May 29 2025
Google’s Query Fan-Out Patent: Thematic Search via @sejournal, @martinibuster

A patent that Google filed in December 2024 presents a close match to the Query Fan-Out technique that Google’s AI Mode uses. The patent, called Thematic Search, offers an idea of how AI Mode answers are generated and suggests new ways to think about content strategy.

The patent describes a system that organizes related search results to a search query into categories, what it calls themes, and provides a short summary for each theme so that users can understand the answers to their questions without having to click a link to all of the different sites.

The patent describes a system for deep research, for questions that are broad or complex. What’s new about the invention is how it automatically identifies themes from the traditional search results and uses an AI to generate an informative summary for each one using both the content and context from within those results.

Thematic Search Engine

Themes is a concept that goes back to the early days of search engines, which is why this patent caught my eye a few months ago and caused me to bookmark it.

Here’s the TL/DR of what it does:

  • The patent references its use within the context of a large language model and a summary generator.
  • It also references a thematic search engine that receives a search query and then passes that along to a search engine.
  • The thematic search engine takes the search engine results and organizes them into themes.
  • The patent describes a system that interfaces with a traditional search engine and uses a large language model for generating summaries of thematically grouped search results.
  • The patent describes that a single query can result in multiple queries that are based on “sub-themes”

Comparison Of Query Fan-Out And Thematic Search

The system described in the parent mirrors what Google’s documentation says about the Query Fan-Out technique.

Here’s what the patent says about generating additional queries based on sub-themes:

“In some examples, in response to the search query 142-2 being generated, the thematic search engine 120 may generate thematic data 138-2 from at least a portion of the search results 118-2. For example, the thematic search engine 120 may obtain the search results 118-2 and may generate narrower themes 130 (e.g., sub-themes) (e.g., “neighborhood A”, “neighborhood B”, “neighborhood C”) from the responsive documents 126 of the search results 118-2. The search results page 160 may display the sub-themes of theme 130a and/or the thematic search results 119 for the search query 142-2. The process may continue, where selection of a sub-theme of theme 130a may cause the thematic search engine 120 to obtain another set of search results 118 from the search engine 104 and may generate narrower themes 130 (e.g., sub-sub-themes of theme 130a) from the search results 118 and so forth.”

Here’s what Google’s documentation says about the Query Fan-Out Technique:

“It uses a “query fan-out” technique, issuing multiple related searches concurrently across subtopics and multiple data sources and then brings those results together to provide an easy-to-understand response. This approach helps you access more breadth and depth of information than a traditional search on Google.”

The system described in the patent resembles what Google’s documentation says about the Query Fan-Out technique, particularly in how it explores subtopics by generating new queries based on themes.

Summary Generator

The summary generator is a component of the thematic search system. It’s designed to generate textual summaries for each theme generated from search results.

This is how it works:

  • The summary generator is sometimes implemented as a large language model trained to create original text.
  • The summary generator uses one or more passages from search results grouped under a particular theme.
  • It may also use contextual information from titles, metadata, surrounding related passages to improve summary quality.
  • The summary generator can be triggered when a user submits a search query or when the thematic search engine is initialized.

The patent doesn’t define what ‘initialization’ of the thematic search engine means, maybe because it’s taken for granted that it means the thematic search engine starts up in anticipation of handling a query.

Query Results Are Clustered By Theme Instead Of Traditional Ranking

The traditional search results, in some examples shared in the patent, are replaced by grouped themes and generated summaries. Thematic search changes what content is shown and linked to users. For example, a typical query that a publisher or SEO is optimizing for may now be the starting point for a user’s information journey. The thematic search results leads a user down a path of discovering sub-themes of the original query and the site that ultimately wins the click might not be the one that ranks number one for the initial search query but rather it may be another web page that is relevant for an adjacent query.

The patent describes multiple ways that the thematic search engine can work (I added bullet points to make it easier to understand):

  • “The themes are displayed on a search results page, and, in some examples, the search results (or a portion thereof) are arranged (e.g., organized, sorted) according to the plurality of themes. Displaying a theme may include displaying the phrase of the theme.
  • In some examples, the thematic search engine may rank the themes based on prominence and/or relevance to the search query.
  • The search results page may organize the search results (or a portion thereof) according to the themes (e.g., under the theme of ‘cost of living”, identifying those search results that relate to the theme of ‘cost of living”).
  • The themes and/or search results organized by theme by the thematic search engine may be rendered in the search results page according to a variety of different ways, e.g., lists, user interface (UI) cards or objects, horizontal carousel, vertical carousel, etc.
  • The search results organized by theme may be referred to as thematic search results. In some examples, the themes and/or search results organized by theme are displayed in the search results page along with the search results (e.g., normal search results) from the search engine.
  • In some examples, the themes and/or theme-organized search results are displayed in a portion of the search results page that is separate from the search results obtained by the search engine.”

Content From Multiple Sources Are Combined

The AI-generated summaries are created from multiple websites and grouped under a theme. This makes link attribution, visibility, and traffic difficult to predict.

In the following citation from the patent, the reference to “unstructured data” means content that’s on a web page.

According to the patent:

“For example, the thematic search engine may generate themes from unstructured data by analyzing the content of the responsive documents themselves and may thematically organize the search results according to the themes.

….In response to a search query (“moving to Denver”), a search engine may obtain search results (e.g., responsive documents) responsive to that search query.

The thematic search engine may select a set of responsive documents (e.g., top X number of search results) from the search results obtained by the search engine, and generate a plurality of themes (e.g., “neighborhoods”, “cost of living”, “things to do”, “pros and cons”, etc.) from the content of the responsive documents.

A theme may include a phrase, generated by a language model, that describes a theme included in the responsive documents. In some examples, the thematic search engine may map semantic keywords from each responsive document (e.g., from the search results) and connect the semantic keywords to similar semantic keywords from other responsive documents to generate themes.”

