However, we’ve seen the role of Schema Markup shift over this past year. This article explores the journey of Schema Markup and its changing role in SEO throughout 2023, emphasizing the growing importance of its semantic value over just rich results.

Early 2023 – Fluctuations in Rich Results

In 2022, the performance of rich results experienced fluctuations, and 2023 has been no different.

In April 2023, Google stopped showing Video rich results on the SERP for pages, favouring YouTube results, or pages where the video was the main element of the page. As such, many organizations can no longer rely on video-rich results to drive traffic to their site.

At the same time, Schema Performance Analytics saw the performance of FAQ rich results declining on mobile for unexplained reasons. However, John Mueller mentioned in an unofficial statement that “sites love adding FAQ markup; it gives them more room to search, and at some point, it makes the results less useful. The right balance makes sense to re-evaluate from time to time, like with any other search element.” This was perhaps Google’s test to see how the removal of FAQ rich results would impact the search experience.

To mitigate the risk from these rich result fluctuations, Schema App’s High Touch Support team made content recommendations that would allow our customers to diversify the types of rich results they were achieving. When it came to making changes with agility, the Schema App Highlighter was used to make quick edits, as it enables the creation of Schema Markup templates that can easily be updated when changes to content occur.

February & May 2023 – The Introduction of Generative AI Search Engines

In February 2023, Microsoft rolled out the new Bing, which included its AI chatbot, Bing Chat. Google followed suit, unveiling the experimental Search Generative Experience (SGE) at the Google I/O conference in May 2023, sparking significant changes in the dynamic of search.

SGE, though in its testing phase, disrupted search results by relegating organic search results, including rich results, lower on the SERP.

Furthermore, these generative AI search engines were prone to hallucinations and biases, resulting in inaccurate search results. This raised critical questions for organizations: How could they control the way generative AI search engines interpreted their content to ensure accurate information on SGE?

The evolving AI landscape generated both excitement and concern, with uncertainties about its impact on SEO. Amidst the ambiguity, the key question persisted on whether Schema Markup aided AI algorithms in comprehending website content.

Preparing for AI-Search

During his Pubcon keynote speech, Fabrice Canel, Principal Program Manager for Bing, shared that SEOs could prepare for this new AI-enabled search by creating great content and annotating it with Schema Markup. As such, many SEO professionals leaned towards implementing Schema Markup.

Recognizing it as the language that search engines used to understand site content, Schema Markup emerged as a concrete way for SEOs to stay relevant during the rapid AI evolution. This approach provided a sense of control over how search engines interpreted content and was adopted by many forward-looking organizations.

The semantic value of Schema Markup, which refers to the underlying meaning and context it adds to the content, became increasingly apparent to SEOs at this time. However, it did so in a somewhat hypothetical manner. Unlike rich results, the quantitative measurement of this semantic value of Schema Markup posed challenges.

At this time, Search Engines didn’t provide any analytics on how content was performing in SGE or Bing Chat. Consequently, SEO teams often relied on rich results as a tangible metric to gauge the effectiveness of their Schema Markup strategy.

August 2023 – FAQ & How-to Deprecation: A Turning Point

Despite the announcement of SGE, the performance of rich results seemed relatively stable on Google. However, between August and September 2023, a significant turning point unfolded as Google deprecated How-to rich results and dramatically reduced the frequency of FAQ rich results. The latter were then restricted to display exclusively on “well-known, authoritative government and health websites.”

Drop in clicks after deprecation of How-To rich results

The deprecation of FAQ, a previously widely utilized rich result, reignited concerns about the continued relevance of Schema Markup. While there are still over 30 rich results available, few had the flexibility like FAQ to use on diverse content.

In a year where marketing budgets were critiqued, and leaders required measurable ROI for initiatives to be approved, the absence of clicks and impressions from rich results made it challenging to quantify the value of Schema Markup. Many marketing teams asked: “Is there a point in implementing Schema Markup if rich results can’t be achieved?” The answer, unequivocally, is yes.

Prioritizing the Semantic Value of Schema Markup

Even without the immediate gratification of rich results, Schema Markup remains important for organizations looking to future-proof for search.

Generative AI search engines like SGE and Bing Chat revolutionized user search experiences. They presented multi-modal answers and provided follow-up questions to user queries.

However, the underlying search algorithms that power both the regular search engines and generative AI search engines have shifted from lexical to semantic. Instead of matching keywords, search engines assess the meaning and intent behind a query, providing searchers with results of the closest relevance.

