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An Agency Guide to Structured Data

image featuring structured data

A quick search on structured data might cause marketers some initial confusion. Even for those who live and breathe these types of concepts at a marketing agency, there seem to be two popular related definitions that are not completely the same.

The more general and correct definition of structured data is information organized in a specific format or layout. This format can store and manipulate data more efficiently, making it easier to process and analyze.

The other definition limits its description of structured data to how it’s most often used to optimize for particular outcomes in search engine results pages (SERPs) and other marketing applications.

Let’s say you have a client running a YouTube Cooking channel who wants to gain more of an audience for their blog. Your agency needs to show the right information to convince searchers that your client is just what they are looking for.

So you encode rich text snippets to tell Google essential information about the recipe such as cook time, serving size, nutrition information, and recipe reviews. Here’s what a recipe schema markup might look like on the backend:

structured data coding showing a recipe schema

While on the front end, it looks like this. As you can probably guess, the websites that use structured data have more chance of making it to the top of the page. 

structured data on the fronted for recipe schema

This article will explore just what structured data is, examples, and how to leverage it for your clients’ success.

First things first: 

What Is Structured Data?

Structured data is any data that’s organized and stored according to a particular set of standardizing parameters. From a technical standpoint, a prevalent example would be data compiled into stored query language (SQL) databases.

Structured data in this broad sense is typically:

  • Stored in a data warehouse

  • Predefined before processing and analysis

  • Limited to certain data types

  • Generally more accessible and prevalent than unstructured data (more on this in a minute).

A familiar example of structured data might be a simple spreadsheet or a CMS, where data points are organized in familiar, less flexible, but useful buckets, such as the name, title, email, and the company name of a prospective customer.

Returning to the example of structured data defined in contemporary marketing speak, consider its use by Google and other search engines as markup metadata for enriching content such as a product page for one of your clients.

In this context, structured data does not appear on the actual web page for your client’s product. But it does provide details hidden in the code for that same page that enable Google and other partners to provide additional information to end users, which has the added benefit of further optimizing marketing and sales efforts.

A different example in this context will highlight this more clearly.

Example of Structured Data

Let’s say you have a hyperlocal client that sells electric bicycles in their community. As one path of their buyer’s journey, consider an ideal customer who types the following query into Google:

ebike for sale near me

Here’s where structured data, and Google’s use of it today, comes into play.

Below are the actual results from this example query, which builds off of what Google does with unstructured data in the form of LocalBusiness Schema markups to offer useful details to end users:

Google Structured Data SERP Result Example

google seatch example of structured data

As you have probably noticed before, as a user yourself, there’s more going on in Google’s SERPs than there would have been in the past, where all you were presented with was a simple list of results. 

Several key elements to the UX in this screenshot originate from structured data included in the code for the sites of the businesses listed:

  • Starred reviews are rich snippets pulled in by Google based on structured data

  • The same can be said for details on items in stock

  • The helpful details providing directions using local landmarks are also based on structured data 

It’s important to note that structured data is not automatically utilized by Google or other search engines. But by including relevant structured data throughout web pages, you can position yourself for optimizations like those listed above when the bots crawl your site to catalog what’s included and how it might be relevant to users.

For this reason, you’ll want to use structured data in your client websites. Despite the lack of certainty that it will be used at all or any control over exactly how you do not want to lose the potential competitive advantage gained when it is included. 

Generally, Google and other search engines are engineered to make the best use of all available data to get users the information they need to make a decision–including the decision to buy your clients’ products.

Structured Data vs. Unstructured Data

Before getting deeper into our discussion of structured data, it’s worth answering the obvious question about how it might differ from unstructured data. Although both structured and unstructured data have applications beyond SEO, such as text messages and contact lists, we’ll focus mainly on the search implications of both. 

Unstructured data is raw data that is not restricted by conventional database structures but can also have powerful applications for your marketing efforts. This means that crawlers, like the ones deployed by Google, must review the content and make their best, machine-learning-based guess about what the content is about. 

However, it offers much more flexibility to create powerful, engaging, and interesting content that goes beyond a list of data points. 

This power comes with its tradeoffs, however, as the lack of standardization among forms of unstructured data requires more work to harness it adequately.

Some common examples of unstructured data include:

  • Blog post paragraph text and corresponding images

  • Social media posts

  • Internet of Things (IoT) sensor data

  • Text, email, or chat data

As you might guess, there are other big differences between structured and unstructured data:

Structured Data

Unstructured Data

Simpler to access by non-experts

Often requires data scientists to process

Formats pre-defined

Variable native formats

Stored in data warehouses (less space, scalable, more expensive to change)

Stored in data lakes (more space, more difficult to scale, less expensive to change)

Limited flexibility and usability

Flexible and quick to gather

More tools available

Fewer tools available

More easily automated with machine learning

Harder or more expensive to process for automated use

As you can see, the main difference between structured and unstructured data is how you use either or both, depending on your client’s business needs. 

