Original research · 2026-07 edition

AI SEO Statistics: Medical Spa (2026-07 edition)

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

See how AI talks about YOUR businessFull data table

The question bank

The questions we tested — sampled from real buyer journeys in medical spa.

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

My skin is looking really dull and tired lately, what kind of medspa treatments help with brightening and texture?
Is it safe to use those at-home chemical peel kits or should I just go to a professional medical spa for a real peel?
What specific certifications or licenses should I look for when choosing a nurse injector for lip fillers?
How much does a full series of laser hair removal usually cost for full legs in a mid-sized city?
What is the difference between standard microneedling and radiofrequency microneedling, and which is better for deep acne scarring?
Are there any reputable medical spas in my area that offer same-day appointments for wrinkle relaxer injections?
What are some red flags I should watch out for during a consultation that suggest a medspa might be cutting corners on safety?
I have a big event in exactly two weeks, is it too late to get filler or will I still be dealing with bruising and swelling?
Show all 15 questions
I have a strict $500 budget for anti-aging, what single treatment will give me the most noticeable results?
What happens if I'm unhappy with my results from a body contouring session, do most places offer free touch-ups or corrections?
Do I need to have a formal consultation with an actual doctor before getting a prescription-strength facial treatment at a spa?
How many months does a typical syringe of cheek filler actually last before the results start to fade significantly?
Which laser hair removal technology is considered the least painful for someone with a very low pain tolerance?
Are there specific medspa treatments that are more effective for men's thicker skin compared to standard female-focused procedures?
Is it better to get a medical-grade hydrafacial or a traditional chemical peel if my main concern is clogged pores and blackheads?

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 medical spa buyers.

Behavior rates across 15 medical spa buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional80%80%40%40%
Suggests DIY first0%7%0%93%
Names specific providers0%0%0%100%
Gives price or cost info13%13%20%73%
Tells to check reviews20%20%7%80%
Tells to verify credentials47%27%27%67%
Mentions case studies / portfolio13%7%0%87%
Mentions local proximity13%13%7%93%
Gives selection criteria60%47%27%47%
Warns about red flags13%33%13%67%
Asks a clarifying question67%73%7%13%
Recommends multiple quotes0%13%0%87%

By model

How each assistant handled Medical Spa questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same medical spa questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 80% (ChatGPT) down to 40% (Gemini), a 40-point gap on an identical question set.

Across the 15 medical spa answers it produced, ChatGPT recommended hiring a professional in 80% 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 66.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 46.7%, averaging 414 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 medical spa 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 13.3% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 26.7%, averaging 276 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 6.7%, 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 13.3%.

Across the 15 medical spa answers it produced, Gemini recommended hiring a professional in 40% 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 20% of the time. Gemini asked a clarifying question before answering in 6.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 237 words per answer. On the remaining cues it told the buyer to check reviews in 6.7%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 26.7% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a medical spa buyer to a professional (80%) and Gemini the least (40%). ChatGPT produced the longest answers, at 414 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 19.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a medical spa buyer happens to ask matters most:

  • Asks a clarifying question: from 6.7% (Gemini) to 73.3% (Claude) — a 67-point spread.
  • Recommends hiring a professional: from 40% (Gemini) to 80% (ChatGPT) — a 40-point spread.
  • Gives selection criteria: from 26.7% (Gemini) to 60% (ChatGPT) — a 33-point spread.
  • Tells the buyer to verify credentials: from 26.7% (Claude) to 46.7% (ChatGPT) — a 20-point spread.
  • Warns about red flags or scams: from 13.3% (ChatGPT) to 33.3% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 67 points — means a medical spa 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 medical spa market.

Where they agree

The points of near-consensus in Medical Spa.

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

  • Names a specific provider: 0% across all three models.
  • Mentions local proximity: 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).
  • Gives price or cost information: 13.3%–20% 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 Medical Spa, averaged across the three models.

The behaviors AI models reproduce most often for medical spa are recommends hiring a professional (66.7% on average), asks a clarifying question (48.9%) and gives selection criteria (44.5%); the rarest are names a specific provider (0%), suggests a DIY approach first (2.2%) and recommends multiple quotes (4.4%). 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: 66.7% on average (ChatGPT 80%, Claude 80%, Gemini 40%) — a 40-point spread.
  • Asks a clarifying question: 48.9% on average (ChatGPT 66.7%, Claude 73.3%, Gemini 6.7%) — a 67-point spread.
  • Gives selection criteria: 44.5% on average (ChatGPT 60%, Claude 46.7%, Gemini 26.7%) — a 33-point spread.
  • Tells the buyer to verify credentials: 33.4% on average (ChatGPT 46.7%, Claude 26.7%, Gemini 26.7%) — a 20-point spread.
  • Warns about red flags or scams: 20% on average (ChatGPT 13.3%, Claude 33.3%, Gemini 13.3%) — a 20-point spread.
  • Tells the buyer to check reviews: 15.6% on average (ChatGPT 20%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Gives price or cost information: 15.5% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 20%) — a 7-point spread.
  • Mentions local proximity: 11.1% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Mentions case studies or portfolio: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 4.4% on average (ChatGPT 0%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Suggests a DIY approach first: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Names a specific provider: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the medical spa buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the medical spa 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 33.4%. Warning about red flags or scams appeared in 20%.

On structuring the decision, a selection-criteria checklist showed up in 44.5% of answers on average and a recommendation to gather multiple quotes in 4.4%. The single least-reproduced protective signal for medical spa is "recommends multiple quotes" at 4.4% 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 Medical Spa providers?

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

The question set

What these 15 Medical Spa questions cover.

The 15 questions behind every percentage on this page were drawn from real medical spa (healthcare 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 medical spa 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 medical spa 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 →