Using Schema Markup to Define Your Entities

Schema Markup plays a crucial role in this understanding by allowing web publishers to define the entities on the site and showcase the relationships between entities in the form of a content knowledge graph.

In the past, when rich result eligibility was the main focus, many organizations would implement Schema Markup, but not in a manner that defined the relationship between the primary things (aka entities) on their pages. They simply added the minimum Schema Markup required to be eligible for the desired rich result rather than applying connected Schema Markup to improve search engine understanding.

In this new world of AI and Large Language models, Schema Markup for rich results alone is insufficient. By implementing proper connected Schema Markup and establishing connections between the entities on your site and on external authoritative knowledge bases, you are creating your organization’s content knowledge graph.

Building Your Content Knowledge Graph

Your content knowledge graph is a structured information data layer that can help search engines disambiguate the entities mentioned on your site. By providing this, you can shape how search engines understand your content, gaining greater control over how users perceive your brand. This ultimately provides users with more accurate and relevant search results.

The value of Schema Markup in ensuring your content is correctly understood is now significantly more strategic than just achieving rich results.

October, November, & December 2023 – Evidence From Google That Rich Results Hold Semantic Value

Don’t believe us? Google is also telling us that the value of Schema Markup is for understanding AND rich results. Search News released a new episode in early October 2023. During the episode, John Mueller talked about how some rich results would go away – in reference to the deprecation of How To rich results and reduction in FAQ rich results – and new rich results would be introduced. And boy did they hold true to their word.

Over the span of October, November, and December 2023, Google rolled out 6 new rich results:

• Oct 16: Added Vehicle Listing structured data documentation.
• Nov 15: Added Course Info structured data documentation.
• Nov 27: Added Profile page structured data documentation, Discussion forum structured data documentation, and expanded recommendations for Q&A Page structured data documentation.
• Nov 29: Added new Organization structured data documentation
• Dec 4: Added Vacation Rental structured data documentation

Structured Data Offering a Mutually Beneficial Relationship Between Google and SEOs

Google’s ongoing rich result opportunities highlight the symbiosis between search engines and SEOs. By offering rich results in exchange for detailed structured data, Google stimulates the supply of credible information, contributing to the development of the semantic web. It’s a win-win!

Google grants rich results as a reward for making it easy and less costly for search engines to comprehend the content on your website. By providing Schema Markup, you inform them about the content and how it relates to other topics on the web and on your website. By providing this information, the search engines don’t have to process the data to infer the meaning or relationships.

This helps search engines understand content without having to spend as many computing resources. This incentive provides content creators with the motivation needed to support the semantic initiatives crucial for the future of search.

Let’s look at two of the new rich results, Profile Page and Organization, and call out their characteristics that emphasize the value they bring to the semantic web.

Profile Page & Organization Structured Data Helps With Disambiguation

Among the newly introduced rich results, Profile Page and Organization stand out, playing a crucial role in disambiguating entities such as organizations, individuals, and authors within your content. These elements tie into E-E-A-T and empower you to assert greater control over structured data, articulating your identity and authority in connection to your content.

Organization structured data enables search engines to understand context, even when your website does not visually present certain information. For instance, properties recommended within the Organization structured data guidelines, such as identifier codes (i.e. duns, taxID, etc.), may not be visible to users but aid Google in disambiguating and identifying your organization within your content.

This background information adds a layer of context, facilitating search engines in relating your content to search queries and other web information through a knowledge graph.

Vehicle Listing and Vacation Rental Structured Data (Feed)

Last but not least, we’re seeing structured data become a data feed. The introduction of the new Vehicle Listing and Vacation Rental Structured Data supports this concept. You can achieve the Vehicle Listing rich result by adding structured data to your site or uploading a vehicle listing feed file to Google. Similarly, you can now achieve the Vacation Rental rich result by adding structured data, instead of the historical method of uploading an XML list feed to Google. Unlike list feeds, structured data is a cleaner way for data providers to transfer data to Google.

While the consecutive release of new rich results is exciting, it’s crucial to recognize that the purpose of structured data has expanded beyond merely attaining rich results, particularly in the past year. Google’s introduction of these rich results reflects a clear effort to improve the semantic nature of the search experience.

As SEOs, our focus should shift towards creating semantically understood content, empowered by Schema Markup.

The Value of Schema Markup Today: Prioritizing Semantic Understanding

Schema Markup remains valuable for search engines in 2023, emphasizing the critical need to prioritize the semantic aspect of structured data over just achieving rich results. While rich results come and go, semantic understanding is instrumental in laying the groundwork for contextual content that is poised to shape the future of search.

