How to Do a Schema Markup Audit at Scale

Structured information is simply a shared vocabulary that helps Google further understand the contented of your site. Also known arsenic schema, these customized bits of codification snippet connection worth to hunt engines and users alike.

While hunt engines get other accusation astir your tract and its content, users get an enhanced acquisition connected the SERP with affluent results.

The hunt effect listing shown beneath has a assortment of affluent effect features acknowledgment to different structured information types, including the prima standing and image.

(This SERP listing’s affluent snippets assistance this look basal retired from different results.)

There are galore antithetic types of schema to take from depending connected what accusation you privation to telephone out, and schema implementation is easier than ever earlier with escaped tools similar Schema Builder.

Lean much astir Schema Builder here: Schema Markup Generator: Build Structured Data Without Developers.

But, it’s important for SEOs to audit their schema to marque definite it's implemented correctly — and this comes with challenges of its own.

In this post, we'll cover:

• The Importance of Schema

• Why Schema Audits are Important

• Schema.org vs Google Structured Data

• The Challenge of Auditing Structured Data

• Steps to Auditing Schema astatine Scale

  • 1. Verifying Schema Implementation

  • 2. Validating Schema Code

Why is Schema Important for SEO?

Incorporating the schema markup gives SEOs the accidental to summation their search visibility.

This is due to the fact that definite schema markups connection a affluent effect (also referred to arsenic affluent snippets), arsenic we covered above.

These affluent results instrumentality up much SERP existent property than a listing without schema. Plus, the affluent results thin to gully the users' eyes and differentiate the SERP listing from others, which tin payment your CTR. 

With truthful overmuch contention to gain a click, schema is besides important to beryllium to users that you tin connection worth to them straight connected the hunt results page.

Take the FAQ schema, for example. Not lone bash the queries (and their respective results) summation the size of your SERP listing, they payment the extremity users.

Here’s an illustration of 2 companies that some connection bookings astatine the aforesaid hotel. Notice which 1 takes up much pixel space, and which a idiosyncratic is much apt to click connected …

Search Listing 1:

Search Listing 2:

As you tin see, enactment 2 provides overmuch much visibility successful the results pages and amended supports your hunt motor optimization efforts.

Why Schema Audits are Important

Unfortunately, not each schema are implemented appropriately — which leads to errors and warnings for your pages, arsenic we'll screen below.

It’s indispensable to audit your schema to guarantee that they've been added correctly truthful that some hunt engines and users tin reap the benefits.

Structured information audits request to usage a operation of structured information markup from schema.org and incorporated validation (i.e. errors and warnings) that Google looks for successful structured schema.

Schema.org vs Google Structured Data: What's the Difference?

Most structured information uses schema.org vocabulary, but Google recommends you travel the developers.google.com documentation alternatively than schema.org’s.

This is due to the fact that definite attributes whitethorn beryllium required connected schema.org, portion they are not required for Google hunt purposes and vice-versa.

Google's documentation describes which properties are required, recommended, oregon optional for structured information with peculiar meaning to Google hunt and the quality of the results.

The Challenge of Auditing Structured Data

If you statesman to incorporated aggregate types of schema or person structured information contiguous connected aggregate web pages (for endeavor sites, this tin beryllium thousands oregon millions!) past auditing for schema becomes a scalability problem.

Plus, determination are 2 steps: One requires verifying the codification is unrecorded connected the leafage and the different requires validating the code.

What instrumentality tin you usage to trial for errors successful a structured information markup?

There is the Google Rich Results instrumentality (that's meant to corroborate a page's eligibility for affluent snippets) but this lone allows for 1 URL astatine a time. This solution is amended suited for smaller sites, due to the fact that for endeavor sites it would beryllium a never-ending process.

(Loading a azygous URL into Google’s schema validator tool.)

Note: You whitethorn person besides heard of Google's structured information investigating tool, but this instrumentality is being deprecated.