Content From Source Pages Are Linked

The documentation states that the thematic search engine links to the URLs of the source pages. It also states that the thematic search result could include the web page’s title or other metadata. But the part that’s important for SEOs and publishers is the part about attribution, links.

“…a thematic search result 119 may include a title 146 of the responsive document 126, a passage 145 from the responsive document 126, and a source 144 of the responsive document. The source 144 may be a resource locator (e.g., uniform resource location (URL)) of the responsive document 126.

The passage 145 may be a description (e.g., a snippet obtained from the metadata or content of the responsive document 126). In some examples, the passage 145 includes a portion of the responsive document 126 that mentions the respective theme 130. In some examples, the passage 145 included in the thematic search result 119 is associated with a summary description 166 generated by the language model 128 and included in a cluster group 172.”

User Interaction Influences Presentation

As previously mentioned, the thematic search engine is not a ranked list of documents for a search query. It’s a collection of information across themes that are related to the initial search query. User interaction with those AI generated summaries influences which sites are going to receive traffic.

Automatically generated sub-themes can present alternative paths on the user’s information journey that begins with the initial search query.

Summarization Uses Publisher Metadata

The summary generator uses document titles, metadata, and surrounding textual content. That may mean that well-structured content may influence how summaries are constructed.

The following is what the patent says, I added bullet points to make it easier to understand:

  • “The summary generator 164 may receive a passage 145 as an input and outputs a summary description 166 for the inputted passage 145.
  • In some examples, the summary generator 164 receives a passage 145 and contextual information as inputs and outputs a summary description 166 for the passage 145.
  • In some examples, the contextual information may include the title of the responsive document 126 and/or metadata associated with the responsive document 126.
  • In some examples, the contextual information may include one or more neighboring passages 145 (e.g., adjacent passages).
  • In some examples, the contextual information may include a summary description 166 for one or more neighboring passages 145 (e.g., adjacent passages).
  • In some examples, the contextual information may include all the other passages 145 on the same responsive document 126. For example, the summary generator may receive a passage 145 and the other passages 145 (e.g., all other passages 145) on the same responsive document 126 (and, in some examples, other contextual information) as inputs and may output a summary description 166 for the passage 145.”

Thematic Search: Implications For Content & SEO

There are two way that AI Mode ends for a publisher:

  1. Since users may get their answers from theme summaries or dropdowns, zero-click behavior is likely to increase, reducing traffic from traditional links.
  2. Or, it could be that the web page that provides the end of the user’s information journey for a given query is the one that receives the click.

I think this means that we really need to re-think the paradigm of ranking for keywords and maybe consider what the question is that’s being answered by a web page, and then identify follow-up questions that may be related to that initial query and either include that in the web page or create another web page that answers what may be the end of the information journey for a given search query.

You can read the patent here:

Thematic Search (PDF)

Read Google’s Documentation Of AI Mode (PDF)

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May 28 2025
Is SEO Still Relevant In The AI Era? New Research Says Yes via @sejournal, @MattGSouthern

New research analyzing 25,000 user searches found that websites ranked #1 on Google appear in AI search answers 25% of the time.

This data demonstrates that traditional SEO remains relevant, despite claims that AI has rendered it obsolete.

Tomasz Rudzki, co-founder of ZipTie, studied real searches across ChatGPT, Perplexity, and Google’s AI Overviews. His findings challenge the widespread belief that AI makes traditional SEO pointless.

Top Rankings Translate To AI Visibility

The data shows a clear pattern: if you rank #1 on Google, you have a 1-in-4 chance of appearing in AI search results. Lower rankings result in lower chances.

Rudzki stated:

“The higher you rank in Google’s top 10, the more likely you are to appear in AI search results across platforms. This isn’t speculation – it’s based on real queries from real users.”

The pattern holds across all major AI search platforms, suggesting that they all rely on traditional rankings when selecting sources.

How AI Search Engines Select Sources

The study detailed how AI search operates, using information from Google’s antitrust trial. The process involves three main steps:

Step 1: Pre-selection
AI systems identify the best documents for each query, favoring pages with higher Google rankings.

Step 2: Content Extraction
The AI extracts relevant information from these top-ranking pages, prioritizing content that directly answers the user’s question.

Step 3: AI Synthesis
The AI synthesizes this information into one clear answer, utilizing Google’s Gemini model for this step.

Google’s internal documents from the trial confirmed a critical fact: using top-ranking content enhances the accuracy of AI responses, which explains why traditional rankings continue to be so significant.

The Query Fan-Out Effect Explained

Sometimes, you’ll come across sources that don’t make it into the top 10. Research identified two reasons why:

Reason 1: Personalization

Search results differ by user. A page might rank high for one user but not for another.

Reason 2: Query Fan-Out

This is the more significant factor. According to Google’s documentation:

“Both AI Overviews and AI Mode may use a ‘query fan-out’ technique — issuing multiple related searches across subtopics and data sources — to develop a response.”

Here’s what that means in simple terms:

When you search for “SEO vs SEM,” the AI discreetly runs multiple searches:

  • “What is SEO?”
  • “SEO explained”
  • “What is PP?C”
  • Plus several other related searches

Pages that perform well for these additional searches can appear in results even if they don’t rank for your primary search.

The research shows we need to think differently about content.

Traditional SEO focused on creating the “best page.” This meant comprehensive guides covering everything about a topic.

AI search wants the “best answer.” This means specific, focused responses to exact questions.

The analysis notes:

“When someone asks specifically about iPhone 15 battery life, you may rank top 1 in Google, but AI doesn’t care about it if you don’t provide a precise, relevant answer to that exact question.”

Marketers need to shift from keyword optimization to answering real questions.