Use cases for structured data will be more common, foundational, and quantitative. For agencies, much of your everyday metrics are built upon structured data that, in its most primary form, could fit into rows and columns. 

When it comes to SEO, structured data means coding content in a way that is easy for the search engines to digest, such as using Schema.org markup. The most common uses of structured data include:

  • Recipe schema markup

  • Event schema markup

  • Product schema markup

  • Article schema markup

  • FAQs 

  • Breadcrumbs 

  • Local business schema markup 

  • Person schema markup

  • Organization schema markup

For each of the above, there are specific requirements regarding what needs data to be structured and how to structure it. For example, we recently dug into the details on Local Business Schema markups.

Conversely, unstructured data may cost more time and money to utilize but could offer deeper qualitative analyses for informing decisions. From an SEO perspective, unstructured data is quicker and easier for an agency to create (think a typical web page or blog post) but harder for a search engine to digest and understand. 

For example, the paragraph text, headings, and images inside a blog post are typically unstructured data, but these are often accompanied by structured areas such as the actual recipe details, breadcrumbs showing the post and the post category, an FAQ at the bottom of the article, or author schema at the end of the post. 

Schema vs. Structured Data

What is the difference between schema and structured data? To put it succinctly, one is part of the other but they are not the same. Most structured data uses schema.org vocabulary, but not all structured data is schema. 

For example, the company's location, phone number, email address, and items may all be marked up with schema so that search engines can interpret the material. 

Structured data is a word used to describe the process of adding code to a website. Rich snippets, Schema, and Twitter cards are all instances of structured data. It specifies the material on your website as well as the activities that site users can do with that content. This is the information you provide search engines for them to comprehend your pages better. 

In summary, if the structured data is not one of the hundreds of structured data items specified in the Google Search Central structured data gallery, it is much less likely to improve how your client appears in Google Search results. And keep in mind that just because something is listed on Schema.org doesn't mean that it's a Google-recognized structured data element. 

How Structured Data is Used by Google

As the continued leader of search by a wide margin, Google dictates much about how structured data is used to supplement SEO.

To recap what we’ve touched on a few times above, Google uses structured data primarily to enable rich elements in their SERPs. 

Sites include structured data in their code that indicate particular key pieces of metatextual information that Google crawls and includes in SERPs at their discretion or the discretion of their algorithms. 

To reiterate, this version of structured data (as opposed to the basic technical definition we started with) relies on markup language developers or plug-ins include on web pages. We’ll get into a bit more detail on how this data is structured in a moment.

How Google uses this structured data can vary. Here are some examples:

Sitelinks

Additional sub-links that show up within SERPs for sites that include clean, relevant structured data for site pages.

Breadcrumbs

With the right structured data, these will show up on mobile searches instead of URLs, next to a site’s favicon.

Featured snippets

Various forms of rich cards, tables, quotes, and lists can be populated by Google based on included structured data.

Carousels

These are Images with captions that can be clicked through for a new SERP result, populated by structured data.

Knowledge panels

Google will try to use structured data to answer questions with the Knowledge Panels that appear to the right of a SERP, as applicable to some queries.

Videos

Relevant videos with the right structured data can be pulled to the top of SERPs in response to the right queries.  

How To

If you have content that instructs users in a How To format and has the right structured data, Google might present your written steps within a SERP along with a link to your content.

FAQ

Similarly, Google will populate answers to FAQ modules included within certain SERPs using structured data to source appropriate answers and links.

With so many opportunities to have your clients’ sites highlighted by Google, the natural next question is how to add structured data to site code so it can be used in one of the above ways.

How To Add Structured Data to a Website

The good news about adding structured data to sites is that some of it can be done for you by SEO plug-ins. 

On that note, it can be helpful to know the main flavors of structured data:

JSON-LD

Preferred by Google and is fairly simple to implement. JSON-LD consolidates all your structured data in one block that can be placed in the <head> or <body> of your site code.

Microdata

Microdata places structured data throughout the code for a site. As a result, it can get more complicated to manage and edit and/or could generate more errors as it interacts more closely with other code for the page.

RDFa

RDFa is not as popular a format for structured data, as it’s not preferred by Google. It works within HTML5 and is more commonly used by Meta/Facebook.

When you have to do it yourself, the most common and simplest format for it (JSON-LD) is easier to work with and less likely to cause any issues with other page elements when adding it to code than you might think.

In terms of actually sourcing and adding what structured data you might need, there’s a standardized resource called Schema.org, where you can copy and customize code to your needs. It will help you define parameters that you then use with your code format of choice out of the above three options.

