Original research · 2026-07 edition

AI SEO Statistics: Window Door Installers (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in window door installers.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

Why is there condensation between my window panes and can it be fixed without replacing the whole thing?
How much does it usually cost to replace 10 double-hung vinyl windows in a standard suburban home?
Is it worth paying extra for triple-pane windows if I live in a climate with mild winters?
What are the main signs that a front door needs to be replaced rather than just repainted or weather-stripped?
How do I know if a window installer is actually licensed and insured in my state?
What's the difference between a full-frame replacement and a pocket window installation?
Can I change the size of my windows during a replacement project or is that too expensive?
How long does it typically take a crew to replace all the windows in a 2,000-square-foot house?
Show all 40 questions
What are the red flags I should look for when a window salesman comes to my house for a quote?
Is it cheaper to buy windows from a big box store and hire a handyman or go through a specialized window company?
My sliding glass door is hard to open; is it better to repair the track or just get a new door?
What kind of warranty should I expect on labor for a professional window installation?
Does replacing old wood windows with vinyl actually increase the resale value of my home?
How much of a mess does window replacement make inside the house and how should I prepare?
Are black window frames just a trend or are they a good long-term investment for curb appeal?
What's the best time of year to schedule window replacement to get the best deals?
How do I compare two quotes that have vastly different prices for what seems like the same window?
What are the pros and cons of fiberglass versus vinyl frames for a coastal home?
Can I replace just the front door and keep the existing frame to save money?
Why do some window companies refuse to give a price over the phone without a home visit?
What permits are usually required for installing a new egress window in a basement?
Is it normal for window installers to ask for a 50% deposit before the materials even arrive?
My windows are drafty but the glass is fine; can I just replace the seals or do I need new units?
How do I choose between a sliding patio door and French doors for a small living room?
What is the U-factor and why does it matter when I'm looking at window energy ratings?
Are there any tax credits or rebates available right now for installing Energy Star certified windows?
What should I do if my new windows were installed and now I see gaps or light coming through the edges?
How much more does it cost to install a bay window compared to a standard flat window?
Do I need to be home the entire time the crew is installing my new doors and windows?
What's the average lead time from ordering custom windows to the actual installation day?
Can I install a storm door myself or is it easy to mess up the alignment?
How can I tell if a window company uses their own employees or just hires random subcontractors?
What are the best soundproofing windows if I live near a busy highway?
Is it possible to replace windows in the middle of winter without freezing out the whole house?
Why is my front door sticking at the top every time it rains?
What's the difference between low-E glass and regular glass and is the price jump worth it?
How do I handle a dispute with a window installer who damaged my siding during the job?
Are there specific window types that are known for being better for high-wind or hurricane zones?
Should I replace all my windows at once or is it okay to do them one side of the house at a time?
What questions should I ask about the caulking and insulation techniques they use during installation?

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 window door installers buyers.

Behavior rates across 40 window door installers buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional70%40%15%35%
Suggests DIY first18%13%10%93%
Names specific providers0%5%8%90%
Gives price or cost info30%33%25%68%
Tells to check reviews18%15%0%80%
Tells to verify credentials15%20%5%85%
Mentions case studies / portfolio10%5%0%90%
Mentions local proximity35%28%3%55%
Gives selection criteria53%43%18%40%
Warns about red flags8%18%10%83%
Asks a clarifying question58%60%0%20%
Recommends multiple quotes30%18%0%63%

By model

How each assistant handled Window Door Installers questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same window door installers questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 70% (ChatGPT) down to 15% (Gemini), a 55-point gap on an identical question set.

Across the 40 window door installers answers it produced, ChatGPT recommended hiring a professional in 70% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 30% of the time. ChatGPT asked a clarifying question before answering in 57.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 15%, averaging 514 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 35%; a selection-criteria checklist appeared in 52.5% of its answers and a recommendation to gather multiple quotes in 30%.

Across the 40 window door installers answers it produced, Claude recommended hiring a professional in 40% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 32.5% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 20%, averaging 293 words per answer. On the remaining cues it told the buyer to check reviews in 15%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 27.5%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 17.5%.

