AI SEO Statistics: Car Detailing (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 car detailing.
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 car detailing buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 67% | 73% | 47% | 40% |
| Suggests DIY first | 27% | 27% | 13% | 67% |
| Names specific providers | 0% | 0% | 0% | 100% |
| Gives price or cost info | 53% | 47% | 20% | 53% |
| Tells to check reviews | 20% | 7% | 0% | 80% |
| Tells to verify credentials | 7% | 7% | 7% | 100% |
| Mentions case studies / portfolio | 20% | 13% | 0% | 73% |
| Mentions local proximity | 20% | 13% | 7% | 73% |
| Gives selection criteria | 33% | 40% | 33% | 33% |
| Warns about red flags | 7% | 27% | 13% | 73% |
| Asks a clarifying question | 73% | 40% | 0% | 20% |
| Recommends multiple quotes | 13% | 13% | 0% | 80% |
By model
How each assistant handled Car Detailing questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same car detailing questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (Claude) down to 46.7% (Gemini), a 27-point gap on an identical question set.
Across the 15 car detailing answers it produced, ChatGPT recommended hiring a professional in 66.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 53.3% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 6.7%, averaging 460 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 car detailing answers it produced, Claude recommended hiring a professional in 73.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 46.7% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 6.7%, averaging 287 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 13.3%.
Across the 15 car detailing answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 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 13.3%, and told the buyer to verify credentials in 6.7%, averaging 285 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 6.7%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, Claude is the assistant most likely to route a car detailing buyer to a professional (73.3%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 460 words on average. No model named a specific provider in more than 0% of answers.
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 car detailing buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
- Gives price or cost information: from 20% (Gemini) to 53.3% (ChatGPT) — a 33-point spread.
- Recommends hiring a professional: from 46.7% (Gemini) to 73.3% (Claude) — a 27-point spread.
- Tells the buyer to check reviews: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
- Mentions case studies or portfolio: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 73 points — means a car detailing 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 car detailing market.
Where they agree
The points of near-consensus in Car Detailing.
On other behaviors the three models move almost in lockstep — the points of near-consensus for car detailing, where all three landed within a few points of each other:
- Names a specific provider: 0% across all three models.
- Tells the buyer to verify credentials: 6.7% across all three models.
- Gives selection criteria: 33.3%–40% across all three (a 7-point spread).
- Mentions local proximity: 6.7%–20% across all three (a 13-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).
Every behavior, measured
All twelve coded behaviors for Car Detailing, averaged across the three models.
The behaviors AI models reproduce most often for car detailing are recommends hiring a professional (62.2% on average), gives price or cost information (40%) and asks a clarifying question (37.8%); the rarest are names a specific provider (0%), tells the buyer to verify credentials (6.7%) and recommends multiple quotes (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:
- Recommends hiring a professional: 62.2% on average (ChatGPT 66.7%, Claude 73.3%, Gemini 46.7%) — a 27-point spread.
- Gives price or cost information: 40% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 20%) — a 33-point spread.
- Asks a clarifying question: 37.8% on average (ChatGPT 73.3%, Claude 40%, Gemini 0%) — a 73-point spread.
- Gives selection criteria: 35.5% on average (ChatGPT 33.3%, Claude 40%, Gemini 33.3%) — a 7-point spread.
- Suggests a DIY approach first: 22.2% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
- Warns about red flags or scams: 15.6% on average (ChatGPT 6.7%, Claude 26.7%, Gemini 13.3%) — a 20-point spread.
- Mentions local proximity: 13.3% on average (ChatGPT 20%, Claude 13.3%, Gemini 6.7%) — a 13-point spread.
- Mentions case studies or portfolio: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
- Tells the buyer to check reviews: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
- Recommends multiple quotes: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
- Tells the buyer to verify credentials: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
- Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the car detailing buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the car detailing buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 15.6%.
On structuring the decision, a selection-criteria checklist showed up in 35.5% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for car detailing is "tells the buyer to verify credentials" at 6.7% 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 Car Detailing providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 car detailing answers, a specific provider was named in 0% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for car detailing: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Car Detailing questions cover.
The 15 questions behind every percentage on this page were drawn from real car detailing (automotive 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 car detailing 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 car detailing 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 →