Original research · 2026-07 edition

AI SEO Statistics: Cell Phone Repair (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in cell phone repair.

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

My phone fell in the sink and won't turn on, what are the first steps before taking it to a pro?
Is it cheaper to replace a cracked glass back or just put a case over it?
How can I tell if my battery just needs a recalibration or a full hardware replacement?
What is the average turnaround time for a screen replacement if I drop it off today?
Are those mall kiosks reliable for fixing high-end smartphone cameras?
Does getting a third-party screen replacement void my manufacturer warranty?
I have a tiny hairline crack, will it eventually spread across the whole screen?
What questions should I ask to make sure a repair shop uses high-quality OEM-grade parts?
Show all 40 questions
Is it safe to leave my phone at a repair shop with all my personal photos and banking apps?
My charging port is loose, is that a quick cleaning job or a full part replacement?
Why is there a huge price difference between different repair shops for the same screen fix?
Can a repair technician recover data from a phone that has a completely dead motherboard?
Should I try a DIY screen repair kit from the internet if I have never opened a phone before?
What are the signs that a repair shop is using cheap, low-quality replacement screens?
My phone is three years old, is it worth spending $150 on a repair or should I just upgrade?
Do I need to back up my data before handing my phone over for a battery swap?
Are there mobile repair services that come to your house to fix a screen in their van?
What kind of warranty or guarantee should a reputable repair shop offer on their work?
My screen is black but the phone still vibrates, is that just a loose cable or a broken panel?
How much does it typically cost to fix a broken internal speaker or microphone?
If a shop breaks my face ID while fixing the screen, are they responsible for the replacement?
Can a software glitch cause the screen to stay frozen or is that always a hardware issue?
What is the best way to clean out a speaker grille without damaging the internals?
I spilled coffee on my phone and now the buttons are sticky, can a shop clean that out?
Is it normal for a phone to get really hot after a battery replacement?
How do I know if my phone is still water-resistant after it has been opened for a repair?
Should I trust a repair shop that asks for my lock screen passcode to do a fix?
What should I do if my touch screen stops responding in certain spots after a repair?
Is it possible to replace just the glass if the OLED display underneath is still working fine?
Why does my phone say unknown part in the settings after I got it fixed at a local shop?
Are there specialized shops that deal specifically with data recovery for liquid-damaged phones?
How long does a typical smartphone battery last before it is considered end of life?
Can a cracked camera lens be replaced without opening up the entire phone?
What is the risk of using a non-certified charger after getting a new battery installed?
Do I need an appointment for a screen repair or can I just walk in to most places?
If my phone is bent, can a repair technician straighten the frame or does it need a new housing?
How can I verify if the replacement screen I paid for is actually an original part?
Is it better to go to an authorized service provider or a highly-rated independent shop?
My phone screen is flickering green lines, is that a permanent hardware failure?
What are the red flags I should look for when reading reviews for a local phone repair business?

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 cell phone repair buyers.

Behavior rates across 40 cell phone repair buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional83%65%30%43%
Suggests DIY first40%28%23%78%
Names specific providers15%20%23%65%
Gives price or cost info15%15%15%73%
Tells to check reviews20%25%3%70%
Tells to verify credentials23%15%0%78%
Mentions case studies / portfolio13%0%0%88%
Mentions local proximity23%13%8%70%
Gives selection criteria48%38%15%55%
Warns about red flags25%25%18%73%
Asks a clarifying question75%55%3%15%
Recommends multiple quotes8%10%0%88%

By model

How each assistant handled Cell Phone Repair questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same cell phone repair questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 82.5% (ChatGPT) down to 30% (Gemini), a 53-point gap on an identical question set.

Across the 40 cell phone repair answers it produced, ChatGPT recommended hiring a professional in 82.5% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 15% of answers (about 0.3 distinct providers per answer) and included price or cost information 15% of the time. ChatGPT asked a clarifying question before answering in 75% of cases, warned about red flags or scams in 25%, and told the buyer to verify credentials in 22.5%, averaging 419 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 22.5%; a selection-criteria checklist appeared in 47.5% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 cell phone repair answers it produced, Claude recommended hiring a professional in 65% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 20% of answers (about 0.5 distinct providers per answer) and included price or cost information 15% of the time. Claude asked a clarifying question before answering in 55% of cases, warned about red flags or scams in 25%, and told the buyer to verify credentials in 15%, averaging 279 words per answer. On the remaining cues it told the buyer to check reviews in 25%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 10%.

