AI SEO Statistics: Smart Home Business (2026-07 edition)

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.

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%).

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.

The 40 questions behind every percentage on this page were drawn from real smart home business (technology / SaaS; 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 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.