Original research · 2026-07 edition

AI SEO Statistics: Gutter Contractor (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in gutter contractor.

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

Why is water overflowing from the middle of my gutters even when they aren't clogged?
Is it worth paying extra for seamless gutters or are the store-bought sections okay for a DIY project?
How much should I expect to pay for a full gutter replacement on a 2,500 square foot ranch style home?
What are the warning signs that my gutters are actually failing versus just needing a good cleaning?
I have huge oak trees overhanging my roof; which type of gutter guards actually handle heavy debris without clogging?
How do I know if a gutter contractor is properly insured and licensed in my state?
Can sagging gutters be repaired or do I have to replace the entire run?
What is the typical warranty period for professional gutter installation labor and materials?
Show all 15 questions
My basement is getting damp after it rains; could my downspouts be the problem even if they look fine?
Should I get 5-inch or 6-inch gutters if I live in an area with very heavy seasonal rainfall?
What questions should I ask during a gutter replacement estimate to make sure I'm not getting ripped off?
Are copper gutters worth the investment for a historic home or is it just for aesthetics?
How long does a professional crew usually take to replace all the gutters on a standard two-story house?
If my fascia boards are rotting, can a gutter company fix that or do I need a separate carpenter first?
Is it cheaper to install gutters in the winter or do prices stay the same year-round?

Model by model

24-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 gutter contractor buyers.

Behavior rates across 15 gutter contractor buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%60%27%20%
Suggests DIY first27%33%20%87%
Names specific providers0%7%13%87%
Gives price or cost info13%20%7%67%
Tells to check reviews13%13%0%80%
Tells to verify credentials33%13%7%73%
Mentions case studies / portfolio27%0%0%73%
Mentions local proximity40%33%0%47%
Gives selection criteria53%47%27%53%
Warns about red flags7%7%13%93%
Asks a clarifying question73%53%0%13%
Recommends multiple quotes13%33%7%73%

By model

How each assistant handled Gutter Contractor questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same gutter contractor questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 26.7% (Gemini), a 60-point gap on an identical question set.

Across the 15 gutter contractor answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 33.3%, averaging 448 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 26.7%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 gutter contractor answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 294 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 33.3%.

Across the 15 gutter contractor answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 13.3% of answers (about 0.3 distinct providers per answer) and included price or cost information 6.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 295 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 0%; 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 gutter contractor buyer to a professional (86.7%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 448 words on average. Specific providers were named most often by Gemini (13.3%) — even there, roughly one answer in 8 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 24.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a gutter contractor buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Recommends hiring a professional: from 26.7% (Gemini) to 86.7% (ChatGPT) — a 60-point spread.
  • Mentions local proximity: from 0% (Gemini) to 40% (ChatGPT) — a 40-point spread.
  • Mentions case studies or portfolio: from 0% (Claude) to 26.7% (ChatGPT) — a 27-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 33.3% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 73 points — means a gutter contractor 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 gutter contractor market.

Where they agree

The points of near-consensus in Gutter Contractor.

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

  • Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
  • Suggests a DIY approach first: 20%–33.3% across all three (a 13-point spread).
  • Names a specific provider: 0%–13.3% across all three (a 13-point spread).
  • Gives price or cost information: 6.7%–20% across all three (a 13-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 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).

Every behavior, measured

All twelve coded behaviors for Gutter Contractor, averaged across the three models.

The behaviors AI models reproduce most often for gutter contractor are recommends hiring a professional (57.8% on average), gives selection criteria (42.2%) and asks a clarifying question (42.2%); the rarest are names a specific provider (6.7%), warns about red flags or scams (8.9%) and mentions case studies or portfolio (8.9%). Each figure below is the share of a model's 15 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: 57.8% on average (ChatGPT 86.7%, Claude 60%, Gemini 26.7%) — a 60-point spread.
  • Gives selection criteria: 42.2% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 26.7%) — a 27-point spread.
  • Asks a clarifying question: 42.2% on average (ChatGPT 73.3%, Claude 53.3%, Gemini 0%) — a 73-point spread.
  • Suggests a DIY approach first: 26.7% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 20%) — a 13-point spread.
  • Mentions local proximity: 24.4% on average (ChatGPT 40%, Claude 33.3%, Gemini 0%) — a 40-point spread.
  • Tells the buyer to verify credentials: 17.8% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 6.7%) — a 27-point spread.
  • Recommends multiple quotes: 17.8% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 6.7%) — a 27-point spread.
  • Gives price or cost information: 13.3% on average (ChatGPT 13.3%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 8.9% on average (ChatGPT 26.7%, Claude 0%, Gemini 0%) — a 27-point spread.
  • Warns about red flags or scams: 8.9% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 0%, Claude 6.7%, Gemini 13.3%) — a 13-point spread.

Trust signals

How well the models protect the gutter contractor buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the gutter contractor 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 17.8%. Warning about red flags or scams appeared in 8.9%.

On structuring the decision, a selection-criteria checklist showed up in 42.2% of answers on average and a recommendation to gather multiple quotes in 17.8%. The single least-reproduced protective signal for gutter contractor 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.

Referral behavior

Do AI models name Gutter Contractor providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 gutter contractor answers, a specific provider was named in 6.7% 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 gutter contractor: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Gutter Contractor questions cover.

The 15 questions behind every percentage on this page were drawn from real gutter contractor (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 gutter contractor 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 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific gutter contractor 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.

15 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 2026-07-04, 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 →