Original research · 2026-07 edition

AI SEO Statistics: Electrician (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 electrician.

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

Why do my lights flicker every time the refrigerator compressor kicks on?
Is it safe to replace a standard light switch with a dimmer myself or should I hire someone?
What is the average hourly rate for a licensed electrician in a mid-sized city?
I smell something like burning plastic near my electrical panel, is this an emergency?
What are the red flags I should look for when getting a quote for a full house rewire?
How much does it typically cost to install a dedicated 240V outlet for an EV charger in an older garage?
Should I hire a master electrician or is a journeyman okay for installing new recessed lighting?
My circuit breaker keeps tripping even when nothing is plugged in, what could be the cause?
Show all 15 questions
Does an electrician usually handle the building permits for a home renovation or is that my responsibility?
What's the price difference between upgrading to a 200-amp panel versus just adding a sub-panel?
How do I verify if an electrician's license and insurance are actually current before they start?
We are buying an old house with knob and tube wiring, how much should we budget to replace it all?
Is it better to provide my own light fixtures or let the electrician source them for the project?
Half of the outlets in my kitchen stopped working but no breakers are flipped, what should I check first?
What specific questions should I ask an electrician to make sure they are experienced with smart home system installs?

Model by model

24-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 electrician buyers.

Behavior rates across 15 electrician buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%73%67%47%
Suggests DIY first27%20%13%87%
Names specific providers0%0%0%100%
Gives price or cost info27%13%40%67%
Tells to check reviews20%27%0%67%
Tells to verify credentials33%27%27%60%
Mentions case studies / portfolio20%7%0%80%
Mentions local proximity27%20%13%60%
Gives selection criteria47%47%47%60%
Warns about red flags13%20%33%73%
Asks a clarifying question73%47%0%13%
Recommends multiple quotes40%20%0%60%

By model

How each assistant handled Electrician questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same electrician questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 73.3% (ChatGPT) down to 66.7% (Gemini), a 7-point gap on an identical question set.

Across the 15 electrician answers it produced, ChatGPT 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 26.7% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 33.3%, averaging 458 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 40%.

Across the 15 electrician answers it produced, Claude recommended hiring a professional in 73.3% of them and suggested a DIY approach first 20% 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. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 26.7%, averaging 298 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, 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 46.7% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 electrician answers it produced, Gemini recommended hiring a professional in 66.7% 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 40% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 26.7%, averaging 287 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 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route an electrician buyer to a professional (73.3%) and Gemini the least (66.7%). ChatGPT produced the longest answers, at 458 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 23.7 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant an electrician 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 40% (ChatGPT) — a 40-point spread.
  • Gives price or cost information: from 13.3% (Claude) to 40% (Gemini) — a 27-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 26.7% (Claude) — a 27-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 73 points — means an electrician 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 electrician market.

Where they agree

The points of near-consensus in Electrician.

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

  • Names a specific provider: 0% across all three models.
  • Gives selection criteria: 46.7% across all three models.
  • Recommends hiring a professional: 66.7%–73.3% across all three (a 7-point spread).
  • Tells the buyer to verify credentials: 26.7%–33.3% across all three (a 7-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 "asks a clarifying question" (13.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for electrician are recommends hiring a professional (71.1% on average), gives selection criteria (46.7%) and asks a clarifying question (40%); the rarest are names a specific provider (0%), mentions case studies or portfolio (8.9%) and tells the buyer to check reviews (15.6%). 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: 71.1% on average (ChatGPT 73.3%, Claude 73.3%, Gemini 66.7%) — a 7-point spread.
  • Gives selection criteria: 46.7% on average (ChatGPT 46.7%, Claude 46.7%, Gemini 46.7%).
  • Asks a clarifying question: 40% on average (ChatGPT 73.3%, Claude 46.7%, Gemini 0%) — a 73-point spread.
  • Tells the buyer to verify credentials: 28.9% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 26.7%) — a 7-point spread.
  • Gives price or cost information: 26.7% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 40%) — a 27-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 13.3%, Claude 20%, Gemini 33.3%) — a 20-point spread.
  • Suggests a DIY approach first: 20% on average (ChatGPT 26.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Mentions local proximity: 20% on average (ChatGPT 26.7%, Claude 20%, Gemini 13.3%) — a 13-point spread.
  • Recommends multiple quotes: 20% on average (ChatGPT 40%, Claude 20%, Gemini 0%) — a 40-point spread.
  • Tells the buyer to check reviews: 15.6% on average (ChatGPT 20%, Claude 26.7%, Gemini 0%) — a 27-point spread.
  • Mentions case studies or portfolio: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
  • Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the electrician buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the electrician buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.6% of answers on average. Verifying credentials or certifications appeared in 28.9%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 46.7% of answers on average and a recommendation to gather multiple quotes in 20%. The single least-reproduced protective signal for electrician is "tells the buyer to check reviews" at 15.6% 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 Electrician providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 electrician 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 electrician: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Electrician questions cover.

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