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.

Most organizations are trying to stay competitive by embracing the AI changes in the market and identifying ways to leverage “off-the-shelf” Large Language Models (LLMs) to optimize tasks and automate business processes.

However, as the adoption of generative AI accelerates, companies will need to fine-tune their Large Language Models (LLM) using their own data sets to maximize the value of the technology and address their unique needs. There is an opportunity for organizations to leverage their content Knowledge Graphs to accelerate their AI initiatives and get SEO benefits at the same time.

What is an LLM? 

A Large Language Model (LLM) is a type of generative artificial intelligence (AI) that relies on deep learning and massive data sets to understand, summarize, translate, predict and generate new content.

LLMs are most commonly used in natural language processing (NLP) applications like ChatGPT, where users can input a query in natural language and generate a response. Businesses can utilize these LLM-powered tools internally to provide employees with Q&A support or externally to deliver a better customer experience.

Despite the efficiency and benefits it offers, however, LLMs also have their challenges.

LLMs are known for their tendencies to ‘hallucinate’ and produce erroneous outputs that are not grounded in the training data or based on misinterpretations of the input prompt. They are expensive to train and run, hard to audit and explain, and often provide inconsistent answers.

Thankfully, you can use knowledge graphs to help mitigate some of these issues and provide structured and reliable information for the LLMs to use.

What is a Knowledge Graph?

Gartner’s “30 Emerging Technologies That Will Guide Your Business Decisions” report, published in February 2024, highlighted Generative AI and Knowledge Graphs as critical emerging technologies companies should invest in within the next 0-1 years. 

A Knowledge Graph is a collection of relationships between things defined using a standardized vocabulary, from which new knowledge can be gained through inferencing. When knowledge is organized in a structured format, it enables efficiencies in the retrieval of information and improves accuracy.

For instance, most organizations have websites that contain extensive information about the business, such as its products and services, locations, blogs, events, case studies, and more. However, the information is unstructured, because it exists as text on the website.

You can use Structured Data, also known as Schema Markup, to describe the content and entities on each page, as well as the relationships between these entities across your site and beyond. Implementing semantic Schema Markup can:

• Help search engines better understand and contextualize your content, thereby providing users with more relevant results on the SERP
• Help your organization develop a reusable content knowledge graph. This graph can provide valuable structured information to enhance your business’s capabilities with LLMs.

Using an LLM to Generate your Schema Markup

To develop your content knowledge graph, you can create your Schema Markup to represent your content. One of the new ways SEOs can achieve this is to use the LLM to generate Schema Markup for a page. This sounds great in theory however, there are several risks and challenges associated with this approach.

One such risk includes property hallucinations. This happens when the LLM makes up properties that don’t exist in the Schema.org vocabulary. Secondly, the LLM is likely unaware of Google’s required and recommended structured data properties, so it will predict them and jeopardize your chances of achieving a rich result. To overcome this, you need a human to verify the structured data properties generated by the LLM.

LLMs are good at identifying entities on Wikidata. However, it lacks knowledge of entities defined elsewhere on your site. This means the markup created by the LLM will create duplicate entities, disconnected across pages on your site or even within a page, making it even more difficult for you to manage your entities.

In addition to duplicate entities, LLMs lack the ability to manage your Schema Markup at scale. It can only produce static Schema Markup for each page. If you make changes to the content on your site, your Schema Markup will not update dynamically, which results in schema drift.

With all the risks and challenges of this piecemeal approach, the Schema Markup created by the LLM is static and unconnected for a page—it doesn’t help you develop your content knowledge graph.

Instead, you should create your Schema Markup in a connected, scalable way that updates dynamically. That way, you’ll have an up-to-date knowledge graph that can be used not only for SEO but also to accelerate your AI experiences and initiatives.

Synergy Between Knowledge Graphs and LLMs

There are three main ways of leveraging the content knowledge graph to enhance the capabilities of LLMs for businesses.

1. Businesses can train their LLMs using their content knowledge graph.
2. Businesses can use LLMs to query their content knowledge graphs.
3. Businesses can structure their information in the form of a knowledge graph to help the LLM function more effectively.

Training the LLM Using Your Content Knowledge Graph

For a business to thrive in this technological age, connecting with customers through their preferred channel is crucial. LLM-powered AI experiences that answer questions in an automated, context-aware manner can support multi-channel digital strategies. By leveraging AI to support multiple channels, businesses can serve their customers through their preferred channels without having to hire more employees.

