AI SEO Statistics: Basement Waterproofing (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your basement waterproofing 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 basement waterproofing.
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
23.8% 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 basement waterproofing benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 92.5% | 50% | 22.5% | 55% |
| Suggests DIY first | 52.5% | 40% | 22.5% | 38.3% |
| Names specific providers | 2.5% | 5% | 7.5% | 5% |
| Gives price or cost info | 20% | 27.5% | 27.5% | 25% |
| Tells to check reviews | 17.5% | 12.5% | 5% | 11.7% |
| Tells to verify credentials | 25% | 10% | 2.5% | 12.5% |
| Mentions case studies / portfolio | 22.5% | 5% | 0% | 9.2% |
| Mentions local proximity | 25% | 12.5% | 7.5% | 15% |
| Gives selection criteria | 35% | 20% | 15% | 23.3% |
| Warns about red flags | 12.5% | 17.5% | 15% | 15% |
| Asks a clarifying question | 72.5% | 75% | 0% | 49.2% |
| Recommends multiple quotes | 30% | 17.5% | 2.5% | 16.7% |
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 | 25% |
| Suggests DIY first | 67.5% |
| Names specific providers | 87.5% |
| Gives price or cost info | 65% |
| Tells to check reviews | 82.5% |
| Tells to verify credentials | 77.5% |
| Mentions case studies / portfolio | 77.5% |
| Mentions local proximity | 80% |
| Gives selection criteria | 62.5% |
| Warns about red flags | 82.5% |
| Asks a clarifying question | 2.5% |
| Recommends multiple quotes | 62.5% |
By model
How each assistant handled Basement Waterproofing questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same basement waterproofing questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 92.5% (ChatGPT) down to 22.5% (Gemini), a 70-point gap on an identical question set.
Across the 40 basement waterproofing answers it produced, ChatGPT recommended hiring a professional in 92.5% of them and suggested a DIY approach first 52.5% of the time. It named a specific provider in 2.5% 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 72.5% of cases, warned about red flags or scams in 12.5%, and told the buyer to verify credentials in 25%, averaging 580 words per answer. On the remaining cues it told the buyer to check reviews in 17.5%, pointed to case studies or a portfolio in 22.5%, and framed the choice around local proximity in 25%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 30%.
Across the 40 basement waterproofing answers it produced, Claude recommended hiring a professional in 50% of them and suggested a DIY approach first 40% 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 27.5% of the time. Claude asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 10%, averaging 313 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 20% of its answers and a recommendation to gather multiple quotes in 17.5%.
Across the 40 basement waterproofing answers it produced, Gemini recommended hiring a professional in 22.5% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 7.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 27.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 2.5%, averaging 287 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 2.5%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching basement waterproofing toward professional help (92.5%) and Gemini the least (22.5%). ChatGPT produced the longest answers, at 580 words on average. Specific providers were named most often by Gemini (7.5%). Even there, roughly one answer in 13 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 23.8%. 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 the choice of assistant matters most for a buyer researching basement waterproofing:
- Asks a clarifying question: from 0% (Gemini) to 75% (Claude). The spread is 75 points.
- Recommends hiring a professional: from 22.5% (Gemini) to 92.5% (ChatGPT). The spread is 70 points.
- Suggests a DIY approach first: from 22.5% (Gemini) to 52.5% (ChatGPT). The spread is 30 points.
- Recommends multiple quotes: from 2.5% (Gemini) to 30% (ChatGPT). The spread is 28 points.
- Tells the buyer to verify credentials: from 2.5% (Gemini) to 25% (ChatGPT). The spread is 23 points.
The widest single gap concerns asks a clarifying question at 75 points. This means a buyer researching basement waterproofing 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 basement waterproofing market.
Where they agree
The points of near-consensus in Basement Waterproofing.
On other behaviors the three models move almost in lockstep. The points of near-consensus for basement waterproofing, where all three landed within a few points of each other:
- Names a specific provider: 2.5%–7.5% across all three (a 5-point spread).
- Warns about red flags or scams: 12.5%–17.5% across all three (a 5-point spread).
- Gives price or cost information: 20%–27.5% across all three (a 8-point spread).
- Tells the buyer to check reviews: 5%–17.5% 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 87.5% of questions) and least consistently on "asks a clarifying question" (2.5%).
Every behavior, measured
All twelve coded behaviors for Basement Waterproofing, averaged across the three models.
The behaviors AI models reproduce most often for basement waterproofing are recommends hiring a professional (55% on average), asks a clarifying question (49.2%) and suggests a DIY approach first (38.3%); the rarest are names a specific provider (5%), mentions case studies or portfolio (9.2%) and tells the buyer to check reviews (11.7%). 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: 55% on average (ChatGPT 92.5%, Claude 50%, Gemini 22.5%). The spread is 70 points.
- Asks a clarifying question: 49.2% on average (ChatGPT 72.5%, Claude 75%, Gemini 0%). The spread is 75 points.
- Suggests a DIY approach first: 38.3% on average (ChatGPT 52.5%, Claude 40%, Gemini 22.5%). The spread is 30 points.
- Gives price or cost information: 25% on average (ChatGPT 20%, Claude 27.5%, Gemini 27.5%). The spread is 8 points.
- Gives selection criteria: 23.3% on average (ChatGPT 35%, Claude 20%, Gemini 15%). The spread is 20 points.
- Recommends multiple quotes: 16.7% on average (ChatGPT 30%, Claude 17.5%, Gemini 2.5%). The spread is 28 points.
- Mentions local proximity: 15% on average (ChatGPT 25%, Claude 12.5%, Gemini 7.5%). The spread is 18 points.
- Warns about red flags or scams: 15% on average (ChatGPT 12.5%, Claude 17.5%, Gemini 15%). The spread is 5 points.
- Tells the buyer to verify credentials: 12.5% on average (ChatGPT 25%, Claude 10%, Gemini 2.5%). The spread is 23 points.
- Tells the buyer to check reviews: 11.7% on average (ChatGPT 17.5%, Claude 12.5%, Gemini 5%). The spread is 13 points.
- Mentions case studies or portfolio: 9.2% on average (ChatGPT 22.5%, Claude 5%, Gemini 0%). The spread is 23 points.
- Names a specific provider: 5% on average (ChatGPT 2.5%, Claude 5%, Gemini 7.5%). The spread is 5 points.
Trust signals
How well the models protect the basement waterproofing buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the basement waterproofing buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.7% of answers on average. Verifying credentials or certifications appeared in 12.5%. Warning about red flags or scams appeared in 15%.
On structuring the decision, a selection-criteria checklist showed up in 23.3% of answers on average and a recommendation to gather multiple quotes in 16.7%. The single least-reproduced protective signal for basement waterproofing is "tells the buyer to check reviews" at 11.7% 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 Basement Waterproofing providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 basement waterproofing answers, a specific provider was named in 5% of responses on average, or roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for basement waterproofing: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Basement Waterproofing questions cover.
The 40 questions behind every percentage on this page form a frozen basement waterproofing (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 basement waterproofing 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 basement waterproofing 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: Basement Waterproofing (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/basement-waterproofing