Before diving into implementation, ask yourself: what am I aiming to achieve through Schema Markup? Which key content or business entities do I want to highlight in search results? How can Schema Markup support our overall SEO and content strategy?

A thoughtful Schema Markup strategy can help you:

• Target the right rich results
• Develop your content knowledge graph
• Increase organic traffic and CTR in search
• Identify gaps in your content and inform your content strategy

In this article, we outline the steps you can take to create a Schema Markup strategy for your website.

Let’s get started!

Step 1: Identify Key Entities in Your Content for Structured Data Opportunities

The first step to implementing Schema Markup is identifying the key entities that represent your business and where they are located on your site. This process will help you recognize the structured data opportunities across your website.

1.1 List the Key Entities That are Relevant to Your Business

You can start by creating a list of key entities that make up your business, such as:

• Business name and contact information
• Products or services
• Key personnel (e.g., owner, management team)
• Locations (for businesses with multiple branches)
• Authoritative content about your industry or expertise

For example, Pizza Palace is a local pizza restaurant owned by Enrico Picolli with over 20 locations across Ontario, Canada. Pizza Palace sells different types of pizzas (i.e. pepperoni, Hawaiian, etc.) and appetizers through their online site. Pizza Palace, along with its products, locations, and owner are key entities related to the business. These entities are also described across their website content.

1.2 Locate Relevant Web Pages

Once you’ve identified your key entities, you can identify which page on your website best describes each entity. This will help you determine which top-level Schema.org type you should use to mark up each page.

As per our previous example, the Pizza Palace home page has detailed information (i.e. address, logo, telephone number, etc.) about the organization. Therefore, it should be the entity home for the organization Pizza Palace.

Similarly, Pizza Palace has product detail pages for each of its pizzas. Therefore, these product detail pages should be the entity home for each product.

Pro Tip: If you don’t have a page about any of your key entities, this is an opportunity for you to create new content to describe the entity.

1.3 Map Content to Schema.org Types

Once you have your list of entities and where they live on your site, you can identify the corresponding Schema.org type for each entity.

Back to our example— “Pizza Palace,” being the organization’s name, would best correspond with the Organization type. Therefore, you would mark up the homepage with Organization markup to help search engines clearly understand the key information about the Organization.

Pro Tip: If you’re new to the Schema.org vocabulary and need more clarity on identifying types and properties within your content, we recommend reading our guide to the Schema.org vocabulary.

1.4 Create an Entity Mapping Table

Next up, it’s time to organize your findings in a table format for clarity. Here’s an example continuing with our hypothetical local pizza business:

Step 2: Review Eligible Rich Results for Your Pages

Once you’ve identified your key entities, the next step is determining which rich results are available and relevant to your content. This will help you prioritize your Schema Markup efforts for maximum visibility in the search results.

2.1 Understand Available Rich Results

Before you dive into rich results, we recommend familiarizing yourself with the types of rich results offered by Google. Google has over 30 rich results, some more applicable than others. The most common types of rich results include:

• Review Snippet
• Local Business
• Product
• Job Posting

Within Google’s structured data guidelines, you can see which Schema.org properties are required and recommended to achieve your target rich results. If your page does not have the content for the required properties, you must add the content to your site before you markup the required property.

2.2 Match Your Content to Eligible Rich Results

You can review your key content identified in Step 1 and determine which rich results each page might be eligible for.

For example, Pizza Palace has 25 product detail pages (PDPs), each with reviews and ratings related to the relevant product. Since the PDPs have the right content, they should be eligible for a review snippet-rich result when we add AggregateRating and Review markup.

When you’ve identified the rich results each page could be eligible for, you can add it to the previous table you’ve created. Here’s an example continuing with the table we created for the pizza business in Step 1:

2.3 Ensure Your Content Aligns With the Structured Data Requirements for Each Rich Result

Before implementing Schema Markup, it’s crucial to verify that your content meets the requirements for each desired rich result if that is your goal. This step helps ensure your markup efforts are effective and compliant with search engine guidelines.

Other actions to consider in this step include:

Review Google’s guidelines: Check the requirements for each rich result type you’re targeting. These requirements may be content-related (e.g. Aggregate Ratings require the reviewCount), but they also may be more general guidelines (e.g. There should only be one Product or ProductGroup per page). Google provides detailed documentation for each rich result in their structured data documentation.

Audit your content: Compare your existing content against the requirements. Look for any gaps or missing elements.

Update content if necessary: If your content doesn’t fully meet the requirements, now is the time to update it. This might involve adding more detailed information, reorganizing content, or creating new page sections.

Step 3: Assess Implementation Methods for Ease and Coverage

After identifying your key entities and desired rich results, it’s time to consider how to implement your Schema Markup strategy effectively. This step focuses on balancing high-value opportunities with practical and manageable implementation methods.

3.1 Analyze Pages for Individual or Repeating Content

The implementation strategy for one-off or individual pages will likely be different than highly templated or repeating page types. For this reason, we recommend determining whether your content follows single or recurring patterns:

• Individual Page: Unique content, generally dissimilar to other pages on the website (e.g. About Us or Contact Information pages)
• Recurring Pages: Pages with a similar content structure repeated across multiple URLs (e.g. product detail or location pages)

3.2 Assess Existing Capabilities

From here, evaluate your current technical setup:

• Check if your Content Management System (CMS) has built-in Schema Markup functionality
• If it does, determine whether it meets your requirements or needs enhancement from other methods or sources

3.3 Consider Implementation Methods

There are many ways to implement Schema Markup. We recommend exploring the different available approaches to implementing Schema Markup to decide what best suits your organization’s needs and capabilities:

• Manual Implementation: You can author your markup for each page manually and add it directly into the HTML of the page
• Plugin or SEO Tool Implementation: You can use Schema Markup plugins designed to generate Schema Markup (these can be limited in their capabilities)
• Full-Service Schema Markup Solution Providers: You can also hire an end-to-end Schema Markup solution Schema App to help you author, implement and manage your Schema Markup on an ongoing basis

3.4 Assess Automation Possibilities

Implementing Schema Markup manually can be tedious, complex, and time-consuming. This is especially true for large organizations managing thousands of content pages or even multiple domains.

This is when it becomes crucial to identify and leverage opportunities to automate your Schema Markup implementation:

• For recurring content patterns, consider templated solutions like the Schema App Highlighter
• Explore API integrations for dynamic content
• Investigate tools and plugins that can generate and update markup automatically and dynamically

Schema App automatically deploys Schema Markup to your website at scale, so you can save time, make updates faster, and reduce delays caused by waiting for IT/developers. It works with all website platforms because of our integrations with Tag Manager, JavaScript, or our custom add-ons for WordPress, Shopify, BigCommerce, Drupal, etc. Explore our integration options.

3.5 Plan for Scalability

A common goal for most organizations is to grow. Therefore, planning for scalability is a must!

Ensure your implementation strategy can grow with your business:

• Choose methods that can easily accommodate new content or site sections
• Consider future rich result types you may wish to target

By completing this step, you’ll have a clear understanding of how to implement your Schema Markup strategy efficiently and at scale, taking into account your current capabilities and future business needs.

Best Practices for Schema Markup

Here are some things to keep in mind as you develop your Schema Markup strategy and evaluate your site content.

1. Use JSON-LD (rather than microdata or RDFa), as recommended by Google
2. Use the most specific Type possible
3. Only markup content that is visible on the page
4. Identify one key page for each business concept
5. Include an @id attribute to the entities in your Schema Markup
6. Nest the entities in your Schema Markup to accurately showcase their relationships on a page

So now you know how to identify which pages you should optimize and how to determine the best approach depending on the page type.

At Schema App, we help you go beyond the fundamentals of SEO, leveraging structured data to showcase your unique value in search. In a rapidly changing SEO landscape, we introduce agility to your digital team, saving you time and resources for managing other aspects of your business portfolio.

See how our end-to-end Schema Markup and knowledge graph solution can help your website stand out in search.

The post How to Develop a Schema Markup Strategy for a Website appeared first on Schema App Solutions.

However, we’ve seen a lot of changes in rich results and the overall search experience this past year. The uprising of generative AI-powered search engines, accompanied by the volatility in rich results, has prompted our team to dive deeper into the semantic value of Schema Markup and entity linking as it pertains to search today.

In this article, we will share the value of entity linking, the tools enabling you to do it at scale and the results we’ve seen from implementing entity linking with our Enterprise clients.

