Original research · 2026-07 edition

AI SEO Statistics: Salon (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 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 can a professional do to fix the damage?
Is it worth paying for a professional blowout or can I get the same look with a high-end hair tool at home?
What should I look for in a stylist's portfolio if I want a very specific ash blonde balayage?
How much should I expect to pay for a full head of highlights and a trim in a mid-sized city?
What is the actual difference between a keratin treatment and a Brazilian blowout for managing frizzy hair?
How do I find a salon that specializes in curly hair textures and won't just give me a triangle cut?
What are some warning signs that a nail salon isn't properly sanitizing their equipment or tools?
I have a wedding in three days and my spray tan looks splotchy, can a salon fix this quickly?
Show all 15 questions
I have a 200 dollar budget for a hair makeover, what services will give me the most dramatic change for that price?
How can I tell if a lash technician is actually certified and using safe medical-grade adhesive?
My scalp has been really itchy and flaky lately, should I see a dermatologist or can a salon scalp treatment help?
Is it better to get a gel manicure or dip powder if I want my nails to stay strong and not chip for three weeks?
What is the standard tipping etiquette for a salon owner who also performs the service?
I tried to box dye my hair brown but it turned out green, what's the best way to ask a salon for a color correction?
What questions should I ask during a bridal hair trial to make sure the stylist can handle my heavy extensions?

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 salon buyers.

Behavior rates across 15 salon buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional73%80%60%67%
Suggests DIY first7%7%0%93%
Names specific providers0%0%13%87%
Gives price or cost info13%27%27%73%
Tells to check reviews20%27%0%73%
Tells to verify credentials20%13%13%93%
Mentions case studies / portfolio20%20%7%80%
Mentions local proximity20%20%0%73%
Gives selection criteria53%67%60%60%
Warns about red flags40%27%27%67%
Asks a clarifying question73%67%0%7%
Recommends multiple quotes0%7%0%93%

By model

How each assistant handled Salon questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same salon questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (Claude) down to 60% (Gemini), a 20-point gap on an identical question set.

Across the 15 salon answers it produced, ChatGPT 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 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 40%, and told the buyer to verify credentials in 20%, averaging 530 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 20%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 salon answers it produced, Claude recommended hiring a professional in 80% of them and suggested a DIY approach first 6.7% 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. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 13.3%, averaging 292 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 20%, 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 6.7%.

Across the 15 salon answers it produced, Gemini recommended hiring a professional in 60% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 13.3% of answers (about 0.4 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 13.3%, averaging 272 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, Claude is the assistant most likely to route a salon buyer to a professional (80%) and Gemini the least (60%). ChatGPT produced the longest answers, at 530 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 18.5 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a salon buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 26.7% (Claude) — a 27-point spread.
  • Recommends hiring a professional: from 60% (Gemini) to 80% (Claude) — a 20-point spread.
  • Mentions local proximity: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 26.7% (Claude) — a 13-point spread.

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

Where they agree

The points of near-consensus in Salon.

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

  • Suggests a DIY approach first: 0%–6.7% across all three (a 7-point spread).
  • Tells the buyer to verify credentials: 13.3%–20% across all three (a 7-point spread).
  • Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
  • Names a specific provider: 0%–13.3% across all three (a 13-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" (6.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for salon are recommends hiring a professional (71.1% on average), gives selection criteria (60%) and asks a clarifying question (46.7%); the rarest are recommends multiple quotes (2.2%), names a specific provider (4.4%) and suggests a DIY approach first (4.5%). 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 80%, Gemini 60%) — a 20-point spread.
  • Gives selection criteria: 60% on average (ChatGPT 53.3%, Claude 66.7%, Gemini 60%) — a 13-point spread.
  • Asks a clarifying question: 46.7% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 0%) — a 73-point spread.
  • Warns about red flags or scams: 31.1% on average (ChatGPT 40%, Claude 26.7%, Gemini 26.7%) — a 13-point spread.
  • Gives price or cost information: 22.2% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 26.7%) — a 13-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: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Tells the buyer to verify credentials: 15.5% on average (ChatGPT 20%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Mentions local proximity: 13.3% on average (ChatGPT 20%, Claude 20%, Gemini 0%) — a 20-point spread.
  • Suggests a DIY approach first: 4.5% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Names a specific provider: 4.4% on average (ChatGPT 0%, Claude 0%, Gemini 13.3%) — a 13-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 salon buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the salon 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 15.5%. Warning about red flags or scams appeared in 31.1%.

On structuring the decision, a selection-criteria checklist showed up in 60% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for 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 Salon providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 salon answers, a specific provider was named in 4.4% of responses on average — roughly 0.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for salon: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Salon questions cover.

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