What is structured data, and why do you need it for SEO?

The volume of data flowing around us at any given time is staggering. The human race produces, on average, around 2.5 quintillion bytes of new data every day. Within that data flow, Google processes over 20 petabytes of daily data, facilitating around 3.5 billion searches. Meanwhile brands push out new content to their audiences continuously, across a spectrum of digital channels, and everyone competes with everyone else to be heard. 

Long story short: the global data landscape is crowded and confusing, and with so much information flying around, finding a way to make your brand’s content stand out to search engines, generative AI (GenAI) chatbots, and their users can be tricky and frustrating.

Fortunately, there is a way to impose order on that digital chaos, lift your voice above the noise, and cut through to the people, and the algorithms, that you need to reach.

The answer? Structured data.

In this post, we’re going to explore the concept of structured data, its importance for search visibility, particularly in the age of generative AI, and how you can apply it effectively in your content infrastructure.

Let’s dive in.

What is structured data?

Structured data refers to information that is classified and organized into a standardized format. 

For example, information contained in spreadsheets, tables, and databases, and organized in rows, columns, and labels, is structured — whereas information in prose text (like the paragraph you’re reading right now) is unstructured. 

In digital environments, standardized structured data is machine readable, which means that other digital applications and data analysis tools can identify it, organize it, and understand it — and even predict how it relates to other structured data around it. 

When you order a pizza online, for example, you don’t just write down a prose summary of what you want and fire it off to the takeout in the hope they can make sense of it. Instead, you enter the specifics of your order via a form, including your preferred toppings and crust, your first and last name, payment info, address, desired delivery window, and so on. That’s because the takeout needs to structure that information so that its app, and other connected apps, can read and understand it in order to process payments, gauge delivery time, and check routes for delivery drivers.

It would be much more difficult to organize and process that information if it was stored as unstructured data — and, more importantly, it would take hours to get your pizza.   

Why is structured data important for SEO?

The machine readability of structured data extends to search engines, which can use structured data to better identify the subjects of users’ searches, and then deliver the requested information. 

Structured data effectively gives developers and content creators a way to talk directly to search engines about their content, and their websites. 

That communication serves search engine optimization (SEO) perfectly because structured data not only helps search engines better understand your content, but present it in a useful, helpful way on search engine results pages (SERPs). Even better, it can help your page stand out from other results that don’t provide the same contextual depth.  

Creating rich results

In a best case scenario, search engines use structured data to create rich results (also known as rich snippets or featured snippets), which are a sort of supercharged entry on a results page. 

While there’s no guarantee structured data will prompt a search algorithm to create rich results, when it does, the search engine pulls certain information from the web page and presents it on the SERP. For example, if your page is selling a particular product, the rich result might include:

• A product image.

• User review star rating.

• The product’s price. 

• Delivery time. 

There are plenty of other rich results that could be displayed, which you can find here in Google’s Search Gallery. 

Unlocking GEO value 

In an evolving search landscape, SEO now necessarily needs to factor in the visibility of content to AI-generated search results delivered by large language models (LLM) such as Gemini or ChatGPT. This is known as generative engine optimization (GEO).

Where SEO is designed for delivering results for single search queries, and encouraging users to click through, GEO anticipates subsequent questions, and often chains multiple queries together to provide context-rich, multi-step answers to search requests. In order to optimize search engine results (SEO), we need to focus on the crawlability, indexability, and rankability of content, but when it comes to generative results, the focus should be on retrievability — in other words, how effectively AI is able to access, interpret, and prioritize information about a search target when forming its responses. 

We know that structured data is useful to GEO, and so far at least one search engine has officially confirmed that. It’s fairly safe to assume the other big engines also use structured data since they were behind the schema’s creation and rapid adoption. GEO for LLMs leverages structured data in a manner similar to search engines — essentially, enhancing entity recognition and retrieval. And if your content is entity-optimized using structured data, then it is more likely to appear in AI-generated results.

That means the structured data you use in your online content becomes more valuable — because it is (potentially) being used by both search engines and LLMs to generate results.

Using structured data and structured content

Structured data complements brands that build out their digital infrastructure with structured content.

In a structured content model, content itself is broken down into its component parts. A blog, for example, can be structured as a header, an image, a byline, body text, a call to action, and so on. Those components can be reused and reassembled anywhere within a brand’s digital infrastructure, on any digital channel, to add end-to-end efficiency to content workflows. 

Structured data enhances the impact and value of structured content because, no matter where content is used, its SEO-relevant metadata goes with it. That means you can spin up new pages and new campaigns quickly, and leverage structured data to support the SEO value of whatever content you create.

There’s much more to say about the value of structured content to SEO and other aspects of your content strategy but, for now, let’s stay focused on how to apply structured data to your content. 

How does structured data work?

Data isn’t born structured, so how does the communication between structured data and search engines happen?

To structure data, developers must add markup to their web pages in the form of tags and other label elements. Those markup elements classify the code that the page content is built on, and make it machine readable — so that it stands out to, and communicates with, search engines when they crawl the web looking for content to fulfill search requests. 

Think about going to the library (sorry, podcast fans). A library doesn’t organize its contents by throwing every title it holds into a huge and ever-expanding pile on the floor. We’d classify that as unstructured data — and probably also “a mess.”