Growing Importance of Entities in Search

Over the past decade, search engines have shifted from lexical to semantic search to improve the accuracy and relevancy of their search results.

As a result, how we think about search engine optimization also has to change. We have to move away from adding keywords to a page and go towards identifying entities on a page to help search engines and machines understand and contextualize the content on our pages.

Entities are a single, unique, well-defined, distinguishable thing or idea. An entity can be anything from a person to a place to a concept, and they possess defining characteristics or attributes (i.e. colour, price, name). But they need to be described in relation to other things to have meaning. For example, Schema App is an entity that can be described by its name, location, website URL, founders, employees and more.

Your website content contains entities related to your organization, and you can help search engines identify the entities on your page using Schema Markup.

When you implement Schema Markup on your page, you are identifying and describing the entities in your content, which helps search engines better understand your content.

While having entities defined on your site is good, you can go one step further and improve your markup by doing entity linking to build a connected, robust content knowledge graph.

A content knowledge graph is a collection of relationships between the entities defined on your website, defined using a standardized vocabulary like Schema.org. It enables search engines and other machines to gain new knowledge about your organization through inference.

What is Entity Linking?

Entity linking is the act of identifying entities mentioned in text, and linking them to corresponding entities that have been defined in a target knowledge base.

In the context of Schema Markup, entity linking is the act of linking the entities on your site to the corresponding known entities on external authoritative knowledge bases such as Wikipedia, Wikidata and Google’s Knowledge Graph using Schema.org properties. Examples of connector properties include sameAs, mentions, areaServed, and more.

External authoritative knowledge bases can differ by vertical or content type. For example, if you are in the medical or finance industry, there may be a governing body or glossary that best defines the entities within your content.

Entity linking can help you define the terms and entities mentioned in your content more explicitly, thus enabling search engines to disambiguate the entity identified on your site with greater confidence and provide users with more accurate and relevant search results.

For example, if your page talks about ‘London,’ this can be confusing to search engines because there are several cities in the world named London. You can help search engines disambiguate which London you are referring to in your content by linking to the same known entity described on Wikipedia, Wikidata or Google’s Knowledge Graph.

Suppose we are talking about the city of London in Ontario, Canada. In that case, we can use the sameAs property to link the entity on your site to the known entity on Wikipedia, Wikidata and Google’s Knowledge Graph. Doing this entity linking makes it explicit to search engines that the content on the page is about ‘London, Ontario, Canada’ and not ‘London, England’.

  "mentions": {
    "@type”: "Place",
    "name": "London",
    "sameAs": "https://www.wikidata.org/wiki/Q92561",
    "sameAs": "https://en.wikipedia.org/wiki/London,_Ontario",
    "sameAs": "kg:/m/0b1t1",
}

Entity linking is even more vital if your organization is in an industry where being specific is essential (such as defining a medical condition or a specific financial instrument like new construction financing).

Approaches to Entity Linking

You could take two main approaches to entity linking: a general approach and a more strategic one.

General Approach to Entity Linking

You could take a general approach and identify any entity on your site, check if it is a known entity on an external authoritative knowledge base, and, if it is, link that entity to the known entities.

For example, if you are a technology company, your product pages might mention entities like SOC2, Solution, and the United States. Using the general entity linking approach, you can link these entities to the known entities on external authoritative knowledge bases.

Strategic Approach to Entity Linking

Alternatively, you can take a more strategic approach and identify a specific type of entity on your site (for example, locations mentioned on your site or a particular term mentioned on your site), check if it is a known entity on an external authoritative knowledge base, and if it is, link that entity to the known entities.

For example, you can use a place-based entity linking approach to explicitly identify the place entities mentioned on a page and link them to the known entities on Wikipedia, Wikidata and Google’s Knowledge Graph.

If your website has different location-based landing pages for your offering, you can implement place-based entity linking in your Schema Markup. Doing so would help search engines understand the locations that your organization is servicing and enable your page to perform better on ‘near me’ and other location-based searches.

The entities you target with entity linking should be purposeful. Instead of linking all the entities on a page with corresponding known entities, you should focus on linking the most essential ones for clarity.

How we do Entity Linking at Schema App

At Schema App, we believe that entity linking is crucial to developing a robust content knowledge graph. It can add value to your SEO efforts and prepare you to get further insights from your content. So, how can you do entity linking within your markup?

You can manually link the entities on your page to the known entities on external authoritative knowledge bases. However, this solution is not dynamic nor scalable, so keeping the data updated and accurate can be resource-intensive and time-consuming.

The Schema App team developed the Omni LER feature to apply entity linking in a scalable, dynamic manner to solve the scale and accuracy of entity linking.

Omni Linked Entity Recognition (LER) is the automated process of identifying entities mentioned in texts and linking them to the corresponding entities on authoritative knowledge bases (like Wikipedia, Wikidata and the Google Knowledge Graph).

Today, Schema App’s Omni LER feature uses natural language processing to identify entities within a block of text automatically and embed them within the Schema Markup based on the Schema Markup configuration in the Schema App Highlighter.

In the future, we’ll introduce a controlled vocabulary feature to help our customers identify which entities they want to map to for entity linking. This evolution will give organizations even more control over the topics and entities they want to be known for and how they want to define those entities.

Entity Linking Experiments and Results

The impact of entity linking on SEO has yet to be explored widely. This prompted our team to experiment with entity linking to see if it has any measurable impact on SEO metrics.

Using our Omni LER feature, we implemented entity linking on over 60 enterprise customer accounts in healthcare, finance, B2B technology and other industries.

We ran general and place-based entity linking experiments on a variety of pages (i.e. blogs, location pages, medical pages, etc.) over three months and measured the impact on search performance. Here’s what we saw as the results.

General Entity Linking Experiment

We took the general entity linking approach on pages with long-form content, such as blogs. The Omni LER feature within the Schema App Highlighter identified the named entities in the text and embedded the known entities in the markup using the mentions and sameAs properties within the schema markup for the page.

For example, one customer had a blog article about rashes caused by amoxicillin. We used the “mentions” property to identify ‘Amoxicillin’ as an entity on the blog post and further clarified the entity by nesting the equivalent entities defined on Wikipedia and Google’s Knowledge Graph for Amoxicillin.

The Omni LER feature also identified other entities on the page, such as ‘Benadryl’, ‘Keflex’, ‘Mononucleosis’ ‘National Institutes of Health’, and linked these entities to the known entities on Wikipedia, Wikidata and Google’s Knowledge graph under the relevant schema markup property.

After implementing entity linking on that blog article, the customer saw a 336% increase in click-through rate for the query ‘Amoxicillin rash’ and a 390% increase in click-through rate for the query ‘Rash from amoxicillin’. The number of queries for that blog also increased by 86.75%.

Across our customer set, we saw an overall trend where the clicks and click-through rates increased for relevant keywords while the number of irrelevant keywords dropped for each page.

Placed-based Entity Linking Experiment

In a second experiment, we took the placed-based entity linking approach on location-based landing pages. This customer had a set of location-based landing pages to cater to their audiences in different states across the US.

We implemented placed-based entity linking on 11 test pages and kept 4 control pages to compare the results.

On the test pages, we added spatialCoverage and audience property in the markup to identify the state this page pertained to (in this example, it was for the state of California) and then further clarified which ‘California’ we were referring to by nesting the equivalent entities defined on Wikipedia, Wikidata and Google’s knowledge graph using the sameAs property.

After running the experiment for 85 days, the test sites saw an increase in the number of queries containing the state name and ‘near me’, leading to a 46% increase in impressions and a 42% increase in clicks for non-branded queries.

By clarifying the locations serviced on the site, this customer’s pages showed up for more location-based queries.

Do Entity Linking at Scale

Based on the early results we’ve seen, entity linking can help search engines disambiguate the entities mentioned on your site and help your pages show up for more relevant search queries, increasing the clicks and click-through rate to the pages. It is a great way to stand out in search and drive more qualified traffic to your site.

Entity linking can also help your organization build a more descriptive content knowledge graph. You can learn more about content knowledge graphs through our free ‘Content Knowledge Graph Fundamentals’ course.

If you want to implement entity linking at scale or build a content knowledge graph for your site, contact us.

The post Measurable Impact of Scaling Entity Linking for Entity Disambiguation appeared first on Schema App Solutions.

A knowledge graph is a structured representation of knowledge that describes entities and the relationships between them.

Knowledge graphs are a part of “knowledge representation“, a field of Artificial Intelligence (AI) that deals with presenting data in a way that enables machines to engage in reasoning, problem-solving, decision-making, and inferencing.