By connecting entities and developing a Knowledge Graph, organizations can ground Language Models (LLMs), support internal AI initiatives, and build authority by linking to authoritative databases. Schema Markup, originally created for search engine comprehension, continues to play a vital role in navigating the dynamic landscape of digital marketing.

In essence, Schema Markup not only provides quick wins through achieving rich results but also sets the stage for ongoing and agile content optimization as search evolves.

Interested in learning how you can apply Schema Markup to stay agile in search? Get started here.

The post The Evolving Role of Schema Markup: From Rich Results to AI Understanding appeared first on Schema App Solutions.

However, it’s important to recognize that while rich results are a nice benefit of Schema Markup, they don’t fully capture its true value.

The real value of Schema Markup lies in its capacity to provide search engines with a deeper, more semantic understanding of your website’s content. When implemented correctly, Schema Markup allows you to develop your content knowledge graph and take better control of how your content appears in search.

This article will explore how Schema Markup enhances website visibility and search engine understanding of your content through robust knowledge graphs. This, in turn, refines how your content appears for relevant queries with greater accuracy and helpfulness to the user.

Why Rich Results Are Not Enough

Measuring the return on investment from your SEO efforts can be tough. Hence, many SEOs like implementing Schema Markup because they can easily measure the ROI on their Schema Markup efforts through the performance of rich results.

However, implementing Schema Markup solely for the purpose of achieving rich results can be risky due to their ever-changing criteria and eligibility.

Rich Result Volatility

Over the past few years, we’ve seen the performance of rich results fluctuate based on Google’s algorithm changes. This year, Google has also made substantial changes to the rich results available on the SERP and the criteria for achieving certain rich results.

They’ve ceased awarding video rich results to pages that lack video as their primary content and deprecated How-to rich results entirely from the SERP. Similarly, FAQ rich results have been curtailed for most websites, now reserved only for authoritative government and health websites.

These volatile fluctuations and changes can be unsettling for businesses and SEOs who have come to rely heavily on rich results to drive traffic and engagement.

The True Purpose of Schema Markup

While rich results offer visual enhancements and additional SERP information, they play a secondary role to Schema Markup’s core objective.

The main purpose of Schema Markup is to enable search engines to clearly understand and contextualize the content on a page. That way, search engines can better match the content on a page to the searcher’s query, and provide more accurate search results.

Think of Schema Markup as a tool to assist search engines in content comprehension, with rich results being a bonus feature for publishers using specific markups.

By structuring your content with Schema Markup, you’re not just chasing rich results; you’re preparing your content for the future of AI-driven search.

What Else Can You Do With Schema Markup?

By now it’s been made clear that Schema Markup has much greater potential than most have given it credit for. Let’s dive into some of the powerful ways Schema Markup can drive results for your organization and keep you competitive in search as it continues to evolve.

Integrate Your Schema Markup

Once implemented, you can also seamlessly integrate your Schema Markup with other external data sources. This flexibility enables you to provide richer, more comprehensive data experiences in the applications and platforms your business chooses to integrate with.

In addition to integrating it with external data sources, you can also integrate your Schema Markup with internal tools, platforms, or systems. This allows for a more cohesive data management strategy within your organization.

Your Schema Markup can be integrated using APIs or Linked Open Data. For example, an e-commerce website might integrate Schema Markup with their inventory management system via APIs. This would allow the product details (like price, availability, and ratings) to be dynamically updated in real-time based on the Schema Markup.

Another example is integrating through Linked Open Data. A cultural institution, like a museum, might use Schema Markup to describe their exhibits and then integrate this information with global datasets like Wikidata. This would help in providing richer context about the exhibits and potentially drive more visitors.

Reuse Your Schema Markup

Your Schema Markup can be reused in various scenarios. One prime example is with our WordPress plugin feature. By appending ?format=application/ld+json to URLs, you can retrieve the schema for a particular page. This facilitates:

• Mobile Apps: Developers could pull this Schema Markup to display rich content snippets in a mobile app about the company’s services or products.
• Chatbots: Businesses could leverage the schema to answer user queries more accurately, providing detailed information pulled directly from the website.
• Partner Websites: If a business has partnerships with other websites or platforms, they can share the Schema Markup, ensuring consistent and updated information across platforms.