Across the 40 window door installers answers it produced, Gemini recommended hiring a professional in 15% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 7.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 25% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 5%, averaging 289 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 2.5%; a selection-criteria checklist appeared in 17.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a window door installers buyer to a professional (70%) and Gemini the least (15%). ChatGPT produced the longest answers, at 514 words on average. Specific providers were named most often by Gemini (7.5%) — even there, roughly one answer in 13 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 22.2 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a window door installers buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
  • Recommends hiring a professional: from 15% (Gemini) to 70% (ChatGPT) — a 55-point spread.
  • Gives selection criteria: from 17.5% (Gemini) to 52.5% (ChatGPT) — a 35-point spread.
  • Mentions local proximity: from 2.5% (Gemini) to 35% (ChatGPT) — a 33-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 30% (ChatGPT) — a 30-point spread.

The widest single gap — asks a clarifying question, 60 points — means a window door installers 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 door installers market.

Where they agree

The points of near-consensus in Window Door Installers.

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

  • Suggests a DIY approach first: 10%–17.5% across all three (a 8-point spread).
  • Names a specific provider: 0%–7.5% across all three (a 8-point spread).
  • Gives price or cost information: 25%–32.5% across all three (a 8-point spread).
  • Mentions case studies or portfolio: 0%–10% across all three (a 10-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 92.5% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

All twelve coded behaviors for Window Door Installers, averaged across the three models.

The behaviors AI models reproduce most often for window door installers are recommends hiring a professional (41.7% on average), asks a clarifying question (39.2%) and gives selection criteria (37.5%); the rarest are names a specific provider (4.2%), mentions case studies or portfolio (5%) and tells the buyer to check reviews (10.8%). 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:

  • Recommends hiring a professional: 41.7% on average (ChatGPT 70%, Claude 40%, Gemini 15%) — a 55-point spread.
  • Asks a clarifying question: 39.2% on average (ChatGPT 57.5%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Gives selection criteria: 37.5% on average (ChatGPT 52.5%, Claude 42.5%, Gemini 17.5%) — a 35-point spread.
  • Gives price or cost information: 29.2% on average (ChatGPT 30%, Claude 32.5%, Gemini 25%) — a 8-point spread.
  • Mentions local proximity: 21.7% on average (ChatGPT 35%, Claude 27.5%, Gemini 2.5%) — a 33-point spread.
  • Recommends multiple quotes: 15.8% on average (ChatGPT 30%, Claude 17.5%, Gemini 0%) — a 30-point spread.
  • Suggests a DIY approach first: 13.3% on average (ChatGPT 17.5%, Claude 12.5%, Gemini 10%) — a 8-point spread.
  • Tells the buyer to verify credentials: 13.3% on average (ChatGPT 15%, Claude 20%, Gemini 5%) — a 15-point spread.
  • Warns about red flags or scams: 11.7% on average (ChatGPT 7.5%, Claude 17.5%, Gemini 10%) — a 10-point spread.
  • Tells the buyer to check reviews: 10.8% on average (ChatGPT 17.5%, Claude 15%, Gemini 0%) — a 18-point spread.
  • Mentions case studies or portfolio: 5% on average (ChatGPT 10%, Claude 5%, Gemini 0%) — a 10-point spread.
  • Names a specific provider: 4.2% on average (ChatGPT 0%, Claude 5%, Gemini 7.5%) — a 8-point spread.

Trust signals

How well the models protect the window door installers buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the window door installers buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 10.8% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 11.7%.

On structuring the decision, a selection-criteria checklist showed up in 37.5% of answers on average and a recommendation to gather multiple quotes in 15.8%. The single least-reproduced protective signal for window door installers is "tells the buyer to check reviews" at 10.8% 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 Door Installers providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 window door installers answers, a specific provider was named in 4.2% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for window door installers: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Window Door Installers questions cover.

The 40 questions behind every percentage on this page were drawn from real window door installers (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 door installers 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 door installers 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 →