The versatility of knowledge graphs extends across various domains, with use cases that include:

• Recommendation engines
• Targeted advertising
• Data analysis
• Semantic search
• The training of Large Language Models (LLMs)

Knowledge graphs empower machines to extract meaningful knowledge from data by presenting information in a machine-readable format.

But did you know you can also create a “content” knowledge graph that is particularly useful for SEO initiatives? Although structured like a general knowledge graph, a content knowledge graph functions as a representation of the content on your website.

This graph can be published externally for search engines to consume, be employed for internal AI projects, or be used to identify content gaps.

Moreover, these graphs establish a robust foundation for developing more extensive marketing knowledge graphs if you have additional data sources you’d like to bring into the fold.

But before we get into that, this article will explore the basic components of a knowledge graph to enable you to develop your own content knowledge graph using the content on your website.

Anatomy of a Content Knowledge Graph

At its simplest form, a knowledge graph fundamentally consists of nodes and edges.

Nodes represent entities within a knowledge graph, and edges interconnect these nodes, delineating the relationships between them.

To fully understand how a knowledge graph works, it’s important to know the technologies required to build them.

Our focus in this section is to guide you through the key terminology and functions that are critical to the development of a robust content knowledge graph.

Uniform Resource Identifier (URI)

In the realm of knowledge graphs, the Uniform Resource Identifier (URI) plays a crucial role in uniquely identifying entities. A URI is a distinctive string of characters designed to distinguish and disambiguate a specific resource on the web.

Similar to license plates on cars that enable individual identification despite many people sharing the same make and model, URIs serve a similar function by ensuring the unique identification of various resources amidst the vast expanse of the internet.

At Schema App, we generate HTTPS URIs for entities defined in your Schema Markup, as shown in the image below. These URIs appear in the @id attribute. They allow you to link the entities on your site within your markup and enable search engines to identify the entities in your knowledge graph.

This systematic identification enables efficient communication and access to resources across different platforms and technologies. Within the context of a knowledge graph, URIs represent entities.

Entities

An entity, as defined by Google, denotes a single, unique, well-defined, and distinguishable thing or idea. It possesses defining characteristics or attributes such as size, color, and duration. However, an entity’s true significance emerges when it is described in relation to other entities, giving it contextual meaning.

This is where RDF Triples play a pivotal role, providing the framework to represent these interconnected relationships between entities within a knowledge graph. But first, what is RDF?

RDF

RDF, which stands for Resource Description Framework, is a standardized method for expressing data in the form of a directed graph using subject-predicate-object statements, commonly referred to as “triples.”

RDF Triples

The foundational unit of a knowledge graph is the triple. It comprises two nodes that represent entities connected by a single edge to articulate their relationship. Represented as “subject-predicate-object” statements, a triple illustrates how an entity (subject) links to another entity or a simple value (object) through a specific property (predicate).

As these triples combine, they form interconnected graphs of resources, laying the groundwork for a comprehensive knowledge graph. However, to provide meaning to the machine, you must express these triples in a machine-readable format.

You can express RDF triples in a variety of formats, including:

• Turtle
• RDF/XML
• And JSON-LD

The most widely adopted format is JSON-LD, which we utilize here at Schema App.

JSON-LD

JSON-LD, or JSON for Linked Data, is a serialization format for expressing RDF triples. It is relatively easy for humans to read and write and also for machines to consume. It is also the preferred Schema Markup format for search engines like Google.

JSON-LD code allows machines to understand RDF statements about entities.

For example, Mark van Berkel is an author for the Schema App blog, and his author page states that he works for the organization Schema App. On the left is the Schema Markup expressed in JSON-LD telling machines that Mark van Berkel (Person) works for Schema App (Organization). On the right is this same code visualized as an RDF triple, depicting these same entities and illustrating the relationships between them.

Ontologies

The last component in a knowledge graph is an ontology.

In Information Science, an ontology is a “formal, explicit specification of a shared conceptualization,” essentially serving as a blueprint for defining what exists in a data model (i.e. the method for describing contents within a database).

This model typically encompasses three key elements.

First, we have classes, also known as types, representing categories of entities such as an organization, event, or person.

Secondly, attributes, aka properties, are used to describe an entity. For instance, a Person entity might possess a name as one of its attributes.

Lastly, relationships, which are also represented by properties, delineate how one entity connects to another. These are similar to attributes in that they describe an entity, but more specifically, they describe how one entity connects to another entity.

For example, a Person may have a parent, child, or colleague relationship with another Person who will have their own attributes.

A wide variety of ontologies, vocabularies, and glossaries exist for categorizing and relating data, with Schema.org standing out as one of the most widely used in SEO. While technically a vocabulary and not a strict ontology, Schema.org effectively fulfills the role of describing categories of things and the relationships between them.

Building a Content Knowledge Graph with Schema.org

Founded in 2011 by Google, Bing, Yahoo, and Yandex, Schema.org emerged as a collaborative effort to enhance the web by introducing a standardized vocabulary. This initiative aimed to transform human language into a structured, machine-readable language.

All major search engines would support this language, improving their ability to match search queries with relevant results, making it beneficial for SEO purposes.

While SEO strategies commonly employ Schema.org, its utility extends beyond; it can also serve as a robust tool for constructing a knowledge graph.

Leveraging the Schema.org vocabulary allows you to organize your website content into a graph of interconnected entities. To achieve this, you can utilize the types and properties defined by Schema.org to express RDF triples in a machine-readable format like JSON-LD, all while representing your entities with URIs.

See how all of these terms come together?

This amalgamation of elements effectively creates a content knowledge graph for your organization.

Construct a Content Knowledge Graph for Your Organization

Developing your own content knowledge graph is essential for optimizing your semantic SEO strategy. It readies your content for the future of search and drives higher-quality traffic to your site.

Knowledge graphs empower search engines to infer knowledge through additional contextual information, bridging gaps for more relevant results. As such, this deeper comprehension should drive more qualified traffic to your site and boost the CTR for relevant pages.

At Schema App, we specialize in building and managing content knowledge graphs through the use of Schema Markup. Our dynamic authoring solutions ensure your Schema Markup is always descriptive, interconnected, and up-to-date.

Whether you’re integrating Schema Markup into your SEO strategy or aspiring to transform your content into a reusable data layer, Schema App has you covered.

Interested in building a content knowledge graph for your own organization but aren’t sure where to start? Schema App handles the technical aspects, enabling you to reap the benefits of having a well-constructed content knowledge graph without imposing the technical burden on your internal teams.

Contact our team today to get started.

The post The Anatomy of a Content Knowledge Graph appeared first on Schema App Solutions.

According to Google, an entity refers to a single, unique, well-defined, and distinguishable thing or idea.

Entities can be diverse, ranging from tangible elements like people, organizations, and products to abstract concepts and creative works. They possess defining characteristics or attributes, like size, colour, and duration. And most importantly, entities exist in relation to other things/entities.

Take, for example, “xylopental”. This is a string of characters that have no meaning to humans and, therefore, have no meaning to machines. However, if I invented a new musical instrument named “Xylopental,” this string of letters would become an identifiable entity. It is understood in relation to musical instruments, which is also an entity.

Entities need to be described in relation to other entities to have any meaning.

In its information architecture, Google often refers to entities as “topics.” From a content perspective, we can consider entities in SEO as topics within your content that become well-defined by referencing other related things.

Entities and their connections are crucial in developing Google’s Knowledge Graph. Google’s Knowledge Graph is a database that Google uses to quickly retrieve information about specific topics or entities. Any information Google has on a particular entity will show up in the Knowledge Panel, as shown below.

For example, when we search for “Berkshire Hathaway” on Google, we get a knowledge panel that conveys information about Berkshire Hathaway’s owner, stock prices, revenue, and more.

In the “People also ask” section, we can see queries that don’t specifically name Berkshire Hathaway, like “Does Buffett own McDonald’s?”

As the long-time owner and CEO of Berkshire Hathaway, Warren Buffett is often synonymous with his brand. McDonald’s is Buffett’s favorite breakfast meal, and he had previously purchased 4.3% of McDonald’s stocks but sold it in 1999.

This explains the inclusion of the question “Does Buffett own McDonald’s?” even though it doesn’t mention Berkshire Hathaway at all. All this information is derived through context from entities that are related to one another.

Difference Between Entities and Keywords

A common misconception SEOs have is that entities are just like keywords. Keywords are words or phrases that searchers use in their search queries. It can be a single word, a phrase, a sentence or a question. Historically, search engines would rank pages on the SERP using keyword matching.

