Original research · 2026-07 edition

AI SEO Statistics: Carpet Fitters (2026-07 edition)

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

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The question bank

The questions we tested — sampled from real buyer journeys in carpet fitters.

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

How much does it cost to fit carpet in a 3-bedroom house including the landing?
Is it cheaper to buy carpet and underlay myself or get it through the fitter?
What are the signs that my carpet underlay actually needs replacing or can I reuse it?
Can a carpet fitter move heavy furniture like wardrobes or do I have to clear the room first?
How do I fix a bump or ripple in a carpet that was installed a few years ago?
Is it possible to lay new carpet over an old one to save money on removal costs?
What is the average day rate for a professional carpet installer in my area?
How long does it take to carpet a standard living room and a hallway?
Show all 40 questions
Should I get the skirting boards painted before or after the new carpet goes in?
What should I look for in a carpet fitting quote to make sure there are no hidden fees?
Can carpet fitters also trim the bottom of doors if they don't clear the new thick pile?
Do I need to remove the old carpet and grippers myself before the fitter arrives?
How do I tell if a carpet fitter is actually qualified or just a handyman doing it on the side?
What is the best type of underlay for a high-traffic staircase to prevent wear?
Why is my new carpet shedding so much fluff right after installation and is that normal?
Is it worth paying extra for felt-backed carpet or does it still need a separate underlay?
How do I find a carpet fitter who can come on short notice for a rental move-out?
What are the red flags when hiring someone to lay a carpet runner on stairs?
Can a carpet be stretched back into place if it has gone loose near the edges?
Do carpet fitters usually take away the old carpet scraps and waste for disposal?
What is the difference between a power stretcher and a knee kicker for carpet fitting?
How much extra should I expect to pay for fitting carpet on a winding staircase versus a straight one?
Can I hire a fitter just to do the labor if I bought the carpet from an online clearance site?
What should I do if the carpet fitter accidentally damaged my baseboards during the install?
Is there a specific type of carpet that is better for homes with big dogs and heavy claws?
How do I measure a room accurately so I do not buy too much carpet and waste money?
Will a carpet fitter work on a Sunday or are they strictly Monday to Friday businesses?
Are there any local carpet fitters who specialize in natural fibers like sisal or seagrass?
What is the standard warranty or guarantee period for carpet installation labor?
Can I get a carpet repaired if there is a permanent burn mark or a deep stain in the middle?
How much does a carpet fitter charge per square meter versus a flat room rate?
Do I need a special type of underlay if I have water-based underfloor heating?
Is it difficult to install carpet tiles myself compared to a full roll of broadloom?
What happens if the carpet I ordered is slightly too small for the room once they start cutting?
Why is there a visible seam in the middle of my new carpet and can it be hidden better?
Should I tip my carpet fitter and if so how much is considered normal for a day's work?
Can a carpet fitter install carpet over a concrete floor that feels slightly damp?
How do I get rid of the strong chemical new carpet smell after it has been fitted?
What are the pros and cons of using spray adhesive versus traditional grippers?
Can a pro fix a carpet that was pulled up by a plumber to get to some leaking pipes?

Model by model

20-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 carpet fitters buyers.

Behavior rates across 40 carpet fitters buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional68%60%23%43%
Suggests DIY first20%15%28%78%
Names specific providers3%5%15%88%
Gives price or cost info28%20%30%68%
Tells to check reviews10%10%0%85%
Tells to verify credentials15%8%3%88%
Mentions case studies / portfolio10%5%0%90%
Mentions local proximity30%18%20%65%
Gives selection criteria43%35%20%58%
Warns about red flags5%8%3%93%
Asks a clarifying question70%58%3%20%
Recommends multiple quotes23%30%0%68%

By model

How each assistant handled Carpet Fitters questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same carpet fitters questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 67.5% (ChatGPT) down to 22.5% (Gemini), a 45-point gap on an identical question set.

Across the 40 carpet fitters answers it produced, ChatGPT recommended hiring a professional in 67.5% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 2.5% of answers (about 0.2 distinct providers per answer) and included price or cost information 27.5% of the time. ChatGPT asked a clarifying question before answering in 70% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 15%, averaging 444 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 10%, and framed the choice around local proximity in 30%; a selection-criteria checklist appeared in 42.5% of its answers and a recommendation to gather multiple quotes in 22.5%.

