Are Rich Results Dead? Maximize The Semantic Value Of Schema Markup via @sejournal, @marthavanberkel

Last year was the year of change for search, especially for schema markup.

We saw a significant loss in clicks due to changes made to certain rich results, while simultaneously witnessing the most number of new rich results introduced by Google in a year.

In August, I even asked myself, are rich results dead? Good news – the answer is a resounding no!

Not only are we seeing the rich results landscape continue to evolve, but the value of schema markup is now more than just rich results.

It is a data layer that enables semantic understanding for AI-powered search engines and experiences.

What Is Schema Markup?

Schema markup, also known as structured data, is data added to your website using the descriptive Schema.org vocabulary.

By applying schema markup, content creators can clearly articulate the intent and meaning of their content, enabling search engines to better understand the content on their web pages.

Adding defined required and recommended schema markup to a webpage can also make it eligible for an enhanced search result, known as a rich result.

Rich results include reviews, ratings, course info, events, local business, and more.

Screenshot from Google, January 2024

Rich results tend to drive a higher click-through rate from the search engine result page (SERP), resulting in more user engagement.

In 2023, product, review, and FAQ rich results were the top awarded rich results for our customers.

However, we know that the search landscape is unpredictable.

Rich results can come and go at Google’s whim.

Generative AI search engines are disrupting the way we consume information, and the search engines are iterating on the search experience as a result.

This raises the question of whether rich results, the coveted outcome for implementing schema markup, are becoming obsolete.

Are Rich Results Dead?

In the first half of 2023, we saw OpenAI, Bing, and Google introduce their generative AI search engines to the world.

This was a major disruption in the world of search, as these generative AI search engines would present answers in a multi-modal format to users directly in the SERP, giving answers in the search engine.

This was a similar experience to zero-click searches in 2020 but on an even bigger stage. It was clear that big changes were coming.

Then, on August 8, 2023, Google announced that it would be reducing the frequency of FAQ rich results being displayed on the SERP and deprecating HowTo rich results entirely.

FAQ rich results would now be exclusively available for “well-known, authoritative government and health websites.”

Many organizations that previously leveraged FAQ rich results to stand out on the SERP were no longer awarded these rich results. This included several reputable healthcare organizations.

Then, on September 14, 2023, all FAQ rich results were gone from the SERP.

Prior to this change, FAQ rich results were one of the most popular rich results because they enhanced visibility for any content type content and also delighted users with answers directly in the SERP.

Many of the other rich results had specific content requirements (reviews, ratings, video, etc), making them less versatile than FAQ.

Without FAQ and HowTo rich results, the types of content eligible for rich results were greatly reduced. Was this the beginning of the end for rich results?

Don’t Worry; Rich Results Are Not Dead

Google answered the question of whether rich results are dead in October 2023, when it released a new episode of Google Search News.

In the episode, John Mueller stated that new rich results would be added while others may disappear over time.

Staying true to its word, in the last quarter of 2023, Google introduced six new rich results – the most it has ever released in a year.

What can we learn from these new rich results? 

First, they are becoming very industry-specific.

Of the six new rich results released in 2023, only two were ‘general’ rich results like FAQ rich results that organizations leverage.

Second, many of the rich results are complimenting previous data feeds.

This may be the start of Google’s more programmatic consumption of structured data, as mentioned by Ryan Levering in April 2022’s Search Off the Record podcast.

Why Are Rich Results & Schema Markup Important To Google And Bing?

Rich results improve a user’s search experience. However, there is a bigger reason why Google continues to invest in them.

Schema Markup Provides Context About Your Content

As John Mueller pointed out in his Search Update, schema markup is machine-readable code you can add to your web pages to help search engines better understand your content.

Google uses large language models to understand the content on a page, but LLMs are prone to hallucinations and misinterpretations.

Furthermore, the things or entities mentioned in your content can be ambiguous.

For example, the word “Apple” is both a fruit and a brand. Without additional context, it can be hard for the LLM to disambiguate which Apple you’re referring to.

Schema markup can overcome this by providing additional information and context about the entities mentioned in your content.

This makes the content easier to understand and increases the machine’s confidence in the data, allowing it to provide more accurate and relevant search results.

When you implement schema markup on a page, you aren’t just defining the entities on that page. You’re also describing their relationship to other entities on your site and on the web.

This interconnected web of information about your website forms a structured content knowledge graph.

Schema Markup Allows You To Control How Your Content Is Understood

Your content knowledge graph enables search engines to understand and infer new knowledge about your organization.

Generative AI search engines like Gemini, SGE, and ChatGPT benefit significantly from a well-constructed knowledge graph, as shown in the image below.