Practical Implications For Digital Marketers

Here’s what marketers should do based on these findings:

  • Continue your SEO efforts: Top 10 rankings directly impact AI visibility. Do not abandon your SEO strategies.
  • Restructure your content: Divide lengthy guides into sections that address specific questions.
  • Target related searches: Optimize for various versions of your main keywords.
  • Write clearly: AI systems favor straightforward answers over content loaded with keywords.
  • Track everything: Monitor your visibility in both traditional and AI search results.

Industry Impact and Future Considerations

This research comes at the perfect time. AI search is growing rapidly. Understanding how it connects to traditional rankings gives you an edge.

Consider this: Only 25% of #1-ranked content appears in AI results. That means 75% is missing out. This suggests an opportunity for marketers who adapt.

Rudzki concludes:

“Instead of asking ‘How do I rank higher?’ start asking ‘How do I better serve users who have specific questions?’ That mindset shift is the key to thriving in the AI search era.”

For an industry experiencing rapid adoption of AI, these findings provide a strong foundation for informed strategic decisions. Instead of abandoning SEO practices, the evidence suggests building on what already works.

Featured Image: Tada Images/Shutterstock

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May 28 2025
Google’s CEO Says AI Overviews Website Referrals Are Increasing via @sejournal, @martinibuster

Google’s Sundar Pichai said in an interview that AI Overviews sends more traffic to a wider set of websites, insisting that Google cares about the web ecosystem and that he expects AI Mode to continue to send more traffic to websites, a claim that the interviewer challenged.

AI Agents Remove Customer Relationship Opportunities

There is a revolutionary change in how ecommerce that’s coming soon, where AI agents research and make purchase decisions on behalf of consumers. The interviewer brought up that some merchants have expressed concern that this will erode their ability to upsell or develop a customer relationship.

A customer relationship can be things like getting them to subscribe to an email or to receive text messages about sales, offer a coupon for a future purchase or to get them to come back and leave product reviews, all the ways that a human consumer interacts with a brand that an AI agent does not.

Sundar Pichai responded that AI agents present a good user experience and compared the AI agent in the middle between a customer and a merchant to a credit card company that sits in between the merchant and a customer, it’s a price that a merchant is willing to pay to increase business.

Pichai explained:

“I can literally see, envision 20 different ways this could work. Consumers could pay a subscription for agents, and their agents could rev share back. So you know, so that that is the CIO use case you’re talking about. That’s possible. We can’t rule that out. I don’t think we should underestimate, people may actually see more value participating in it.

I think this is, you know, it’s tough to predict, but I do think over time like you know like if you’re removing friction and improving user experience, it’s tough to bet against those in the long run, right? And so I think, in general if you’re lowering friction for it, you know, and and people are enjoying using it, somebody’s going to want to participate in it and grow their business.

And like would brands want to be in retailers? Why don’t they sell directly today? Why don’t they sell directly today? Why won’t they do that? Because retailers provide value in the middle.

Why do merchants take credit cards? There are many parts like and you find equilibrium because merchants take credit cards because they see more business as part of taking credit cards than not, right. And which justifies the increased cost of taking credit cards and may not be the perfect analogy. But I think there are all these kinds of effects going around.”

Pichai Claims That Web Ecosystem Is Growing

The interviewer began talking about the web ecosystem, calling attention to the the “downstream” effect of AI Search and AI search agents on information providers and other sites on the web.

Pichai started his answer by doing something he did in another interview about this same question where he deflected the question about web content by talking about video content.

He also made the claim that Google isn’t killing the web ecosystem and cited that the number of web pages in Google’s index has grown by 45% over the past two years, claiming it’s not AI generated content.

He said:

“I do think people are consuming a lot more information and the web is one specific format. So we should talk about the web, but zooming back out, …there are new platforms like YouTube and others too. So I think people are just consuming a lot more information, right? So it feels like like an expansionary moment. I think there are more creators. People are putting out more content, you know, and so people are generally doing a lot more. Maybe people have a little extra time in their hands. And so it’s a combination of all that.

On the web, look things have been interesting and you know we’ve had these conversations for a while, you know, obviously in 2015 there was this famous, the web is dead. You know, I always have it somewhere around, you know, which I look at it once in a while. Predictions, it’s existed for a while.

I think web is evolving pretty profoundly. When we crawl, when we look at the number of pages available to us, that number has gone up by 45% in the last two years alone. So that’s a staggering thing to think about.”

The interviewer challenged Pichai’s claim by asking if Google is detecting whether that increase in web pages is because they’re AI generated.

Pichai was caught by surprise by that question and struggled to find the answer and then finally responded that Google has many techniques for understanding the quality of web pages, including whether it was machine generated.

He doubled down on his statement that the web ecosystem is growing and then he started drifting off-topic, then he returned to the topic.

He continued:

“That doesn’t explain the trend we are seeing. So, generally there are more web pages. At an underlying level, so I think that’s an interesting phenomenom. I think everybody as a creator, like you do at The Verge, I think today if you’re doing stuff you have to do it in a cross-platform, cross-format way. So I think things are becoming more dynamic cross-format.

I think another thing people are underestimating with AI is AI will make it zero-friction to move from one format to another, because our models are multi-modal.

So I think this notion, the static moment of, you produce content by format, whereas I think machines can help translate it from, almost like different languages and they can go seamlessly between. I think it’s one of the incredible opportunities to be unlocked.

I think people are producing a lot of content, and I see consumers consuming a lot of content. We see it in our products. Others are seeing it too. So that’s probably how I would answer at the highest level.”

Related: The Data Behind Google’s AI Overviews: What Sundar Pichai Won’t Tell You

Search Traffic and Referral Patterns

The interviewer asked Pichai what his response is to people who say that AI Overviews is crushing their business.

Pichai answered:

“AI mode is going to have sources and you know, we’re very committed as a direction, as a product direction, part of why people come to Google is to experience that breadth of the web and and go in the direction they want to, right?