That said, if you want to leverage an AI chatbot to serve your customers, you want it to provide your customers with the right answers at all times. However, LLMs don’t have the ability to perform a fact check. They generate responses based on patterns and probabilities. This results in issues such as inaccurate responses and hallucinations.

To mitigate this issue, businesses can use their content knowledge graphs to train and ground the LLM for specific use cases. In the case of an AI chatbot, the LLMs would need an understanding of what entities and relations you have in your business to provide accurate responses to your customers.

Using the Schema.org Vocabulary to Define Entities

The Schema.org vocabulary is robust, and by leveraging the wide range of properties available in the vocabulary, you can describe the entities on your website and how they are related with more specificity. The collection of website entities forms a content knowledge graph that is a comprehensive dataset that can ground your LLMs. The result is accurate, fact-based answers to enhance your AI experience.

Let’s illustrate how your content knowledge graph can train and inform your AI Chatbot.

A healthcare network in the US has a website with pages on their physicians, locations, specializations, services, etc. The physician page has content relating to the specific physician’s specialties, ratings, service areas and opening hours.

If the healthcare network has a content knowledge graph that captures all the information on their site, when a user searches on the AI Chatbot “I want to book a morning appointment with a neurologist in Minnesota this week”, the AI Chatbot can deduce the information by accessing the healthcare network’s content knowledge graph. The response would be the names of the neurologists who service patients in Minnesota and have morning appointments available with their booking link.

The content knowledge graph is also readily available, so you can quickly deploy your knowledge graph and train your LLM. If you are a Schema App customer, we can easily export your content knowledge graph for you to train your LLM.

Using LLMs to Query Your Knowledge Graph

Instead of training the LLM, you can use the LLM to generate the queries to get the answers directly from your content knowledge graph.

This approach of generating answers through the LLM is less complicated, less expensive and more scalable. All you need is a content knowledge graph and a SPARQL endpoint. (Good news, Schema App offers both of these.)

1. The Schema App application loads the content model from your content knowledge graph, which would be all the Schema.org data types and properties that exist within your website knowledge graph.
2. Then the user would ask the Schema App application a question.
3. The Schema App application combines the question with the content model and asks the LLM to write a SPARQL query. Note: The only thing the LLM does is transform the question into a query.
4. Schema App application then executes the SPARQL against your content knowledge graph and displays the results or requests as a formatted response using the LLM.

This method is possible because the LLMs have a great understanding of SPARQL and can help translate the question from natural language to a SPARQL query.

By doing this, the LLM doesn’t have to hold the data in memory or be trained on the data because the answers exist within the content knowledge graph, which makes it stateless and a less resource-intensive solution. Furthermore, companies can avoid providing all their data to the LLM as this method introduces a control point to the knowledge graph owner to only allow questions on their data that they approve.

Overcoming LLM Restrictions

This approach also overcomes some of the restrictions of the LLMs.

For example,  LLMs have token limits, which restrict the input and output number of words that can be included. This approach eliminates this problem by using the LLMs to build the query/prompt and using the knowledge graph to query. Since SPARQL queries can query gigabytes of data, they don’t have any token limitations. This means you can use an entire content knowledge graph without worrying about the word limit.

By using the LLM for the sole purpose of querying the knowledge graph, you can achieve your AI outcomes in an elegant, cost-effective manner and have control of your data while also overcoming some of the current LLM restrictions.

Optimizing LLMs by Managing Data in the form of a Knowledge Graph

You can machine learn Obama’s birthplace every time you need it, but it costs a lot and you’re never sure it is correct.” – Jamie Taylor, Google Knowledge Graph

One of the most considerable costs of running an LLM is the inference cost (aka the cost of running a query through the LLM).

In comparison to a traditional query, LLMs like ChatGPT have to run on expensive GPUs to answer queries ($0.36 per query according to research), which can eat into profits in the long run.

Businesses can reduce the inference cost of the LLM by storing the historical responses or knowledge generated by the LLM in the form of a knowledge graph. That way, if someone asks the question again, the LLM does not have to exhaust resources to regenerate the same answer. It can simply look up the answer stored in the knowledge graph.

Unstructured data that the LLM is trained on can also cause inefficiencies in the retrieval of information and high inference costs. Therefore, converting unstructured data such as documents and web pages into a knowledge graph can reduce information retrieval time and produce more reliable facts.

