AI SEO Statistics: Smart Home Business (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — a frozen buyer-intent benchmark for smart home business.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
18.6% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 smart home business benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 47.5% | 27.5% | 30% | 35% |
| Suggests DIY first | 37.5% | 27.5% | 22.5% | 29.2% |
| Names specific providers | 40% | 52.5% | 52.5% | 48.3% |
| Gives price or cost info | 12.5% | 7.5% | 5% | 8.3% |
| Tells to check reviews | 10% | 10% | 0% | 6.7% |
| Tells to verify credentials | 10% | 12.5% | 7.5% | 10% |
| Mentions case studies / portfolio | 7.5% | 5% | 2.5% | 5% |
| Mentions local proximity | 7.5% | 5% | 5% | 5.8% |
| Gives selection criteria | 32.5% | 50% | 22.5% | 35% |
| Warns about red flags | 2.5% | 5% | 5% | 4.2% |
| Asks a clarifying question | 40% | 62.5% | 0% | 34.2% |
| Recommends multiple quotes | 7.5% | 0% | 0% | 2.5% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 65% |
| Suggests DIY first | 70% |
| Names specific providers | 50% |
| Gives price or cost info | 80% |
| Tells to check reviews | 82.5% |
| Tells to verify credentials | 82.5% |
| Mentions case studies / portfolio | 95% |
| Mentions local proximity | 85% |
| Gives selection criteria | 42.5% |
| Warns about red flags | 90% |
| Asks a clarifying question | 30% |
| Recommends multiple quotes | 92.5% |
By model
How each assistant handled Smart Home Business questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same smart home business questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 47.5% (ChatGPT) down to 27.5% (Claude), a 20-point gap on an identical question set.
Across the 40 smart home business answers it produced, ChatGPT recommended hiring a professional in 47.5% of them and suggested a DIY approach first 37.5% of the time. It named a specific provider in 40% of answers (about 2.7 distinct providers per answer) and included price or cost information 12.5% of the time. ChatGPT asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 10%, averaging 620 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 7.5%.
Across the 40 smart home business answers it produced, Claude recommended hiring a professional in 27.5% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 52.5% of answers (about 2.8 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 12.5%, averaging 302 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 0%.
Across the 40 smart home business answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 52.5% of answers (about 2.3 distinct providers per answer) and included price or cost information 5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 7.5%, averaging 244 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a smart home business buyer to a professional (47.5%) and Claude the least (27.5%). ChatGPT produced the longest answers, at 620 words on average. Specific providers were named most often by Claude (52.5%) — even there, roughly one answer in 2 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 18.6% — the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a smart home business buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 62.5% (Claude) — a 63-point spread.
- Gives selection criteria: from 22.5% (Gemini) to 50% (Claude) — a 28-point spread.
- Recommends hiring a professional: from 27.5% (Claude) to 47.5% (ChatGPT) — a 20-point spread.
- Suggests a DIY approach first: from 22.5% (Gemini) to 37.5% (ChatGPT) — a 15-point spread.
- Names a specific provider: from 40% (ChatGPT) to 52.5% (Claude) — a 13-point spread.
The widest single gap — asks a clarifying question, 63 points — means a smart home business 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 smart home business market.
Where they agree
The points of near-consensus in Smart Home Business.
On other behaviors the three models move almost in lockstep — the points of near-consensus for smart home business, where all three landed within a few points of each other:
- Mentions local proximity: 5%–7.5% across all three (a 3-point spread).
- Warns about red flags or scams: 2.5%–5% across all three (a 3-point spread).
- Tells the buyer to verify credentials: 7.5%–12.5% across all three (a 5-point spread).
- Mentions case studies or portfolio: 2.5%–7.5% across all three (a 5-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 95% of questions) and least consistently on "asks a clarifying question" (30%).
Every behavior, measured
All twelve coded behaviors for Smart Home Business, averaged across the three models.
The behaviors AI models reproduce most often for smart home business are names a specific provider (48.3% on average), recommends hiring a professional (35%) and gives selection criteria (35%); the rarest are recommends multiple quotes (2.5%), warns about red flags or scams (4.2%) and mentions case studies or portfolio (5%). 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:
- Names a specific provider: 48.3% on average (ChatGPT 40%, Claude 52.5%, Gemini 52.5%) — a 13-point spread.
- Recommends hiring a professional: 35% on average (ChatGPT 47.5%, Claude 27.5%, Gemini 30%) — a 20-point spread.
- Gives selection criteria: 35% on average (ChatGPT 32.5%, Claude 50%, Gemini 22.5%) — a 28-point spread.
- Asks a clarifying question: 34.2% on average (ChatGPT 40%, Claude 62.5%, Gemini 0%) — a 63-point spread.
- Suggests a DIY approach first: 29.2% on average (ChatGPT 37.5%, Claude 27.5%, Gemini 22.5%) — a 15-point spread.
- Tells the buyer to verify credentials: 10% on average (ChatGPT 10%, Claude 12.5%, Gemini 7.5%) — a 5-point spread.
- Gives price or cost information: 8.3% on average (ChatGPT 12.5%, Claude 7.5%, Gemini 5%) — a 8-point spread.
- Tells the buyer to check reviews: 6.7% on average (ChatGPT 10%, Claude 10%, Gemini 0%) — a 10-point spread.
- Mentions local proximity: 5.8% on average (ChatGPT 7.5%, Claude 5%, Gemini 5%) — a 3-point spread.
- Mentions case studies or portfolio: 5% on average (ChatGPT 7.5%, Claude 5%, Gemini 2.5%) — a 5-point spread.
- Warns about red flags or scams: 4.2% on average (ChatGPT 2.5%, Claude 5%, Gemini 5%) — a 3-point spread.
- Recommends multiple quotes: 2.5% on average (ChatGPT 7.5%, Claude 0%, Gemini 0%) — a 8-point spread.
Trust signals
How well the models protect the smart home business buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the smart home business buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 10%. Warning about red flags or scams appeared in 4.2%.
On structuring the decision, a selection-criteria checklist showed up in 35% of answers on average and a recommendation to gather multiple quotes in 2.5%. The single least-reproduced protective signal for smart home business is "recommends multiple quotes" at 2.5% 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 Smart Home Business providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 smart home business answers, a specific provider was named in 48.3% of responses on average — roughly 2.6 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for smart home business: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Smart Home Business questions cover.
The 40 questions behind every percentage on this page form a frozen smart home business (technology / SaaS; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact smart home business 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 smart home business 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Smart Home Business (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/technology/smart-home-business