Original research · 2026-07 edition

AI SEO Statistics: Lighting (2026-07 edition)

40 questions · 120 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 lighting.

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

Why is my living room so dark even with all the lamps on?
Is it worth hiring a lighting designer or should I just buy fixtures at a big box store?
How much does it typically cost to install 10 recessed lights in a kitchen?
What are the signs that my home's lighting wiring is outdated and unsafe?
Can I swap out a heavy chandelier by myself or do I need a pro?
What should I ask a contractor before hiring them for outdoor landscape lighting?
Is there a difference between an electrician and a lighting specialist for a high-end remodel?
My dimmer switch is buzzing and getting hot, is this an emergency?
Show all 40 questions
How do I choose the right color temperature for a home office to reduce eye strain?
What's the average hourly rate for a licensed electrician to install outdoor security lights?
I want to switch my whole house to smart lighting, where do I start?
Are there any rebates for upgrading to energy-efficient LED lighting in a residential home?
Why do my LED bulbs keep flickering when I turn on the microwave?
How much should I budget for a full outdoor lighting setup for a medium-sized backyard?
What are the red flags when looking at a lighting installer's portfolio?
Can a lighting pro help me hide ugly wires in an old brick house?
Is it cheaper to buy the fixtures myself and just pay for labor?
What kind of lighting do I need for a windowless basement to make it feel natural?
Do I need a permit to add new light outlets in my ceiling?
How long does it take to install under-cabinet lighting in a standard kitchen?
What's the best way to light a staircase for safety without it looking industrial?
Should I get a warranty on labor for lighting installation?
My outdoor lights stopped working after a heavy rain, who do I call to fix it?
Can I convert my old track lighting to something more modern without tearing out the ceiling?
How do I vet a lighting company to make sure they aren't overcharging for parts?
What’s the price difference between basic pot lights and high-end gimbal recessed lighting?
I have high vaulted ceilings, how do I find someone with the right equipment to change the fixtures?
Is smart lighting actually worth the investment for home resale value?
What are the pros and cons of low voltage vs line voltage for garden lights?
How many recessed lights do I need for a 20x20 foot master bedroom?
Can an electrician help me design a mood for my dining room or do I need an interior designer?
What does a typical consultation fee for a lighting designer look like?
Why are my new smart bulbs losing connection to the bridge constantly?
I'm DIYing my kitchen, at what point do I legally have to call an electrician for the lights?
How can I make my house look more expensive using just exterior lighting?
What is the most durable type of outdoor lighting for a coastal area with salt air?
Is it possible to install a chandelier where there is currently no ceiling outlet?
How do I compare quotes from three different lighting contractors?
What's the best way to light a home art gallery to prevent fading the paintings?
Are there any specific certifications I should look for in a landscape lighting specialist?

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 lighting buyers.

Behavior rates across 40 lighting buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional75%53%40%50%
Suggests DIY first25%28%18%70%
Names specific providers8%13%5%88%
Gives price or cost info20%18%23%70%
Tells to check reviews10%10%0%85%
Tells to verify credentials23%15%8%70%
Mentions case studies / portfolio13%15%5%80%
Mentions local proximity13%15%10%80%
Gives selection criteria38%30%25%53%
Warns about red flags8%5%10%88%
Asks a clarifying question63%73%0%20%
Recommends multiple quotes10%5%3%85%

By model

How each assistant handled Lighting questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same lighting questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 75% (ChatGPT) down to 40% (Gemini), a 35-point gap on an identical question set.