As the volume of data in the hybrid cloud environment continues to grow exponentially, knowledge graphs play a crucial role in data management and organization. They contribute to the ‘Big Convergence,’ which combines data management and knowledge management to ensure efficient information organization and retrieval.

Build Your Knowledge Graph Through Schema App

In summary, the integration of knowledge graphs with LLMs can significantly enhance decision-making accuracy, especially in the realm of Marketing.

The content knowledge graph is an excellent foundation to leverage schema data in LLM tools, leading to more AI-ready platforms. It’s an investment that could pay off handsomely, especially in a world increasingly reliant on AI and knowledge management.

At Schema App, we can help you quickly implement your Schema Markup data layer and develop a semantically relevant and ready-to-use content knowledge graph to prepare your organization for AI.

Regardless of whether you use Schema App to author your Schema Markup, we can produce a content knowledge graph for you. Schema App can capture the Schema.org data from your existing implementation using our Schema App Analyzer to develop your marketing knowledge graph.

Get in touch with our team to find out more about how Schema App can help you build your marketing knowledge graph to enhance your LLM.

The post How to Leverage Your Content Knowledge Graph for LLMs Like ChatGPT 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.

While we remain uncertain about the impact of these new experiences on search engine optimization, there are a few things that we know:

• Google’s Search Generative Experience (SEG) will feature source links.
• Generative AI is in its infancy, but it is not going away.
• AI uses large language models to train. Large language models use Knowledge Graphs to learn and ground their data to increase accuracy.
• Schema Markup when done in a connected way, creates a knowledge graph that AI can consume.

Organizations should use Schema Markup (also known as Structured Data) on their websites if they want to be prepared and thrive in AI search.

The last point is especially important because AI is going to need help understanding and contextualizing the content on a website to provide searchers with the right answers.

Before exploring deeper into the threats and opportunities of this new technology, let’s talk about generative AI in the context of search.

What Is Generative AI for Search?

Generative AI is a specific type of artificial intelligence algorithm that is designed to generate new content and outputs based on data they have been trained on. Some of the content that generative AI can create includes text, audio, code, and images.

Bard and ChatGPT are prime examples of generative AI chatbots that have been trained on large quantities of unlabeled, unstructured data. While generative AI chatbots are a groundbreaking development in the world of artificial intelligence, they are only the beginning.

During Google I/O 2023, the search engine giant showcased how it has incorporated generative AI technologies into its search engine and other products such as Workspace. This newly integrated AI technology will have a significant impact on Google’s responses to queries and the composition of the search engine results page (SERP).

There is considerable excitement surrounding the AI revolution. That said, there are also significant concerns around bias, plagiarism, trustworthiness, and accuracy of generative AI search results.

Currently, generative AI search engines are largely trained on unlabeled/unstructured data which can lead to inaccurate results. Adding structured data or Schema Markup to your site can allow search engines to:

• train on quality data,
• improve the accuracy of their answers,
• and give you a control point to inform generative AI on your web content.

There are underlying risks and exciting opportunities associated with the implementation of any new technology, especially when that technology is as powerful and sophisticated as generative AI.

Google’s new Search Generative Experience will have an impact on the way SEO is done. So is generative AI a threat or an opportunity for search engine optimization?

Let’s explore some of the potential threats and clear opportunities that have emerged in the wake of generative AI search.

Is AI Search an Opportunity or Threat for SEOs?

At first glance, Google’s new generative AI search has a few glaring threats to SEO.

Threats

1. Potential Loss of Visibility on the SERP

There are a lot of unanswered questions about how website performance will be measured with AI search.

Google’s generative AI search engine does showcase links that attribute information back to reputable source content. However, users may no longer need to click to read this content (resulting in zero-click searches), which limits your visibility and overall influence over the customer journey.

Ultimately, rich results, featured snippets and other top-ranked content may be overshadowed by generative AI results. In turn, this could lead to a reduction in rankings, clicks, and impressions.

2. Success Metrics of a Website Are Going to Fundamentally Change

Generative AI has the potential to condense your sales funnel. The line between clicks, impressions, and conversions could become blurred, making it difficult to attribute sales or leads to specific marketing activities.

Bing and Google have yet to reveal how sites can measure their performance in generative AI search results. For now, you will have to draw conclusions using existing metrics.

3. Hyper Long and Specific Search Queries

If you haven’t watched the Google I/O segment on search yet, we strongly recommend doing so. In their demonstration, we saw that the Search Generative Experience (SGE) allows for more natural, specific conversational search queries in comparison to the traditional keywords or questions we see today.