So I view us as giving more context. Yes, there are certain questions which may get answers, but overall that’s the pattern we see today. And if anything over the last year, it’s clear to us the breadth of where we are sending people to is increasing. And, so I expect that to be true with AI Mode as well.”

The interviewer immediately responded by noting that if everything Pichai said was true, people would be less angry with him.

Pichai dismissed the question, saying:

“You’re always going to have areas where people are robustly debating value exchanges, etc. … No one sends traffic to the web the way we do.”

See also: Google’s AI Overviews Slammed By News Publishers

Oh, Really?

What do you think? Are Google’s AI features prioritizing sending traffic to web sites?

Watch the Sundar Pichai interview here:

Featured image is screenshot from video

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May 28 2025
The Overlooked Traffic Drop Caused by AI Overviews [Webinar] via @sejournal, @lorenbaker

If your rankings are stable but your clicks are fading, AI Overviews could be the reason. 

These AI-powered summaries now show up on nearly half of Google searches. While they aim to help users, they may be shifting attention away from your site.

The problem is not just visibility. It is visibility without engagement. And the only way to fix it is to know exactly where the drop is happening.

That is what this session is designed to do.

AIO Hurting Traffic? How To Identify True Loss With GA4, GSC and Rank Tracking
Live on June 11, 2025 | Sponsored by STAT SA

Join us for a tactical webinar that breaks down how to track, measure, and respond to traffic loss caused by AI Overviews. You will explore how to use GA4, GSC and rank tracking to separate what has changed and what still works.

What you will take away from this session

✅ A method for separating AIO traffic from traditional organic clicks.
 ✅ A clear process for identifying traffic loss that is often hidden.
 ✅ Steps to update your SEO strategy based on your actual data.
 ✅ A framework to turn assumptions into insights you can act on.

Tom Capper, Senior Search Scientist at STAT SA, will guide you through the same tools and techniques used by leading SEO teams to evaluate AIO impact and protect long-term search performance.

This is not about guesswork. This is about clarity. If your site is losing visibility in subtle ways, now is the time to find out why and what to do next.

Can’t make it live

Register anyway, and we will send you the full recording to watch on your own schedule.

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May 27 2025
Google’s Sergey Brin Says AI Can Synthesize Top 1,000 Search Results via @sejournal, @martinibuster

Google co-founder Sergey Brin says AI is transforming search from a process of retrieving links to one of synthesizing answers by analyzing thousands of results and conducting follow-up research. He explains that this shift enables AI to perform research tasks that would take a human days or weeks, changing how people interact with information online.

Machine Learning Models Are Converging

For those who are interested in how search works, another interesting insight he shared was that algorithms are converging into a single model. In the past, Googlers have described a search engine as multiple engines, multiple algorithms, thousands of little machines working together on different parts of search.

What Brin shared is that machine learning algorithms are converging into models that can do it all, where the learnings from specialist models are integrated into the more general model.

Brin explained:

“You know, things have been more converging. And, this is sort of broadly through across machine learning. I mean, you used to have all kinds of different kinds of models and whatever, convolutional networks for vision things. And you know, you had… RNN’s for text and speech and stuff. And, you know, all of this has shifted to Transformers basically.

And increasingly, it’s also just becoming one model.”

Google Integrates Specialized Model Learnings Into General Models

His answer continued, shifting to explaining how it’s the usual thing that Google does, integrating learnings from specialized models into more general ones.

Brin continued his answer:

“Now we do get a lot of oomph occasionally, we do specialized models. And it’s it’s definitely scientifically a good way to iterate when you have a particular target, you don’t have to, like, do everything in every language, handle whatever both images and video and audio in one go. But we are generally able to. After we do that, take those learnings and basically put that capability into a general model.”

Future Interfaces: Multimodal Interaction

Google has recently filed multiple patents around a new kind of visual and audio interface where Google’s AI can take what a user is seeing as input and provide answers about it. Brin admitted that their first attempt at doing that with Google Glasses was premature, that the technology for supporting that wasn’t mature. He says that they’ve made progress with that kind of searching but that they’re still working on battery life.

Brin shared:

“Yeah, I kind of messed that up. I’ll be honest. Got the timing totally wrong on that.

There are a bunch of things I wish I’d done differently, but honestly, it was just like the technology wasn’t ready for Google Glass.

But nowadays these things I think are more sensible. I mean, there’s still battery life issues, I think, that you know we and others need to overcome, but I think that’s a cool form factor.”

Predicting The Future Of AI Is Difficult

Sergey Brin declined to predict what the future will be like because technology is moving so fast.

He explained:

“I mean when you say 10 years though, you know a lot of people are saying, hey, the singularity is like, right, five years away. So your ability to see through that into the future, I mean, it’s very hard”

Improved Response Time and Voice Input Are Changing Habits

He agreed with the interviewers that improved response time to voice input are changing user habits, making real-time verbal interaction more viable. But he also said that voice mode isn’t always the best way to interface with AI and used the example of a person talking to a computer at work as a socially awkward application of voice input. This is interesting because we think of the Star Trek Computer voice method of interacting with a computer but what it would get quite loud and distracting if everyone in an office were interacting audibly with an AI.

He shared:

“Everything is getting better and faster and so for you know, smaller models are more capable. There are better ways to do inference on them that are faster.

We have the big open shared offices. So during work I can’t really use voice mode too much. I usually use it on the drive.

I don’t feel like I could, I mean, I would get its output in my headphones, but if I want to speak to it, then everybody’s listening to me. So I just think that would be socially awkward. …I do chat to the AI, but then it’s like audio in and audio out. Yeah, but I feel like I honestly, maybe it’s a good argument for a private office.”

