What is structured data? Definition and examples

Structured data is machine-readable markup, usually schema.org terms written as JSON-LD, that states what a page describes: a product, company or article.

How it works

A page's visible text is written for people. Structured data adds a parallel, labeled version for software: this is an organization, this is its logo, this product costs this much. Google calls it "a standardized format for providing information about a page and classifying the page content" (Google Search Central).

Most markup uses the shared vocabulary at Schema.org, with types such as Organization, Product, SoftwareApplication, Article and BreadcrumbList. It can be written in three formats: JSON-LD, Microdata or RDFa. Google recommends JSON-LD where your setup allows it, while saying all three are equally fine if the markup is valid.

Markup makes a page eligible for enhanced search features (rich results); it doesn't guarantee them.

Why it matters for your website

The search payoff has narrowed. Google has retired several rich result types: How-to results were dropped in 2023, and the FAQ rich result stopped appearing in Google Search on May 7, 2026, with its documentation removed in June 2026 (Google Search updates). Google also says there is no special schema.org markup you need to add to appear in its AI features (Google Search Central).

Structured data is still worth doing well, for three reasons:

• Remaining rich results. Product, breadcrumb, organization and other features still use it.
• Unambiguous facts. Name, logo, prices and product type stated once, in a standard format, are easier for any system to parse, including AI tools and shopping agents.
• Consistency checks. Writing markup forces you to state prices and plan names precisely. Keep them identical to the visible page; markup that contradicts the page breaks Google's guidelines and confuses readers of both.

Example

JSON-LD for a software product page, placed in the page's HTML inside a script tag of type application/ld+json:

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Acme Analytics",
  "applicationCategory": "BusinessApplication",
  "operatingSystem": "Web",
  "offers": {
    "@type": "Offer",
    "price": "49.00",
    "priceCurrency": "USD"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Acme Inc.",
    "url": "https://www.example.com"
  }
}

Validate it with a structured data testing tool before publishing, and update it whenever the visible price changes.

Related terms

• Answer engine optimization (AEO): making answers easy to lift from your pages.
• Generative engine optimization (GEO): being cited by AI-written answers.
• Agentic commerce: agents that compare products and prices.
• llms.txt: a separate, Markdown-based guide for AI tools.