Image Credit: Google

This capability will lead users to conduct very specific queries. And as a result, question fragments and traditional keyword searches will become less prominent.

These long specific queries are a shift from traditional keywords and will likely have little to no search volume. It also reflects SGE’s ability to understand the context behind a query rather than the keywords used. Therefore, it is crucial for content publishers to focus on utilizing Schema Markup helping search engines to understand and contextualize the on-page content.

Furthermore, Google has previously emphasized the importance of a website focusing on content around a specific topic as a way of creating people-first content.

Instead of focusing heavily on optimizing for keywords, focus on creating content for topics within your expertise and around your customers’ needs. You can then use Schema Markup to communicate your expertise, experience, authority and trustworthiness with the AI search engine.

Opportunities

The world of SEO is constantly evolving. Long-time marketing professionals understand that this level of change is not a new phenomenon. To stay relevant in the world of organic search and differentiate yourself from the competition, you must adapt to the latest trends (such as generative AI) and capitalize on the opportunities they provide.

The key opportunities created by generative AI search include the following:

1. Provide a Better Customer Experience

You can provide customers with a better experience by answering their queries directly on the search engine results page. Today, content publishers can already utilize rich results such as FAQ and How-To as well as other features like Featured snippets to answer the questions in search.

However, the generative AI search engine can help you reduce friction with your customers by showing users the key information they need to know or providing follow-up information.

The eCommerce industry is one of the first industries to be truly disrupted by this because Google already has great eCommerce data from Google’s Shopping Graph for their AI search engine to train on.

Image credit: Google

Generative AI can shorten the consumer funnel by allowing searchers to convert immediately through the SERP and purchase an item directly from the SERP instead of having to navigate the site. This will mean that conversion data and source-to-conversion pages will be one of the key metrics during this evolution.

2. Links Are Not Going Away

Based on the demo and the responses from the early experimental rollout of Search Generative Experience, Google’s generative AI search engine is linking back to sources more frequently than initially expected. In fact, Google’s SGE demo gave a lot of real estate to the websites where the content was originally sourced from.

Though far from perfect, this is an opportunity for search engine optimizers to capitalize. We believe that content publishers can focus on optimizing their website for search by following Google’s guidelines of creating people-first content and E-E-A-T, optimizing on-page experiences for your audience, and monitoring or optimizing core web vitals.

It doesn’t matter whether you are new to search or simply want to stay ahead of the pack. You should refine all these key SEO areas to gain an edge over the competition.

3. Schema Markup Is Key to Helping AI Search Engines Understand Your Content

In a keynote address at Pubcon 2023, Fabrice Canel from Bing expressed that annotating great content with Schema Markup is how search engine optimization experts should prepare for generative AI search.

Thus, you should craft compelling, people-first content before implementing Schema Markup across all web pages. Schema Markup helps search engines understand the content on your site so it can provide users with useful results.

While many SEO teams already use Structured Data to achieve rich results, few implement proper semantic Schema Markup across their entire site. To help generative AI search engines truly understand and contextualize your content, you need to ensure your Schema Markup is connected to other entities on your site and other external authoritative sources.

Implementing connected Schema Markup can help you develop your knowledge graph that generative AI search engines can then utilize to infer new knowledge.

At Schema App, we are a semantic technology company. Since we started doing Schema Markup at scale in 2016, we’ve focused on implementing proper, connected Schema Markup across our customers’ sites to help search engines fully understand and contextualize their content.

If you are looking to optimize your website for AI search using Schema Markup, get in touch with our team for more information.

4. Focus on Content Quality

Over the previous year, Google launched a helpful content system intended to ensure that content is “people-first” instead of “search-engine first.” This emphasis on content made by people, for people, demonstrates Google’s commitment to providing users with relevant results that address their pain points. These changes are aligned with the type of content that Google SGE and the new Bing experience are highlighting.

Generative AI is still in its early stages and the accuracy of their answers is still questionable. In light of this, there is an opportunity for content publishers to create high-quality, trustworthy content for Google’s generative AI search engine to train on.

You should also ensure that your content is created by human writers and that each piece of copy addresses specific topics relevant to your audience.

SEO Is Changing

The bottom line is that the SEO landscape is evolving quickly. Google’s Search Generative Experience is still in its experimental phase and we don’t know its full impact on SEO or the SERP.