However, the method of lexical search presented a few challenges.

1. Keywords tend to be ambiguous because certain words can have multiple meanings. For example, the word ‘Java’ can refer to either the programming language or the island of Indonesia.
2. Different languages tend to phrase the same things differently. For example, the term ‘rebord de fenêtre’ in French translates directly to ‘edge of window’ in English. But it is actually referring to a windowsill.

As a result, the old search algorithms were producing less relevant and accurate results for searchers.

Entities, on the other hand, are universally understood concepts that are not bounded by language or ambiguity. They are broader topics that keywords can stem from. They are distinguishable, especially through their relation to other things. Unlike keywords, entities have an additional layer of context, which can provide greater clarity to search engines.

How do Entities Relate to SEO?

Search engines are evolving toward a more semantic approach, analyzing the concepts and meanings within user queries. They identify relevant pages that answer the entities in question with greater context and accuracy.

As search engines advance in their understanding, there is inevitable demand for SEO strategies to also become more semantic to better align with this sophisticated and nuanced way of search. The good news is that you can assist search engines in grasping the entities and context of the content on your site.

Your website serves as the information hub about things related to your organization. The services provided by your organization, your postal address, your customer reviews, your blog articles – these are all entities related to your organization.

However, the content often exists in the form of plain text, images, videos and infographics. Humans can consume this form of information but machines and search engines cannot comprehend information in this unstructured manner.

Creating Machine-Readable Content

To bridge this gap between human understanding and machine interpretation, implementing semantic Schema Markup to define, describe and connect your entities is crucial. By meticulously defining entities within your content, you are essentially structuring your data in a format that search engines and machines can understand.

You can also further define the entities on your site by linking them to other linked entities in external authoritative databases like Google’s Knowledge Graph, Wikipedia, or Wikidata. This helps search engines disambiguate the entities on your page.

Defining these entities ensures your content is contextually understood by machines. This contextual understanding allows search engines to display your content for a broader range of relevant queries, expanding your site’s visibility and attracting a more qualified audience.

If you leave AI search engines to their own devices without informing them about the entities on your site, you are leaving it to them to decide on what is “true” for your content. You can control how machines interpret your content by defining your entities to prevent hallucinations and inaccuracies from being presented about your organization. This strategic approach safeguards your organization’s E-E-A-T and credibility.

So, now you know why you should define your entities, but how do you do it?

How to Identify and Define Page Entities

Author and Deploy Schema Markup

To have your content topics recognized as entities by search engines, use the Schema.org vocabulary to structure your data. You can use the Schema.org Types and properties to describe the entities across your site.

Many organizations tend to use a Schema Markup plugin to automate their Schema Markup process. However, many of these plugins will only markup certain page Types or properties. As such, you cannot customize your markup to properly define your entities or link them to other entities on your site.

If you want to provide search engines with a clear understanding of your content, you need to describe your entities thoroughly and leverage as many relevant properties as possible. The Schema App Editor and Highlighter are two great options if you want to implement custom semantic Schema Markup on your site.

Add Unique Identifiers to Schema Markup

For your entity to be identifiable and retrievable, it must have a distinct Uniform Resource Identifier (URI). URIs can help machines identify unique resources (like entities) and enable data interlinking.

In JSON-LD, this is expressed with the ‘@id’ attribute. By adding the ‘@id’ attribute to the entities in your Schema Markup, you can easily connect and refer back to other entities on your site so that search engines can clearly understand the relationship between different entities on your site.

For example, the author page for Mark van Berkel contains all the information about the person Mark van Berkel. Therefore, we can use Person markup on that page and define the entity ‘Mark van Berkel’ using the Schema.org properties. When we create the markup, we can add an ‘@id’ so that any connections to Mark can be indicated using the @id.

Search engines like Google can still read and qualify your page for a rich result if you don’t include an @id for your entities. However, you wouldn’t be able to connect the entities on your site in a machine-readable manner.

When you publish your Schema Markup using the Schema App Highlighter or Editor, our tool automatically generates HTTPs URIs for the entities defined in your Schema Markup.

Connect Your Entities

Connecting these entities on your website to explain how they are related, and extending these connections to external knowledge graphs, such as Google’s Knowledge Graph, Wikipedia, or Wikidata, helps search engines to disambiguate the entities on your site.

For example, Mark is one of the founders of the organization Schema App. We can leverage the ‘founder’ property under the Organization type to express that Mark is the founder of Schema App. And since we’ve already defined the entity Mark on his author page, we can link the entity ‘Mark’ using his @id to the entity ‘Schema App’ in the Organization markup.

That way, search engines know that this specific entity, Mark van Berkel, which is described on this page (https://www.schemaapp.com/author/vberkel/#Person), is the founder of Schema App.

As mentioned earlier, you can also connect your entities to external knowledge graphs to distinguish the entities on your site. External knowledge graphs are authoritative databases comprising millions of entities and their relationships. These entities link to other entities across the web which is why they are referred to as “linked entities”.

The linked entities identified in these external knowledge graphs also have unique identifiers, enabling connections to your own entities.

For example, Vancouver is the name of a city in British Columbia, Canada and also the name of a city in Washington State, US.

If your organization is a restaurant based in Vancouver, BC, you can describe your organization’s areaServed property by linking it to the right entity on:

• Google’s knowledge graph (/m/080h2), OR
• Wikipedia (https://en.wikipedia.org/wiki/Vancouver), OR
• Wikidata (https://www.wikidata.org/wiki/Q24639).

That way, search engines can clearly understand which Vancouver you’re referring to.

By establishing these relationships, you empower machines not only to comprehend existing information deeply but also to infer new knowledge based on this contextual understanding.

How do Entities Relate to Knowledge Graphs?

This process of defining and connecting entities effectively constructs a robust knowledge graph for your organization, providing a comprehensive and accurate representation of your content from a digital scope. Entities serve as the foundational building blocks of information that knowledge graphs organize into explicit relationships.

By capturing these complex relationships between entities and building context, knowledge graphs provide machines with a robust understanding of how different entities are related. Linking your entities internally and externally enriches the information available to search engines to create a holistic view of your organization.

This approach also helps prevent misrepresentation of your content and avoids machine confusion between ambiguous entities. Consider the thing, “Apple”, as an example; it could refer to the fruit or the brand. By linking your entity to the relevant external definition using the sameAs property, you offer an explicit distinction and enable search engines to align your content accurately with user queries.

Schema App Helps Define Your Entities & Develop Your Knowledge Graph

You can help search engines further understand, contextualize and distinguish the entities on your site using Schema Markup. If you are looking to leverage semantic Schema Markup to define your entities and develop a robust marketing knowledge graph for your organization, we can help.

At Schema App, we help enterprise SEO teams leverage semantic Schema Markup to define and link their entities, develop their knowledge graph, and improve search performance. Visit our website to learn more about our Schema Markup and knowledge graph solution.

Curious about how we can support your organization? Fill out this form to get started and connect with us.

The post What is an Entity in SEO? appeared first on Schema App Solutions.

This past August, Google first removed How-To rich results on mobile. As a result, we saw a huge decline in clicks and impressions for How-To rich results across our customers. This updated announcement will undoubtedly remove all traffic and impressions from the rich result.

Why is this happening?

Prior to this change, content publishers would add HowTo Schema Markup to pages with instructional content that defined the steps needed to successfully complete a task. If appropriate, Google would then award the page with a How-To rich result that outlined the steps in the SERP.

However, we’ve often found How-To rich results to be somewhat controversial. On one hand, rich results were supposed to increase user engagement and drive click-through rates to a site. On the other hand, How-To rich results usually provided users with the answer directly on the SERP, resulting in a lower click-through rate. As such, How-To rich results were not as widely adopted as other rich results like product, review snippets and FAQ.

That said, How-To rich results still provided users with valuable information on the SERP and could help organizations improve their customer journey. So why is Google removing this rich result from the SERP?

In their announcement, Google mentioned that this was a continued effort on their end to ‘simplify search results’. This year, Google has made some significant changes to the SERP.

• They introduced Search Generative Experience (SGE) – their generative AI-powered search interface.
• They stopped awarding video rich results to pages that did not have a video as their main content.
• They stopped awarding FAQ rich results on both desktop and mobile for most websites and will only be showing FAQ rich results for well-known, authoritative government and health websites.
• They deprecated How-To rich results entirely from the SERP.

However, this begs the question: What does Google mean by simplifying search results?