Instead, librarians consider the information that defines each book (subject, author, publication date, etc.), and carefully assign it a place on a shelf, in a specific department, on a specific floor, based on the nature of that content. So, when you arrive to find a book, you don’t spend all afternoon searching because the helpful labeling process gives you a good idea where you’re going to find the information you’re looking for — even if you don’t have a specific title in mind. 

That’s essentially what structured data does for search engines. When you add structured data markup to the unstructured data in your page, you’re announcing what your page contains and how it should be displayed — rather than forcing the search engine to trawl through a vast landscape of irrelevant information, or make best guesses.  

And, unlike a librarian, you don’t have to add markup to your content manually: there are digital tools available to help brands and developers add markup to content automatically. 

Structured data SEO benefits

Now that we understand how it works, let’s summarize the specific benefits of working with structured content. 

• Machine readability: Structured data is easy for computers to process, which makes it ideal for use on the web. 

• Communication with search engines: Structured data provides search engines with key information about, and a better understanding of, your website and its content.

• Search engine results pages: When used correctly, structured data enhances the appearance of search results, which can help improve your website’s rankings and click-through rate from SERPs. 

• GenAI search results: Using structured data makes your content more likely to appear in AI-generated answers from LLMs. 

• Enhanced EEAT signals: By reinforcing the credibility and authority of your content, structured data can influence Google’s search quality ratings — which are based on experience, expertise, authority, and trust (EEAT), and are assessed by humans.   

What is schema markup?

We’ve talked about adding markup to web pages as a way to turn them into structured data and help them talk to search engines, but what do you need them to say to those search engines? 

Fortunately, we know quite a lot about what kind of data search engines are looking for when they run searches — thanks to Schema.org. 

Schema.org is a "collaborative community activity with a mission to create, maintain, and promote schemas for structured data” across the digital landscape. Founded by Google, Microsoft, Yahoo, and Yandex, Schema.org represents a shared vocabulary which can be used to markup code as structured data so that search engines are more likely to use it to create rich results. 

Using schema code you can add markup to a range of common content types, including: 

• Articles

• Books

• Breadcrumbs

• Courses

• Events

• FAQs

• How-tos

• Local businesses

• Movies

• Reviews

• Podcasts

• Products

• Recipes

• Services

That list isn’t exhaustive but provides a sense of the type of structured data that you could add to your digital content. If you’re running an ecommerce store that sells furniture, for example, you could add markup to the unstructured data on your product pages that signals important customer-relevant information such as furniture type, material, price, color, and so on. 

You can explore the full Schema.org hierarchy here to find the markup you need for your content. Google’s own structured data search gallery is another useful resource, presenting  structured data features as a visual list.   

Using schema markup with your digital content

There’s certainly a bit of a learning curve when it comes to using Schema.org to unlock the possibilities of structured data. From a marketing perspective, a good starting point is to simply take a look at which types of schema markup your competitors are using.

Once you’ve determined what type of structured data you want to include in your content, you’ll need to decide which data format you're going to use to embed the markup in your page. You can choose from:

• JSON-LD: Uses JSON to encode linked data within HTML.

• Microdata: Adds metadata directly within HTML code. 

• RDFa: Supports multiple markup vocabularies.

The key differences between these formats are the ways they are embedded in the page code. While JSON-LD uses a JavaScript object, Microdata uses inline HTML tags and attributes. 

Google supports all three formats, but we typically recommend the use of JSON-LD because it can be loaded asynchronously and therefore doesn't impact page performance. 

Implementing, testing, and monitoring structured data

There are a number of ways that you can transform unstructured data by adding structured data markup to your pages.

To kick things off, take a look at Google's Structured Data Markup Helper. The tool will help you determine what type of data your page contains, and how it should be marked up. 

After markup code has been added to a page, you can use Google's Structured Data Testing Tool to make sure that it has been implemented correctly. You could also use Google’s Rich Results Test tool to see if your page supports rich results. 

Finally, you can also use Google Search Console to monitor for structured data issues through the Enhancements report feature, which you can find at the bottom of the Overview tab, or by using the URL Inspection tool.

The added value of structured data

Remember: even if you’ve implemented all your structured data markup correctly, there's no guarantee that search engines will use your structured data to enhance search results, so don’t despair if your efforts don’t deliver an immediate SEO boost. The data that Google uses to produce search results changes frequently, and can also vary significantly from user to user based on location, search history and search intent.

Whether your markup generates rich results or not, you can rest assured that it’s having a meaningful SEO impact, and adding value, because it’s sending a strong signal to search engines that you’re making an effort to help them understand your content — and that you’re maintaining a living, breathing site that hosts quality content. 

Using structured data with Contentful 

Contentful makes it easy for you to structure data across every part of your content infrastructure, adding markup quickly and easily without worrying about coding complications. 

With Contentful, you’ll be able to leverage an API-first content management system (CMS) to build flexible content models that align with Schema.org vocabulary, and dynamically generate structured JSON-LD data across every page, and every digital experience, that you create.

Ready to see structured data in action? Sign up for a free Contentful account or check out our SEO Guide and Developer Portal for more resources and tutorials.

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