Build Your Knowledge Graph

A knowledge graph is a collection of relationships between the entities defined using a standardized vocabulary, from which new knowledge can be gained through inferencing.

For additional clarity, an entity is a thing that has specific attributes. For example, your postal address is a thing that can be described by the country, region, postal code and street address.

When you implement Schema Markup on your site, you are essentially using the Schema.org Types and properties to describe the entities on your site. Each entity is then identifiable through a Uniform Resource Identifier (URI) to ensure that it can be referenced to other items in your graph.

You can develop a knowledge graph by using the Schema.org vocabulary to connect the entities on your site to other entities on your site and other external authoritative knowledge bases like Wikidata or Wikipedia. By doing so, you are establishing your entity and defining how it connects to other things that exist in the world.

Download our guide to learn how to connect the entities on your site using Schema Markup.

What Makes Knowledge Graphs So Valuable?

At Schema App, we leverage Schema Markup to enable you to present your data in the form of a semantic knowledge graph, but the real magic lies in how you choose to use this connected data.

Your knowledge graph is a versatile resource that opens up a world of possibilities tailored to your specific business objectives.

For instance, you can harness the power of SPARQL Queries to extract precise data and information from your knowledge graph. This capability enables tasks such as generating insightful reports, counting the number of pages related to a particular topic, or tracking external entities linked to your Schema Markup.

These reports not only offer valuable insights but also serve as a foundation for identifying content gaps within your domain. By analyzing your existing content against your knowledge graph, you can determine which topics are well-covered and which areas require further exploration.

This strategy helps you build your authority by pinpointing opportunities for content expansion.

Enhance User Experience with Better Content-Query Alignment

When left to their own devices, search engines rely on natural language processing to parse the information on a site, which can lead to inaccuracies. When the information on your site is organized in a structured knowledge graph using the schema.org vocabulary, it makes it easier for search engines to understand and contextualize your site content.

This leads to more precise matches between your content and search queries, ultimately improving user experience and the quality of traffic you are getting to your site.

Our Customer Success team has even experimented with linking entities on a page to external authoritative knowledge bases like Wikidata and Google’s knowledge graph. This approach has yielded positive results, increasing click-through rates for queries related to those entities.

While it might not necessarily boost the visibility of your pages like a rich result, it does ensure that the clicks are from users who are genuinely interested in your content.

Integrate Your Knowledge Graph

Your knowledge graph can also seamlessly integrate into your workflow, serving as a backbone for various tools and applications.

At Schema App, for instance, our Editor tool relies on the knowledge graph to provide a comprehensive experience. All of the information in that interface is part of our knowledge graph. Any changes made to data items in our tool directly impact and update the knowledge graph.

Additionally, you can leverage your content knowledge graph to build custom web applications. This is accomplished by providing data for new apps and enabling developers to create user interfaces that utilize the wealth of information within your knowledge graph.

Ground and Train Your Internal LLMs

In the realm of AI search engines, one significant challenge is the potential for incorrect inferences leading to hallucinations. Hallucinations occur when Large Language Models (LLMs) making up false information that is not based on real data.

You have the power to mitigate this major risk by using your knowledge graph as a control point to define your content more precisely to AI search engines. 

Although major search engines have yet to officially confirm this, there’s potential to train AI search engines to provide more accurate results by grounding their understanding with your knowledge graph.

Another interesting use case for knowledge graphs is that you can reuse them to train your own internal LLMs. An example of this is the use of AI chatbots on your site to address common customer queries. 

Grounding your LLMs with a knowledge graph enhances the performance of customer queries. It also ensures the accuracy of the information provided, since the LLM is restricted to the statements (RDF triples) expressed in your knowledge graph. 

You can clearly define entities in your content knowledge graph to ground it with factual and accurate information about your organization.

Leveraging the True Power of Schema Markup

As search engines become more sophisticated and semantic, they attempt to grasp the nuances of human language, meaning and intention.

Schema Markup serves as a bridge between your content and these semantic search engines.  It enables your content to be interpreted more accurately, leading to improved relevance in search results.

While rich results undoubtedly hold distinctive value and can elevate your content’s visibility, they should be seen as a bonus rather than the sole objective of Schema Markup.

Schema Markup’s true value lies in its ability to help search engines understand your content’s context and intent. When you implement Schema Markup with machine comprehension in mind, you not only enhance your chances of securing rich results but also ensure your content remains resilient and relevant in an ever-changing search landscape.

Looking to develop your very own marketing knowledge graph through the power of Schema Markup?

Get started today to learn about our solution.

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