AI SEO Statistics: Window Company (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same window company questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 13.3% (Gemini), a 47-point gap on an identical question set.

Across the 15 window company answers it produced, ChatGPT recommended hiring a professional in 60% of them and suggested a DIY approach first 6.7% 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 73.3% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 26.7%, averaging 539 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 73.3% of its answers and a recommendation to gather multiple quotes in 53.3%.

Across the 15 window company answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 13.3% of answers (about 0.5 distinct providers per answer) and included price or cost information 40% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 13.3%, averaging 293 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 13.3%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 window company answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.2 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 20%, and told the buyer to verify credentials in 0%, averaging 273 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 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Taken together, ChatGPT is the assistant most likely to route a window company buyer to a professional (60%) and Gemini the least (13.3%). ChatGPT produced the longest answers, at 539 words on average. Specific providers were named most often by Claude (13.3%) — even there, roughly one answer in 8 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for window company, where all three landed within a few points of each other:

• Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
• Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
• Names a specific provider: 0%–13.3% across all three (a 13-point spread).
• Gives price or cost information: 33.3%–46.7% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the window company 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 13.3%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 24.4%. The single least-reproduced protective signal for window company is "tells the buyer to check reviews" at 8.9% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 15 questions behind every percentage on this page were drawn from real window company (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 window company 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.