AI SEO Statistics: Garage Floor Epoxy (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your garage floor epoxy 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 garage floor epoxy.
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 40 questions
Model by model
14.6% 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 40 garage floor epoxy benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 57.5% | 25% | 15% | 32.5% |
| Suggests DIY first | 15% | 12.5% | 12.5% | 13.3% |
| Names specific providers | 0% | 2.5% | 5% | 2.5% |
| Gives price or cost info | 15% | 7.5% | 12.5% | 11.7% |
| Tells to check reviews | 5% | 2.5% | 0% | 2.5% |
| Tells to verify credentials | 5% | 5% | 2.5% | 4.2% |
| Mentions case studies / portfolio | 2.5% | 7.5% | 2.5% | 4.2% |
| Mentions local proximity | 12.5% | 5% | 7.5% | 8.3% |
| Gives selection criteria | 22.5% | 25% | 12.5% | 20% |
| Warns about red flags | 2.5% | 7.5% | 5% | 5% |
| Asks a clarifying question | 65% | 67.5% | 0% | 44.2% |
| Recommends multiple quotes | 5% | 2.5% | 0% | 2.5% |
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 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 40% |
| Suggests DIY first | 82.5% |
| Names specific providers | 92.5% |
| Gives price or cost info | 87.5% |
| Tells to check reviews | 95% |
| Tells to verify credentials | 92.5% |
| Mentions case studies / portfolio | 92.5% |
| Mentions local proximity | 85% |
| Gives selection criteria | 70% |
| Warns about red flags | 90% |
| Asks a clarifying question | 15% |
| Recommends multiple quotes | 95% |
By model
How each assistant handled Garage Floor Epoxy questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same garage floor epoxy questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 57.5% (ChatGPT) down to 15% (Gemini), a 43-point gap on an identical question set.
Across the 40 garage floor epoxy answers it produced, ChatGPT recommended hiring a professional in 57.5% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 15% of the time. ChatGPT asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 2.5%, and told the buyer to verify credentials in 5%, averaging 507 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 5%.
Across the 40 garage floor epoxy answers it produced, Claude recommended hiring a professional in 25% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 2.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 67.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 5%, averaging 292 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 2.5%.
Across the 40 garage floor epoxy answers it produced, Gemini recommended hiring a professional in 15% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 12.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 2.5%, 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 2.5%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 12.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a garage floor epoxy buyer to a professional (57.5%) and Gemini the least (15%). ChatGPT produced the longest answers, at 507 words on average. Specific providers were named most often by Gemini (5%). Even there, roughly one answer in 20 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 14.6%. 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 garage floor epoxy buyer happens to ask matters most:
- Asks a clarifying question: from 0% (Gemini) to 67.5% (Claude). The spread is 68 points.
- Recommends hiring a professional: from 15% (Gemini) to 57.5% (ChatGPT). The spread is 43 points.
- Gives selection criteria: from 12.5% (Gemini) to 25% (Claude). The spread is 13 points.
- Gives price or cost information: from 7.5% (Claude) to 15% (ChatGPT). The spread is 8 points.
- Mentions local proximity: from 5% (Claude) to 12.5% (ChatGPT). The spread is 8 points.
The widest single gap concerns asks a clarifying question at 68 points. This means a garage floor epoxy 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 garage floor epoxy market.
Where they agree
The points of near-consensus in Garage Floor Epoxy.
On other behaviors the three models move almost in lockstep. The points of near-consensus for garage floor epoxy, where all three landed within a few points of each other:
- Suggests a DIY approach first: 12.5%–15% across all three (a 3-point spread).
- Tells the buyer to verify credentials: 2.5%–5% across all three (a 3-point spread).
- Names a specific provider: 0%–5% across all three (a 5-point spread).
- Tells the buyer to check reviews: 0%–5% across all three (a 5-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (15%).
Every behavior, measured
All twelve coded behaviors for Garage Floor Epoxy, averaged across the three models.
The behaviors AI models reproduce most often for garage floor epoxy are asks a clarifying question (44.2% on average), recommends hiring a professional (32.5%) and gives selection criteria (20%); the rarest are recommends multiple quotes (2.5%), tells the buyer to check reviews (2.5%) and names a specific provider (2.5%). 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:
- Asks a clarifying question: 44.2% on average (ChatGPT 65%, Claude 67.5%, Gemini 0%). The spread is 68 points.
- Recommends hiring a professional: 32.5% on average (ChatGPT 57.5%, Claude 25%, Gemini 15%). The spread is 43 points.
- Gives selection criteria: 20% on average (ChatGPT 22.5%, Claude 25%, Gemini 12.5%). The spread is 13 points.
- Suggests a DIY approach first: 13.3% on average (ChatGPT 15%, Claude 12.5%, Gemini 12.5%). The spread is 3 points.
- Gives price or cost information: 11.7% on average (ChatGPT 15%, Claude 7.5%, Gemini 12.5%). The spread is 8 points.
- Mentions local proximity: 8.3% on average (ChatGPT 12.5%, Claude 5%, Gemini 7.5%). The spread is 8 points.
- Warns about red flags or scams: 5% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 5%). The spread is 5 points.
- Tells the buyer to verify credentials: 4.2% on average (ChatGPT 5%, Claude 5%, Gemini 2.5%). The spread is 3 points.
- Mentions case studies or portfolio: 4.2% on average (ChatGPT 2.5%, Claude 7.5%, Gemini 2.5%). The spread is 5 points.
- Names a specific provider: 2.5% on average (ChatGPT 0%, Claude 2.5%, Gemini 5%). The spread is 5 points.
- Tells the buyer to check reviews: 2.5% on average (ChatGPT 5%, Claude 2.5%, Gemini 0%). The spread is 5 points.
- Recommends multiple quotes: 2.5% on average (ChatGPT 5%, Claude 2.5%, Gemini 0%). The spread is 5 points.
Trust signals
How well the models protect the garage floor epoxy buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the garage floor epoxy buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.5% of answers on average. Verifying credentials or certifications appeared in 4.2%. Warning about red flags or scams appeared in 5%.
On structuring the decision, a selection-criteria checklist showed up in 20% of answers on average and a recommendation to gather multiple quotes in 2.5%. The single least-reproduced protective signal for garage floor epoxy is "tells the buyer to check reviews" at 2.5% 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 Garage Floor Epoxy providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 garage floor epoxy answers, a specific provider was named in 2.5% of responses on average, or roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for garage floor epoxy: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Garage Floor Epoxy questions cover.
The 40 questions behind every percentage on this page form a frozen garage floor epoxy (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 garage floor epoxy 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 40 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific garage floor epoxy 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 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 2026-07-06 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: Garage Floor Epoxy (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/garage-floor-epoxy