Med Spa — 9 Schemas, 47 Pages, One Connected Graph
The situation
A 4-location med spa specializing in injectables, laser hair removal, body contouring, and IV therapy had basic Organization schema on their homepage and not much else. Treatment pages had no schema. Provider bios had no Person schema. The FAQ content existed but wasn't wrapped in FAQPage JSON-LD. Reviews existed but weren't structured.
AI engines answering queries like “best botox provider near me,” “med spa with RN injectors [city],” or “CoolSculpting cost [city]” couldn't find structured signals to recommend the client. They defaulted to directory aggregators and competitors that had basic LocalBusiness schema deployed.
What we did
1. Schema audit + gap analysis
Inspected every page type — homepage, location pages, treatment pages, provider bios, FAQ pages, reviews. Logged what was present, what was missing, what was malformed. Pre-deploy: client failed Google Rich Results Test on 2 of 4 location pages.
2. Designed a linked schema graph (not isolated blocks)
Most agencies deploy schemas as independent islands. We connected every schema with @id references so AI engines parse them as a single coherent business graph. LocalBusiness → Person (provider) → MedicalProcedure → Offer → Review, all linked.
3. Deployed 9 schema types across 47 pages
(1) LocalBusiness × 4 locations · (2) MedicalBusiness subtype × 4 · (3) MedicalProcedure × 12 treatments · (4) Service × 5 memberships/packages · (5) Offer × 17 (nested in Service/Procedure) · (6) Person × 6 providers · (7) FAQPage × 12 · (8) SpeakableSpecification × 47 (sitewide) · (9) Review × 12 (treatment-level).
4. Three schema types most agencies skip — and we included
SpeakableSpecification: flags content as voice-search ready. Google AI Overviews preferentially quote pages with this schema. Person (per provider): the “board-certified” / “RN” signal AI engines look for when answering medical or aesthetic queries. Review (treatment-level): most sites only deploy AggregateRating at the business level — per-treatment Review schema means AI can recommend specific treatments based on actual patient feedback.
5. Validated against Google Rich Results Test + Schema.org linter
Every page passed cleanly. 0 errors, 0 warnings post-deploy. Treatment pages went from “not detected” to validating MedicalProcedure + Offer + FAQPage on every single one.
The results
60-day citation tracking on 8 representative queries (no llms.txt was deployed in this engagement — client wanted to test schema impact in isolation):
- “Best botox provider near me” — went from “not cited” to cited by Gemini + Perplexity.
- “Med spa with RN injectors [city]” — cited by all 4 AI platforms post-deploy.
- “CoolSculpting cost [city]” — featured Offer in ChatGPT + Gemini snippets.
- “Best laser hair removal [city]” — cited by Claude + Gemini.
- “Med spa membership [city]” — cited by ChatGPT (Service schema win).
- Review schema rendered in Google snippet — stars + ratings now appearing directly in the SERP.
Schema-only proof: No llms.txt was deployed in this engagement. All citation gains came from JSON-LD alone — meaning the AI engines were extracting brand, service, provider, and pricing data directly from structured markup, then surfacing it as recommendations.
What we learned
Isolated schema blocks are 30% of the value. A linked schema graph (using @id references) is 100% of the value. AI engines that parse JSON-LD don't just look at individual blocks — they reconstruct the relationships between entities. A standalone Person schema for “Dr. Smith” doesn't help much. But a Person schema linked via @id to a LocalBusiness, a MedicalProcedure, and a Review schema gives the AI a complete picture: who, where, what they do, and how good they are.
For medical/aesthetic businesses specifically, Person schema is the most under-deployed and highest-leverage schema type. The “board-certified” / “RN” / “MD” / “dietitian-led” signal is what AI engines look for when answering medical queries — and almost no med spa has it deployed.
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