AI SEO Statistics: Audiologist (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 audiologist.
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
17-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 audiologist buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 83% | 85% | 48% | 60% |
| Suggests DIY first | 25% | 18% | 5% | 73% |
| Names specific providers | 8% | 15% | 23% | 73% |
| Gives price or cost info | 5% | 10% | 28% | 75% |
| Tells to check reviews | 3% | 5% | 3% | 98% |
| Tells to verify credentials | 13% | 8% | 5% | 88% |
| Mentions case studies / portfolio | 3% | 0% | 0% | 98% |
| Mentions local proximity | 15% | 10% | 5% | 75% |
| Gives selection criteria | 35% | 35% | 25% | 63% |
| Warns about red flags | 10% | 15% | 10% | 85% |
| Asks a clarifying question | 58% | 58% | 0% | 25% |
| Recommends multiple quotes | 8% | 10% | 0% | 90% |
By model
How each assistant handled Audiologist questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same audiologist questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 85% (Claude) down to 47.5% (Gemini), a 38-point gap on an identical question set.
Across the 40 audiologist answers it produced, ChatGPT recommended hiring a professional in 82.5% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 7.5% of answers (about 0.2 distinct providers per answer) and included price or cost information 5% of the time. ChatGPT asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 12.5%, averaging 435 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 2.5%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 7.5%.
Across the 40 audiologist answers it produced, Claude recommended hiring a professional in 85% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 15% of answers (about 0.5 distinct providers per answer) and included price or cost information 10% of the time. Claude asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 7.5%, averaging 282 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 10%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 10%.
Across the 40 audiologist answers it produced, Gemini recommended hiring a professional in 47.5% of them and suggested a DIY approach first 5% of the time. It named a specific provider in 22.5% of answers (about 0.8 distinct providers per answer) and included price or cost information 27.5% 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 5%, averaging 285 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 0%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, Claude is the assistant most likely to route an audiologist buyer to a professional (85%) and Gemini the least (47.5%). ChatGPT produced the longest answers, at 435 words on average. Specific providers were named most often by Gemini (22.5%) — even there, roughly one answer in 4 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 16.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an audiologist buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 57.5% (ChatGPT) — a 58-point spread.
- Recommends hiring a professional: from 47.5% (Gemini) to 85% (Claude) — a 38-point spread.
- Gives price or cost information: from 5% (ChatGPT) to 27.5% (Gemini) — a 23-point spread.
- Suggests a DIY approach first: from 5% (Gemini) to 25% (ChatGPT) — a 20-point spread.
- Names a specific provider: from 7.5% (ChatGPT) to 22.5% (Gemini) — a 15-point spread.
The widest single gap — asks a clarifying question, 58 points — means an audiologist 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 audiologist market.
Where they agree
The points of near-consensus in Audiologist.
On other behaviors the three models move almost in lockstep — the points of near-consensus for audiologist, where all three landed within a few points of each other:
- Tells the buyer to check reviews: 2.5%–5% across all three (a 3-point spread).
- Mentions case studies or portfolio: 0%–2.5% across all three (a 3-point spread).
- Warns about red flags or scams: 10%–15% across all three (a 5-point spread).
- Tells the buyer to verify credentials: 5%–12.5% across all three (a 8-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 97.5% of questions) and least consistently on "asks a clarifying question" (25%).
Every behavior, measured
All twelve coded behaviors for Audiologist, averaged across the three models.
The behaviors AI models reproduce most often for audiologist are recommends hiring a professional (71.7% on average), asks a clarifying question (38.3%) and gives selection criteria (31.7%); the rarest are mentions case studies or portfolio (0.8%), tells the buyer to check reviews (3.3%) and recommends multiple quotes (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: 71.7% on average (ChatGPT 82.5%, Claude 85%, Gemini 47.5%) — a 38-point spread.
- Asks a clarifying question: 38.3% on average (ChatGPT 57.5%, Claude 57.5%, Gemini 0%) — a 58-point spread.
- Gives selection criteria: 31.7% on average (ChatGPT 35%, Claude 35%, Gemini 25%) — a 10-point spread.
- Suggests a DIY approach first: 15.8% on average (ChatGPT 25%, Claude 17.5%, Gemini 5%) — a 20-point spread.
- Names a specific provider: 15% on average (ChatGPT 7.5%, Claude 15%, Gemini 22.5%) — a 15-point spread.
- Gives price or cost information: 14.2% on average (ChatGPT 5%, Claude 10%, Gemini 27.5%) — a 23-point spread.
- Warns about red flags or scams: 11.7% on average (ChatGPT 10%, Claude 15%, Gemini 10%) — a 5-point spread.
- Mentions local proximity: 10% on average (ChatGPT 15%, Claude 10%, Gemini 5%) — a 10-point spread.
- Tells the buyer to verify credentials: 8.3% on average (ChatGPT 12.5%, Claude 7.5%, Gemini 5%) — a 8-point spread.
- Recommends multiple quotes: 5.8% on average (ChatGPT 7.5%, Claude 10%, Gemini 0%) — a 10-point spread.
- Tells the buyer to check reviews: 3.3% on average (ChatGPT 2.5%, Claude 5%, Gemini 2.5%) — a 3-point spread.
- Mentions case studies or portfolio: 0.8% on average (ChatGPT 2.5%, Claude 0%, Gemini 0%) — a 3-point spread.
Trust signals
How well the models protect the audiologist buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the audiologist buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 3.3% of answers on average. Verifying credentials or certifications appeared in 8.3%. Warning about red flags or scams appeared in 11.7%.
On structuring the decision, a selection-criteria checklist showed up in 31.7% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for audiologist is "tells the buyer to check reviews" at 3.3% 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 Audiologist providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 audiologist answers, a specific provider was named in 15% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for audiologist: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Audiologist questions cover.
The 40 questions behind every percentage on this page were drawn from real audiologist (healthcare 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 audiologist 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 audiologist 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 →