The GEO Analytics Stack: What We Should Be Measuring (But Aren’t)

Welcome back to our GEO series!

Over the course of this series, we’ve looked at the different factors that can influence whether content appears in generative search.

Now, we need to think about how we measure them. How can we understand whether a page is being considered for a query, or whether its content is likely to be retrieved and cited?

In this article, we’ll bring those factors together and look at what a GEO analytics stack could actually look like.

Only got 30 seconds? Here are the key takeaways:

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The problem with traditional SEO analytics

Traditional SEO analytics largely focus on rankings, assuming an indexed page is eligible to compete and that its position explains its visibility.

In our experience, generative search complicates this. A page can be indexed and highly relevant to a query without its content appearing in a generated response. That’s because other points in the retrieval process can affect whether it is considered or used.

Simply put, the reason a page doesn’t appear may not be something a ranking metric can show us. If we only measure the outcome, we have little insight into the factors that may have affected its visibility.

A GEO-aligned mental model

A GEO analytics stack, therefore, needs to account for the different factors that can affect whether content appears in a generative response. That starts with whether content is available and accessible, alongside the four areas we’ve explored so far in this series.

Some of these apply at particular points in retrieval, while others can influence more than one part of the process. For analytics purposes, we can organize them into five layers: 

1. Availability and indexability
2. Candidate page selection
3. Semantic relevance
4. Chunkability and retrieval
5. Authority and trust

For each layer, we’ve included an importance score based on how much we believe it can influence GEO performance, alongside our confidence in the role it plays within this model.

The GEO analytics stack (layer by layer)

1. Availability & Indexability layer

Before looking at the other layers, we need to know whether the content is accessible in the first place.

That means looking at crawlability and indexability, along with whether the content can be successfully rendered and extracted from the page.

Problems this early on can prevent a system from reliably accessing the content it needs, making the other factors in the stack largely irrelevant until they’re resolved.

• Importance: 9/10
• Confidence: 9/10

2. Candidate page selection layer

At the candidate page selection layer, we want to understand whether a page is likely to be considered for a particular query.

Here, we’d want to look at: 

• Lexical alignment in the title and H1,
• Page-level semantic alignment with the query, and
• How clearly the page is focused on a single topic.

We could also look at whether authority may affect the page’s eligibility for that query.

Together, these could feed into a Candidate Eligibility Score for each query, giving us a way to assess how likely a page is to be considered a candidate.

• Importance: 9/10
• Confidence: 9/10

Learn about candidate page selection and why traditional SEO tools don’t measure it

3. Semantic relevance

Semantic relevance needs to be measured in context because it can play different roles across candidate selection, retrieval, and citation.

For candidate selection, that means measuring page-level aboutness and how closely the overall page aligns with the query, whereas retrieval looks at the similarity of individual chunks. Citation focuses on how precisely the retrieved evidence supports the cited information.

A relevance score therefore needs context. Page-level aboutness, chunk-level similarity and citation-level precision are measuring different things at different points in the process.

• Importance: 9/10
• Confidence: 8/10

Discover why semantic relevance serves multiple purposes across retrieval stages

4. Chunkability and retrieval

At the chunkability and retrieval layer, we want to understand how suitable individual passages are for retrieval and citation. That means looking at:

• Topical cohesion
• Boundary clarity
• Answer density
• Whether a chunk contains enough context to make sense on its own.

These factors could feed into a ‘Chunk Citability Score’, designed to indicate how suitable a passage is for use as supporting evidence in a generative response.

• Importance: 10/10
• Confidence: 9/10

Learn about why some content gets cited, and how to spot whether your content is failing

5. Authority and trust

The authority and trust layer looks at whether the source itself may affect its eligibility for a particular query. As we covered in the previous article, the influence of authority can vary depending on the query and the level of risk involved.

To measure this, we could look at:

• Domain provenance 
• How well the source’s level of authority aligns with the risk of the query
• The authority gap between the source and others competing for the same query 

This gives us a way to identify where authority may be limiting a source’s ability to compete, even when its content performs well in other areas of the stack.

• Importance: 8.5/10
• Confidence: 8/10

Learn more about authority in GEO and why it’s a filter, not a score

What a GEO dashboard should look like

Bringing these measurements into one dashboard would give GEO teams a clearer view of where content may be succeeding or struggling. Alongside ranking position, we could start asking more specific questions:

• Which pages are likely to be eligible for which queries?
• Where could semantic relevance be falling short?
• Which chunks are most suitable for citation?
• Where could authority be limiting eligibility?

Together, these give us a more detailed view of GEO performance than the final visibility outcome alone.

Making GEO performance actionable

Separating GEO analytics into layers helps because different visibility problems can point to different causes. For example, a page that appears to have an eligibility problem calls for a different response than content that appears eligible but lacks the precision needed for retrieval or citation.

Being able to make that distinction gives teams a clearer idea of where to focus their efforts. Without it, we risk optimizing for the outcome without understanding what may have caused it.

Bringing the framework together

Taken together, these layers give you a more complete way to measure GEO performance than citations alone.

They also give us a natural next question: what happens when your content still isn’t being cited?

Next, we’ll turn this framework into a practical diagnostic checklist to identify where the problem might lie.