How schema formatting builds trust and increases information gain

In this article, we explore how schema markup, specifically using descriptive markup, linking entities, and connecting to external data sources, can build trust with Google and potentially reduce the likelihood of de-indexing.

In addition, we discuss the role of schema formatting in increasing the likelihood of inclusion Seeking Generative Experience (SGE) and increasing information gain scores, which can be crucial for visibility in conversational search environments.

How Schema Markup Benefits Google and Website Owners

Google is facing rising costs due to the rapid growth of spam and the upcoming adoption of generative AI in SERPs. Implementing SGE comes at a significantly higher cost compared to current methods, potentially impacting advertising revenue by meeting searchers’ needs more quickly, reducing the number of placement opportunities.

One approach to reducing costs that I’ve observed is a stricter policy on what gets included in Google’s index. Anecdotally, customers with complex business models who use template pages see de-indexing more often, even for pages that have been stable for years.

In some cases, JavaScript is the culprit. However, Google has already figured out how to deal with sites with large amounts of injection. Google needs to identify these pages, add them to the rendering queue, and crawl them after the data is injected.

So why isn’t this happening? Could this be another resource that Google is having difficulty managing efficiently due to the growing amount of spam?

Getting deindexed is a nightmare for website owners and SEO professionals. It’s like you’re completely taken out of the game.

However, experience has taught me that aligning with Google’s initiatives can deliver significant benefits to our customers’ sites.

The solution is simple: schema markup. In addition to helping search engines understand content more efficiently, it can also result in major cost savings for Google.

Schema markup helps Google’s crawlers and machine learning algorithms understand web content more efficiently and cost-effectively.

It can play a role in reducing Google’s operating costs. The premise is that helping Google minimize the resources needed to crawl, index, and understand your site will lead to better visibility.

Building trust with schema formatting

Implement detailed schema markup, using @id to link entities and connect these entities to external data sameAs can build trust and reduce the cost of data exchange between a company and Google.

• Descriptive schema formatting: Detailed and accurate schema markup that is not deceptive helps Google by reducing NLP costs, made possible by defining the entities and their relationships on the page.
• Linking entities: Of @idare entities linked together, improving the description of their relationships.
• Linking to external data: Using sameAs external data can be linked to entities on a web page to increase the descriptiveness of the entity and prove its legitimacy.

While there is a lot of information about writing detailed schema markup and linking entities, I don’t think we pay enough attention to connecting data from external sources.

Using sameAs goes beyond just linking social media profiles to an organization’s schema; it is crucial for linking to external data. This has led many SEOs to connect to knowledge bases such as Wikidata, Google’s Knowledge Graph, and many more.

In today’s SERP environment, creating trust is necessary and one of the easiest ways to prove you’re not spammy. This can be done by using the sameAs property to connect entities to trusted and authenticated data sources.

When thinking about organizations with complex business structures, the Better Business Bureau, City of Chambers Business Directories, and EIN Numbers are among some of the options SEOs can leverage to prove legitimacy and gain trust.

Ultimately, the goal is to minimize ambiguity so that Google can easily verify and trust your content. In the age of generative AI, trust is built on verifiable information. If Google can’t verify it, it won’t trust it, which simply undermines our cost-saving efforts.

Implementation schedule for potential SGE inclusion

Natural Language Processing (NLP) has come a long way, especially with the addition of LLMs, yet making sense of the vast amount of information on the Internet remains a challenge for any search engine.

The more structured data companies provide to Google, the more verified information they will have to effectively train machine learning algorithms like LLMs.

Increasing your chances of admission to SGE is a hot topic. However, my perspective is that visibility will increase as more information from a specific source is included in the training data.

Ultimately, simply helping Google understand information isn’t enough.

If you were building a library at home with limited space, would you buy the same book again just because you found another copy? I certainly wouldn’t do that, and I don’t think Google would either.

To be included in SGE you have to share something new, even if that’s just a new perspective.

This brings us to information gaina concept used by Google to improve the user experience across multiple searches and encourage the discovery of new information with each search.

This score can be useful in SGE because conversational search inherently creates a multi-search environment. A high Information Gain score can increase the chances of your content being included in the SGE results because there is less competition for that information to be included in Google’s machine learning algorithms.

Combining new, unique content with schema markup aligns perfectly with Google’s goal to deploy SGE efficiently, aiming for a neutral or positive impact on revenue while reducing operational costs.