AI SEO Statistics: Window Treatment (2026-07 edition)
40 questions · 120 AI responses · 3 models · measured 2026-07-06
The question bank
The questions we tested — sampled from real buyer journeys in window treatment.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
19-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 window treatment buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 50% | 33% | 18% | 65% |
| Suggests DIY first | 20% | 15% | 13% | 85% |
| Names specific providers | 20% | 30% | 40% | 58% |
| Gives price or cost info | 25% | 10% | 23% | 70% |
| Tells to check reviews | 5% | 5% | 0% | 93% |
| Tells to verify credentials | 10% | 10% | 8% | 88% |
| Mentions case studies / portfolio | 10% | 3% | 0% | 88% |
| Mentions local proximity | 28% | 18% | 10% | 63% |
| Gives selection criteria | 60% | 53% | 35% | 50% |
| Warns about red flags | 5% | 3% | 0% | 95% |
| Asks a clarifying question | 80% | 63% | 0% | 18% |
| Recommends multiple quotes | 10% | 5% | 0% | 90% |
By model
How each assistant handled Window Treatment questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same window treatment questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 50% (ChatGPT) down to 17.5% (Gemini), a 33-point gap on an identical question set.
Across the 40 window treatment answers it produced, ChatGPT recommended hiring a professional in 50% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 20% of answers (about 0.5 distinct providers per answer) and included price or cost information 25% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 10%, averaging 513 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 27.5%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 10%.
Across the 40 window treatment answers it produced, Claude recommended hiring a professional in 32.5% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 30% of answers (about 0.8 distinct providers per answer) and included price or cost information 10% of the time. Claude asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 10%, averaging 270 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 17.5%; a selection-criteria checklist appeared in 52.5% of its answers and a recommendation to gather multiple quotes in 5%.
Across the 40 window treatment answers it produced, Gemini recommended hiring a professional in 17.5% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 40% of answers (about 1.3 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 0%, and told the buyer to verify credentials in 7.5%, averaging 270 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 10%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a window treatment buyer to a professional (50%) and Gemini the least (17.5%). ChatGPT produced the longest answers, at 513 words on average. Specific providers were named most often by Gemini (40%) — even there, roughly one answer in 3 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
The divergence index for this study is 18.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a window treatment buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT) — a 80-point spread.
- Recommends hiring a professional: from 17.5% (Gemini) to 50% (ChatGPT) — a 33-point spread.
- Gives selection criteria: from 35% (Gemini) to 60% (ChatGPT) — a 25-point spread.
- Names a specific provider: from 20% (ChatGPT) to 40% (Gemini) — a 20-point spread.
- Mentions local proximity: from 10% (Gemini) to 27.5% (ChatGPT) — a 18-point spread.
The widest single gap — asks a clarifying question, 80 points — means a window treatment 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 window treatment market.
Where they agree
The points of near-consensus in Window Treatment.
On other behaviors the three models move almost in lockstep — the points of near-consensus for window treatment, where all three landed within a few points of each other:
- Tells the buyer to verify credentials: 7.5%–10% across all three (a 3-point spread).
- Tells the buyer to check reviews: 0%–5% across all three (a 5-point spread).
- Warns about red flags or scams: 0%–5% across all three (a 5-point spread).
- Suggests a DIY approach first: 12.5%–20% across all three (a 8-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "warns about red flags or scams" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (17.5%).
Every behavior, measured
All twelve coded behaviors for Window Treatment, averaged across the three models.
The behaviors AI models reproduce most often for window treatment are gives selection criteria (49.2% on average), asks a clarifying question (47.5%) and recommends hiring a professional (33.3%); the rarest are warns about red flags or scams (2.5%), tells the buyer to check reviews (3.3%) and mentions case studies or portfolio (4.2%). 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:
- Gives selection criteria: 49.2% on average (ChatGPT 60%, Claude 52.5%, Gemini 35%) — a 25-point spread.
- Asks a clarifying question: 47.5% on average (ChatGPT 80%, Claude 62.5%, Gemini 0%) — a 80-point spread.
- Recommends hiring a professional: 33.3% on average (ChatGPT 50%, Claude 32.5%, Gemini 17.5%) — a 33-point spread.
- Names a specific provider: 30% on average (ChatGPT 20%, Claude 30%, Gemini 40%) — a 20-point spread.
- Gives price or cost information: 19.2% on average (ChatGPT 25%, Claude 10%, Gemini 22.5%) — a 15-point spread.
- Mentions local proximity: 18.3% on average (ChatGPT 27.5%, Claude 17.5%, Gemini 10%) — a 18-point spread.
- Suggests a DIY approach first: 15.8% on average (ChatGPT 20%, Claude 15%, Gemini 12.5%) — a 8-point spread.
- Tells the buyer to verify credentials: 9.2% on average (ChatGPT 10%, Claude 10%, Gemini 7.5%) — a 3-point spread.
- Recommends multiple quotes: 5% on average (ChatGPT 10%, Claude 5%, Gemini 0%) — a 10-point spread.
- Mentions case studies or portfolio: 4.2% on average (ChatGPT 10%, Claude 2.5%, Gemini 0%) — a 10-point spread.
- Tells the buyer to check reviews: 3.3% on average (ChatGPT 5%, Claude 5%, Gemini 0%) — a 5-point spread.
- Warns about red flags or scams: 2.5% on average (ChatGPT 5%, Claude 2.5%, Gemini 0%) — a 5-point spread.
Trust signals
How well the models protect the window treatment buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the window treatment buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 3.3% of answers on average. Verifying credentials or certifications appeared in 9.2%. Warning about red flags or scams appeared in 2.5%.
On structuring the decision, a selection-criteria checklist showed up in 49.2% of answers on average and a recommendation to gather multiple quotes in 5%. The single least-reproduced protective signal for window treatment is "warns about red flags or scams" 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 Window Treatment providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 window treatment answers, a specific provider was named in 30% of responses on average — roughly 0.9 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for window treatment: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Window Treatment questions cover.
The 40 questions behind every percentage on this page were drawn from real window treatment (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 treatment 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 window treatment 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 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 2026-07-06, 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 →