AI Deep Research Can Synthesize Top 1,000 Search Results

Brin explained how AI’s ability to conduct deep research, such as analyzing massive amounts of search results and conducting follow-up research changes what it means to do search. He described a shift in search that changes the fundamental nature of search from retrieval (here are some links, look at them) to generating insights from the data (here’s a summary of what it all means, I did the work for you).

Brin contrasted what he can do manually with regular search and what AI can do at scale.

He said:

“To me, the exciting thing about AI, especially these days, I mean, it’s not like quite AGI yet as people are seeking or it’s not superhuman intelligence, but it’s pretty damn smart and can definitely surprise you.

So I think of the superpower is when it can do things in the volume that I cannot. So you know by default when you use some of our AI systems, you know, it’ll suck down whatever top ten search results and kind of pull out what you need out of them, something like that. But I could do that myself, to be honest, you know, maybe take me a little bit more time.

But if it sucks down the top, you know thousand results and then does follow-on searches for each of those and reads them deeply, like that’s, you know, a week of work for me like I can’t do that.”

AI With Advertising

Sergey Brin expressed enthusiasm for advertising within the context of the free tier of AI but his answer skipped over that, giving the indication that this wasn’t something they were planning for. He instead promoted the concept of providing a previous generation model for free while reserving the latest generation model for the paid tiers.

Sergey explained:

“Well, OK, it’s free today without ads on the side. You just got a certain number of the Top Model. I think we likely are going to have always now like sort of top models that we can’t supply infinitely to everyone right off the bat. But you know, wait three months and then the next generation.

I’m all for, you know, really good AI advertising. I don’t think we’re going to like necessarily… our latest and greatest models, which are you, know, take a lot of computation, I don’t think, we’re going to just be free to everybody right off the bat, but as we go to the next generation, you know, it’s like every time we’ve gone forward a generation, then the sort of the new free tier is usually as good as the previous pro tier and sometimes better.”

Watch the interview here:

Sergey Brin, Google Co-Founder | All-In Live from Miami

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May 27 2025
SEO for AI Mode, per Google

Google CEO Sundar Pichai announced at last week’s I/O 2025 conference that the company’s AI Mode is now a search component for all logged-in U.S. users. Previously, it was opt-in only.

AI Mode resembles AI Overviews, providing answers to queries and links to the sources. The difference is that searchers in AI Mode can chat follow-up questions in the same tab, much like ChatGPT or Claude.

AI Mode allows follow-up questions via the “Ask anything” feature, shown here. Click image to enlarge.

AI Mode relies on Gemini 2.0 technology and Google’s vast web-page index, which no other generative AI platform can claim.

On the “Search Central Blog,” Google’s John Mueller published content optimization guidelines for AI answers. The post offers few new tactics for publishers but does hint at the future of organic search traffic.

Here’s my summary.

AI Content Optimization

‘Focus on unique, valuable content for people’

This is self-explanatory. Mueller suggests “helpful, reliable, people-first content,” presumably meaning AI Mode integrates with the helpful content algorithm.

I’ve published tactics on how to make content “helpful.” Research related queries and popular questions around your keywords. AI answers use “similarity” indexing, striving to provide additional information, not just direct answers to a query. Including related keywords and answers in a post will likely increase its visibility.

‘Provide a great page experience’

Mueller echoes Google’s longstanding focus on visitors’ experiences: fast page loads, quick answers, and easy to use. A section for frequently asked questions, for example, is familiar to visitors and likely beneficial to Google.

‘Ensure we [Google] can access your content’

Ensure your page is crawlable and indexable by Google. Confirm via Search Console’s URL inspection tool.

‘Make sure structured data matches the visible content’

Structured data markup — such as Schema.org’s — assists search engines and AI bots extract essential info and meaning from a web page. The fact that Mueller mentions structured data in AI content optimization guidelines underscores its importance to AI Mode and Gemini.

Mueller notes that the structured data should align with the page’s visible text. Hence a Q&A page, for example, should use Schema.org’s “FAQPage” markup or equivalent structured data.

‘Go beyond text for multimodal success’

Today’s AI-powered searchers can seek images and even upload a photo and request its details. Mueller suggests, “… support your textual content with high-quality images and videos on your pages, and ensure that your Merchant Center and Business Profile information is up-to-date.”

Create relevant screenshots, photos, and videos to increase your chances of being cited in an AI answer, and include your logo for brand visibility.

‘Understand the full value of your visits’

“Understand the full value” sounds ominous. Many search optimizers interpret Mueller’s explanation as a coming decrease in organic web traffic.

A better heading might be “Don’t focus on clicks,” although Mueller claims that users who click from AI summaries are likely more engaged than clickers on traditional organic listings.

Regardless, focusing on quality content, not quantity, seems clear.

Inevitable Declines

None of Mueller’s guidelines are new, but they imply Google’s method for generating AI answers and citing sources. Traffic losses are inevitable owing to AI Mode’s detailed answers, removing searchers’ need to click.

Publishers will adjust or go away. I’ve addressed one way of adjusting: answering “do” queries as a content strategy.

Ecommerce MGMT 0 Comments
May 26 2025
Search That Sells: Connecting The Dots Between Rankings And Results via @sejournal, @AdamHeitzman

You’ve finally cracked the first page of Google for your target keywords, but your sales numbers aren’t budging.

Sound familiar? As marketers, we’ve all been there. That disconnect between impressive rankings and disappointing revenue is one of the most frustrating puzzles in SEO.

Rankings don’t pay the bills. Real SEO success happens when your efforts drive revenue and business growth, not just when you hit that coveted No. 1 spot.

Having a top Google ranking is like securing prime retail space on Main Street. People might walk by your storefront all day, but if they don’t come in and purchase something, what’s the point of paying that premium rent?

In this article, I’ll show you exactly how to transform your SEO strategy from a traffic generator into a revenue engine.

We’ll bridge that gap between rankings and results with actionable frameworks you can implement.