Are they trying to declutter the search engine results page? They did reduce the visibility of video and FAQ rich results in the past few months, possibly because people were abusing them. However, the SERP is still littered with advertisements, making it tougher for users to identify the most appropriate result for their query.

Or could they be simplifying search results that SGE can also provide? As seen in SEO expert, Glenn Gabe’s tweet, the content from the same How-To was shown in SGE and in the first position in the SERP as a How-To rich result.

Ah, yes. But this is about the desktop version being removed. This is how it looked. See my listing below SGE (when HowTo was still there on desktop). pic.twitter.com/S1nqtYukNa

— Glenn Gabe (@glenngabe) September 14, 2023

One of the glowing features of SGE is its ability to provide users with answers and additional relevant information that they might need. If you search up how to perform a task, SGE can provide you the steps to perform the task successfully and links to a few pages that also capture those steps.

If you search for the top Italian restaurants, SGE can provide you with a list of restaurants together with a map showing where they’re located in proximity to you, and links to aggregator sites that also have a list of top Italian restaurants in your city. These are just two of the many examples of how SGE creates helpful experiences based on the wealth of information on the web.

At its core, Google’s mission is to organize the world’s information and make it universally accessible and useful. Rich results were first introduced to provide users with more useful information in search, help them make better decisions and find answers. It was also a way for Google to incentivize website owners to add structured data to their sites to help search engines understand the content on a page.

But with SGE providing the information in a simplified way, more rich results could be rendered obsolete in the coming years. That said, this does not mean that you should abandon adding Schema Markup to your site.

What should you do next?

Schema Markup helps machines understand and contextualize the content and information on your website.

Even though you will no longer achieve a How-To rich result on your page, you should still add Schema Markup to your pages to futureproof your organization for search.

This is a paradigm shift that requires SEOs to think about the value of Schema Markup beyond rich results. 

Over the past few years, search algorithms have shifted from lexical to semantic search. Instead of ranking pages based on keyword matching, search engines are ranking pages based on the relevance of the concepts and entities in the page’s content to the searcher’s query.

And how do you identify and define the entities on your website for search engines? You can define the entities on your website using Schema Markup.

By marking up the content on your site, you are helping search engines understand the concepts and entities on your website and providing them with contextual information about these entities. In return, they can better match your page to a query and ideally improve your ranking on search in the long run.

If you are interested in learning more about entities and semantic search, you can tune in to our recent webinar with Mike King or Schema Markup expert, Dave Ojeda’s latest interview on iPullRank’s Rankable podcast.

Generative AI search engines like SGE and the new Bing still face hallucination challenges resulting in inaccurate results. At Schema App, we’ve been advising our customers to think about the semantic value of Schema Markup.

Instead of implementing Schema Markup on a handful of pages for the sole purpose of achieving a rich result, you should implement Schema Markup across your site to define the entities and concepts on your site and link them to develop your very own marketing knowledge graph.

Knowledge graphs are a structured and organized information data layer that can help search engines to improve the accuracy of their answers and provide your organization with a control point to inform generative AI on your web content. Your marketing knowledge graph can also be reused for other AI initiatives.

As the SEO industry continues to see changes from Google and on search, organizations need to prepare to stay ahead of the competition. If you are looking to learn more about semantic Schema Markup, we can help.

Contact us to see how we can help your organization build a marketing knowledge graph and future proof your organization for AI search.

If you are a Schema App customer with concerns regarding the changes in rich results, please get in touch with your Customer Success Manager to see how we can support your organization through these changes.

The post Say Goodbye to How-To Rich Results on Google appeared first on Schema App Solutions.

Fast forward to today, search engines now prioritize positive user experience and ‘people-first’ content. Search engines consider content depth, meaning (aka semantics), and how it answers user questions by providing the desired information.

Businesses must adapt to this evolution of search. As search engines become more sophisticated, incorporating semantic understanding into your search engine optimization (SEO) strategy is crucial to keep up with the changing landscape. This will help ensure your content remains relevant and visible to your target audience.

Understanding Semantic SEO

The word ‘semantic’ is all about understanding the meaning of language.

When people use the term ‘arguing about semantics’, they’re usually debating the interpretation (or misunderstanding) of words or phrases. Semantics is a field that examines how language conveys meaning and follows certain rules for effective communication.

Semantic SEO is the process of giving more meaning and context to your web content to help search engines gain a better understanding of your content.

Why is Semantic SEO important?

The way that search engines understand your content has changed

Historically, Google solely used keywords to evaluate a web page’s topic and relevance to a search query. As of Google’s algorithm changes made in 2013, however, instead of only looking at keywords to understand what the page is about, search engines now read and understand a page’s overall topic.

This change allowed search engines to provide users with a better search experience and ensure that the results presented are providing users with the answers they are looking for.

To improve your ranking and web traffic

By utilizing semantic SEO, search engines can better understand your content and more accurately relate it to search queries. In return, your pages can rank higher on relevant searches, leading to more impressions and, ideally, more clicks. 

Because it presents users with the most relevant information based on their queries, those who do visit your pages are more easily converted into customers. This is because it’s more likely to be exactly the information/product/service they were seeking. 

To keep up with generative AI search

Semantic SEO is the future of search, and that future has already begun. The emergence of powerful generative AI search engines like Google’s Search Generative Experience, has propelled semantic technology to unprecedented heights.

In this transformative era with the AI revolution and search generative experience, search engines are gaining an unprecedented ability to interpret the nuances and meaning of human language. As a result, search queries are now returning dynamic and tailored results with the potential for conversational follow-up answers.

While traditional SEO practices, including keyword research, remain valuable in digital marketing, integrating semantic technologies like Schema Markup into your strategy can provide a competitive advantage.

By doing so, your pages become more visible and comprehensible to the intelligent systems that bridge the gap between your content and human users.

How is Semantic SEO Different From Traditional SEO?

Where traditional SEO prioritizes content that is keyword-based, semantic SEO is a topic-based approach that increases the likelihood of connecting users to information that is most relevant to their search query. 

It accomplishes this by focusing on both the meaning behind queries and the contextual information and relationships in the content being retrieved. This results in a better user experience which can lead to a lower bounce rate, as those who end up on your page from search have a higher intent to consume the information presented.

Semantic SEO is the bridge between your content and users’ intent. This is the biggest difference between Traditional SEO and Semantic SEO. – WeDevs

Moving from a keyword-based to a topic-based approach with your content can seem a bit abstract at first. After all, it’s simple enough to do some keyword research, find a list of terms, and then write content to string the terms together.

These same skills are still essential when it comes to semantic SEO, with one key difference: entities.

What are Entities?

To put it plainly: entities are things, and things have dimensions! 

They take up space (be it physical, digital, or conceptual). They also have attributes (like colour, size, duration) and, most importantly, they are understood in relation to other things.

Take, for example, “bestgihrtie”. This is a string of characters and it means nothing to a human brain, so it won’t mean anything to a search engine either. But if I decide it’s the name of my new album, snackfood, or generative AI tool, this jumble of letters now becomes an identifiable entity. In other words, the string becomes a thing.

However, that entity needs to be described for it to have any meaning. “ChatGPT” didn’t mean anything until we started hearing about it in relation to generative AI, chatbots, and productivity. 

This same entity took on a different meaning when we heard about it in relation to hallucinations, misinformation, algorithmic bias, and plagiarism. The word “relation” is doing the heavy lifting in this example since what it’s providing is context. 

We as humans use context clues to make sense of new things and search engines are doing the same thing.

That being said, machines, including search engines, aren’t good at understanding in the same way that human brains can. Search engines use natural language processing (NLP) to analyze the proximity and frequency of certain terms, phrases and entities.

There are ways, however, to make statements about entities more explicit for search engines.

Elevating Search with Entities

As previously stated, semantic search goes beyond traditional keyword matches and focuses on delivering topically relevant search results.

Instead of simply providing “plain blue links” to web pages, it can present information in various formats, such as Knowledge Panels, Featured Snippets, and Rich Results, all centered around the primary entity being searched.

This approach aims to provide users with more comprehensive and contextually relevant information related to their search query. Let’s look at an example of how a search for “Gibson Les Paul” yields results about this particular entity.

Under the “People also ask” section, we can see queries that don’t blatantly name the type of guitar, like: “How much did Kirk Hammet pay for Greeny?”. 

Greeny is a 1959 Gibson Les Paul Standard, named after its original owner, Peter Green. It happened to be purchased by Kirk Hammet, the guitarist of Metallica, which also explains the inclusion of the question “Who is the richest member of Metallica?”, which has nothing to do with guitars at all.

