AI SEO Statistics: Life Coaches (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in life coaches.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 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 life coaches buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 80% | 63% | 48% | 63% |
| Suggests DIY first | 8% | 10% | 0% | 83% |
| Names specific providers | 10% | 10% | 10% | 85% |
| Gives price or cost info | 10% | 10% | 8% | 85% |
| Tells to check reviews | 20% | 15% | 5% | 73% |
| Tells to verify credentials | 48% | 33% | 15% | 50% |
| Mentions case studies / portfolio | 35% | 18% | 0% | 60% |
| Mentions local proximity | 20% | 10% | 3% | 80% |
| Gives selection criteria | 68% | 63% | 38% | 40% |
| Warns about red flags | 30% | 38% | 13% | 60% |
| Asks a clarifying question | 68% | 65% | 0% | 15% |
| Recommends multiple quotes | 10% | 3% | 0% | 88% |
By model
How each assistant handled Life Coaches questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same life coaches questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 47.5% (Gemini), a 33-point gap on an identical question set.
Across the 40 life coaches answers it produced, ChatGPT recommended hiring a professional in 80% of them and suggested a DIY approach first 7.5% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 30%, and told the buyer to verify credentials in 47.5%, averaging 529 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 35%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 67.5% of its answers and a recommendation to gather multiple quotes in 10%.
Across the 40 life coaches answers it produced, Claude recommended hiring a professional in 62.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 10% of answers (about 0.4 distinct providers per answer) and included price or cost information 10% of the time. Claude asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 37.5%, and told the buyer to verify credentials in 32.5%, averaging 285 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 17.5%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 62.5% of its answers and a recommendation to gather multiple quotes in 2.5%.
Across the 40 life coaches answers it produced, Gemini recommended hiring a professional in 47.5% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 10% of answers (about 0.3 distinct providers per answer) and included price or cost information 7.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 15%, averaging 280 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 2.5%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a life coaches buyer to a professional (80%) and Gemini the least (47.5%). ChatGPT produced the longest answers, at 529 words on average. Specific providers were named most often by ChatGPT (10%) — even there, roughly one answer in 10 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 life coaches buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 67.5% (ChatGPT) — a 68-point spread.
- Mentions case studies or portfolio: from 0% (Gemini) to 35% (ChatGPT) — a 35-point spread.
- Recommends hiring a professional: from 47.5% (Gemini) to 80% (ChatGPT) — a 33-point spread.
- Tells the buyer to verify credentials: from 15% (Gemini) to 47.5% (ChatGPT) — a 33-point spread.
- Gives selection criteria: from 37.5% (Gemini) to 67.5% (ChatGPT) — a 30-point spread.
The widest single gap — asks a clarifying question, 68 points — means a life coaches 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 life coaches market.
Where they agree
The points of near-consensus in Life Coaches.
On other behaviors the three models move almost in lockstep — the points of near-consensus for life coaches, where all three landed within a few points of each other:
- Names a specific provider: 10% across all three models.
- Gives price or cost information: 7.5%–10% across all three (a 3-point spread).
- Suggests a DIY approach first: 0%–10% across all three (a 10-point spread).
- Recommends multiple quotes: 0%–10% across all three (a 10-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "recommends multiple quotes" (identical coding in 87.5% of questions) and least consistently on "asks a clarifying question" (15%).
Every behavior, measured
All twelve coded behaviors for Life Coaches, averaged across the three models.
The behaviors AI models reproduce most often for life coaches are recommends hiring a professional (63.3% on average), gives selection criteria (55.8%) and asks a clarifying question (44.2%); the rarest are recommends multiple quotes (4.2%), suggests a DIY approach first (5.8%) and gives price or cost information (9.2%). 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: 63.3% on average (ChatGPT 80%, Claude 62.5%, Gemini 47.5%) — a 33-point spread.
- Gives selection criteria: 55.8% on average (ChatGPT 67.5%, Claude 62.5%, Gemini 37.5%) — a 30-point spread.
- Asks a clarifying question: 44.2% on average (ChatGPT 67.5%, Claude 65%, Gemini 0%) — a 68-point spread.
- Tells the buyer to verify credentials: 31.7% on average (ChatGPT 47.5%, Claude 32.5%, Gemini 15%) — a 33-point spread.
- Warns about red flags or scams: 26.7% on average (ChatGPT 30%, Claude 37.5%, Gemini 12.5%) — a 25-point spread.
- Mentions case studies or portfolio: 17.5% on average (ChatGPT 35%, Claude 17.5%, Gemini 0%) — a 35-point spread.
- Tells the buyer to check reviews: 13.3% on average (ChatGPT 20%, Claude 15%, Gemini 5%) — a 15-point spread.
- Mentions local proximity: 10.8% on average (ChatGPT 20%, Claude 10%, Gemini 2.5%) — a 18-point spread.
- Names a specific provider: 10% on average (ChatGPT 10%, Claude 10%, Gemini 10%).
- Gives price or cost information: 9.2% on average (ChatGPT 10%, Claude 10%, Gemini 7.5%) — a 3-point spread.
- Suggests a DIY approach first: 5.8% on average (ChatGPT 7.5%, Claude 10%, Gemini 0%) — a 10-point spread.
- Recommends multiple quotes: 4.2% on average (ChatGPT 10%, Claude 2.5%, Gemini 0%) — a 10-point spread.
Trust signals
How well the models protect the life coaches buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the life coaches buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 13.3% of answers on average. Verifying credentials or certifications appeared in 31.7%. Warning about red flags or scams appeared in 26.7%.
On structuring the decision, a selection-criteria checklist showed up in 55.8% of answers on average and a recommendation to gather multiple quotes in 4.2%. The single least-reproduced protective signal for life coaches is "recommends multiple quotes" at 4.2% 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 Life Coaches providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 life coaches answers, a specific provider was named in 10% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for life coaches: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Life Coaches questions cover.
The 40 questions behind every percentage on this page were drawn from real life coaches (professional 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 life coaches 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 life coaches 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 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 2026-07-06, 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 →