Original research · 2026-07 edition

AI SEO Statistics: The Pet Industry (2026-07 edition)

34 questions · 102 AI responses · 3 models · measured 2026-07-06

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The question bank

The questions we tested — sampled from real buyer journeys in the pet industry.

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

My dog has a hard lump on his leg that doesn't seem to hurt, should I get it biopsied or just monitor it?
Is it worth the extra cost to have a mobile vet come to the house for a cat that gets terrified in the car?
How much does a typical senior dog wellness exam with full bloodwork cost on average?
What are the red flags I should look for when visiting a new animal hospital for the first time?
Can I treat a mild hot spot on my dog at home with OTC meds or does it definitely need a prescription?
What specific questions should I ask a surgeon before my dog undergoes a TPLO or ACL procedure?
Is pet insurance actually worth the monthly premium if my dog is already five years old and healthy?
How do I find a local vet who is experienced with exotic pets like sugar gliders or reptiles?
Show all 34 questions
What is the difference between a standard vet clinic and one that is Fear Free certified?
My cat hasn't touched her food in 24 hours, is this an immediate emergency or can I wait for a regular appointment?
Are there any low-cost clinics for kitten vaccinations that don't charge a massive office visit fee?
What should I do if my current vet isn't giving me clear answers about my dog's chronic skin allergies?
How do I know if my dog's teeth really need a professional cleaning under anesthesia versus just a scale?
Do most emergency vets offer payment plans like Scratchpay or CareCredit for unexpected surgeries?
What are the pros and cons of choosing a large corporate veterinary chain over a small family-owned practice?
My puppy has had diarrhea twice today but is still playing, do I need to rush to the clinic?
How can I verify if a veterinarian is actually board-certified in a specialty like oncology or cardiology?
What is a reasonable price range for a dog spay in a major metropolitan area?
Should I seek a second opinion if my vet recommends a $3,000 surgery right after the first exam?
What is the best way to transfer medical records and history when moving to a new vet in a different state?
How do I handle a situation where the final vet bill is significantly higher than the initial written estimate?
Is it safe to buy heartworm and flea prevention from online pharmacies or should I always buy from the vet?
Are there any 24-hour emergency vets that still allow owners to stay with their pets during the triage process?
What are the signs that a vet clinic is understaffed or that the quality of care might be slipping?
How much does an initial consultation with a certified veterinary behaviorist usually cost?
Does an indoor-only cat really need the same battery of vaccines as a cat that goes outside?
How do I find a vet who is open-minded and supportive of raw feeding or alternative diets?
What documents should I bring to a first-time vet appointment for a rescue dog with no known medical history?
Is it generally cheaper to take my pet to a vet in a rural area compared to a downtown city clinic?
My dog is limping after a long hike, can I give him a small dose of human aspirin or is that toxic?
How do I filter through online reviews for a vet to see which ones are legitimate patient experiences?
What are the most common hidden fees that show up on a veterinary surgical quote?
Can I legally request my pet's digital X-rays and lab results to be sent to me for my own records?
Why does my vet require an expensive blood test every year just to refill a prescription my dog has been on for years?

Model by model

21-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 the pet industry buyers.

Behavior rates across 34 the pet industry buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional65%59%50%68%
Suggests DIY first32%18%9%71%
Names specific providers6%12%21%65%
Gives price or cost info15%15%27%74%
Tells to check reviews15%9%3%77%
Tells to verify credentials27%21%21%74%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity29%27%15%74%
Gives selection criteria50%41%29%47%
Warns about red flags21%27%15%82%
Asks a clarifying question56%56%0%21%
Recommends multiple quotes21%24%3%71%

By model

How each assistant handled The Pet Industry questions.

Reading the 102 answers model by model shows how differently the three assistants treat the same the pet industry questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 64.7% (ChatGPT) down to 50% (Gemini), a 15-point gap on an identical question set.

Across the 34 the pet industry answers it produced, ChatGPT recommended hiring a professional in 64.7% of them and suggested a DIY approach first 32.4% of the time. It named a specific provider in 5.9% of answers (about 0 distinct providers per answer) and included price or cost information 14.7% of the time. ChatGPT asked a clarifying question before answering in 55.9% of cases, warned about red flags or scams in 20.6%, and told the buyer to verify credentials in 26.5%, averaging 445 words per answer. On the remaining cues it told the buyer to check reviews in 14.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 29.4%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 20.6%.

Across the 34 the pet industry answers it produced, Claude recommended hiring a professional in 58.8% of them and suggested a DIY approach first 17.6% of the time. It named a specific provider in 11.8% of answers (about 0.4 distinct providers per answer) and included price or cost information 14.7% of the time. Claude asked a clarifying question before answering in 55.9% of cases, warned about red flags or scams in 26.5%, and told the buyer to verify credentials in 20.6%, averaging 288 words per answer. On the remaining cues it told the buyer to check reviews in 8.8%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 26.5%; a selection-criteria checklist appeared in 41.2% of its answers and a recommendation to gather multiple quotes in 23.5%.