But if we think about this information as being derived from entities that are related to one another, the inclusion of these “People also ask” queries make sense.

And if we search for “Greeny guitar”, we’ll get a Knowledge Panel conveying some of the attributes of this particular guitar, including the fact that its manufacturer is “Gibson”.

Leverage Schema Markup to Improve Your Semantic SEO

There are many things you can do to implement semantic SEO. A lot of it involves creating clusters of content surrounding the topic that you want to be known for.

However, in addition to this, you need to ensure search engines understand what your content is about and how the entities in your content are connected. Implementing Schema Markup allows you to categorize entities and explicitly relate them to each other, providing search engines with helpful contextual information about your content.

Schema Markup, also known as structured data, is a standardized vocabulary that search engines analyze to understand the content on your web pages. By implementing Schema Markup through code, such as JSON-LD, search engines can contextualize your content and present it to users searching for relevant and related topics.

While machines don’t interpret information like humans do, Schema Markup helps bridge the gap. It does this by providing explicit details about the content on your pages, ensuring search engines accurately comprehend the topics of information your website offers.

One of the most common uses of structured data is the application of the Schema.org vocabulary expressed in JSON-LD. It’s usually found under the “technical SEO” umbrella, and most would know it as the “Thing” responsible for rich results.

Rich results can drive higher click-through rates with their engaging visuals, but if that’s the extent of your Schema Markup application, your semantic SEO strategy is missing out!

So how can you leverage Schema Markup to improve your semantic SEO?

1. Implement more specific Schema Markup to clearly explain what your page is about

To be semantic, search engines need to clearly understand your content.

Content publishers often use generic Schema Markup plugins to add default Schema Markup on certain pages like blog articles, product pages, home page, etc. However, the downside of doing this is the lack of control over your Schema Markup.

Generic Article markup autogenerated by plugins won’t give your content the richly descriptive Schema Markup that best supports the search engines.

Plugins are usually CMS-specific and tend to map more general properties to available metadata (like author, or datePublished). While these properties are still helpful, they don’t describe the content with as much depth as more specific properties like about or mentions, which can be used to call out topics and entities in an Article.

Your markup will also often be disconnected. Each page may have Schema Markup describing the content, but not necessarily how that content relates to other pages across your website.

2. Add @ids in your Schema Markup

Your Schema Markup can be generated and authored without including identifiers (@id). Search engines like Google will still read it and make it eligible for rich results.

In the JSON-LD syntax, @id is used to provide URIs (uniform resource identifiers) to entities in your Schema Markup. These identifiers allow you to refer back to entities as you build your knowledge graph.

In the example below, the Organization entity created for Schema App’s homepage has the @id “https://www.schemaapp.com/#Organization”. If a blog post on another page wants to say that it was published by the Organization Schema App, the Schema Markup for that page would say the publisher is “https://www.schemaapp.com/#Organization”.

@ids give the entities in your markup unique identifiers.

Think of it like your social insurance number! There may be 10 different people named “Jane Doe” in your organization, but each of them will have a unique ID that differentiates them. Schema App auto-generates @ids for every entity, so you can link the unique entities across your website.

Therefore, if you want to improve your semantic SEO, you should add @ids to your JSON-LD Schema Markup.

3. Connect your Schema Markup to develop your knowledge graph

Establishing a connection between your Schema Markup elements is crucial for developing a comprehensive knowledge graph. Knowledge graphs are necessary for describing how things on your site are related to each other, as well as other things on the internet.

It makes your content more semantic and provides search engines with contextual knowledge about your content.

Connect Your Entities On Your Website

On your website, you can connect different entities to one another. For instance, if you have a law firm with multiple service pages, it’s important to connect those service pages to your organization. This indicates that your organization provides all of those services despite them being on separate pages.

To ensure accurate representation, it’s vital to describe the relationships between marked-up entities in detail. For example, you need to clarify if an article is about a specific topic or if it simply mentions it.

Schema App offers a free Schema Path tool that helps identify available properties to connect your entities effectively.

Connect Entities to External Authoritative Knowledge Bases

You can also connect entities on your site to external authoritative knowledge bases such as Wikidata or Wikipedia. By doing so, you are clearly explaining what your entity is about.

For example, let’s say your page talks about football. Football can mean two different sports to different readers. In America, football is American football while in Europe, football is soccer.

So if your page is about American football, you can link it to the Wikidata entity (https://www.wikidata.org/wiki/Q41323) for American football in your Schema Markup using the sameAs property. This will help search engines understand that your page is referring to American football and reduces the risk of misinterpretation.

By connecting entities on your site to other entities and external knowledge bases, you are forming your own knowledge graph. The @ids that we mentioned earlier clearly identify the entities in your content, allowing you to connect them and build context.

With Schema App, you have the flexibility to add these entities either manually through our Editor or automatically through the Highlighter, utilizing the Linked Entity Recognition feature. For WordPress users, our WordPress plugin can automatically identify and link entities that you have included in your tags and categories.

Download our Guide to Connected Schema Markup to learn how to connect the entities on your site and build your knowledge graph. 

The Future is Semantic

When creating website content for SEO, it’s important to prioritize semantic SEO that focuses on topics rather than just keywords. Search engines now understand context, relationships, and user intent better than ever before.

To stay competitive on SERPs, you need to create relevant, high-quality content that targets specific topics and use connected Schema Markup to help search engines understand how your content relates to user intent, search queries, and other information on the internet.

By embracing semantic SEO, you align your strategy with search engines’ evolving understanding. This leads to better visibility on the SERP and the delivery of highly-tailored content to your target audience.

If you’re looking to implement connected Schema Markup at scale for your site, get in touch with our team to learn about our solution.

The post Semantic SEO: What You Need to Know appeared first on Schema App Solutions.

On Thursday, October 28th, we held our first Fireside Chat with Simon Yohe, the Senior Director of E-commerce and CRM at Holiday Inn Club Vacations. Prior to this role, Simon Yohe was the Director of Web Marketing Strategy at AdventHealth, which was formerly Florida Hospital and a part of the Adventis Health System. Simon was brought on to AdventHealth because they were doing a national rebrand and wanted to mitigate any loss in organic traffic during their website consolidation. 

The Challenge

AdventHealth had dozens of hospitals in multiple locations scattered across nine states, and wanted to bring them all together as one health system. This would require consolidating over 800 different hospital and medical websites that were all branded differently into one AdventHealth.com domain and brand.

In order to do this, Simon and his team needed to put a system in place to help Google and other search engines be able to easily identify those legacy names without losing any organic search traffic in the process. They also wanted to ensure that customers were still able to locate the hospitals, medical pavilions, imaging centres, etc throughout the consolidation. One of the ways they solved this challenge was through the use of structured data. 

The Solution

For AdventHealth, the rebranding and consolidation was one piece, but where the structured data really played to AdventHealth’s advantage was the connectedness of entities within AdventHealth’s company knowledge graph. It wasn’t simply a rebrand from the term “Florida Hospital” to “AdventHealth”; it was a complete restructure of how Google contextualized those hospitals, their geographical areas, and how they relate to this new consolidated health system.

For Holiday Inn Club Vacations, Simon and his team wanted to build out and grow their digital platform in a way that could be self-sufficient. It wasn’t just about building functionality that would allow customers to book online reservations or creating a new member portal; they were architecting ways that they could overall enhance and improve how their website is found by using structured data to inform their content strategy.

Working together with Simon and his team to understand the unique value these initiatives proposed, we were able to design customized schema markup strategies that would help them achieve these online business goals.

Structured data became a strategic opportunity to achieve these goals, but the AdventHealth team wasn’t sure how they were going to implement it and were limited in regards to their development team, as they were actively working on developing the new platforms.

Through their design agency, AdventHealth came across Schema App and loved the idea of a “plug and play” option instead of managing the software themselves. A development team can build this solution in-house, and a lot of companies do, but where it can become a struggle is that you only have a finite amount of developers.

When you’re working on different website features and functionality, it becomes harder to make sure that you have those dedicated resources focused on your structured data markup. You also have to have someone that’s knowledgeable and understanding of what you’re trying to mark up, and someone who can maintain, monitor, track and optimize it. 

Why Schema App?

As a beginner, structured data can be challenging. That’s why Schema App was created in the first place: to provide a scalable solution paired with expert support so that structured data could become a more approachable practice for any SEO team.