Image from arxiv.org, January 2024

By providing factual and connected schema markup in a graph, your website has now become a data layer for machines to consume, giving you a control point on how you want your data understood and enabling them to make accurate inferences about your brand.

Start Maximizing The Semantic Value of Schema Markup

Rich results are not dead. In fact, they have never been more alive!

Google uses the schema markup on your site to understand your content while helping you stand out in the SERP, creating an engaging search experience for your consumers.

Google will continue releasing new rich results in exchange for your data because their AI-powered search experiences depend on access to your data to deliver accurate, relevant answers.

However, the value of schema markup extends far beyond achieving rich results.

By implementing robust, connected schema markup, you are essentially creating a data layer that allows you to control how search engines and machines consume and understand your content.

That way, the LLMs that power search experiences like SGE, ChatGPT, Gemini, and the new Bing can make accurate inferences about your content, preparing your organization for future search experiences.

One could argue that schema markup has never been more strategic for companies aiming to maintain a control point in preparation for AI.

More resources: 

Featured Image: Gracia Chua/Schema App

Google and EEAT, Explained

Google hires thousands of personnel to view and evaluate web pages for organic search rankings. A key metric for those human raters is “Experience, Expertise, Authoritativeness, and Trustworthiness.” Google emphasizes pages that demonstrate those qualities.

What’s unclear, however, is how Google integrates EEAT into its algorithm. Google executives have shared conflicting views.

In 2022, Google’s vice president of search, Hyung-Jin Kim, stated that EAT was a “core part.” (Google originally coined the metric as “Expertise, Authoritativeness, and Trustworthiness.” In December 2022, it added “Experience,” morphing “EAT” to “EEAT.”)

In 2024, Google’s search liaison, Danny Sullivan, tweeted it had never been a ranking factor.

Google’s “Search Central Blog” states:

Search raters have no control over how pages rank. Rater data is not used directly in our ranking algorithms. Rather, we use them as a restaurant might get feedback cards from diners. The feedback helps us know if our systems seem to be working.

Hence EEAT is for Google’s quality control. But it’s also for creators. The blog post adds, “… the guidelines may help you self-assess how your content is doing from an E-E-A-T perspective, improvements to consider… .”

The bottom line is Google wants to rank web pages with strong EEAT. The guidelines (PDF) include:

• Expertise of the author. Add details such as work history.

• Research methods. Explain how the content was created. For example, for product reviews, state how many you tested and how.

• Purpose. Address the rationale for publishing the content. It’s a vague suggestion, although it reinforces the goal of helping humans, not search engines. Potential reasons might include client interactions, outside commentary, new data, and more.

EEAT applies to informational content, not product or category pages. Moreover, Google scrutinizes content that impacts “the health, financial stability, or safety of people, or the welfare or well-being of society.” Google calls such content “Your Money or Your Life” — YMYL.

Google tells its raters that YMYL pages require much expertise and authority. So if you publish financial or health advice, closely follow the guidelines.

EEAT in Brief

Here’s my interpretation of EEAT in practice. The underlying principles existed long before the acronym. Many are more or less common sense: Ensure your content is trustworthy, thorough, authentic, and helpful to humans.

What Data Science Can Do for Site Architectures via @sejournal, @artios_io

The past decade has marked the shift of SEO from spreadsheet-driven, anecdotal best practices to a more data-driven approach, evidenced by the greater numbers of SEO pros learning Python.

As Google’s updates increase in number (11 in 2023), SEO professionals are recognizing the need to take a more data-driven approach to SEO, and internal link structures for site architectures are no exception.

In a previous article, I outlined how internal linking could be more data-driven, providing Python code on how to evaluate the site architecture statistically.

Beyond Python, data science can help SEO professionals more effectively uncover hidden patterns and key insights to help signal to search engines the priority of content within a website.

Data science is the intersection of coding, math, and domain knowledge, where the domain, in our case, is SEO.

So while math and coding (invariably in Python) are important, SEO is by no means diminished in its importance, as asking the right questions of the data and having the instinctive feel of whether the numbers “look right” are incredibly important.

Align Site Architecture To Support Underlinked Content

Many sites are built like a Christmas tree, with the home page at the very top (being the most important) and other pages in descending order of importance in subsequent levels.

For the SEO scientists among you, you’ll want to know what the distribution of links is from different views. This can be visualized using the Python code from the previous article in several ways, including:

• Site depth.
• Content type.
• Internal Page Rank.
• Conversion Value/Revenue.

Image by author, December 2023

The boxplot effectively shows how many links are “normal” for a given website at different site levels. The blue boxes represent the interquartile range (i.e., the 25th and 75th quantiles) which is where most (67% to be precise) of the number of inbound internal links lie.