While it is unclear what these changes will mean for your SEO strategy, now is the time to start preparing for this change. In a world where many circumstances are out of your control, you should focus on the factors that you can manage – such as the quality of the Schema Markup on your website.

Schema Markup is one of the SEO tactics you can implement immediately to prepare for AI Search and Schema App can help your business implement semantic Schema Markup using our end-to-end solution. Get in touch with our team to learn more about how we can help you prepare for generative AI search.

The post Is Google’s Generative AI Search an Opportunity or Threat for SEOs? appeared first on Schema App Solutions.

2023 started off on a calm note with very few changes in rich results performance, but since the March Broad Core Update, we’ve seen a few big swings happen on the search engine results page, particularly with Video and FAQ rich results.

Based on our industry data, we’ve seen most of the changes happen after an announcement from Google – whether that be a core update or a documentation update on how they are displaying specific rich results. However, there are also changes on the search engine results page that happen unannounced by Google.

Here’s what our industry data is showing us from the past few months.

Video Rich Result Changes

Effective April 13, 2023, Google has simplified the video presentation on the search engine results page. Prior to this change, video thumbnails were shown in two different ways.

1. Pages that had a video as the main content of the page would show up with this listing format on the SERP.

2. Pages that had a video present on a page but not as the main element of the page would show up with this listing format on the SERP.

Today, Google is no longer awarding a video rich result to pages that do not have the video as the main content. However, if the video is the main content of the page, the video rich results will continue to show.

After April 13, we’ve seen the clicks and impressions for Video Rich Results drop by over 95% for all our clients and the numbers slowly improved after April 21st, as a result of customers making video the main content on a page.

Read Google’s Video SEO best practices guide to learn how you can help Google find your video and optimize it for search. If you are a Schema App enterprise customer, your Customer Success Manager will provide you with content recommendations to optimize your videos.

FAQ Rich Results Fluctuations

Our relationship with FAQ rich results is tumultuous at best.

Around April 5th 2023, we saw the clicks and impressions for FAQ rich results on a downward trend.

This drop was mainly for FAQ rich results on mobile as mentioned by other SEOs.

Quick update on the drop in FAQ snippets on mobile & the drop in video snippets in the SERPs.

FAQ snippets remain down on mobile based on the change on 4/5. And video snippets have plummeted for sites where the videos are not the main content (starting on 4/13). No change there: pic.twitter.com/M0406FPGWG

— Glenn Gabe (@glenngabe) April 29, 2023

Even though the numbers show signs of slow recovery after April 18, we saw the clicks and impressions for FAQ rich results on desktop and mobile dropped drastically on the weekend of May 7. We’ve yet to see any confirmed algorithm changes or comments from Google regarding this issue.

Despite being one of the top-performing rich results on the search engine results page, FAQ rich results have historically been notorious with their fluctuations. We saw the performance for FAQ rich results fluctuate in May, August, September and October of 2022.

We have yet to isolate the reason for this decline but it looks like Google is reducing how often it is awarding a rich result and the queries that might be achieving them.

Moreover, we aren’t seeing this across all our clients, in fact some clients are seeing gains. This leads us to believe that it could be based on the quality of content.

In an unofficial statement on Mastodon, John Mueller also mentioned that “sites love adding FAQ markup, it gives them more room in 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.”

As a result, our Customer Success team keeping a close eye on the situation and revisiting the schema markup strategy to make content recommendations to drive results. This is also why we urge our customers to diversify their content and rich results, to reduce dependency on individual rich results to drive traffic.

Google Updates: Generative AI on Search

Google’s I/O developer conference also happened today and recent reports from Wall Street Journal and New York Times have leaked potential changes that Google might be making to the search engine results page prior to the conference.

During the keynote speech at the conference, Google unveiled the addition of generative AI to their search engine results page. Users will still need to type a query, but the generative AI search engine will provide users will answer directly on the SERP with links to websites, snippets of content and ads. Users can also ask follow-up questions to get more specific responses.

Image: Google

This change is a departure from the traditional search results and could explain the recent changes in FAQ and video rich results. But more importantly, Google’s Search Generative Experience will still require a data source to identify information from trusted websites and sources. This is where Schema Markup comes to play.

Watch our ‘How Marketers Can Leverage Schema Markup and Prepare for AI-Search’ webinar recording to learn why Schema Markup and your Knowledge graph are important for AI and machine learning.

The post April 2023 Rich Results Weather Report: Video & FAQ Fluctuations appeared first on Schema App Solutions.