Original research · 2026-07 edition

AI SEO Statistics: Plumber (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 plumber.

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

My kitchen sink is draining really slow and I've already tried a plunger, what's the next step before calling someone?
Is it worth paying extra for a master plumber or is a journeyman fine for a simple faucet install?
How much should I expect to pay for an emergency call-out on a Sunday night for a burst pipe?
What are the warning signs that my main sewer line is backed up instead of just a local clog?
I have a $2,000 budget to upgrade my guest bathroom fixtures, will that cover labor and mid-range parts?
Can a plumber help with a gas leak or do I need to call the utility company first?
What questions should I ask a plumbing company to make sure they're actually licensed and insured?
Why does my water heater keep making a popping noise and is it about to explode?
Show all 15 questions
I'm buying an old house from the 1950s, what plumbing red flags should I look for during the walkthrough?
Should I get a tankless water heater if I have a family of five, or will we run out of hot water?
Is it cheaper to repipe the whole house at once or just fix leaks as they happen?
My garbage disposal is humming but not turning, is that a DIY fix or a professional job?
How do I compare two plumbing quotes when one is a flat rate and the other is hourly?
What's the average lifespan of a sump pump and how do I know if mine is failing?
Can a plumber move a toilet about three feet to the left during a remodel, or is that too expensive?

Model by model

17-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 plumber buyers.

Behavior rates across 15 plumber buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional100%87%27%27%
Suggests DIY first47%40%20%73%
Names specific providers0%0%0%100%
Gives price or cost info27%40%40%67%
Tells to check reviews13%13%0%87%
Tells to verify credentials33%20%13%80%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity20%20%7%80%
Gives selection criteria27%33%27%80%
Warns about red flags13%20%13%93%
Asks a clarifying question60%53%0%27%
Recommends multiple quotes13%7%0%87%

By model

How each assistant handled Plumber questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same plumber questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 100% (ChatGPT) down to 26.7% (Gemini), a 73-point gap on an identical question set.

Across the 15 plumber answers it produced, ChatGPT recommended hiring a professional in 100% of them and suggested a DIY approach first 46.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 26.7% of the time. ChatGPT asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 33.3%, averaging 501 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 20%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 plumber answers it produced, Claude recommended hiring a professional in 86.7% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 0% of answers (about 0 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 20%, and told the buyer to verify credentials in 20%, averaging 297 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 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 plumber 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 0% of answers (about 0 distinct providers per answer) and included price or cost information 40% 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 13.3%, averaging 265 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 0%.

Taken together, ChatGPT is the assistant most likely to route a plumber buyer to a professional (100%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 501 words on average. No model named a specific provider in more than 0% of answers.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 16.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a plumber buyer happens to ask matters most:

  • Recommends hiring a professional: from 26.7% (Gemini) to 100% (ChatGPT) — a 73-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 60% (ChatGPT) — a 60-point spread.
  • Suggests a DIY approach first: from 20% (Gemini) to 46.7% (ChatGPT) — a 27-point spread.
  • Tells the buyer to verify credentials: from 13.3% (Gemini) to 33.3% (ChatGPT) — a 20-point spread.
  • Gives price or cost information: from 26.7% (ChatGPT) to 40% (Claude) — a 13-point spread.

The widest single gap — recommends hiring a professional, 73 points — means a plumber 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 plumber market.

Where they agree

The points of near-consensus in Plumber.

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

  • Names a specific provider: 0% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Gives selection criteria: 26.7%–33.3% across all three (a 7-point spread).
  • Warns about red flags or scams: 13.3%–20% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "names a specific provider" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for plumber are recommends hiring a professional (71.1% on average), asks a clarifying question (37.8%) and suggests a DIY approach first (35.6%); the rarest are mentions case studies or portfolio (0%), names a specific provider (0%) and recommends multiple quotes (6.7%). 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: 71.1% on average (ChatGPT 100%, Claude 86.7%, Gemini 26.7%) — a 73-point spread.
  • Asks a clarifying question: 37.8% on average (ChatGPT 60%, Claude 53.3%, Gemini 0%) — a 60-point spread.
  • Suggests a DIY approach first: 35.6% on average (ChatGPT 46.7%, Claude 40%, Gemini 20%) — a 27-point spread.
  • Gives price or cost information: 35.6% on average (ChatGPT 26.7%, Claude 40%, Gemini 40%) — a 13-point spread.
  • Gives selection criteria: 28.9% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 26.7%) — a 7-point spread.
  • Tells the buyer to verify credentials: 22.2% on average (ChatGPT 33.3%, Claude 20%, Gemini 13.3%) — a 20-point spread.
  • Mentions local proximity: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Warns about red flags or scams: 15.5% on average (ChatGPT 13.3%, Claude 20%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the plumber buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the plumber 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 22.2%. Warning about red flags or scams appeared in 15.5%.

On structuring the decision, a selection-criteria checklist showed up in 28.9% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for plumber is "recommends multiple quotes" at 6.7% 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 Plumber providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 plumber answers, a specific provider was named in 0% of responses on average — roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for plumber: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Plumber questions cover.

The 15 questions behind every percentage on this page were drawn from real plumber (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 plumber 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 plumber 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 →