The aforesaid applies to the Bing Markup Validator, which is simply a portion of its webmaster tools. A markup validator lone allows for 1 leafage astatine a time. 

In short, each hunt engine’s structured information validator has the aforesaid problem: they’re not feasible for auditing astatine scale.

Adding to the trouble are the assorted schema format options. Some sites usage JSON-LD (which is the astir popular), portion others usage Microdata oregon RDFa. Others adjacent usage a operation of these antithetic schema types, which combines assorted specifications and makes the audit process adjacent much difficult.

Recommended Reading: 7 Common Issues with Implementing Structured Data

Steps to Auditing Schema At Scale

A structured information analysis essentially consists of 2 steps:

1. Verify the schema exists connected the page
2. Validate the structured information snippet

Below, we'll amusement you however to implicit some stages of the process astatine scale.

1. Verifying Schema Implementation astatine Scale

To audit your schema astatine scale, you tin usage an SEO platform similar seoClarity. Not lone does the level let you to get this occupation done efficiently, it opens the doorway to collaboration with different departments wrong your organization.

In this instance, you whitethorn payment from sharing your findings with the contented team, oregon whichever squad is liable for the schema implementation.

For this demonstration, we’ll amusement you a structured schema audit investigation utilizing our Site Audits feature.

The crawler automatically captures each structured schema recovered per leafage successful a crawl.

Structured Schema

This summary container uses a donut illustration to amusement you the full fig of pages crawled, and however galore pages had structured data.

In this example, retired of the 1,534 pages recovered successful the crawl, lone 6 of them had structured data.

This is utile for visualizing the percent of pages that person schema implemented. Perhaps the fig is importantly little than you expected it to beryllium — which could mean the schema is not implemented correctly.

Schema Types Found

Easily spot which schema types were recovered successful the crawl, the fig of URLs that had that schema benignant implemented connected the page.

Here, a pastry illustration makes the summary investigation seamless and lets you visualize the beingness of these assorted schema types successful narration to 1 another. 

(The fig of schema types recovered successful the schema audit.)

Schema Format

We enactment each methods for adding structured information (i.e. JSON-LD, Microdata, and RDFa). This summary container shows you however galore of each format benignant were recovered successful the crawl.

(The format of the schema found, including JSON-LD.)

Granular Analysis astatine the Page Level

After you’ve digested the summary report, navigate to the URL array for a person look astatine circumstantial pages’ structured information implementation.

This allows users to cheque the fig of structured schema for each page.

(This database shows the circumstantial pages wherever schema were found.)

There are 2 main points to see here:

• Page rubric and URL
• Schema Found

2. Validating Schema Code

Now that you've verified that the schema exists connected the page, it's clip to validate the structured information to guarantee determination aren't immoderate errors.

If the schema exists connected the leafage but does not incorporate the required fields, it won't get picked up by Google since it would beryllium missing captious accusation that Google displays successful the SERP. 

This is wherefore it's important to validate the code.   

Most sites instrumentality schema utilizing a template oregon an automated script. So alternatively of validating each schema benignant implemented connected each leafage you conscionable request to validate your template pages. 

If 1 of your templated pages is validated, it's apt that each the pages with the aforesaid template besides person the schema correctly implemented.

To bash this, leverage Schema Tester — a structured information investigating situation with a corresponding Chrome plug-in.

The web leafage below, for example, has the breadcrumb enabled (with nary errors oregon warnings).

(Schema Tester instantly validates the schema types recovered connected a page.)

Conclusion

Schema connection benefits to users, hunt engines, and SEOs alike. With the summation of schema, you tin amended pass hunt engines connected what your leafage is astir and springiness users an enhanced acquisition with affluent results successful the SERPs.

However, beryllium definite to regularly show the implementation of your schema. While determination are tools to usage successful replacement of Google's structured information investigating tool, endeavor SEO requires much power.

For much penetration into however our Site Audits capableness makes it casual to execute a schema audit astatine scale, docket a escaped demo today!