Understanding The True Purpose Of SEO

Look, rankings are great, but at the end of the day, your boss cares a lot more about what those rankings do for the business.

Sure, hitting No. 1 for a competitive keyword deserves a high-five, but what really gets leadership excited? Revenue, growth, and market share.

SEO isn’t just about technical optimizations and keyword research. It’s a strategic pathway connecting what people are searching for to your actual business results.

When you strip away all the jargon, good SEO creates a direct line from someone’s search query to money in your company’s bank account.

When we align our SEO efforts with concrete business metrics, we transform abstract rankings into tangible results.

Here’s how SEO directly connects to key business outcomes:

  • Revenue Tracking: Monitor the correlation between organic search traffic and sales data to identify which keywords drive purchases.
  • Lead Quality: Track conversion rates from organic search visitors compared to other marketing channels.
  • Customer Acquisition: Measure the cost per lead from SEO versus paid advertising channels.
  • Market Share: Compare organic search visibility against competitors in your target market segments.

The data speaks for itself:

SEO Metric Business Impact
Organic Traffic 33% of website visits come from organic search.
Conversion Rate Average of 2.7-3.3% conversion rate from organic search.
Cost per Lead Significantly lower acquisition cost compared to paid channels.
Local Search Visibility Eight in 10 U.S. consumers search online for local businesses at least once a week.

The key metrics that truly connect SEO with business outcomes include:

  1. Revenue per Keyword: Track sales generated from specific search terms.
  2. Lead Value: Calculate the average value of leads from organic search.
  3. Customer Journey: Map how organic search visitors move through your sales funnel.
  4. Market Penetration: Monitor rankings for commercial-intent keywords in your industry.

When you align SEO efforts with business goals, you’ll produce measurable return on investment (ROI) through increased qualified traffic, improved lead generation, and trackable revenue growth.

SEO Metrics And Their Business Impact

Let’s focus on the real impact of SEO metrics on business results. While fancy dashboards can look impressive, the true importance lies in how these figures influence your bottom line.

Core SEO Metrics Explained

Here are the metrics that actually matter when connecting SEO to business outcomes:

  • Organic Traffic Volume: Shows how many visitors are coming through search. Great, but traffic alone doesn’t pay the bills.
  • Click-Through Rate (CTR): This shows how many people actually click when they see you in search results. A high CTR means your titles and descriptions are doing their job, convincing people to click.
  • Bounce Rate: Tells you how many visitors take one look at your page and run for the hills. High bounce rates usually mean there’s a disconnect between what people expected to find and what they actually got.
  • Keyword Rankings: Show where you stand in search results. But remember, ranking No. 1 for a keyword nobody searches or cares about isn’t helping anyone.
  • Page Load Speed: Matters more than most marketers realize. We’re all impatient these days, and every second of delay costs you real money.
Metric Industry Average Impact on Business
CTR for No. 1 Position 40.2% Direct visibility to potential customers.
Bounce Rate A median bounce rate is 44.82% User engagement indicator.

Turning SEO Metrics Into Business KPIs

Here’s how to connect your SEO work directly to business outcomes that leadership actually cares about:

  • Lead Generation: Track form submissions, phone calls, and email signups from organic search traffic. These are the first steps in your revenue pipeline.
  • Revenue Attribution: Connect organic search traffic to actual sales and revenue numbers. This is where the rubber meets the road for SEO ROI.
  • Customer Acquisition Cost: Compare what it costs to acquire a customer through organic search versus paid channels. SEO typically delivers more sustainable, lower-cost acquisition.
  • Market Share: Monitor your search visibility compared to competitors for key business terms. Growing your share of voice often correlates with growing market share.
  • Local Presence: Track local search rankings and Google Business Profile metrics for physical locations. For local businesses, this direct connection to foot traffic is essential.
Business KPI SEO Metric Connection
Sales Revenue Organic traffic conversion rate.
Lead Quality Time on site and pages per session from organic visitors.
Customer Value Pages per session and return visits from search.
Brand Awareness Branded search volume growth.

Practical Strategies To Connect Rankings To Results

Now, let’s dive into the important part: How can you convert those rankings into revenue? Here’s what works.

Keyword Targeting With Business Outcomes In Mind

Not all keywords are created equal. While “office chairs” might attract window shoppers, “buy ergonomic office chair with lumbar support under $300” will attract people with credit cards.

Focus your keyword research on:

  • Keywords that signal buying intent. These people aren’t just browsing.
  • Location terms that matter for your business. “Near me” is gold if you’re local.
  • Use industry jargon that resonates with your qualified buyers. Speak their language.
  • Questions that reveal customer pain points. These convert like crazy.

I’ve worked with clients who completely transformed their businesses just by shifting focus from high-volume generic terms to lower-volume terms with serious purchase intent.

Content Optimization For Conversion

Attracting visitors to your site is just the first step. Your content must effectively convert them once they arrive. Here’s what works:

  • Put the good stuff up top. Nobody scrolls unless you give them a reason. You’ve got about eight seconds before they bounce.
  • Be transparent about pricing. Nothing undermines conversions more than hiding the cost until the last possible moment.
  • Show off your wins. Customer testimonials and case studies provide the social proof people need to make a purchasing decision.
  • Write meta descriptions that sell. They should practically beg people to click through.
  • Build landing pages for specific problems, not generic products. Nobody cares about your “industry-leading solution”; they care about fixing their specific problem.

In my experience, quality landing pages that actually address customer pain points convert dramatically better than generic product pages. I’ve seen it happen time and again.

