: from 33.3% (Gemini) to 66.7% (ChatGPT) — a 33-point spread.
- Tells the buyer to verify credentials: from 20% (Gemini) to 46.7% (ChatGPT) — a 27-point spread.
- Mentions case studies or portfolio: from 0% (Claude) to 26.7% (ChatGPT) — a 27-point spread.
- Names a specific provider: from 6.7% (ChatGPT) to 33.3% (Gemini) — a 27-point spread.
The widest single gap — asks a clarifying question, 60 points — means a health wellness 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 health wellness store market.
Where they agree
The points of near-consensus in Health Wellness Store.
On other behaviors the three models move almost in lockstep — the points of near-consensus for health wellness store, where all three landed within a few points of each other:
- Gives price or cost information: 13.3%–20% across all three (a 7-point spread).
- Mentions local proximity: 13.3%–20% across all three (a 7-point spread).
- Warns about red flags or scams: 20%–26.7% 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 "recommends multiple quotes" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).
Every behavior, measured
All twelve coded behaviors for Health Wellness Store, averaged across the three models.
The behaviors AI models reproduce most often for health wellness store are gives selection criteria (62.2% on average), recommends hiring a professional (53.3%) and asks a clarifying question (40%); the rarest are recommends multiple quotes (2.2%), mentions case studies or portfolio (8.9%) and tells the buyer to check reviews (11.1%). 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: 62.2% on average (ChatGPT 60%, Claude 73.3%, Gemini 53.3%) — a 20-point spread.
- Recommends hiring a professional: 53.3% on average (ChatGPT 66.7%, Claude 60%, Gemini 33.3%) — a 33-point spread.
- Asks a clarifying question: 40% on average (ChatGPT 60%, Claude 60%, Gemini 0%) — a 60-point spread.
- Tells the buyer to verify credentials: 31.1% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 20%) — a 27-point spread.
- Warns about red flags or scams: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
- Gives price or cost information: 17.8% on average (ChatGPT 13.3%, Claude 20%, Gemini 20%) — a 7-point spread.
- Mentions local proximity: 17.8% on average (ChatGPT 20%, Claude 20%, Gemini 13.3%) — a 7-point spread.
- Suggests a DIY approach first: 15.6% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
- Names a specific provider: 15.6% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 33.3%) — a 27-point spread.
- Tells the buyer to check reviews: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
- Mentions case studies or portfolio: 8.9% on average (ChatGPT 26.7%, Claude 0%, Gemini 0%) — a 27-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 health wellness store buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the health wellness store buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.1% of answers on average. Verifying credentials or certifications appeared in 31.1%. Warning about red flags or scams appeared in 24.5%.
On structuring the decision, a selection-criteria checklist showed up in 62.2% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for health wellness store 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 Health Wellness Store providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 health wellness store answers, a specific provider was named in 15.6% of responses on average — roughly 0.6 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for health wellness store: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Health Wellness Store questions cover.
The 15 questions behind every percentage on this page were drawn from real health wellness store (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 health wellness 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 health wellness 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 →