AI SEO Statistics: Appliance Repair (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 appliance repair.
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 appliance repair buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 67% | 60% | 40% | 53% |
| Suggests DIY first | 27% | 33% | 27% | 93% |
| Names specific providers | 0% | 0% | 7% | 93% |
| Gives price or cost info | 33% | 33% | 47% | 73% |
| Tells to check reviews | 27% | 27% | 7% | 67% |
| Tells to verify credentials | 40% | 20% | 0% | 53% |
| Mentions case studies / portfolio | 0% | 0% | 0% | 100% |
| Mentions local proximity | 27% | 20% | 27% | 53% |
| Gives selection criteria | 47% | 53% | 40% | 47% |
| Warns about red flags | 13% | 13% | 7% | 80% |
| Asks a clarifying question | 80% | 60% | 0% | 13% |
| Recommends multiple quotes | 20% | 20% | 0% | 67% |
By model
How each assistant handled Appliance Repair questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same appliance repair 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 40% (Gemini), a 27-point gap on an identical question set.
Across the 15 appliance repair 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 33.3% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 40%, averaging 427 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, 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 46.7% of its answers and a recommendation to gather multiple quotes in 20%.
Across the 15 appliance repair answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 33.3% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 20%, averaging 283 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 20%.
Across the 15 appliance repair answers it produced, Gemini recommended hiring a professional in 40% 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 46.7% 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 0%, averaging 284 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 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route an appliance repair buyer to a professional (66.7%) and Gemini the least (40%). ChatGPT produced the longest answers, at 427 words on average. Specific providers were named most often by Gemini (6.7%) — even there, roughly one answer in 15 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 an appliance repair buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT) — a 80-point spread.
- Tells the buyer to verify credentials: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
- Recommends hiring a professional: from 40% (Gemini) to 66.7% (ChatGPT) — a 27-point spread.
- Tells the buyer to check reviews: from 6.7% (Gemini) to 26.7% (ChatGPT) — a 20-point spread.
- Recommends multiple quotes: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
The widest single gap — asks a clarifying question, 80 points — means an appliance repair 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 appliance repair market.
Where they agree
The points of near-consensus in Appliance Repair.
On other behaviors the three models move almost in lockstep — the points of near-consensus for appliance repair, where all three landed within a few points of each other:
- Mentions case studies or portfolio: 0% across all three models.
- Suggests a DIY approach first: 26.7%–33.3% across all three (a 7-point spread).
- Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
- Names a specific provider: 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 Appliance Repair, averaged across the three models.
The behaviors AI models reproduce most often for appliance repair are recommends hiring a professional (55.6% on average), gives selection criteria (46.7%) and asks a clarifying question (46.7%); the rarest are mentions case studies or portfolio (0%), names a specific provider (2.2%) and warns about red flags or scams (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:
- Recommends hiring a professional: 55.6% on average (ChatGPT 66.7%, Claude 60%, Gemini 40%) — a 27-point spread.
- Gives selection criteria: 46.7% on average (ChatGPT 46.7%, Claude 53.3%, Gemini 40%) — a 13-point spread.
- Asks a clarifying question: 46.7% on average (ChatGPT 80%, Claude 60%, Gemini 0%) — a 80-point spread.
- Gives price or cost information: 37.8% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 46.7%) — a 13-point spread.
- Suggests a DIY approach first: 28.9% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 26.7%) — a 7-point spread.
- Mentions local proximity: 24.5% on average (ChatGPT 26.7%, Claude 20%, Gemini 26.7%) — a 7-point spread.
- Tells the buyer to check reviews: 20% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
- Tells the buyer to verify credentials: 20% on average (ChatGPT 40%, Claude 20%, Gemini 0%) — a 40-point spread.
- Recommends multiple quotes: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
- Warns about red flags or scams: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
- Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 0%, Gemini 6.7%) — 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 appliance repair buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the appliance repair buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 20% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 11.1%.
On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 13.3%. The single least-reproduced protective signal for appliance repair is "warns about red flags or scams" at 11.1% 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 Appliance Repair providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 appliance repair answers, a specific provider was named in 2.2% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for appliance repair: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Appliance Repair questions cover.
The 15 questions behind every percentage on this page were drawn from real appliance repair (home 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 appliance repair 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 appliance repair 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 →