No model names specific home services providers with any real frequency (0-2.5%), so AI visibility in this industry is about shaping how a business is described generically, not securing brand mentions.
AI SEO Statistics: Home Services (2026-07 edition)
Across 120 AI responses to 40 home services questions, ChatGPT, Claude, and Gemini diverge sharply on whether to recommend hiring a professional (87.5% vs 67.5% vs 27.5%) and on how much guidance they give around credentials, reviews, and multiple quotes. No model names specific providers with any consistency, meaning AI visibility in this industry hinges on being described favorably in generic terms rather than being cited by name. With a divergence index of 22.1, home services businesses need model-specific strategies rather than a single AI-SEO playbook.
40 questions · 120 AI responses · 3 models · measured 2026-07-02
Key statistics
Every number below is measured, anchored, and sourced.
The question bank
The questions we tested — sampled from real buyer journeys in home services.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
By service
Not all home services services are treated the same by AI.
We ran the same measurement on 86 distinct home services services. The rate at which ChatGPT, Claude and Gemini push buyers toward a professional swings widely, and that gap is exactly where authority is won or lost.
Measured across ChatGPT, Claude and Gemini · 15 buyer questions per service × 3 models · Authority Specialist AI Study. Free to cite with attribution.
Model by model
22-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 home services buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 88% | 68% | 28% | 35% |
| Suggests DIY first | 33% | 30% | 18% | 83% |
| Names specific providers | 0% | 0% | 3% | 98% |
| Gives price or cost info | 45% | 35% | 35% | 50% |
| Tells to check reviews | 15% | 5% | 5% | 90% |
| Tells to verify credentials | 33% | 13% | 8% | 65% |
| Mentions case studies / portfolio | 8% | 3% | 0% | 90% |
| Mentions local proximity | 28% | 30% | 15% | 60% |
| Gives selection criteria | 38% | 20% | 18% | 63% |
| Warns about red flags | 8% | 8% | 13% | 90% |
| Asks a clarifying question | 80% | 55% | 3% | 18% |
| Recommends multiple quotes | 33% | 13% | 3% | 63% |
What this means
What this means for home services businesses.
ChatGPT is by far the most directive model, recommending professionals (87.5%), verifying credentials (32.5%), and suggesting multiple quotes (32.5%) at rates 2-3x higher than Claude or Gemini, meaning content optimized for ChatGPT's framing may not transfer to other assistants.
Gemini rarely asks clarifying questions (2.5%) and gives the least selection guidance across every category measured, producing shorter, less structured answers (258 words) that leave less room for businesses to be represented by proxy criteria like credentials or reviews.
Trust-and-safety guidance -- reviews (5-15%), red flags (7.5-12.5%), and credential checks (7.5-32.5%) -- is inconsistently surfaced by all three models, representing an open content opportunity for home services brands to fill via their own sites and listings.
The 22.1-point divergence index confirms that model choice materially changes the advice a consumer receives, so businesses should audit their visibility separately across ChatGPT, Claude, and Gemini rather than assuming uniform AI behavior.
AI visibility is measurable. We just measured it for your industry.
Open your dashboard to see how ChatGPT, Claude and Gemini describe YOUR business — mentions, recommendations, citations, gaps.
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 2026-07-02, 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 →