Schema markup, also known as structured data or schema, helps make the information on your website search-engine-friendly and machine-readable so AI models can understand and use it in generative search responses. The information is readable because it's structured in a standardized code embedded in a webpage's HTML. AI models can recognize the content, context, and relationships between information sets on the page because of the structures the schema markup defines.
Why schema markup is important in modern search
Schema markup previously helped traditional search engines read web pages and display SERPs. Now, it also guides voice assistants, AI-driven searches, and new search platforms. When brands implement schema markup on their web pages, the structured data schema gives AI models clues about what page elements mean (e.g., hours of operation, production specifications, reviews).
Again, when AI models recognize what's in your brand information and content, they can surface your data when it's relevant to customer prompts and search queries.
But AI doesn't rely on schema alone. AI models analyze both structured data (like schema-marked content) and unstructured data (like images and videos) to pick up clues about what your web page is communicating.
While schema markup does not convert every piece of unstructured content into structured data, it highlights specific parts of the content. This makes it easier for AI to find and interpret your brand's semantic relevance for customer search queries.
Schema markup examples
Google's AI Overviews may recognize that there's an image on your local listing's website. Still, without schema markup, it can't tell if the image is your brand logo or a photo of your brick-and-mortar storefront, suggesting local relevance.
Another example: ChatGPT may "look" at web pages featuring the word 'tombstone' to generate an answer, but how does the model powering it know with certainty if 'tombstone' references an object, the movie, or the pizza brand? Schema markup helps to eliminate this confusion.
How schema markup improves SEO and brand engagement
Schema markup improves search engine optimization (SEO) by enhancing how search engines interpret, display, and rank web content, delivering rich snippets in SERP. More than 72% of sites on Google's first page use schema, but research estimates that only 50% of brands use schema markup. However, brands that use schema markup see significantly higher click-through rates (25%-82%) than brands that don't.
Meanwhile, as search shifts to AI-driven platforms and search "experiences" keep developing, schema markup helps these platforms understand and use the information on web pages because it's been structured in a standardized way.
Schema markup can power rich results (also called rich snippets). Rich results contain elements like star ratings, reviews, images, pricing, product availability, FAQs, and more. These elements can make your listing more visible on a page or in AI-generated content, and they're full of content that customers view as helpful and informative, especially when compared to brands that don't surface with rich results.
How schema markup contributes to RAG in AI search
Schema markup also plays a role in supporting Retrieval-Augmented Generation (RAG)in AI search. RAG combines two key behaviors to make AI smarter and give customers better responses in AI search:
Retrieval: RAG pulls relevant, accurate information from reliable sources like knowledge graphs, websites, or reviews.
Augmented generation: Using the data RAG retrieves, the AI model crafts conversational responses grounded in facts. By using schema markup to structure content that feeds into Retrieval-Augmented Generation workflows, brands can improve three things customers value:
Precision: RAG enhances an AI model's ability to interpret and communicate domain-specific information for specialized, regulated industries like healthcare or legal services, where accurate semantic relationships matter deeply.
Accuracy: RAG reduces errors and hallucinations using schema and knowledge graphs to deliver verified data.
Relevance: RAG can enrich the context of both prompts and responses, ensuring that AI generates answers that match customer search intent no matter where they are in the customer journey.
Types of schema markup
Schema markup was developed collaboratively by major search brands like Google, Bing, and Yahoo. The collaboration led to a standardized vocabulary held by Schema.org. There are approximately 800 types of schema and nearly 1,500 schema properties.
Here are some examples of schema types that help improve local SEO:
AreaServed schema: Important for staking out geographical areas and neighborhoods served by your brand location
OpeningHours schema: Indicates when your business is open/closed so customers get accurate data
LocalBusiness schema: Structures essential business information like NAP, operating hours, and products/services offered
FAQ schema: Ideal for answering common questions and responding conversationally with specifics in response to high-intent searches
Review schema: Displays ratings and reviews to attract attention, build trust, and give customers the at-a-glance reassurance they need to engage
Recipe schema: Provides ingredients, cooking time, etc., so your differentiators stand out with high-intent search
Product or Service schema: Highlights product details like price and availability to improve click-through rates (CTR) and foot traffic
New frameworks for thinking about schema markup for SEO
Schema markup is increasingly more valuable — and critical — for brand visibility. As search fragments across platforms, schema helps listings stand out in traditional SERPs, and the structured data in schema markup penetrates voice and AI-driven search so your brand can surface with authority in conversational prompts.
Examples of what schema markup does for traditional SEO
Enhanced relevance: Schema markup improves a search engine's ability to index and rank pages.
Rich search results: Schema fuels features like star ratings, prices, or FAQs in search engine results pages (SERPs). Customers engage with these results because of their visually appealing, informative features.
Improved content categorization: By explicitly defining entities (e.g., products, events), schema markup shows search engines how to associate the right content with relevant queries.
Examples of what schema markup does for AI-driven search
Context, context, context: Schema markup gives AI clear signals about the content's purpose and context.
Advanced features: Schema markup informs and generates rich, conversational features like FAQ answer boxes, voice search results, and reverse prompting from AI agents.
Voice search optimization: Schema helps AI voice assistants like Alexa or Google Assistant extract concise, relevant answers to queries.
Schema markup can give you an edge in SEO and AI search discoverabilityhe structured data in a schema. Ultimately, this improves the accuracy of AI-generated responses.S
Your schema markup can give you an edge in SEO and AI search discoverability
Despite its benefits, only a small percentage of brands and websites use schema markup. But, using schema at scale can give brands a competitive edge. Schema markup makes content more attractive, informative, and understandable than your competitors'. As AI search keeps fragmenting and the customer journey diversifies, schema markup is an unsung hero, helping brands meet customers everywhere they search.
Here are some key questions to ask when schema tagging a site for traditional search engines:
What do users hope to find on this page?
What keywords relate to the brand or site's content?
What keywords are customers likely to associate with this brand, location, product, or service?
By comparison, here are the types of questions to ask when schema tagging for AI and emerging search platforms:
What content on this page is most valuable to users and search engines?
Is the schema markup tested and free of errors?
What types of schema are most relevant to this page's purpose?
Does the schema markup match the visible content on the page?
Are all the required and recommended properties included in the schema?
Is there an opportunity to use specific subtypes of schema?
Is this page optimized for local SEO with location-specific schema?
How can this page's schema support voice search or AI-driven queries?
Are there opportunities to include review or rating data?
How frequently should this schema be updated?
Takeaways
Schema markup is a foundational layer that bridges unstructured queries with structured knowledge frameworks. But, manually tagging content is complex, time-consuming, and full of risk. Schema is central to future-proofing SEO and brand visibility, especially when managed at scale.
Yext automates schema markup to keep brand data accurate and structured. Yext also simplifies schema markup at scale. Learn how to implement the new strategy for AI search using schema markup and manage it seamlessly in a knowledge graph. Read the post