Structured data is a shared vocabulary that helps Google further understand the content of your site. Also known as schema, these custom bits of code snippet offer value to search engines and users alike.
While search engines get extra information about your site and its content, users get an enhanced experience on the SERP with rich results.
The SERP listing shown below has a variety of rich result features thanks to different schema, including the star rating and image.
(This SERP listing’s rich snippets help it stand out from other results.)
There are a variety of schema to choose from depending on what information you wish to call out, and schema implementation is easier than ever before with free tools like Schema Builder.
Recommended Reading: Schema Markup Generator: Implement Structured Data Without Developers
But, it’s important to audit your schema to make sure they’re implemented correctly, and this comes with challenges of its own.
In this post, we'll cover:
- The Importance of Schema (and a Schema Audit)
- The Difference Between Schema.org and Google Structured Data
- The Challenge of Auditing Structured Data
- Auditing Schema at Scale
So, let's get started …
The Importance of Schema (and a Schema Audit)
Incorporating the schema markup gives you the opportunity to increase your search visibility.
This is because certain schema markups offer a rich result (also referred to as rich snippets), as we covered above. These rich results take up more SERP real estate than a listing without schema. Plus, the rich results tend to draw the users' eyes and differentiate the SERP listing from others, which can benefit your CTR.
With so much competition to earn a click, schema is also important to prove to users that you can offer value to them directly on the search results page. Take the FAQ schema, for example. Not only do the questions (and their respective answers) increase the size of your SERP listing, they benefit the end users.
Here’s an example of two companies that both offer bookings at the same hotel. Notice which one takes up more pixel space, and which a user is more likely to click on …
Search Listing 1:
Search Listing 2:
Unfortunately, not all schema are implemented appropriately — which leads to errors and warnings for your pages, as we'll cover below.
It’s necessary to audit your schema to ensure that they've been added correctly, so that both search engines and users can reap the benefits.
Structured data audits need to use a combination of structured data markup from schema.org and incorporate validation (i.e. errors and warnings) that Google looks for in structured schema.
What’s the Difference Between Schema.org and Google Structured Data?
Most search structured data uses schema.org vocabulary, but Google recommends you follow the developers.google.com documentation rather than schema.org’s.
This is because certain attributes may be required on schema.org, while they are not required for Google search purposes and vice-versa.
Google's documentation describes which properties are required, recommended, or optional for
structured data with special meaning to Google search and the appearance of the results.
Schema at Scale: The Challenge of Auditing Structured Data
If you begin to incorporate multiple types of schema, or have structured data present on multiple web pages (for enterprise sites, this can be thousands or millions!) then auditing for schema becomes a scalability problem.
There is Google’s Rich Results test, but this only allows for one URL at a time. This solution is better suited for smaller sites, because for enterprise sites it would be a never-ending process.
(Loading a single URL into Google’s schema validator tool.)
The same applies to the Bing Markup Validator, which is a part of its webmaster tools.
Each search engine’s structured data validator has the same problem: they’re not feasible for auditing at scale.
Adding to the difficulty are the various schema format options. Some sites use JSON-LD (which is the most popular), while others use Microdata or RDFa. Others even use a combination of these different schema types, which combines various specifications and makes the audit process even more difficult.
Recommended Reading: 7 Common Issues with Implementing Structured Data
Auditing Schema at Scale with seoClarity
To audit your schema at scale, you can use an SEO platform like seoClarity. Not only does the platform allow you to get this job done efficiently, it opens the door to collaboration with other departments within your organization.
In this instance, you may benefit from sharing your schema audit findings with the content team, or whichever team is responsible for the schema implementation.
For the purposes of this demonstration, we’ll show you a structured schema audit analysis using Clarity Audits, our advanced site audit feature.
Top-Level Summary of Your Structured Data
For a quick and easy summary of your structured data, you’re immediately shown a visual representation of your structured data.
This includes the amount of structured data that was found on your site, the schema types that were found, and the different formats used for the schema.
Let’s break each of these down.
This summary box uses a donut chart to show you the total number of pages crawled, and how many pages had structured data.
In this example, out of the 1,534 pages found in the crawl, only 6 of them had structured data.
This is useful for visualizing the percentage of pages that have schema implemented. Perhaps the number is significantly lower than you expected it to be — which could mean the schema is not implemented correctly.
Schema Types Found
Easily see which schema types were found in the crawl, the number of URLs that had that schema type implemented on the page, and errors/warnings per schema type.
Here, a pie chart makes the summary analysis seamless, and lets you visualize the presence of these various schema types in relation to one another.
In the example below, it shows you that the product schema may not have been implemented correctly as indicated by the large number of errors and warnings found for that schema type.
We support all methods for adding structured data (i.e. JSON-LD, Microdata, and RDFa). This summary box shows you how many of each format type were found in the crawl.
Granular Analysis at the URL Level
After you’ve digested the summary report, navigate to the URL table for a closer look at specific pages’ structured data implementation.
This allows users to check the code related to the structured schema for each page.
(The URL list shows the specific pages where schema were found.)
There are four main points to consider here:
- Page title and URL
- Schema Found
We follow Google’s classification of errors and warnings, which means that errors need to be fixed, while warnings are recommended additives, but don’t necessarily need to be fixed for the structured data to work.
To dive even deeper into the data, simply click on the number associated with any page’s schema found, errors, or warnings. Here’s what that looks like for the first URL shown in the table above.
Our schema has no errors, but you can still explore the item itself — in this case, you can see the explicit questions and answers used for the FAQ schema.
Schema offer benefits to users and search engines alike. With a quick addition of a code, you can better inform search engines on what your page is about, and give users an enhanced experience with rich results in the SERPs.
However, be sure to regularly monitor the implementation of your schema. If there are errors in the code, you won’t be able to reap the benefits that the structured data offers.