Think of the bell curve, but instead of viewing it from the side (as you would a mountain), you’re viewing it like a bird flying overhead.

For example, the chart shows that for pages that are two levels down from the home page, the blue box indicates that 67% of URLs have between five and nine inbound internal links. We can also see this is considerably (and perhaps unsurprisingly) much lower than pages that are one hop away from the home page.

The thick line that cuts the blue box is the median (50th quantile), representing the middle value. Going with the above example, the median inbound internal links are 7 for site level 2 pages, which is about 5,000 times less than those in site level 1!

On a side note, you may notice that the median line isn’t visible for all blue boxes, the reason being the data is skewed (i.e., not normally distributed like a bell-shaped curve).

Is This Good? Is This Bad? Should SEO Pros Be Worried?

A data scientist with no knowledge of SEO might decide that it’d be better to redress the balance by working out the distribution of internal links to pages by site level.

From there, any pages that are, say, below the median or the 20th percentile (quantile in data science speak) for their given site level, a data scientist might conclude that these pages require more internal links.

As such, this often means that pages that share the same number of hops from the home page (i.e., same site depth level) are of equal importance.

However, from a search value perspective, this is unlikely to be true, especially when you consider that some pages on the same level simply have more search demand than others.

Thus, the site architecture should prioritize those pages with more search demand than those with less demand regardless of their default place in the hierarchy – whatever their level!

Revising True Internal Page Rank (TIPR)

True Internal Page Rank (TIPR), as popularised by Kevin Indig, has taken a rather more sensible approach by incorporating the external PageRank, i.e., earned from backlinks. In simple maths terms:

TIPR = Internal Page Rank x Page Level Authority of Backlinks

Although the above is the non-scientific version of his metric, it’s nonetheless a much more useful and empirical way of modeling what is the normal value of a page’s value within a website architecture. If you’d like the code to compute this, please see here.

Furthermore, rather than applying this metric to site levels, it’s far more instructive to apply this by content type. For an ecommerce client, we see the distribution of TIPR by content type below:

Image by author, December 2023

The plot in this online store’s case is that the median TIPR for categories content or Product Listing Pages (PLPs) is about two TIPR points.

Admittedly, TIPR is a bit abstract, as how does that translate to the amount of internal links required? It doesn’t – at least not directly.

Abstraction notwithstanding, this is still a more effective construct for shaping site architecture.

If you wanted to see which categories were underperforming for their rank position potential, you’d simply see that PLP URLs were below the 25th quantile and perhaps look for internal links from pages of a higher TIPR value.

How many links and what TIPR? With some modeling, that’s an answer for another post.

Introducing Revenue Internal Page Rank (RIPR)

The other important question worth answering is: which content deserves higher rank positions?

Kevin also advocated a more enlightened approach to align internal link structures towards conversion values, which many of you are hopefully already applying to your clients; I must heartily agree.

A simple non-scientific solution is to take the ratio of the ecommerce revenue to the TIPR i.e.

RIPR = Revenue / TIPR

The above metric helps us see what normal revenue per page authority looks like, as visualized below:

Image by author, December 2023

As we can see, the picture changes somewhat; suddenly, we see no box (i.e., distribution) for blog content because no revenue is recorded against that content.

Practical applications? If we use this as a model by content type, any pages that are higher than the 75th quantile (i.e., north of their blue box) for their respective content type should have more internal links added to them.

Why? Because they have high revenue but are very low in Page Authority, meaning they have a very high RIPR and should therefore be given more internal links to get it closer to the median.

By contrast, those with lower revenue but too many significant internal links will have a lower RIPR and should thus have links taken away from them to allow the higher revenue content to be assigned more importance by the search engines.

A Caveat

RIPR has some assumptions built in, such as analytics revenue tracking being set up properly so that your model forms the basis for effective internal link recommendations.

Of course, as in TIPR, one should model what an internal link is worth in terms of how much RIPR an internal link is worth from any given page.

That’s before we even get to the location of the internal link placement itself.

More resources: 

Featured Image: NicoElNino/Shutterstock

Google’s Core Update vs. Helpful Content

Google’s algorithm updates used to be straightforward. If rankings were impacted, search engine optimizers knew the cause. For example, the Panda update in 2011 affected sites with low-value content. The Penguin update in 2012 addressed spammy links.

But those causes are no longer apparent. Google’s explanations of algorithm updates are often fuzzy. It rarely offers actionable solutions or even targeted areas, such as content, usability, or links.

Thus diagnosing and fixing an organic search drop is much harder.

Google now broadly classifies algorithm updates as “core” or “helpful content.” Understanding the difference can make recovery easier and quicker.

Core Algorithm Update

A core algorithm update is Google’s reevaluation of an algorithm component. It doesn’t focus on specific sites or pages.

