Original research · 2026-07 edition

AI SEO Statistics: Tree Service (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 tree service.

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

My maple tree has these weird black spots on the leaves, does it need to be cut down or can it be saved?
Is it cheaper to have a tree removed in the winter or does the price stay the same year-round?
I have a 30-foot pine leaning slightly toward my neighbor's fence, is that an emergency or can it wait?
What's the average cost to remove a medium-sized oak tree and do they usually include hauling away the wood?
Can I just rent a chainsaw and trim the high branches myself or is that too dangerous for a DIYer?
What specific questions should I ask a tree service to make sure they won't damage my lawn with their heavy equipment?
If a tree is on the property line, who is legally responsible for paying to have it trimmed?
I got a quote for $2000 to remove a dead ash tree, does that sound like a fair price or am I getting ripped off?
Show all 15 questions
Do I need to get a permit from the city before I hire someone to take down a large tree in my backyard?
What is the difference between tree pruning and tree topping, and why do some people say topping is bad?
A guy knocked on my door saying my tree looks diseased and offered a discount if I pay cash today, is this a scam?
Does homeowners insurance usually cover the cost of removing a tree that fell during a storm but didn't hit anything?
How do I know if a tree service is actually licensed and insured or if they are just saying they are?
I want to keep the wood for my fireplace after a tree is cut down, will the company give me a discount since they don't have to haul it?
How close to my house's foundation can a large tree be before the roots start causing structural problems?

Model by model

23-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 tree service buyers.

Behavior rates across 15 tree service buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%73%20%20%
Suggests DIY first7%13%7%93%
Names specific providers0%0%0%100%
Gives price or cost info13%27%27%80%
Tells to check reviews13%7%0%87%
Tells to verify credentials60%27%13%33%
Mentions case studies / portfolio0%7%0%93%
Mentions local proximity53%33%7%47%
Gives selection criteria40%13%20%47%
Warns about red flags13%7%20%87%
Asks a clarifying question53%33%0%33%
Recommends multiple quotes27%20%0%67%

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.

The divergence index for this study is 23 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a tree service buyer happens to ask matters most:

  • Recommends hiring a professional: from 20% (Gemini) to 86.7% (ChatGPT) — a 67-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT) — a 53-point spread.
  • Tells the buyer to verify credentials: from 13.3% (Gemini) to 60% (ChatGPT) — a 47-point spread.
  • Mentions local proximity: from 6.7% (Gemini) to 53.3% (ChatGPT) — a 47-point spread.
  • Gives selection criteria: from 13.3% (Claude) to 40% (ChatGPT) — a 27-point spread.

The widest single gap — recommends hiring a professional, 67 points — 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%) — a 67-point spread.
  • Tells the buyer to verify credentials: 33.3% on average (ChatGPT 60%, Claude 26.7%, Gemini 13.3%) — a 47-point spread.
  • Mentions local proximity: 31.1% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 6.7%) — a 47-point spread.
  • Asks a clarifying question: 28.9% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 0%) — a 53-point spread.
  • Gives selection criteria: 24.4% on average (ChatGPT 40%, Claude 13.3%, Gemini 20%) — a 27-point spread.
  • Gives price or cost information: 22.2% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 26.7%) — a 13-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%) — a 27-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 20%) — a 13-point spread.
  • Suggests a DIY approach first: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • 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 — 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 — 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 were drawn from real tree service (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 tree service 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 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 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 →