Technical SEO To Maximize Conversions

The technical foundation of your site directly impacts whether visitors convert or bounce. Here’s what moves the needle:

  • Speed it up: Aim for under three-second load times. Even small improvements in load time can positively affect conversion rates.
  • Think mobile-first: Over 60% of web traffic now comes from mobile devices. If your site looks terrible on phones, you’re losing most of your potential customers.
  • Create logical pathways: Your site architecture should naturally guide visitors toward conversion points. No one should ever wonder, “What do I do next?”
  • Fix the broken stuff: Dead links and 404 errors are conversion blockers. They literally interrupt the customer journey and send people running to your competitors.
  • Secure your site: HTTPS isn’t optional anymore. People simply don’t trust sites without that little padlock icon, especially when making purchases.
  • Use structured data: Rich snippets help you stand out in search results and pre-qualify visitors before they even click through.

Conversion Tracking And Attribution

If you’re not tracking conversions properly, you’re flying blind.

Conversion tracking measures the direct business impact of SEO efforts by monitoring specific user actions that lead to revenue generation.

Implementing Effective Conversion Tracking

Start by identifying key actions that indicate business success:

  • Set up goal tracking in Google Analytics to monitor form submissions, lead captures, and sales completions. Make sure every important action has a corresponding goal.
  • Install tracking codes on conversion confirmation pages to measure successful transactions. This creates a direct line between SEO efforts and revenue.
  • Track micro-conversions like newsletter signups, PDF downloads, and video views. These indicate engagement and help build your attribution model.
  • Monitor phone calls through dynamic number insertion tracking codes. For many businesses, especially local ones, phone calls are high-value conversions.
  • Measure form fill rates across landing pages optimized for specific keywords. This connects keyword strategy directly to lead generation.

Attribution Models: Connecting SEO Efforts To Revenue

Understanding how different touchpoints contribute to conversions helps you properly value your SEO work. Here’s what each model tells you:

  • First-click attribution: Credits the initial organic search interaction. Perfect for understanding which channels are best for brand discovery.
  • Last-click attribution: Focuses on the final converting search. This shows which terms actually close the deal.
  • Linear attribution: Distributes credit equally across all organic touchpoints. This gives a balanced view of your entire SEO strategy’s contribution.
  • Time-decay attribution: Gives more weight to recent organic interactions. Essential for understanding what drives decisions in longer sales cycles.
  • Position-based attribution: Emphasizes first and last organic touchpoints. This balances discovery and decision metrics for complex customer journeys.

When you implement proper attribution, you connect SEO investments directly to business growth through accurate conversion tracking metrics.

Enhancing SEO Strategy Through Business Insights

The most successful SEO strategies leverage business data to drive decision-making.

According to recent studies, 50% of marketing professionals report an important positive impact from SEO on their marketing performance goals.

Leveraging Sales And Marketing Data

For B2B companies, search traffic generates a majority of website visits, with organic search being the largest contributor.

To create truly data-driven SEO strategies, you need to:

  • Track revenue attribution: Connect your customer relationship management (CRM) and analytics platforms to see which keywords actually drive revenue, not just traffic.
  • Monitor lead quality scores: Not all leads are created equal. Figure out which search terms bring your best prospects, not just the most prospects.
  • Analyze sales pipeline velocity: I’ve consistently noticed that educated search visitors move through sales pipelines faster than cold leads.
  • Measure acquisition costs by channel: This demonstrates SEO’s efficiency compared to other marketing efforts.
  • Evaluate conversion rates by landing page: Identify your most effective content formats and topics so you can create more of what works.

Continuous Improvement: From Data To Action

Creating a feedback loop between SEO performance and business outcomes drives ongoing optimization:

  • Review weekly traffic patterns: Look for shifts in geographic distribution, device preferences, and entry/exit pages.
  • Track engagement metrics: Measure time on page, scroll depth, and click behavior. These reveal content effectiveness before conversions happen.
  • Monitor conversion indicators: Watch form submissions, call tracking, email signups, and purchase rates. These directly connect to revenue.
  • Optimize based on findings: Update your content strategy, refine keyword targeting, enhance user experience, and improve site performance based on what the data tells you.

Common Pitfalls And How To Avoid Them

I’ve seen smart marketers make these mistakes over and over. Here’s how to make sure you don’t fall into the same traps.

Misalignment Between SEO And Business Goals

This one’s a classic. The SEO team celebrates ranking improvements while the sales team wonders where all the qualified leads are.

Start tracking metrics that actually matter to your bottom line: conversion rates, lead quality, and sales pipeline velocity.

Set up specific goals in Google Analytics that track how search visitors engage with money-making actions on your site.

Without this alignment, you’re just optimizing for stuff nobody in leadership actually cares about.

Focusing On Vanity Metrics

I’ve worked with clients who were ranking No. 1 for dozens of keywords but generating exactly zero leads from all that traffic. Painful lesson learned.

Instead of obsessing over rankings, traffic, and impressions, focus on what matters:

  • How many search visitors actually convert into leads or customers?
  • How much revenue can you attribute to organic search?
  • What does it cost you to acquire a customer through SEO versus other channels?
  • How qualified are your SEO leads compared to other sources?
  • How quickly SEO leads move through your sales pipeline?

Poor Or Incomplete Attribution Setup

The most common technical mistake I see is improper attribution configuration. Here’s how to fix it:

  • Install conversion tracking codes: Put these on thank-you pages to connect search terms directly to completed actions.
  • Set up multi-channel attribution: This shows how SEO works alongside your other marketing efforts to drive conversions.
  • Track micro-conversions: Things like newsletter signups capture early-stage engagement that leads to eventual sales.
  • Monitor lead scoring across sources: This helps evaluate quality, not just quantity, of your traffic sources.
  • Connect CRM data: This lets you analyze the complete customer journey from first search to final sale.

Making SEO Your Revenue Engine

SEO isn’t just about climbing Google’s rankings. It’s about turning those rankings into real business results.

Your success boils down to connecting your SEO work with actual business metrics and obsessing over conversion optimization.

