Schema Markup
Schema markup is structured metadata embedded in a webpage (using the schema.org vocabulary, typically as JSON-LD) that tells search engines and AI assistants exactly what a page is about — product, article, FAQ, organisation, person, recipe, event, etc.
Why it matters
Schema markup is the difference between an AI assistant guessing what your page is and reading a labelled fact card. Without it, AI synthesis is brittle and inconsistent across crawls; with it, your brand has a stable, machine-readable identity that AI engines extract verbatim.
How it works
You add a <script type="application/ld+json"> tag to each page containing JSON-LD structured data. Search engines and AI crawlers parse the JSON and treat it as authoritative metadata about the page — bypassing the need to infer meaning from prose.
Schemas that matter for AEO
- Organization — the company entity (name, URL, logo, social profiles).
- SoftwareApplication / Service — the product entity, including features, audience, category.
- Article / BlogPosting — for blog posts, with author, dates, citations.
- FAQPage — Q&A blocks AI assistants extract directly into 'how do I' answers.
- HowTo — step-by-step processes (still consumed by Perplexity, ChatGPT, Bing/Copilot even after Google deprecated rich results).
- Offer / AggregateOffer — pricing tiers, currency, billing duration.
- Review / AggregateRating — testimonials and ratings.
- Claim — sourced statistics with author and citation appearance.
- SpeakableSpecification — flags content blocks AI should read aloud.
The @id pattern
Give each entity a stable @id URI so other entities can reference it across pages. An Offer's itemOffered can point to a SoftwareApplication's @id instead of duplicating its data — this is the schema-graph pattern.
Validation
Use Google's Rich Results Test and schema.org validator on every change. Broken schema is worse than no schema — AI engines may downweight a page entirely.
Related terms
