Structured data: what it is and how to use it in SEO
By Tiago CostaUpdated on July 2, 2026
Structured data is standardized code that explains the content of a page to the search engine, field by field. In practice, it:
- uses the Schema.org vocabulary to label each piece of information;
- tells Google whether a snippet is a price, a rating, an author or a date;
- enables rich results (stars, FAQ, images, prices) in search;
- does not change the visible text, only how the machine reads it.
What structured data is
Structured data is information organized in a standardized format that a machine can read without ambiguity. In the context of SEO, the term has a very specific meaning: it is the code markup you add to the HTML to describe, explicitly, what each part of the content represents.
An example makes it clear. For a human, the phrase "4.8 stars from 320 reviews" is obvious. For a search engine, it is just text. With structured data, you label those numbers as a rating value (ratingValue) and a review count (reviewCount), and Google starts to understand it as an actual rating, and can show the stars in the result.
It helps to separate the SEO meaning from the database meaning. In technology, "structured data" also describes information organized in rows and columns (like a spreadsheet or a SQL table), as opposed to unstructured data. In this glossary, the focus is structured data markup for search, the pillar that connects your content to rich results and to technical SEO.
How structured data works
All structured data for SEO follows a shared vocabulary, Schema.org, created jointly by Google, Microsoft, Yahoo and Yandex. This vocabulary defines types (such as Product, Article, Recipe, FAQPage, Event) and properties (such as name, price, author, datePublished) that search engines recognize.
This vocabulary can be written in three formats, but one has become the recommended standard: JSON-LD, a block of code in JavaScript Object Notation inserted in the head or body of the page. Google prefers JSON-LD because it stays separate from the visible HTML, is easy to maintain and does not risk breaking with small layout changes.
The flow is simple: the crawler reads the page, finds the schema markup block, interprets each property and, if the content is eligible, displays an enhanced feature in the result. Structured data also feeds Google's knowledge graph, helping the search engine connect entities (people, brands, products) to one another.
Structured, semi-structured and unstructured data
Outside SEO, "structured data" is one of the three broad categories of how information can be organized. Understanding the difference helps you avoid mixing up the two meanings of the term.
| Type | How it is organized | Examples |
|---|---|---|
| Structured | In a fixed model, with defined rows, columns and fields. | Spreadsheets, SQL tables, records. |
| Semi-structured | No rigid table, but with markers that add organization. | JSON and XML files, and SEO JSON-LD itself. |
| Unstructured | No predefined format, hard to query directly. | Free text, images, audio, video. |
Note that SEO markup is, technically, semi-structured data (JSON with labels), used precisely to give machine-readable structure to a page that, at its core, is unstructured content. This is how the search engine turns loose text into organized information.
Main types and examples of markup
Google only displays rich results for content types it already officially supports. Knowing the most useful ones helps you prioritize the effort:
- Article: marks up articles and blog posts, with author, title and date. The basis for performing well in news and editorial content.
- Product and Offer: describe products with price, availability and reviews. This is what generates the stars and the price in e-commerce results.
- FAQPage: marks up questions and answers, which can appear expanded in search and reinforce the chance of the featured snippet.
- Recipe: recipes with prep time, calories and rating, widely used on cooking sites.
- Event, LocalBusiness and BreadcrumbList: describe events, local businesses and the site navigation trail.
Each type has required and recommended properties. Filling in the recommended ones, not just the minimum, greatly increases the chance of the rich feature being displayed.
How to implement structured data step by step
Adding markup does not have to be complicated. A routine that works for most sites:
- Choose the right type: identify what the page is (an article, a product, a FAQ) and select the matching type on Schema.org.
- Generate the JSON-LD: write the block by hand or use a plugin or generator. On platforms like WordPress, SEO extensions insert the markup automatically.
- Fill the fields with real data: never mark up a review that does not exist on the page, that violates the guidelines and can trigger a penalty.
- Test before publishing: validate the markup in the Rich Results Test and in the Schema.org validator to catch errors and warnings.
- Monitor in Search Console: follow the enhancements report to see which pages are eligible and fix issues.
Once published, the search engine needs to crawl the page again to recognize the markup, so rich results can take a few days to appear.
Benefits: rich results, CTR and AI visibility
The biggest gain from structured data is eligibility for rich results, which take up more space, draw more attention and tend to receive more clicks. The effect shows up in numbers from serious sources.
According to the case studies gathered in the Google Search Central documentation, Nestlé measured an 82% higher click-through rate on pages shown as rich results compared with pages without that format, and Rotten Tomatoes recorded 25% more clicks on the pages with markup applied to 100,000 URLs.
There is also a more recent benefit: generative search engines and AI assistants use semantic structure to understand and cite content with more confidence. A well marked-up page tends to be easier to summarize and reference inside AI Overviews, which adds value from both traditional SEO and AI optimization.