Across the 40 lighting answers it produced, ChatGPT recommended hiring a professional in 75% of them and suggested a DIY approach first 25% of the time. It named a specific provider in 7.5% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 62.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 22.5%, averaging 511 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 12.5%, 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 lighting answers it produced, Claude recommended hiring a professional in 52.5% of them and suggested a DIY approach first 27.5% of the time. It named a specific provider in 12.5% of answers (about 0.1 distinct providers per answer) and included price or cost information 17.5% of the time. Claude asked a clarifying question before answering in 72.5% of cases, warned about red flags or scams in 5%, and told the buyer to verify credentials in 15%, averaging 286 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 15%, and framed the choice around local proximity in 15%; a selection-criteria checklist appeared in 30% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 lighting answers it produced, Gemini recommended hiring a professional in 40% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 5% of answers (about 0.2 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 7.5%, averaging 273 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 25% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route a lighting buyer to a professional (75%) and Gemini the least (40%). ChatGPT produced the longest answers, at 511 words on average. Specific providers were named most often by Claude (12.5%) — 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 20.1 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a lighting buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 72.5% (Claude) — a 73-point spread.
  • Recommends hiring a professional: from 40% (Gemini) to 75% (ChatGPT) — a 35-point spread.
  • Tells the buyer to verify credentials: from 7.5% (Gemini) to 22.5% (ChatGPT) — a 15-point spread.
  • Gives selection criteria: from 25% (Gemini) to 37.5% (ChatGPT) — a 13-point spread.
  • Suggests a DIY approach first: from 17.5% (Gemini) to 27.5% (Claude) — a 10-point spread.

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

Where they agree

The points of near-consensus in Lighting.

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

  • Gives price or cost information: 17.5%–22.5% across all three (a 5-point spread).
  • Mentions local proximity: 10%–15% across all three (a 5-point spread).
  • Warns about red flags or scams: 5%–10% across all three (a 5-point spread).
  • Names a specific provider: 5%–12.5% across all three (a 8-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 87.5% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

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

The behaviors AI models reproduce most often for lighting are recommends hiring a professional (55.8% on average), asks a clarifying question (45%) and gives selection criteria (30.8%); the rarest are recommends multiple quotes (5.8%), tells the buyer to check reviews (6.7%) and warns about red flags or scams (7.5%). 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: 55.8% on average (ChatGPT 75%, Claude 52.5%, Gemini 40%) — a 35-point spread.
  • Asks a clarifying question: 45% on average (ChatGPT 62.5%, Claude 72.5%, Gemini 0%) — a 73-point spread.
  • Gives selection criteria: 30.8% on average (ChatGPT 37.5%, Claude 30%, Gemini 25%) — a 13-point spread.
  • Suggests a DIY approach first: 23.3% on average (ChatGPT 25%, Claude 27.5%, Gemini 17.5%) — a 10-point spread.
  • Gives price or cost information: 20% on average (ChatGPT 20%, Claude 17.5%, Gemini 22.5%) — a 5-point spread.
  • Tells the buyer to verify credentials: 15% on average (ChatGPT 22.5%, Claude 15%, Gemini 7.5%) — a 15-point spread.
  • Mentions local proximity: 12.5% on average (ChatGPT 12.5%, Claude 15%, Gemini 10%) — a 5-point spread.
  • Mentions case studies or portfolio: 10.8% on average (ChatGPT 12.5%, Claude 15%, Gemini 5%) — a 10-point spread.
  • Names a specific provider: 8.3% on average (ChatGPT 7.5%, Claude 12.5%, Gemini 5%) — a 8-point spread.
  • Warns about red flags or scams: 7.5% on average (ChatGPT 7.5%, Claude 5%, Gemini 10%) — a 5-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 10%, Claude 10%, Gemini 0%) — a 10-point spread.
  • Recommends multiple quotes: 5.8% on average (ChatGPT 10%, Claude 5%, Gemini 2.5%) — a 8-point spread.

Trust signals

How well the models protect the lighting buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the lighting buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 15%. Warning about red flags or scams appeared in 7.5%.

On structuring the decision, a selection-criteria checklist showed up in 30.8% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for lighting 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 Lighting providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 lighting answers, a specific provider was named in 8.3% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for lighting: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Lighting questions cover.

The 40 questions behind every percentage on this page were drawn from real lighting (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 lighting 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 lighting 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 →