AI SEO Statistics: Concrete Contractor (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
Apply these findings to your concrete contractor SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for concrete contractor.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 15 questions
Model by model
24.4% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 15 concrete contractor benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 73.3% | 66.7% | 26.7% | 55.6% |
| Suggests DIY first | 13.3% | 13.3% | 13.3% | 13.3% |
| Names specific providers | 0% | 0% | 6.7% | 2.2% |
| Gives price or cost info | 20% | 33.3% | 26.7% | 26.7% |
| Tells to check reviews | 6.7% | 6.7% | 0% | 4.5% |
| Tells to verify credentials | 33.3% | 13.3% | 6.7% | 17.8% |
| Mentions case studies / portfolio | 33.3% | 13.3% | 0% | 15.5% |
| Mentions local proximity | 33.3% | 26.7% | 6.7% | 22.2% |
| Gives selection criteria | 66.7% | 46.7% | 26.7% | 46.7% |
| Warns about red flags | 33.3% | 33.3% | 26.7% | 31.1% |
| Asks a clarifying question | 80% | 40% | 0% | 40% |
| Recommends multiple quotes | 26.7% | 20% | 0% | 15.6% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 15 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 33.3% |
| Suggests DIY first | 100% |
| Names specific providers | 93.3% |
| Gives price or cost info | 66.7% |
| Tells to check reviews | 86.7% |
| Tells to verify credentials | 66.7% |
| Mentions case studies / portfolio | 66.7% |
| Mentions local proximity | 46.7% |
| Gives selection criteria | 40% |
| Warns about red flags | 73.3% |
| Asks a clarifying question | 13.3% |
| Recommends multiple quotes | 73.3% |
By model
How each assistant handled Concrete Contractor questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same concrete contractor questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 73.3% (ChatGPT) down to 26.7% (Gemini), a 47-point gap on an identical question set.
Across the 15 concrete contractor answers it produced, ChatGPT recommended hiring a professional in 73.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 80% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 33.3%, averaging 543 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 33.3%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 26.7%.
Across the 15 concrete contractor answers it produced, Claude recommended hiring a professional in 66.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 33.3% 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 326 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, 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 concrete contractor answers it produced, Gemini recommended hiring a professional in 26.7% of them and suggested a DIY approach first 13.3% 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. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 6.7%, averaging 271 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 concrete contractor buyer to a professional (73.3%) and Gemini the least (26.7%). ChatGPT produced the longest answers, at 543 words on average. Specific providers were named most often by Gemini (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.
Question-level model disagreement is 24.4%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where which assistant a concrete contractor buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 80% (ChatGPT). The spread is 80 points.
- Recommends hiring a professional: from 26.7% (Gemini) to 73.3% (ChatGPT). The spread is 47 points.
- Gives selection criteria: from 26.7% (Gemini) to 66.7% (ChatGPT). The spread is 40 points.
- Mentions case studies or portfolio: from 0% (Gemini) to 33.3% (ChatGPT). The spread is 33 points.
- Recommends multiple quotes: from 0% (Gemini) to 26.7% (ChatGPT). The spread is 27 points.
The widest single gap concerns asks a clarifying question at 80 points. This means a concrete 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 concrete contractor market.
Where they agree
The points of near-consensus in Concrete Contractor.
On other behaviors the three models move almost in lockstep. The points of near-consensus for concrete contractor, where all three landed within a few points of each other:
- Suggests a DIY approach first: 13.3% across all three models.
- Warns about red flags or scams: 26.7%–33.3% across all three (a 7-point spread).
- Names a specific provider: 0%–6.7% across all three (a 7-point spread).
- Tells the buyer to check reviews: 0%–6.7% across all three (a 7-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 100% of questions) and least consistently on "asks a clarifying question" (13.3%).
Every behavior, measured
All twelve coded behaviors for Concrete Contractor, averaged across the three models.
The behaviors AI models reproduce most often for concrete contractor are recommends hiring a professional (55.6% on average), gives selection criteria (46.7%) and asks a clarifying question (40%); the rarest are names a specific provider (2.2%), tells the buyer to check reviews (4.5%) and suggests a DIY approach first (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: 55.6% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 26.7%). The spread is 47 points.
- Gives selection criteria: 46.7% on average (ChatGPT 66.7%, Claude 46.7%, Gemini 26.7%). The spread is 40 points.
- Asks a clarifying question: 40% on average (ChatGPT 80%, Claude 40%, Gemini 0%). The spread is 80 points.
- Warns about red flags or scams: 31.1% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 26.7%). The spread is 7 points.
- Gives price or cost information: 26.7% on average (ChatGPT 20%, Claude 33.3%, Gemini 26.7%). The spread is 13 points.
- Mentions local proximity: 22.2% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 6.7%). The spread is 27 points.
- Tells the buyer to verify credentials: 17.8% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 6.7%). The spread is 27 points.
- Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%). The spread is 27 points.
- Mentions case studies or portfolio: 15.5% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 0%). The spread is 33 points.
- Suggests a DIY approach first: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
- Tells the buyer to check reviews: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%). The spread is 7 points.
- Names a specific provider: 2.2% on average (ChatGPT 0%, Claude 0%, Gemini 6.7%). The spread is 7 points.
Trust signals
How well the models protect the concrete contractor buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the concrete contractor buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.5% of answers on average. Verifying credentials or certifications appeared in 17.8%. Warning about red flags or scams appeared in 31.1%.
On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for concrete contractor is "tells the buyer to check reviews" at 4.5% on average. This is 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 Concrete Contractor providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 concrete contractor answers, a specific provider was named in 2.2% of responses on average, or roughly 0 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for concrete contractor: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Concrete Contractor questions cover.
The 15 questions behind every percentage on this page form a frozen concrete contractor (home services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact concrete contractor question set rather than 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. It is not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific concrete 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 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-04 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Concrete Contractor (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/concrete-contractor