Across the 34 the pet industry answers it produced, Gemini recommended hiring a professional in 50% of them and suggested a DIY approach first 8.8% of the time. It named a specific provider in 20.6% of answers (about 0.5 distinct providers per answer) and included price or cost information 26.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 14.7%, and told the buyer to verify credentials in 20.6%, averaging 266 words per answer. On the remaining cues it told the buyer to check reviews in 2.9%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 14.7%; a selection-criteria checklist appeared in 29.4% of its answers and a recommendation to gather multiple quotes in 2.9%.

Taken together, ChatGPT is the assistant most likely to route a the pet industry buyer to a professional (64.7%) and Gemini the least (50%). ChatGPT produced the longest answers, at 445 words on average. Specific providers were named most often by Gemini (20.6%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 21.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a the pet industry buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 55.9% (ChatGPT) — a 56-point spread.
  • Suggests a DIY approach first: from 8.8% (Gemini) to 32.4% (ChatGPT) — a 24-point spread.
  • Gives selection criteria: from 29.4% (Gemini) to 50% (ChatGPT) — a 21-point spread.
  • Recommends multiple quotes: from 2.9% (Gemini) to 23.5% (Claude) — a 21-point spread.
  • Recommends hiring a professional: from 50% (Gemini) to 64.7% (ChatGPT) — a 15-point spread.

The widest single gap — asks a clarifying question, 56 points — means a the pet industry 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 the pet industry market.

Where they agree

The points of near-consensus in The Pet Industry.

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

  • Mentions case studies or portfolio: 0% across all three models.
  • Tells the buyer to verify credentials: 20.6%–26.5% across all three (a 6-point spread).
  • Gives price or cost information: 14.7%–26.5% across all three (a 12-point spread).
  • Tells the buyer to check reviews: 2.9%–14.7% across all three (a 12-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" (20.6%).

Every behavior, measured

All twelve coded behaviors for The Pet Industry, averaged across the three models.

The behaviors AI models reproduce most often for the pet industry are recommends hiring a professional (57.8% on average), gives selection criteria (40.2%) and asks a clarifying question (37.3%); the rarest are mentions case studies or portfolio (0%), tells the buyer to check reviews (8.8%) and names a specific provider (12.8%). Each figure below is the share of a model's 34 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: 57.8% on average (ChatGPT 64.7%, Claude 58.8%, Gemini 50%) — a 15-point spread.
  • Gives selection criteria: 40.2% on average (ChatGPT 50%, Claude 41.2%, Gemini 29.4%) — a 21-point spread.
  • Asks a clarifying question: 37.3% on average (ChatGPT 55.9%, Claude 55.9%, Gemini 0%) — a 56-point spread.
  • Mentions local proximity: 23.5% on average (ChatGPT 29.4%, Claude 26.5%, Gemini 14.7%) — a 15-point spread.
  • Tells the buyer to verify credentials: 22.6% on average (ChatGPT 26.5%, Claude 20.6%, Gemini 20.6%) — a 6-point spread.
  • Warns about red flags or scams: 20.6% on average (ChatGPT 20.6%, Claude 26.5%, Gemini 14.7%) — a 12-point spread.
  • Suggests a DIY approach first: 19.6% on average (ChatGPT 32.4%, Claude 17.6%, Gemini 8.8%) — a 24-point spread.
  • Gives price or cost information: 18.6% on average (ChatGPT 14.7%, Claude 14.7%, Gemini 26.5%) — a 12-point spread.
  • Recommends multiple quotes: 15.7% on average (ChatGPT 20.6%, Claude 23.5%, Gemini 2.9%) — a 21-point spread.
  • Names a specific provider: 12.8% on average (ChatGPT 5.9%, Claude 11.8%, Gemini 20.6%) — a 15-point spread.
  • Tells the buyer to check reviews: 8.8% on average (ChatGPT 14.7%, Claude 8.8%, Gemini 2.9%) — a 12-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the the pet industry buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the the pet industry buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.8% of answers on average. Verifying credentials or certifications appeared in 22.6%. Warning about red flags or scams appeared in 20.6%.

On structuring the decision, a selection-criteria checklist showed up in 40.2% of answers on average and a recommendation to gather multiple quotes in 15.7%. The single least-reproduced protective signal for the pet industry is "tells the buyer to check reviews" at 8.8% 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 The Pet Industry providers?

For service providers the decisive question is whether these systems name anyone at all. Across 102 the pet industry answers, a specific provider was named in 12.8% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for the pet industry: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 34 The Pet Industry questions cover.

The 34 questions behind every percentage on this page were drawn from real the pet industry (healthcare 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 the pet industry 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 34 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific the pet industry 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.

34 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 2026-07-06, 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 →