Original research · 2026-07 edition

AI SEO Statistics: Services (2026-07 edition)

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

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The question bank

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

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

My kitchen sink is gurgling every time the dishwasher runs, is this a major plumbing issue or something I can clear myself?
Is it safe to replace a light fixture on my own if the house has older wiring, or should I definitely call an electrician?
What specific certifications or insurance documents should I ask to see before hiring a roofing company for a full replacement?
What is the average hourly rate for a handyman in a mid-sized city for small odd jobs like hanging shelves and fixing a door?
Should I keep repairing my 12-year-old HVAC system or is it finally more cost-effective to just replace the whole unit?
Water is leaking from my ceiling right now, what are the immediate steps I should take while waiting for an emergency plumber?
What are some red flags that a home contractor might be overcharging me or planning to cut corners on a bathroom remodel?
I have a $5,000 budget for landscaping; what are the best projects to prioritize to increase curb appeal before I sell my house?
Show all 15 questions
What is the actual difference between a deep clean and a standard clean when I'm looking at professional maid services?
How can I verify if a contractor's license is currently valid and check if they have any recent consumer complaints on file?
I want to paint my two-story foyer; is it worth buying the tall ladders and safety gear myself or just hiring pros for the weekend?
Do most pest control companies require a yearly contract for ants, or is it possible to just pay for a one-time treatment?
My AC stopped working during a heatwave; how do I find a technician who offers 24/7 service without charging a massive emergency premium?
There's a weird musty smell in my basement after it rains, should I call a mold remediation specialist or a foundation expert first?
What specific questions should I ask during a walkthrough with a flooring contractor to ensure they'll handle the subfloor prep correctly?

Model by model

26-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 services buyers.

Behavior rates across 15 services buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%53%53%33%
Suggests DIY first33%20%27%80%
Names specific providers7%0%7%87%
Gives price or cost info27%40%40%67%
Tells to check reviews20%20%0%67%
Tells to verify credentials27%13%0%67%
Mentions case studies / portfolio13%13%0%73%
Mentions local proximity27%13%0%73%
Gives selection criteria47%33%20%47%
Warns about red flags13%33%20%53%
Asks a clarifying question53%40%0%33%
Recommends multiple quotes20%27%0%60%

By model

How each assistant handled Services questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same services 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 53.3% (Claude), a 33-point gap on an identical question set.

Across the 15 services answers it produced, ChatGPT recommended hiring a professional in 86.7% 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 26.7% of the time. ChatGPT asked a clarifying question before answering in 53.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 530 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 services answers it produced, Claude recommended hiring a professional in 53.3% 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. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 13.3%, averaging 318 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 services answers it produced, Gemini recommended hiring a professional in 53.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% 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 20%, and told the buyer to verify credentials in 0%, averaging 252 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 20% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a services buyer to a professional (86.7%) and Claude the least (53.3%). ChatGPT produced the longest answers, at 530 words on average. Specific providers were named most often by ChatGPT (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 25.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a services buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT) — a 53-point spread.
  • Recommends hiring a professional: from 53.3% (Claude) to 86.7% (ChatGPT) — a 33-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
  • Mentions local proximity: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
  • Gives selection criteria: from 20% (Gemini) to 46.7% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 53 points — means a services 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 services market.

Where they agree

The points of near-consensus in Services.

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

  • Names a specific provider: 0%–6.7% across all three (a 7-point spread).
  • Suggests a DIY approach first: 20%–33.3% across all three (a 13-point spread).
  • Gives price or cost information: 26.7%–40% across all three (a 13-point spread).
  • Mentions case studies or portfolio: 0%–13.3% across all three (a 13-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 86.7% of questions) and least consistently on "asks a clarifying question" (33.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for services are recommends hiring a professional (64.4% on average), gives price or cost information (35.6%) and gives selection criteria (33.3%); the rarest are names a specific provider (4.5%), mentions case studies or portfolio (8.9%) and mentions local proximity (13.3%). 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: 64.4% on average (ChatGPT 86.7%, Claude 53.3%, Gemini 53.3%) — a 33-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: 33.3% on average (ChatGPT 46.7%, Claude 33.3%, Gemini 20%) — a 27-point spread.
  • Asks a clarifying question: 31.1% on average (ChatGPT 53.3%, Claude 40%, Gemini 0%) — a 53-point spread.
  • Suggests a DIY approach first: 26.7% on average (ChatGPT 33.3%, Claude 20%, Gemini 26.7%) — a 13-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 20%) — a 20-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 20%, Claude 26.7%, Gemini 0%) — a 27-point spread.
  • Tells the buyer to check reviews: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Tells the buyer to verify credentials: 13.3% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 0%) — a 27-point spread.
  • Mentions local proximity: 13.3% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 0%) — a 27-point spread.
  • Mentions case studies or portfolio: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Names a specific provider: 4.5% on average (ChatGPT 6.7%, Claude 0%, Gemini 6.7%) — a 7-point spread.

Trust signals

How well the models protect the services buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the services buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 13.3% of answers on average. Verifying credentials or certifications appeared in 13.3%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for services is "tells the buyer to check reviews" at 13.3% 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 Services providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 services answers, a specific provider was named in 4.5% 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 services: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Services questions cover.

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