Original research · 2026-07 edition

AI SEO Statistics: Hair Salon (2026-07 edition)

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

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The question bank

The questions we tested — sampled from real buyer journeys in hair salon.

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

My hair is super dry and breaking after bleaching it at home, what professional treatment should I ask for at a salon?
Is it worth paying for a professional gloss treatment or can I just use a drugstore toner to get the same shine?
What should I look for in a stylist's portfolio if I want a natural-looking balayage on dark hair?
How much does a full head of highlights usually cost in a mid-sized city including the blowout?
What is the actual difference between a keratin treatment and a Brazilian blowout in terms of long-term maintenance?
How do I find a local salon that specifically specializes in curly hair and uses sulfate-free products?
What are some warning signs that a stylist might over-process my hair during a complex color correction?
I have a wedding in three days and my DIY dye turned orange, is it possible for a salon to fix this in one session?
Show all 15 questions
I have a $150 budget for a haircut and color, is that realistic for a high-end salon or should I look for a junior stylist?
Do I need to book a separate consultation before getting hair extensions for the first time or can it all happen at once?
How often will I realistically need to come back for touch-ups if I decide to go from natural brown to platinum blonde?
What is the lived-in color technique and why do some salons charge so much more for it than traditional foils?
Are there specific questions I should ask a stylist to ensure they have experience working with 4C hair textures?
Is it better to go to a specialized blow-dry bar or a full-service salon for an updo for a formal event?
If I am unhappy with how my layers turned out, is it standard practice for a salon to offer a free adjustment within the first week?

Model by model

15-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 hair salon buyers.

Behavior rates across 15 hair salon buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional87%73%73%60%
Suggests DIY first0%7%0%93%
Names specific providers0%7%13%87%
Gives price or cost info13%20%27%80%
Tells to check reviews13%13%0%87%
Tells to verify credentials7%13%7%93%
Mentions case studies / portfolio33%27%13%80%
Mentions local proximity20%20%7%87%
Gives selection criteria67%40%40%73%
Warns about red flags27%20%27%80%
Asks a clarifying question73%60%0%13%
Recommends multiple quotes0%7%0%93%

By model

How each assistant handled Hair Salon questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same hair salon questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 73.3% (Claude), a 13-point gap on an identical question set.

Across the 15 hair salon answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 0% 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. 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 6.7%, averaging 468 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 33.3%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 hair salon answers it produced, Claude recommended hiring a professional in 73.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 270 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 26.7%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 hair salon answers it produced, Gemini recommended hiring a professional in 73.3% of them and suggested a DIY approach first 0% 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 26.7% 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 6.7%, averaging 264 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a hair salon buyer to a professional (86.7%) and Claude the least (73.3%). ChatGPT produced the longest answers, at 468 words on average. Specific providers were named most often by Gemini (13.3%) — 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 15.2 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a hair salon buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Gives selection criteria: from 40% (Claude) to 66.7% (ChatGPT) — a 27-point spread.
  • Mentions case studies or portfolio: from 13.3% (Gemini) to 33.3% (ChatGPT) — a 20-point spread.
  • Recommends hiring a professional: from 73.3% (Claude) to 86.7% (ChatGPT) — a 13-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 26.7% (Gemini) — a 13-point spread.

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

Where they agree

The points of near-consensus in Hair Salon.

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

  • Tells the buyer to verify credentials: 6.7%–13.3% across all three (a 7-point spread).
  • Suggests a DIY approach first: 0%–6.7% across all three (a 7-point spread).
  • Warns about red flags or scams: 20%–26.7% across all three (a 7-point spread).
  • Recommends multiple quotes: 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 "suggests a DIY approach first" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (13.3%).

Every behavior, measured

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

The behaviors AI models reproduce most often for hair salon are recommends hiring a professional (77.8% on average), gives selection criteria (48.9%) and asks a clarifying question (44.4%); the rarest are recommends multiple quotes (2.2%), suggests a DIY approach first (2.2%) and names a specific provider (6.7%). 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: 77.8% on average (ChatGPT 86.7%, Claude 73.3%, Gemini 73.3%) — a 13-point spread.
  • Gives selection criteria: 48.9% on average (ChatGPT 66.7%, Claude 40%, Gemini 40%) — a 27-point spread.
  • Asks a clarifying question: 44.4% on average (ChatGPT 73.3%, Claude 60%, Gemini 0%) — a 73-point spread.
  • Warns about red flags or scams: 24.5% on average (ChatGPT 26.7%, Claude 20%, Gemini 26.7%) — a 7-point spread.
  • Mentions case studies or portfolio: 24.4% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 13.3%) — a 20-point spread.
  • Gives price or cost information: 20% on average (ChatGPT 13.3%, Claude 20%, Gemini 26.7%) — a 13-point spread.
  • Mentions local proximity: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Tells the buyer to check reviews: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Tells the buyer to verify credentials: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 0%, Claude 6.7%, Gemini 13.3%) — a 13-point spread.
  • Suggests a DIY approach first: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Recommends multiple quotes: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the hair salon buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the hair salon buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 8.9% of answers on average. Verifying credentials or certifications appeared in 8.9%. Warning about red flags or scams appeared in 24.5%.

On structuring the decision, a selection-criteria checklist showed up in 48.9% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for hair salon is "recommends multiple quotes" 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 Hair Salon providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 hair salon answers, a specific provider was named in 6.7% 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 hair salon: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Hair Salon questions cover.

The 15 questions behind every percentage on this page were drawn from real hair salon (beauty 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 hair salon 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 hair salon 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 →