Original research · 2026-07 edition

AI SEO Statistics: Tire Shop (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in tire shop.

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

How can I tell if my tires just need a balance or if I actually need a wheel alignment?
Is it safe to drive on a tire with a nail in it to a shop, or should I get it towed?
What's the average price for mounting and balancing four tires if I buy them online myself?
I have a slow leak in one tire; is it usually cheaper to patch it or do most places make you buy a new one?
What are the red flags I should look for when a tire shop tells me I need to replace all four tires instead of just two?
Can a mobile tire service change my tires in my office parking lot, or do they need a special lift?
How do I check the manufacture date on tires at the shop to make sure they aren't selling me old stock?
Is it worth paying extra for nitrogen inflation at a tire shop or is that just a gimmick?
Show all 15 questions
My steering wheel shakes at 60 mph, does that mean I need new tires or just a rotation?
What's a fair labor rate for a tire rotation and oil change bundle in a mid-sized city?
Should I buy the road hazard warranty from the tire dealer or does my car insurance usually cover flat repairs?
I'm on a tight budget; are used tires from a local shop safe for highway driving?
How long does a typical tire installation appointment take if I have an appointment?
If I'm switching to winter tires, will the shop store my summer tires for me or do I have to take them home?
What questions should I ask to see if a tire shop is experienced with electric vehicles and their specific weight requirements?

Model by model

19-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 tire shop buyers.

Behavior rates across 15 tire shop buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional60%53%47%60%
Suggests DIY first33%33%13%73%
Names specific providers13%13%27%67%
Gives price or cost info33%40%53%60%
Tells to check reviews7%0%0%93%
Tells to verify credentials13%0%0%87%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity20%27%20%80%
Gives selection criteria53%53%47%73%
Warns about red flags27%33%27%73%
Asks a clarifying question73%40%0%27%
Recommends multiple quotes27%20%0%73%

By model

How each assistant handled Tire Shop questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same tire shop questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 46.7% (Gemini), a 13-point gap on an identical question set.

Across the 15 tire shop answers it produced, ChatGPT recommended hiring a professional in 60% of them and suggested a DIY approach first 33.3% 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 33.3% 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 13.3%, averaging 414 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 20%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 tire shop answers it produced, Claude recommended hiring a professional in 53.3% of them and suggested a DIY approach first 33.3% 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 40% of the time. Claude asked a clarifying question before answering in 40% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 0%, averaging 275 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 26.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 tire shop answers it produced, Gemini recommended hiring a professional in 46.7% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 26.7% of answers (about 0.8 distinct providers per answer) and included price or cost information 53.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 0%, averaging 303 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 20%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a tire shop buyer to a professional (60%) and Gemini the least (46.7%). ChatGPT produced the longest answers, at 414 words on average. Specific providers were named most often by Gemini (26.7%) — even there, roughly one answer in 4 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 18.9 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a tire shop buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
  • Suggests a DIY approach first: from 13.3% (Gemini) to 33.3% (ChatGPT) — a 20-point spread.
  • Gives price or cost information: from 33.3% (ChatGPT) to 53.3% (Gemini) — a 20-point spread.
  • Names a specific provider: from 13.3% (ChatGPT) to 26.7% (Gemini) — a 13-point spread.

The widest single gap — asks a clarifying question, 73 points — means a tire shop 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 tire shop market.

Where they agree

The points of near-consensus in Tire Shop.

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

  • Gives selection criteria: 46.7%–53.3% across all three (a 7-point spread).
  • Warns about red flags or scams: 26.7%–33.3% across all three (a 7-point spread).
  • Tells the buyer to check reviews: 0%–6.7% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

All twelve coded behaviors for Tire Shop, averaged across the three models.

The behaviors AI models reproduce most often for tire shop are recommends hiring a professional (53.3% on average), gives selection criteria (51.1%) and gives price or cost information (42.2%); the rarest are mentions case studies or portfolio (2.2%), tells the buyer to check reviews (2.2%) and tells the buyer to verify credentials (4.4%). 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: 53.3% on average (ChatGPT 60%, Claude 53.3%, Gemini 46.7%) — a 13-point spread.
  • Gives selection criteria: 51.1% on average (ChatGPT 53.3%, Claude 53.3%, Gemini 46.7%) — a 7-point spread.
  • Gives price or cost information: 42.2% on average (ChatGPT 33.3%, Claude 40%, Gemini 53.3%) — a 20-point spread.
  • Asks a clarifying question: 37.8% on average (ChatGPT 73.3%, Claude 40%, Gemini 0%) — a 73-point spread.
  • Warns about red flags or scams: 28.9% on average (ChatGPT 26.7%, Claude 33.3%, Gemini 26.7%) — a 7-point spread.
  • Suggests a DIY approach first: 26.6% on average (ChatGPT 33.3%, Claude 33.3%, Gemini 13.3%) — a 20-point spread.
  • Mentions local proximity: 22.2% on average (ChatGPT 20%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Names a specific provider: 17.8% on average (ChatGPT 13.3%, Claude 13.3%, 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.
  • Tells the buyer to verify credentials: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.
  • Tells the buyer to check reviews: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the tire shop buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the tire shop buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 2.2% of answers on average. Verifying credentials or certifications appeared in 4.4%. Warning about red flags or scams appeared in 28.9%.

On structuring the decision, a selection-criteria checklist showed up in 51.1% of answers on average and a recommendation to gather multiple quotes in 15.6%. The single least-reproduced protective signal for tire shop is "tells the buyer to check reviews" at 2.2% 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 Tire Shop providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 tire shop answers, a specific provider was named in 17.8% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for tire shop: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Tire Shop questions cover.

The 15 questions behind every percentage on this page were drawn from real tire shop (automotive 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 tire shop 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 tire shop 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 →