Working with Schema App and structured data even ended up helping Simon and his team with their content strategy, and with how they organized content as they built their new consolidated website. Through schema markup and the linking of entities across your site, you’re helping Google categorize and understand your content. Two areas that really impacted AdventHealth were the facility pages and the physician profile pages.

The ability to connect physicians with their services, the physicians with their locations and the locations to the services—structured data markup gave Simon and his team a better understanding of how to structurally organize and display content on their website.

The Return on Investment

One of the most effective ways to show the impact of structured data is by looking through search engine page results (SERPs). You can visually see how adding structured data enhances your content in the SERPs, like the FAQ rich result for AdventHealth below. 

The visual change is clear, but structured data also empowers your SEO team with measurable results. AdventHealth saw a 90% increase in clicks and a 40% increase in impressions that could be attributed to the addition of schema markup to their website. 

Structured data is a long term investment in the success of your website. You’re building meaningful connections with Google through well-defined entities, and with your customers through enhanced content in the SERPs. By using structured data opportunities to inform your content strategy, you’re ensuring that your website is organized in a way that search engines can easily understand your content and then show that information to the right customers at the right time.

We help you leverage structured data to showcase the unique value of your health system in search. By working cross-functionally with different areas of your organization, we introduce agility to your team in the rapidly changing landscape of search engine optimization. We’ve worked with some of the top healthcare leaders in the industry, such as Sharp Healthcare, AdventHealth, and Henry Ford Health System—executing business results using our expertise and technology. Let’s start building some meaningful connections!

The post How Structured Data Empowers your Content Strategy: A Fireside Chat appeared first on Schema App Solutions.

The Rich Results status reports identify any errors in your structured data that could be preventing content from being eligible for rich results in search engine page results. 

What has changed in the Google Search Console rich results status reports?

Google Search Console is not reporting on any new errors related to rich result eligibility. The recent change is specifically adding more information about errors it already checks for, including:

• Invalid attribute string length
• Invalid attribute enum value
• Invalid object
• Type conversion failed
• Out of numeric range

What’s exciting is that these additional details may not have been exposed in Google’s now deprecated Structured Data Testing Tool (SDTT), which has been replaced with the Schema Markup Validator (SMV). Google is helping to make these errors more actionable for SEOs, so that we can ensure that our structured data is valid and eligible for rich results.

That being said, eligibility does not mean that Google will always reward your content with a rich snippet, but you are setting your content up for the opportunity to be awarded with one. You’re doing all you can from a technical standpoint, but you should also focus on building more credibility with search engines to set your website up for long-term success. For example, by increasing your E-E-A-T (experience, expertise, authoritativeness, and trustworthiness), Google will start to understand how your website and business overall relates to entities in its knowledge graph. This streamlines Google’s ability to match a user’s search query to the most relevant content from your site!

We keep an eye on updates to Google’s structured data documentation so that you don’t have to! If you’re looking for help with your structured data implementation, we’d love to hear from you. We help you go beyond the fundamentals of search engine optimization, leveraging structured data to showcase your unique value in search. In a rapidly changing SEO environment, we introduce agility to your digital team, saving you time and resources for managing other aspects of your business portfolio. We deliver to your online business goals using our structured data expertise and advanced technology.

The post New Error Reporting in Google Search Console appeared first on Schema App Solutions.

Applying schema markup allows you to define entities across your site and link them to other entities across the Web. You’re creating data points for search engines, helping Google connect the dots about you, your company, and the people who work there. Well-defined entities can also be included in Google’s Knowledge Graph, which is a knowledge base of entities and the relationships between them. 

For medical clinics and health systems, you have the opportunity to create entities for your physicians and connect them to the medical organizations or clinics they work for through schema markup. These connections create context, streamlining Google’s ability to match relevant search queries with your services and medical practitioners. Your content can also be eligible for rich results and enhanced search features like star ratings or frequently asked questions.

To be eligible for these rich results, you need to mark up visible content on your web pages and follow Google’s structured data guidelines. When you achieve a rich snippet, Google will display additional information from your markup through richer features beneath the standard page title, meta description, and URL. 

What is really exciting are the opportunities for combining multiple rich results into one striking snippet. 

Schema markup takes your content beyond the standard search result, adding enhanced search features to help your brand stand out from the competition. For many customers, their health journey begins in search. The additional information included in your structured data presents customers with the opportunity to learn more about your services directly in search results. According to BrightLocal’s 2020 Local Consumer Review Survey, 89% of consumers look at reviews in the healthcare industry, and 87% of consumers believe reviews are important for the industry. 

How you can achieve multiple rich results for your physician pages

Let’s take a look at how the physician rich result has evolved over time, and how each search enhancement contributes to the power of this striking snippet.

Stage One: Title Tag, Meta Description, Page URL

Without structured data, your search engine page result will display the page title, the meta description, or other information chosen by Google, and the page URL.

Stage Two: LocalBusiness (Physician) Schema Markup

When using Google to search for a local business, you may have noticed profiles displaying on the right-hand side, called the right rail, of search engine page results. These are Google Local Knowledge Panels. Local knowledge panels are information boxes that appear when Google interprets a search query to have local intent and recognizes a well-defined entity which is already connected to Google’s Knowledge Graph. Google Business Profile is the catalyst for a local knowledge panel to show in search results. 

Structured data is not a direct trigger for Google Knowledge Panels, but it does feed and enhance them. Adding schema markup to your web pages means that your website can be an authoritative source of information for Google’s Knowledge Graph, rather than relying on a Wikipedia page (which you don’t own) or a LinkedIn or Twitter profile (which you only semi-own). Schema markup gives you more control over how your brand and website appear in search results. 

For any LocalBusiness schema markup, you want to use the most specific schema.org type that you can to explain to Google what your organization is and does. The physician pages on your website should be marked up with the Physician schema.org type, which is a subclass of LocalBusiness and has all the attributes of LocalBusiness schema within it.

With LocalBusiness schema markup, you want to use the schema.org type that is as specific to your business as possible. Learn more about the benefits of LocalBusiness schema markup in our Ultimate Guide.

Stage Three: LocalBusiness (Physician) + aggregateRating

You’ve probably already noticed star ratings showing up for products or local businesses in search results. Those little stars are achieved through aggregateRating schema markup. You can nest aggregate ratings into another schema.org type using the aggregateRating property, such as within an instance of Physician.

Stage Four: LocalBusiness (Physician) + aggregateRating + FAQ

Star ratings are one of the more eye-catching rich snippets you can achieve through schema markup. To build on your aggregateRating structured data, we recommend next adding FAQPage schema. Frequently asked questions are an effective way for customers to engage with your brand directly in search engine page results. Consider what questions your customers may ask, and dedicate a section for these frequently asked questions and answers on your physician page. Then, mark up these FAQs with FAQPage structured data. For physician pages, this could be their credentials or area served as seen in our example below:

Adding frequently asked questions to your website to unlock FAQ rich results is just one example of how structured data should inform your content strategy.

Physician Rich Result: What’s Next?

As you can see, structured data can be layered and nested to achieve different search appearances for your content. The schema.org vocabulary is evolving, and with Google testing how different rich results behave in search, there are many exciting opportunities ahead for schema markup. For example, during a limited time in early August of 2021, we saw Physician rich results with aggregateRating, FAQ, AND an image of the physician in live search engine page results. 

These tests may be a glimpse into what could soon be coming for certain types of content, and how they’re marked up with structured data, in search. If you’re interested in learning more about where to start with healthcare structured data, check out our article, “The Value of Schema Markup for Healthcare Organizations.” 

Why structured data for healthcare?

Schema markup explains to search engines how the physician pages on your website relate back to your healthcare organization. These connections create relevance as Google crawls your web pages, which leads to more relevant search queries being matched with services like yours. The benefits of structured data extend beyond achieving multiple rich results in search. Those benefits are visible and effective, as we’ve demonstrated above. To maximize your search engine optimization performance, it’s important to understand how structured data really works. 

You’re actually taking control of how the data on your website is defined to show the best information in search results, and not just hoping Google will figure it out on its own. This control helps medical organizations—and their physicians—retain credibility with customers and search engines alike.

Through schema markup, you can also increase your E-E-A-T, establishing your brand as a trustworthy source of information. Schema markup is all about context and relevance, and when done effectively, you can increase both the quantity and quality of leads coming through your website. For healthcare organizations, this means more appointments booked by customers who are well-aligned with the services you offer.

