AI SEO Statistics: Tree Service (2026-07 edition)
15 questions · 45/45 expected AI responses · 3 models · measured 2026-07-04
Apply these findings to your tree service 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 tree service.
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
23% 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 tree service benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 86.7% | 73.3% | 20% | 60% |
| Suggests DIY first | 6.7% | 13.3% | 6.7% | 8.9% |
| Names specific providers | 0% | 0% | 0% | 0% |
| Gives price or cost info | 13.3% | 26.7% | 26.7% | 22.2% |
| Tells to check reviews | 13.3% | 6.7% | 0% | 6.7% |
| Tells to verify credentials | 60% | 26.7% | 13.3% | 33.3% |
| Mentions case studies / portfolio | 0% | 6.7% | 0% | 2.2% |
| Mentions local proximity | 53.3% | 33.3% | 6.7% | 31.1% |
| Gives selection criteria | 40% | 13.3% | 20% | 24.4% |
| Warns about red flags | 13.3% | 6.7% | 20% | 13.3% |
| Asks a clarifying question | 53.3% | 33.3% | 0% | 28.9% |
| 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 | 20% |
| Suggests DIY first | 93.3% |
| Names specific providers | 100% |
| Gives price or cost info | 80% |
| Tells to check reviews | 86.7% |
| Tells to verify credentials | 33.3% |
| Mentions case studies / portfolio | 93.3% |
| Mentions local proximity | 46.7% |
| Gives selection criteria | 46.7% |
| Warns about red flags | 86.7% |
| Asks a clarifying question | 33.3% |
| Recommends multiple quotes | 66.7% |
By model
How each assistant handled Tree Service questions.
Reading the 45 answers model by model shows how differently the three assistants treat the same tree service 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 20% (Gemini), a 67-point gap on an identical question set.
Across the 15 tree service answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% 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 60%, averaging 461 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 53.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 26.7%.
Across the 15 tree service answers it produced, Claude 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 26.7% of the time. Claude asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 26.7%, averaging 276 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 6.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 13.3% of its answers and a recommendation to gather multiple quotes in 20%.
Across the 15 tree service answers it produced, Gemini recommended hiring a professional in 20% of them and suggested a DIY approach first 6.7% 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. 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 13.3%, averaging 289 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 20% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a tree service buyer to a professional (86.7%) and Gemini the least (20%). ChatGPT produced the longest answers, at 461 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.
Question-level model disagreement is 23%. 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 tree service buyer happens to ask matters most:
- Recommends hiring a professional: from 20% (Gemini) to 86.7% (ChatGPT). The spread is 67 points.
- Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT). The spread is 53 points.
- Tells the buyer to verify credentials: from 13.3% (Gemini) to 60% (ChatGPT). The spread is 47 points.
- Mentions local proximity: from 6.7% (Gemini) to 53.3% (ChatGPT). The spread is 47 points.
- Gives selection criteria: from 13.3% (Claude) to 40% (ChatGPT). The spread is 27 points.
The widest single gap concerns recommends hiring a professional at 67 points. This means a tree service 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 tree service market.
Where they agree
The points of near-consensus in Tree Service.
On other behaviors the three models move almost in lockstep. The points of near-consensus for tree service, where all three landed within a few points of each other:
- Names a specific provider: 0% across all three models.
- Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
- Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
- Tells the buyer to check reviews: 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 100% of questions) and least consistently on "recommends hiring a professional" (20%).
Every behavior, measured
All twelve coded behaviors for Tree Service, averaged across the three models.
The behaviors AI models reproduce most often for tree service are recommends hiring a professional (60% on average), tells the buyer to verify credentials (33.3%) and mentions local proximity (31.1%); the rarest are names a specific provider (0%), mentions case studies or portfolio (2.2%) and tells the buyer to check reviews (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: 60% on average (ChatGPT 86.7%, Claude 73.3%, Gemini 20%). The spread is 67 points.
- Tells the buyer to verify credentials: 33.3% on average (ChatGPT 60%, Claude 26.7%, Gemini 13.3%). The spread is 47 points.
- Mentions local proximity: 31.1% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 6.7%). The spread is 47 points.
- Asks a clarifying question: 28.9% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 0%). The spread is 53 points.
- Gives selection criteria: 24.4% on average (ChatGPT 40%, Claude 13.3%, Gemini 20%). The spread is 27 points.
- Gives price or cost information: 22.2% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 26.7%). The spread is 13 points.
- Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%). The spread is 27 points.
- Warns about red flags or scams: 13.3% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 20%). The spread is 13 points.
- Suggests a DIY approach first: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%). The spread is 7 points.
- Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%). The spread is 13 points.
- Mentions case studies or portfolio: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%). The spread is 7 points.
- Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).
Trust signals
How well the models protect the tree service buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the tree service buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 33.3%. Warning about red flags or scams appeared in 13.3%.
On structuring the decision, a selection-criteria checklist showed up in 24.4% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for tree service is "tells the buyer to check reviews" at 6.7% 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 Tree Service providers?
For service providers the decisive question is whether these systems name anyone at all. Across 45 tree service answers, a specific provider was named in 0% 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 tree service: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 15 Tree Service questions cover.
The 15 questions behind every percentage on this page form a frozen tree service (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 tree service 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 tree service 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: Tree Service (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/home/tree-service