Across the 40 carpet fitters answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 20% of the time. Claude 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 7.5%, averaging 273 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 17.5%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 30%.

Across the 40 carpet fitters answers it produced, Gemini recommended hiring a professional in 22.5% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 15% of answers (about 0.4 distinct providers per answer) and included price or cost information 30% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 2.5%, averaging 291 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 20%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a carpet fitters buyer to a professional (67.5%) and Gemini the least (22.5%). ChatGPT produced the longest answers, at 444 words on average. Specific providers were named most often by Gemini (15%) — even there, roughly one answer in 7 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a carpet fitters buyer happens to ask matters most:

  • Asks a clarifying question: from 2.5% (Gemini) to 70% (ChatGPT) — a 68-point spread.
  • Recommends hiring a professional: from 22.5% (Gemini) to 67.5% (ChatGPT) — a 45-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 30% (Claude) — a 30-point spread.
  • Gives selection criteria: from 20% (Gemini) to 42.5% (ChatGPT) — a 23-point spread.
  • Suggests a DIY approach first: from 15% (Claude) to 27.5% (Gemini) — a 13-point spread.

The widest single gap — asks a clarifying question, 68 points — means a carpet fitters 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 carpet fitters market.

Where they agree

The points of near-consensus in Carpet Fitters.

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

  • Warns about red flags or scams: 2.5%–7.5% across all three (a 5-point spread).
  • Gives price or cost information: 20%–30% across all three (a 10-point spread).
  • Tells the buyer to check reviews: 0%–10% across all three (a 10-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 "warns about red flags or scams" (identical coding in 92.5% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

All twelve coded behaviors for Carpet Fitters, averaged across the three models.

The behaviors AI models reproduce most often for carpet fitters are recommends hiring a professional (50% on average), asks a clarifying question (43.3%) and gives selection criteria (32.5%); the rarest are warns about red flags or scams (5%), mentions case studies or portfolio (5%) and tells the buyer to check reviews (6.7%). 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: 50% on average (ChatGPT 67.5%, Claude 60%, Gemini 22.5%) — a 45-point spread.
  • Asks a clarifying question: 43.3% on average (ChatGPT 70%, Claude 57.5%, Gemini 2.5%) — a 68-point spread.
  • Gives selection criteria: 32.5% on average (ChatGPT 42.5%, Claude 35%, Gemini 20%) — a 23-point spread.
  • Gives price or cost information: 25.8% on average (ChatGPT 27.5%, Claude 20%, Gemini 30%) — a 10-point spread.
  • Mentions local proximity: 22.5% on average (ChatGPT 30%, Claude 17.5%, Gemini 20%) — a 13-point spread.
  • Suggests a DIY approach first: 20.8% on average (ChatGPT 20%, Claude 15%, Gemini 27.5%) — a 13-point spread.
  • Recommends multiple quotes: 17.5% on average (ChatGPT 22.5%, Claude 30%, Gemini 0%) — a 30-point spread.
  • Tells the buyer to verify credentials: 8.3% on average (ChatGPT 15%, Claude 7.5%, Gemini 2.5%) — a 13-point spread.
  • Names a specific provider: 7.5% on average (ChatGPT 2.5%, Claude 5%, Gemini 15%) — a 13-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 10%, Claude 10%, Gemini 0%) — a 10-point spread.
  • Mentions case studies or portfolio: 5% on average (ChatGPT 10%, Claude 5%, Gemini 0%) — a 10-point spread.
  • Warns about red flags or scams: 5% on average (ChatGPT 5%, Claude 7.5%, Gemini 2.5%) — a 5-point spread.

Trust signals

How well the models protect the carpet fitters buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the carpet fitters 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 8.3%. Warning about red flags or scams appeared in 5%.

On structuring the decision, a selection-criteria checklist showed up in 32.5% of answers on average and a recommendation to gather multiple quotes in 17.5%. The single least-reproduced protective signal for carpet fitters is "warns about red flags or scams" at 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 Carpet Fitters providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 carpet fitters answers, a specific provider was named in 7.5% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for carpet fitters: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Carpet Fitters questions cover.

The 40 questions behind every percentage on this page were drawn from real carpet fitters (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 carpet fitters 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 carpet fitters 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 →