Schema markup
Schema markup is structured data (typically JSON-LD) that tells search engines and AI engines explicitly what a page is about.
Schema markup is the common language Google, Bing and AI engines use to semantically understand web pages. The vocabulary comes from schema.org, a joint initiative of major search engines since 2011. Rather than letting a search engine guess ('is this a recipe, a product or an article?'), you tell it explicitly via JSON-LD in the page head. For GEO, schema markup is extra important: LLMs can read HTML, but structured data is drastically easier to parse and far less error-prone. Pages with well-filled Article, FAQPage, HowTo, Product and Organization schema get cited more consistently and correctly in AI answers.
Example
A FAQPage schema looks like this: a JSON-LD script with @type FAQPage and a mainEntity array of Question objects, each containing an acceptedAnswer with Answer text. Adding it takes minutes; the impact on AI citation rate is significant.
Frequently asked questions
Which schema types have the most impact?
Per content type: Article (blogs), FAQPage (FAQ blocks), HowTo (step instructions), Product (shops), Organization (homepage), LocalBusiness, Person (author bios), BreadcrumbList, Review/AggregateRating, DefinedTerm/DefinedTermSet (for glossaries).
In which format should I add schema?
JSON-LD is Google's recommended form — inline in the head as a script tag. Alternatives (microdata, RDFa) still work but are harder to maintain.
Related terms
Further reading
- → Our service: SEO
- → Blog: Bitcoin & YMYL: how Google judges crypto sites
- → Blog: Fintech: highest AI Overview exposure of all sectors
- → Blog: GEO for Bitcoin & fintech: why you need it