4 Common Schema Markup Examples for Structured Data

Schema markups are useful for many types of content like events, people, restaurants, movies, and more. There are hundreds of markup types you can use to boost your clients’ search rankings. 

Person Markup

The schema person markup lists a person’s key details all on ‘Wikipedia.’ A Google search for ‘Walt Disney’ brings up this card with most of what you’d like to know when searching for a person. 

person markup example structured data

Organization Schema Markup

An organization schema markup, on the other hand, is all about a company’s information, with information on their location, what they do, content produced, and social profiles.

organization schema markup structured data example

Event Schema Markup

The event schema markup gives essential information about concerts, shows, festivals, and more types of events. Search your favorite musician’s concerts, for instance, and you’ll find their key concert location and date details at the top of your search page results. 

event schema markup structured data example Dan Mangan concert

A breadcrumbs markup might be less intuitive than the other names. The BreadcrumbList schema markup is “an ItemList consisting of a chain of linked Web pages, typically described using at least their URL and their name, and typically ending with the current page.”

It helps reduce bounce rate and helps users see information like location. On the backend, it looks like this: 

<script type=”application/ld+json”>

{“@context”:”http://schema.org”,

“@type”:”BreadcrumbList”,

“itemListElement”:[{“@type”:”ListItem”,”position”:1,”item”:{“@id”:”http://time.com/money/section/travel/”,”name”:”Travel”,”image”:null}},

{“@type”:”ListItem”,”position”:2,”item”:{“@id”:”http://time.com/money/tag/tourism/”,”name”:”Tourism”,”image”:null}}]}

</script>

On the front end:

Time.com > Travel > Tourism

Which Type of Structured Data Should You Use

In terms of type, agencies should scan Schema.org and other resources to determine which structured data to implement for which clients, based on their unique needs. 

Here is a quick overview of some of the most commonly used types of Schema markups, so you can see how many can be used for your clients. 

WebPage Schema Entity

LocalBusiness Schema Entity

MobileApplication Schema Entity

SportsOrganization Schema Entity

Blog Schema Entity

WebSite Schema Entity

Sport Schema Entity

MusicComposition Schema Entity

Organization Schema Entity

Review Schema Entity

Game Schema Entity

Series Schema Entity

Person Schema Entity

Thing Schema Entity

SportsEvent Schema Entity

BookSeries Schema Entity

BlogPosting Schema Entity

Rating Schema Entity

AlbumRelease Schema Entity

Currency Schema Entity

Product Schema Entity

Brand Schema Entity

Url Schema Entity

MusicRelease Schema Entity

Offer Schema Entity

Place Schema Entity

Address Schema Entity

MovieSeries Schema Entity

Article Schema Entity

Event Schema Entity

SportsTeam Schema Entity

VideoGameSeries Schema Entity

SearchAction Schema Entity

NewsArticle Schema Entity

VideoGame Schema Entity

Amount Schema Entity

CreativeWork Schema Entity

Recipe Schema Entity

Periodical Schema Entity

Athlete Schema Entity

AggregateRating Schema Entity

Movie Schema Entity

Action Schema Entity

Coach Schema Entity

When it comes to implementation, because it’s preferred by Google and simple to learn, we recommend using JSON-LD. Note that while structured data doesn’t positively impact search ranking, failure to code with it accurately could trigger action by Google that suppresses your rankings due to code errors.

Structured Data Tools and Testing

Several tools help you utilize and test your structured data (and other code). Use these to help place relevant data and to ensure any code added to your sites is clean and ready to be used by Google and other search engines.

Google’s Structured Data Markup Helper

This tool from Google will help you add structured data to a web page.

Google’s Rich Results Test

Use this tool to see which rich results can be produced by Google based on your structured data and preview how they might look in Google Search.

Schema.org Markup Validator

Validate markup on your page, including any coded structured data, with this tool.

Bing Markup Validator

Assess your site’s code, check your structured data, and work on your overall search ranking on the world’s second-largest search engine.

Keep Competitive by Utilizing Structured Data

Adding structured data might seem daunting for non-data scientist marketing agencies, but it certainly goes a long way in improving the ranking in the SERPs and gaining traction for your clients’ online presence. 

Structured data improves SEO efforts. Track your progress using SEO reporting software and live dashboards to keep track of keyword rankings for your clients automatically.

Written by

Michael DiBiasio

Michael DiBiasio is a Marketing and Storytelling Consultant and Content Strategist working out of New York City and Burlington, Vermont. With experience as a Head of Marketing (SaaS, eComm) and as a Video Marketing Lead (professional services), he helps stakeholders find the sweet spot between audience needs and values and their own sales and service goals.

Read more posts by Michael DiBiasio ›

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