In this article, we’ll explore how schema markup, particularly the use of descriptive markup, linking entities and connecting to external data sources, can establish trust with Google and potentially reduce the chances of being deindexed.
Additionally, we’ll discuss the role of schema markup in increasing the likelihood of inclusion in Search Generative Experience (SGE) and boosting information gain scores, which can be crucial for visibility in conversational search environments.
How schema markup benefits Google and website owners
Google faces escalating costs with the rapid growth of spam and the impending adoption of generative AI in SERPs. Implementing SGE incurs significantly higher costs compared to current methods, potentially impacting ad revenue as searchers’ needs are met more quickly reducing the number of placement opportunities.
One approach to reducing costs I’ve observed is a more stringent policy for what gets included in Google’s index. Anecdotally, clients with complex business models using template pages are seeing more frequent deindexing, 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 high 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 is this not happening? Could this be another resource that Google is struggling to manage efficiently with the growing amount of spam?
Being deindexed is a nightmare for website owners and SEO professionals. It’s like being taken out of the game entirely.
However, experience has taught me that aligning with Google’s initiatives can significantly benefit our clients’ sites.
The solution is straightforward: schema markup. Aside from aiding search engines in understanding content more efficiently, it can also provide a great cost-saving for Google.
Schema markup aids Google’s crawlers and machine learning algorithms in understanding web content more efficiently and cost-effectively.
It can play a role in reducing Google’s operational costs. The premise is that aiding Google in minimizing the resources needed for crawling, indexing and understanding your site will lead to improved visibility.
Building trust with schema markup
Implementing detailed schema markup, utilizing @id to link entities and connecting these entities to external data through sameAs can establish trust and decrease the cost of data exchange between a business and Google.
Descriptive schema markup: Detailed and accurate schema markup that is not deceitful aids Google by lowering NLP costs, facilitated by defining the entities and their relationships on the page.
Linking entities: With @id, entities are interlinked, enhancing the description of their relationships.
Linking to external data: Using sameAs external data can be connected to entities on a webpage to increase the descriptiveness of the entity and prove its legitimacy.
While there’s a lot of information about writing detailed schema markup and linking entities, I think we’re not paying enough attention to connecting data from external sources.
Utilizing sameAs extends beyond just linking social media profiles to an organization’s schema; it’s pivotal for linking to external data. This has led many SEOs to connect to knowledge bases like Wikidata, Google’s Knowledge Graph and many more.
In the current SERP environment, creating trust is necessary and is one of the easiest ways to prove that you are 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 Directories and EIN numbers are among a few opportunities that SEOs can leverage to prove legitimacy and gain trust.
Ultimately, the goal is to minimize ambiguity, allowing Google to easily verify and trust your content. In the era 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-reduction efforts.
Implementing schema for potential SGE inclusion
Natural language processing (NLP) has come a long way, especially with the addition of LLMs, yet still comprehending the web’s vast amount of information remains challenging for any search engine.
The more structured data that businesses provide Google, the more verified information they will have to train machine learning algorithms like LLMs effectively.
Boosting 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, simply helping Google understand information is not enough.
If you were compiling a library in your home with limited space, would you buy the same book again just because you found another copy? I certainly wouldn’t and I don’t think Google would either.
To be included in SGE, you must share something new, even if that is simply a new perspective.
This brings us to information gain, a concept used by Google to improve user experience across multiple searches, encouraging the discovery of new information with each query.
This score can be helpful in SGE as conversational search inherently creates a multi-search environment. A high information gain score could boost your content’s chances of being included in SGE results as there is less competition for that information’s inclusion in Google’s machine learning algorithms.
Combining fresh, unique content with schema markup aligns perfectly with Google’s aim to deploy SGE efficiently, which aims for a neutral or positive revenue impact while reducing operational costs.
