Standardized format for providing information about a page to search engines, typically using schema.org vocabulary.
Structured data is a standardized format for providing explicit information about a page's content to search engines, typically using schema.org vocabulary to help them understand context and relationships. By implementing structured data markup, websites can communicate directly with search engines about their content's meaning, whether that's a product, event, recipe, organization, or any other entity type.
Search engines use this structured information to create enhanced search results called rich snippets, which can include star ratings, prices, event dates, and other relevant details displayed directly in search results. This enhanced presentation often leads to higher click-through rates and better visibility in competitive search environments.
Why It Matters for AI SEO
AI-powered search systems rely heavily on structured data to understand content context and entity relationships. Google's AI models, including BERT and MUM, use structured markup as training signals to better comprehend page content and determine appropriate search result features. When AI systems encounter well-marked content, they can more accurately categorize it and determine when to trigger enhanced SERP features. Modern AI search experiences like Google's AI Overviews and answer engines increasingly depend on structured data to source accurate information for direct answers. Websites with comprehensive structured markup become more likely candidates for featured snippets, knowledge panels, and AI-generated response citations, making structured data implementation crucial for maintaining visibility in AI-dominated search landscapes.
How It Works
Structured data implementation typically uses JSON-LD format embedded in a page's HTML head section, though microdata and RDFa formats remain valid alternatives. The most common schema types include Product, Organization, Person, Event, Recipe, FAQ, and How-to markup. Tools like Schema Pro and WordLift can automate much of this implementation, while Google's Rich Results Test validates markup accuracy. Effective structured data strategy involves mapping your content types to appropriate schema vocabularies, then implementing comprehensive markup that covers all relevant properties. For e-commerce sites, this means marking up products with prices, availability, and reviews. For local businesses, it involves Organization and LocalBusiness schema with complete NAP information. Content sites benefit from Article schema, FAQ markup for common questions, and How-to schema for instructional content.
Common Mistakes
The most frequent error is implementing incomplete schema markup that covers only basic properties while omitting valuable details that could enhance rich snippets. Many sites add Product schema but forget to include crucial elements like aggregate ratings, availability status, or detailed descriptions. Another common mistake is using outdated or incorrect schema types, such as applying Article schema to product pages or using deprecated properties that search engines no longer recognize. Validation issues also plague many implementations, with syntax errors, missing required properties, or conflicting information between markup and visible page content creating problems for search engine interpretation.