AI SEO Statistics: Aesthetic Clinics (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same aesthetic clinics questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 85% (ChatGPT) down to 35% (Gemini), a 50-point gap on an identical question set.

Across the 40 aesthetic clinics answers it produced, ChatGPT recommended hiring a professional in 85% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 2.5% of answers (about 0 distinct providers per answer) and included price or cost information 5% of the time. ChatGPT asked a clarifying question before answering in 65% of cases, warned about red flags or scams in 35%, and told the buyer to verify credentials in 55%, averaging 451 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 37.5%, and framed the choice around local proximity in 17.5%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 5%.

Across the 40 aesthetic clinics answers it produced, Claude recommended hiring a professional in 67.5% of them and suggested a DIY approach first 10% of the time. It named a specific provider in 2.5% of answers (about 0.2 distinct providers per answer) and included price or cost information 12.5% of the time. Claude asked a clarifying question before answering in 55% of cases, warned about red flags or scams in 30%, and told the buyer to verify credentials in 30%, averaging 278 words per answer. On the remaining cues it told the buyer to check reviews in 12.5%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 40 aesthetic clinics answers it produced, Gemini recommended hiring a professional in 35% of them and suggested a DIY approach first 2.5% of the time. It named a specific provider in 5% of answers (about 0.1 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 25%, and told the buyer to verify credentials in 15%, averaging 266 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 12.5%, and framed the choice around local proximity in 2.5%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route an aesthetic clinics buyer to a professional (85%) and Gemini the least (35%). ChatGPT produced the longest answers, at 451 words on average. Specific providers were named most often by Gemini (5%) — even there, roughly one answer in 20 carried a name.

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

• Names a specific provider: 2.5%–5% across all three (a 3-point spread).
• Recommends multiple quotes: 0%–5% across all three (a 5-point spread).
• Suggests a DIY approach first: 2.5%–12.5% across all three (a 10-point spread).
• Warns about red flags or scams: 25%–35% across all three (a 10-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 97.5% of questions) and least consistently on "asks a clarifying question" (20%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the aesthetic clinics buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.7% of answers on average. Verifying credentials or certifications appeared in 33.3%. Warning about red flags or scams appeared in 30%.

On structuring the decision, a selection-criteria checklist showed up in 38.3% of answers on average and a recommendation to gather multiple quotes in 2.5%. The single least-reproduced protective signal for aesthetic clinics is "recommends multiple quotes" at 2.5% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real aesthetic clinics (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 aesthetic clinics 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.