Original research · 2026-07 edition

AI SEO Statistics: Pest Control (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

The question bank

The questions we tested — sampled from real buyer journeys in pest control.

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

I just found some tiny black droppings in my kitchen cabinets, does this mean I have mice or is it just roaches?
Is it actually possible to get rid of a German cockroach infestation on my own using stuff from Home Depot?
What are the biggest red flags to look out for when a pest control company does their initial walkthrough?
How much should I expect to pay for a quarterly pest service in a 2,000 square foot house?
Are the pesticides used for professional flea treatments safe to be around if I have a newborn and two senior dogs?
My neighbor has termites and I'm worried they'll move to my house, should I get a preventative treatment now?
I have a massive wasp nest under my deck and I'm allergic, how quickly do companies usually respond to emergency removals?
Is a one-time spray enough for a spider problem or do I really need to sign a year-long contract?
Show all 15 questions
What's the difference between a liquid termite barrier and those bait stations they put in the yard?
Can a pest control company guarantee that bed bugs won't come back after a heat treatment?
I keep seeing silverfish in my bathroom even though I keep it clean, what is attracting them and how do I stop it?
Should I be worried if a technician won't tell me the specific names of the chemicals they are spraying around my baseboards?
Is it better to hire a local family-owned pest business or one of the big national chains?
How long do I have to stay out of the house after a professional fogging for fruit flies?
Does home insurance ever cover the cost of repairing structural damage caused by carpenter ants?

Model by model

20-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 pest control buyers.

Behavior rates across 15 pest control buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional93%73%33%27%
Suggests DIY first27%27%20%80%
Names specific providers0%0%13%87%
Gives price or cost info7%7%20%80%
Tells to check reviews20%7%0%80%
Tells to verify credentials40%20%7%53%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity27%20%13%87%
Gives selection criteria60%47%27%67%
Warns about red flags27%20%13%80%
Asks a clarifying question73%33%0%20%
Recommends multiple quotes20%20%7%80%

By model

How each assistant handled Pest Control questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same pest control questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 93.3% (ChatGPT) down to 33.3% (Gemini), a 60-point gap on an identical question set.

Across the 15 pest control answers it produced, ChatGPT recommended hiring a professional in 93.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 40%, averaging 510 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 pest control answers it produced, Claude recommended hiring a professional in 73.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 6.7% of the time. Claude asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 279 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 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 pest control answers it produced, Gemini recommended hiring a professional in 33.3% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 13.3% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 279 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 13.3%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Taken together, ChatGPT is the assistant most likely to route a pest control buyer to a professional (93.3%) and Gemini the least (33.3%). ChatGPT produced the longest answers, at 510 words on average. Specific providers were named most often by Gemini (13.3%) — even there, roughly one answer in 8 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a pest control buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Recommends hiring a professional: from 33.3% (Gemini) to 93.3% (ChatGPT) — a 60-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 40% (ChatGPT) — a 33-point spread.
  • Gives selection criteria: from 26.7% (Gemini) to 60% (ChatGPT) — a 33-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 73 points — means a pest control 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 pest control market.

Where they agree

The points of near-consensus in Pest Control.

On other behaviors the three models move almost in lockstep — the points of near-consensus for pest control, where all three landed within a few points of each other:

  • Mentions case studies or portfolio: 0% across all three models.
  • Suggests a DIY approach first: 20%–26.7% across all three (a 7-point spread).
  • Names a specific provider: 0%–13.3% across all three (a 13-point spread).
  • Gives price or cost information: 6.7%–20% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

All twelve coded behaviors for Pest Control, averaged across the three models.

The behaviors AI models reproduce most often for pest control are recommends hiring a professional (66.6% on average), gives selection criteria (44.5%) and asks a clarifying question (35.5%); the rarest are mentions case studies or portfolio (0%), names a specific provider (4.4%) and tells the buyer to check reviews (8.9%). 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: 66.6% on average (ChatGPT 93.3%, Claude 73.3%, Gemini 33.3%) — a 60-point spread.
  • Gives selection criteria: 44.5% on average (ChatGPT 60%, Claude 46.7%, Gemini 26.7%) — a 33-point spread.
  • Asks a clarifying question: 35.5% on average (ChatGPT 73.3%, Claude 33.3%, Gemini 0%) — a 73-point spread.
  • Suggests a DIY approach first: 24.5% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Tells the buyer to verify credentials: 22.2% on average (ChatGPT 40%, Claude 20%, Gemini 6.7%) — a 33-point spread.
  • Mentions local proximity: 20% on average (ChatGPT 26.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Warns about red flags or scams: 20% on average (ChatGPT 26.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Recommends multiple quotes: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Gives price or cost information: 11.1% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 20%) — a 13-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
  • Names a specific provider: 4.4% on average (ChatGPT 0%, Claude 0%, Gemini 13.3%) — a 13-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the pest control buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the pest control buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 22.2%. Warning about red flags or scams appeared in 20%.

On structuring the decision, a selection-criteria checklist showed up in 44.5% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for pest control is "tells the buyer to check reviews" at 8.9% 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 Pest Control providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 pest control answers, a specific provider was named in 4.4% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for pest control: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Pest Control questions cover.

The 15 questions behind every percentage on this page were drawn from real pest control (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 pest control 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 pest control 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 →