Across the 40 cell phone repair answers it produced, Gemini recommended hiring a professional in 30% of them and suggested a DIY approach first 22.5% of the time. It named a specific provider in 22.5% of answers (about 0.6 distinct providers per answer) and included price or cost information 15% of the time. Gemini asked a clarifying question before answering in 2.5% of cases, warned about red flags or scams in 17.5%, and told the buyer to verify credentials in 0%, averaging 295 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 15% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a cell phone repair buyer to a professional (82.5%) and Gemini the least (30%). ChatGPT produced the longest answers, at 419 words on average. Specific providers were named most often by Gemini (22.5%) — 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 22.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a cell phone repair buyer happens to ask matters most:

  • Asks a clarifying question: from 2.5% (Gemini) to 75% (ChatGPT) — a 73-point spread.
  • Recommends hiring a professional: from 30% (Gemini) to 82.5% (ChatGPT) — a 53-point spread.
  • Gives selection criteria: from 15% (Gemini) to 47.5% (ChatGPT) — a 33-point spread.
  • Tells the buyer to check reviews: from 2.5% (Gemini) to 25% (Claude) — a 23-point spread.
  • Tells the buyer to verify credentials: from 0% (Gemini) to 22.5% (ChatGPT) — a 23-point spread.

The widest single gap — asks a clarifying question, 73 points — means a cell phone repair 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 cell phone repair market.

Where they agree

The points of near-consensus in Cell Phone Repair.

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

  • Gives price or cost information: 15% across all three models.
  • Names a specific provider: 15%–22.5% across all three (a 8-point spread).
  • Warns about red flags or scams: 17.5%–25% across all three (a 8-point spread).
  • Recommends multiple quotes: 0%–10% across all three (a 10-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 87.5% of questions) and least consistently on "asks a clarifying question" (15%).

Every behavior, measured

All twelve coded behaviors for Cell Phone Repair, averaged across the three models.

The behaviors AI models reproduce most often for cell phone repair are recommends hiring a professional (59.2% on average), asks a clarifying question (44.2%) and gives selection criteria (33.3%); the rarest are mentions case studies or portfolio (4.2%), recommends multiple quotes (5.8%) and tells the buyer to verify credentials (12.5%). Each figure below is the share of a model's 40 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: 59.2% on average (ChatGPT 82.5%, Claude 65%, Gemini 30%) — a 53-point spread.
  • Asks a clarifying question: 44.2% on average (ChatGPT 75%, Claude 55%, Gemini 2.5%) — a 73-point spread.
  • Gives selection criteria: 33.3% on average (ChatGPT 47.5%, Claude 37.5%, Gemini 15%) — a 33-point spread.
  • Suggests a DIY approach first: 30% on average (ChatGPT 40%, Claude 27.5%, Gemini 22.5%) — a 18-point spread.
  • Warns about red flags or scams: 22.5% on average (ChatGPT 25%, Claude 25%, Gemini 17.5%) — a 8-point spread.
  • Names a specific provider: 19.2% on average (ChatGPT 15%, Claude 20%, Gemini 22.5%) — a 8-point spread.
  • Tells the buyer to check reviews: 15.8% on average (ChatGPT 20%, Claude 25%, Gemini 2.5%) — a 23-point spread.
  • Gives price or cost information: 15% on average (ChatGPT 15%, Claude 15%, Gemini 15%).
  • Mentions local proximity: 14.2% on average (ChatGPT 22.5%, Claude 12.5%, Gemini 7.5%) — a 15-point spread.
  • Tells the buyer to verify credentials: 12.5% on average (ChatGPT 22.5%, Claude 15%, Gemini 0%) — a 23-point spread.
  • Recommends multiple quotes: 5.8% on average (ChatGPT 7.5%, Claude 10%, Gemini 0%) — a 10-point spread.
  • Mentions case studies or portfolio: 4.2% on average (ChatGPT 12.5%, Claude 0%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the cell phone repair buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the cell phone repair buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.8% of answers on average. Verifying credentials or certifications appeared in 12.5%. Warning about red flags or scams appeared in 22.5%.

On structuring the decision, a selection-criteria checklist showed up in 33.3% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for cell phone repair is "recommends multiple quotes" at 5.8% 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 Cell Phone Repair providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 cell phone repair answers, a specific provider was named in 19.2% 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 cell phone repair: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Cell Phone Repair questions cover.

The 40 questions behind every percentage on this page were drawn from real cell phone repair (technology / SaaS; 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 cell phone repair 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 40 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 cell phone repair 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.

40 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 →