We help you leverage structured data to showcase the unique value of your health system in search. By working cross-functionally with different areas of your organization, we introduce agility to your team in the rapidly changing landscape of search engine optimization. We’ve worked with some of the top healthcare leaders in the industry, such as Sharp Healthcare, AdventHealth, and Baptist Health—executing business results using our expertise and technology.

The post Healthcare Schema Markup: Evolution of the Physician Rich Result appeared first on Schema App Solutions.

According to Microsoft’s Work Trend Index, which surveys over 30,000 people across 31 countries and analyzes activity across Microsoft 365 platforms, over 40% of the global workforce is considering leaving their current employer this year (2021).

The Great Resignation is very real and widespread across many industries, with healthcare and high-tech being some of the hardest hit. From March 2020 to March 2021, high-tech companies saw a 4.5% rise in resignations, and healthcare saw a 3.61% increase in the same time period. Stress, anxiety and burnout were impacting healthcare workers long before the pandemic. The added stresses and challenges of an ongoing global health crisis continue to impact nurses and physicians, with 75 to 80% of physicians in North America experiencing burnout. 

As employees quit their jobs, businesses experience productivity losses. While your team may encounter short term instability during unprecedented times, future-proofing your digital strategy with structured data can reverse some of those negative effects. During a period like The Great Resignation, you have to focus on things you can control and that are measurable, like using structured data to inform your content and SEO strategy. Structured data consistently helps you stand out at the beginning of the customer journey by creating multiple touchpoints in search results through different Google search enhancements.

Managing your digital presence through times of change—whether in user behavior, Google algorithm updates, or as people are leaving their jobs—can be challenging. With structured data, your digital team will be empowered with the control, data, and agility to maximize results from search.

Structured data supports recruiting

If you are looking to fill a position or find new prospects, adding structured data to job postings will improve your chances of finding qualified candidates. JobPosting schema markup will make your job postings eligible for a special user experience in search results. You can also integrate with Google using a third party job site. See the following example for nurses near Buffalo, NY:

Job applicants are able to filter by different criteria, such as location or job title, leading to more engaged and qualified candidates for your posting. This additional job posting avenue increases your chances of finding more applicants, as well as displaying your company logo, ratings, and the job description. Learn more about adding structured data to your job postings here.

Structured data supports changes in User Behaviour

During COVID-19, people have changed their search behaviour. It’s no surprise that in 2020, Coronavirus was in 3 of the top 5 Google search queries for the United States. Staying on top of what users are searching for will help you plan and execute on the content your website should provide to your target market.

Remaining on top of new content and ensuring you’re including new questions your customers may be searching for will not only drive more traffic to your website, but will support your position as a credible industry leader. Through FAQ structured data, the frequently asked questions and answers for your customers will also be eligible for rich results.

As new content is added to your FAQ pages, tools like the Schema App Highlighter can dynamically deploy schema markup to help inform users in search results without missing a beat.

Structured data keeps you aligned with Google’s changes

Paying attention to Google’s updates will ensure your website follows SEO best practices. Google made two notable changes during the course of the pandemic: Google’s Core Web Vitals update and the addition of SpecialAnnouncement rich results. 

SpecialAnnouncement schema markup empowers locally-based organizations, like medical clinics or schools, to update the public with timely information concerning COVID-19. In the height of vaccinations, this Google search enhancement, and other forms of schema markup, drove higher user engagement for medical websites. For example, AdventHealth saw an 8% increase in clicks during the first 3 months of the pandemic, directly correlating with the addition of COVID-19 structured data to their website.

You can take control with structured data

In these turbulent times, you need to maintain your web presence. If you are investing in search engine optimization, following the best practices set out by search engines, such as Google’s structured data documentation, will maximize and maintain your SEO results:

Google Search works hard to understand the content of a page. You can help us by providing explicit clues about the meaning of a page to Google by including structured data on the page.”

Structured data gives you control over how your brand appears in search

Structured data is a form of metadata that can be added to the backend of your website, giving you more control over what information search engines will display in search results.

For AdventHealth, structured data provided stability during a massive rebrand that would consolidate over 800 websites using approximately 35 different CMS platforms under a single brand and URL. To mitigate the risk of a  drop in organic traffic, AdventHealth decided to use structured data as a key strategy to help manage the transition within Google search, to lead the health industry in rich results, and to stand out as an innovative brand. 

Schema App used schema markup to notify search engines about the rebranding, while also bolstering visibility in search through FAQ markup and physician reviews on their physician pages. 

We have seen significant increases in how our physicians are being found. Physician bio clips increased 90% from 150,000 clicks to about 285,000 clicks and we saw a 38% increase in the click-through rate of the search results as well”

 — Brandi West, Executive Director, Digital Brand & Content Strategy I Digital Marketing, AdventHealth

After the rebranding—and thanks to the addition of schema markup—AdventHealth experienced a 90% increase in clicks across all pages, a 40% increase in impressions, a 38% increase in CTR, and top position in the rankings. As a result of structured data deployed for COVID-19 announcements, they experienced an additional 8% increase in clicks. Read about their journey in our case study. 

Structured data delivers results, and at Schema App we customize your schema strategy based on your unique online business goals. 

Structured data delivers results for your digital strategy

The great thing about structured data markup is that the results are measurable and informative. Through enhanced analytics of existing schema markup, we are even able to consider different user trends to drive content decisions for a website. For Henry Ford Health System, this meant identifying groups of pages that had content regarding the same medical specialties and health topics through our enhanced analytics reporting. This resulted in insight into what their patients had been searching for and engaging with informed them of what topics they needed to produce more content for.

As things are changing, you want to focus on what is constant and what you can control. The structured data on your site is entirely in your control, as long as you follow Google’s guidelines.

Structured data empowers your digital team with agility

Having a diverse rich result portfolio means that your digital team will have the agility to pivot from one SEO opportunity to the next during times of change. For example, even though the SpecialAnnouncement rich result is driving significant traffic for our healthcare customers, the structured data across their websites was so robust that when it temporarily dropped off on May 20th, 2021, we were already prepared to shift that focus to other types of rich results like FAQs, Review Snippets, and more! 

The challenge with adding manual structured data is that it’s time-consuming, complicated, and requires IT support. Schema App is a SaaS technology solution that’s paired with flexible, high-touch support to a managed service. You don’t have to wait to get started with structured data through these changes—we’re here to help you with your unique SEO challenges!

Going the extra mile to be solution oriented and always coming up with a solution is so helpful. (You are) always staying ahead and paying attention to the details. I’ve learned so much. (You’re) really being a partner through it all, not just fulfilling a task.”

— Rachael Jones, SEO Strategist, Sharp Healthcare

Get started with structured data for healthcare

If you’re experiencing the Great Resignation and/or want to future-proof your digital presence, structured data is an advanced SEO strategy worth investing in.

For healthcare, structured data provides more control over how your organization appears in search results and creates engaging touchpoints in search for existing and new customers to connect with you, leading to more appointments booked. There are rich result opportunities for every industry that has an online presence, with innovative ways to stand out from your competitors.

Ready to get started? Set up a FREE call with one of our technical advisors today to determine the readiness of your website for structured data.

Frequently Asked Questions

What are the best rich results for healthcare organizations?

There are many different rich result opportunities for healthcare organizations! Some of our favorites are frequently asked questions (FAQs), job postings, local business, review snippets (which you can even use for your physician pages), and articles for your expert blog. 

How can you get started with structured data?

To get started with structured data, analyze your overall online business goals and determine what challenges you are looking to solve. Then, explore Google’s Search Gallery to see what rich results your existing or future content may be eligible for. For our enterprise clients, a partnership with Schema App means that we will do the structured data markup for you, so your team can focus on strategy and defining your online business goals. We work cross-functionally with your team to ensure that we are an indispensable business partner for your organization, with advanced technology and high-touch support to a managed service. Get started today!

Where can I learn more about customers who’ve got results from structured data?

Any industry with an online presence can benefit from structured data markup. We’ve worked with industry leaders in healthcare, e-commerce, SaaS technology, and more—delivering results customized to each organization’s ROI goals. Learn more about our customers’ success stories in our case studies. 

We help you leverage structured data to showcase the unique value of your health system in search. By working cross-functionally with different areas of your organization, we introduce agility to your team in the rapidly changing landscape of search engine optimization. We’ve worked with some of the top healthcare leaders in the industry, such as Sharp Healthcare, AdventHealth, and Baptist Health —executing business results using our expertise and technology. 

The post The Great Resignation: Future-Proof your Digital Strategy with Structured Data appeared first on Schema App Solutions.