For example, a core update could target political bias in search results or a shift in query intent. It is a broad change to the algorithm, not a penalty. Per Google’s Search Central blog:

… there’s nothing in a core update that targets specific pages or sites. Instead, the changes are about improving how our systems assess content overall.

Core updates can address user experiences or on-page optimization tactics affecting the entire algorithm, not particular sites.

Hence sites don’t typically require action to recover from core updates. Sites can lose or gain traffic with each update without doing anything.

Yet sometimes entire sites are impacted — all pages lose traffic. That’s a good reason to assess your site’s overall content and usability for out-of-favor (to Google) tactics or elements.

Helpful Content

Helpful content is more of a penalty. It looks for content that targets search engines instead of humans and can penalize the whole site if most pages are presumed unhelpful.

Thus recovering from a helpful content update requires action. The next rollout won’t alter rankings without content changes sitewide.

Removing unhelpful content pages from your site may help. Regardless, a traffic drop coinciding with a helpful content update requires immediate action. There’s no recovery without it.

But a drop from a core update is more about analyzing organic listings and newly ranking URLs.

The helpful content algorithm is Google’s attempt to reward material that benefits humans. An update doesn’t elevate the rankings of superior pages; it penalizes unhelpful ones.

Commonalities

Both updates:

• Are automated and machine-based. No human reviews or penalizes.
• May impact the entire site. Recoveries occur only with the next update.
• Are intent-based — to satisfy a searcher’s intent better. Intent analysis and optimization are key.

In short, while “core” and “helpful content” algorithm updates can focus on similar aspects, recovery from each is different. Helpful content addresses your site. Core updates address the algorithm.

How Google Uses Keywords

Years ago Google ranked organic listings by exactly matching search queries to text on a web page. Search engine optimization involved creating multiple pages containing all sorts of keyword variations.

The result was an avalanche of inferior content ranking at or near the top for long-tail queries.

Search engines no longer rely on exact-match keywords, instead attempting to understand the intent and context behind a query. It’s common for Google to rank a page that doesn’t contain a single query word.

For example, a Google search for “affordable airfare” produces page-1 organic results mainly containing “cheap flights,” “last minute flight deals,” and similar. None that I saw included the exact phrase “affordable airfare.”

And searching “trails for kids upstate ny” generates organic results for “family-friendly hikes in Albany and “easy hikes near NYC” — both likely satisfying the searcher’s intent.

Searching “trails for kids upstate ny” generates results for “family-friendly hikes in Albany and “easy hikes near NYC.”

Google’s “How search works” portal explains the process:

To return relevant results, we first need to establish what you’re looking for — the intent behind your query. To do this, we build language models to try to decipher how the relatively few words you enter into the search box match up to the most useful content available.

This involves steps as seemingly simple as recognizing and correcting spelling mistakes and extends to trying to our sophisticated synonym system that allows us to find relevant documents even if they don’t contain the exact words you used.

Keyword Research

Is keyword research important for SEO in 2024?

While it no longer relies on exact-match queries, Google’s algorithm still uses keywords. In the same “How search works” page, Google states:

The most basic signal that information is relevant is when content contains the same keywords as your search query. For example, with webpages, if those keywords appear on the page, or if they appear in the headings or body of the text, the information might be more relevant.

Further down Google says it also uses “other relevancy signals” (presumably beyond keywords), “interaction data to assess whether search results are relevant to queries,” and machine learning to make sense of the data.

Google provides an example:

Just think: when you search for “dogs,” you likely don’t want a page with the word “dogs” on it hundreds of times. With that in mind, algorithms assess if a page contains other relevant content beyond the keyword “dogs” — such as pictures of dogs, videos, or even a list of breeds.

Yet the benefits of keyword research extend beyond SEO.

Keywords:

• Help understand the needs of target customers.
• Guage demand — high search volume signals higher demand for a product or service.
• Guide site structure — keywords with heavy search volume could be categories; lower volume subcategories.
• Identify potential high-traffic content ideas.
• Inform new products or categories.

Keyword Optimization

Optimizing for exact-match keywords is not just dated. It may be counterproductive owing to Google’s Helpful Content algorithm, which devalues websites targeting search engines rather than humans.

Instead:

• Search Google for topics and formats it considers helpful to searchers.
• Prompt ChatGPT to create personas of searchers using your keywords, including their intent and likely needs. Google uses AI similarly to identify which content better serves searchers.
• Identify related keywords for products that solve similar problems.
• Brainstorm content elements for a keyword that improve engagement, such as a table or checklist.

In short, keywords remain the “most basic signal that information is relevant” to the query, per Google. Use a primary keyword in a page’s title and meta description, but don’t force it.