In this article, we’ll explore how schema markup, especially using descriptive tagging, linking entities, and connecting to external data sources, can establish trust with Google and potentially reduce your chances of being de-indexed.
Additionally, we’ll discuss the role of schema tagging in increasing the likelihood of inclusion in the Search Generative Experience (SGE) and increasing insight scores, which can be crucial for visibility in search environments conversational
How schema markup benefits Google and website owners
Google faces rising costs with the rapid growth of spam and the imminent adoption of generative AI in the SERPs. Implementing SGE involves significantly higher costs compared to current methods, which can impact advertising revenue as searcher needs are met more quickly and reduce the number of placement opportunities.
One cost-cutting approach I’ve observed is a stricter policy for what is included in Google’s index. Anecdotally, customers with complex business models using template pages see more frequent de-indexing, 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 should identify these pages, add them to the render queue, and crawl them after the data has been injected.
So why doesn’t this happen? Could this be another resource that Google is struggling to manage efficiently with the growing amount of spam?
Being de-indexed is a nightmare for website owners and SEO professionals. It’s like they took it out of the game entirely.
However, experience has taught me that aligning with Google initiatives can significantly benefit our clients’ sites.
The solution is simple: markup of schemes. Aside from helping search engines understand your content more efficiently, it can also be a huge cost-saver 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 improve visibility.
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Build trust with schema tagging
Implementing detailed schema markup, using @id to link entities, and connecting those entities to external data using sameAs can establish trust and reduce the cost of data exchange between a company and Google.
Descriptive outline marking: Detailed and accurate schema tagging that is not misleading helps Google reduce NLP costs by making it easier to define entities and their relationships on the page.
Link entities: With @id, entities are interconnected, improving the description of their relationships.
Link to external data: The use of external data sameAs can be connected to entities on a web page to increase the description of the entity and demonstrate its legitimacy.
While there is a lot of information on how to write detailed schema markup and link entities, I don’t think we are paying enough attention to connecting data from external sources.
The use of sameA extends beyond linking social media profiles to an organization’s schema; is essential 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, building trust is necessary and one of the easiest ways to prove you’re not spam. This can be done by using the sameAs property to connect entities to trusted and verified data sources.
When thinking about organizations with complex business structures, the Better Business Bureau, City of Chambers Business Directory, and EIN numbers are some of the opportunities that SEOs can take advantage of to demonstrate legitimacy and gain trust.
Ultimately, the goal is to minimize ambiguity, allowing Google to easily verify and trust your content. In the age of generative AI, trust is based on verifiable information. If Google can’t verify it, they won’t trust it, which simply undermines our cost-cutting efforts.
Implementation scheme for possible SGE inclusion
Natural language processing (NLP) has come a long way, especially with the addition of LLM, but still understanding the vast amount of information on the web remains a challenge for any search engine.
The more structured data companies provide to Google, the more verified information they have to effectively train machine learning algorithms like LLMs.
Increasing your chances of inclusion in 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, it’s not enough to help Google understand information.
If you were building a library in your home with limited space, would you buy the same book again just because you found another one? I sure wouldn’t and I don’t think Google would either.
To be included in SGE, you have to share something new, even if it’s simply a new perspective.
This brings us to insights, a concept used by Google to improve the user experience across multiple searches, encouraging the discovery of new information with each query.
This score can be useful in SGE, as conversational search inherently creates a multiple search environment. A high Insight Gain score could increase your content’s chances of being included in SGE results, as there is less competition to include that information in Google’s machine learning algorithms.
Combining fresh, unique content with schema markup aligns perfectly with Google’s goal of efficiently deploying SGE, which aims for a neutral or positive revenue impact while reducing operational costs.
The views expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