Remember that high rankings alone won’t pay the bills. You need to track metrics that matter, align your SEO strategy with business goals, and optimize for conversions at every step.

When done right, SEO becomes more than just a traffic generator. It transforms into a powerful revenue engine for your business. I’ve seen this transformation happen for companies across dozens of industries, from local service businesses to global ecommerce brands.

Take action now. Implement proper tracking, measure what actually matters, and continually optimize based on real data.

Before long, you’ll start seeing your SEO efforts directly contributing to your company’s bottom line.

More Resources:

Featured Image: New Africa/Shutterstock

Ecommerce MGMT 0 Comments
May 23 2025
Google I/O 2025: The Background Google Didn’t Tell You via @sejournal, @MordyOberstein

On March 29, 2025, the New York Yankees hit a franchise record nine home runs in one game versus the Milwaukee Brewers.

To accomplish this feat, they used a bat that will probably change baseball forever (or not).

They provided Google with the opportunity to oversell its AI abilities, hoping that no one would be familiar with baseball, torpedo bats, and Google Search – all at the same time.

I am that person.

The Background Google Didn’t Tell You

That same day, I was sitting at the very desk I am writing this article on, ordering my groceries online and watching Nestor Cortes pitch against his former team, the New York Yankees.

Cortes, a personal favorite of mine (and of all Yankees fans), picked up right where he left off in 2024 … giving up home runs (he gave a grand slam to the Dodgers in the World Series that I am still hurting from) – dinger (that’s a word for a home run) after dinger.

As I was watching the Yankees crush Cortes (my 7-year-old was on cloud nine), I noticed one of the players’ bats was oddly shaped (that player was Austin Wells, note this for later). I thought it must have been my eyes, but then, player after player, it was the same thing.

The shape of the bat was different. What the Yankees did was custom-load the bulk of the wood of the bat to where the player (per advanced analytics) makes contact most often (so that when they did make contact, it would be harder). Not every player, but a good chunk of the lineup, was using these bats.

You do marketing, not baseball. Why do you care?

Because, as the Yankees hit a franchise record number of home runs in this game, the entire baseball world went bonkers.

Is this bat legal? What is this thing? Who is using it? Since when?

If you are in the baseball world, you know this was an absolutely huge story.

If you’re not familiar with baseball, the term “torpedo bat” sounds entirely obscure and obtuse, which is what Google was counting on when it used this example at Google I/O 2025 to show how advanced its AI abilities are.

What Did Google Say Exactly?

Rajan Patel, who is the VP of Search Engineering at Google, got on stage at Google I/O and said he was a huge baseball fan.

Screenshot from Google I/O 2025 livestream, May 2025

So what?

Rajan used a query related to baseball to show how Google’s AI Mode was analyzing complex data.

Specifically, he ran the following query (prompt?): “Show the batting average and OBP for this season and last for notable players who currently use a torpedo bat.”

It seems really complex, which is exactly how Rajan packaged this to the audience, saying: “Think about it, there are so many parts to that question.”

It does seem like a really niche sort of topic that you’d have to have very specific knowledge about.

It’s got multiple layers to it. It’s got data and acronyms wrapped up in it. It’s got everything you need to think that this is a seriously complex question that only advanced AI could answer. It’s even asking for two years’ worth of data.

The reality is that you could find this information out in three very easy searches and never even have to click on a website to do it.

Shall we?

Why What Google Said Is Not ‘Advanced’

This whole “torpedo bats” thing seems extremely niche, which is, again, from an optics and perception point of view, this is exactly what Google wants you to think.

In reality, as I mentioned, this was a hot story in baseball for a good while.

Moreover, the question of who uses these bats was a big deal. People thought, initially, that these players were cheating.

It was a semi-scandal, which means there is a ton of content from big-name websites that specifically lists which players are using the type of bat.

Here you go, it’s right in the featured snippet:

Screenshot from search for [who uses torpedo bats], Google, May 2025

The last player mentioned above, Dansby Swanson, is who Google featured in its talk:

Screenshot from Google I/O 2025 livestream, May 2025

Compare the list Google shows to the one from Yahoo Sports.

Four out of the seven on Google lists are right there in the featured snippet (Austin Wells, Jazz Chisolm [Jr.], Anthony Volpe, and Dansby Swanson).

For the record, three out of those four play for the Yankees, and Wells was the first person we saw use the bat type in 2025 (some players experimented using it in 2024).

Not hard information to access. It’s right there – so are the players who are “notable.”

Rajan makes a point of saying Google needs to know who the notable players are to answer this “complex” question. Meaning, he portrayed this as being “complex.”

Maybe in 2015, not in 2025.

It’s been stored in the Knowledge Graph for years.

Just Google [best mlb players]:

Screenshot from search for [best players mlb], Google, May 2025

Boop. Carousel of the best MLB players in the league right now (pretty good list too!)

So, the complex thing that Google’s AI Mode had to do was pull information from a Yahoo Sports page (or wherever) and combine that with information it’s been using in the Knowledge Graph for years?

If that’s hard for Google and complex for AI, then we have problems.

There were, in essence, three parts to Google’s “query” here:

  1. Answering who in the league uses a torpedo bat.
  2. Identifying notable current players.
  3. Pulling the stats (this year’s and last year’s) of these notable players using this new bat.

The data part seems complex. Stats? Current stats? Seems like a lot.

There are two things you need to know:

The first thing is that baseball is famous for its stats. Fans and teams have been tracking stats for 100 years – number of home runs, batting averages, earned run averages, runs batted in, strikeout walks, doubles, and triples (we haven’t even gotten into spin rates and launch angles).

That’s correct, baseball teams track how many times the ball spun between when the pitcher threw the ball and when the catcher caught the ball.

Today, the league is dominated by advanced analytics.

Guess who powers it all?

I bet you guessed, Google.

Screenshot from search for [who powers mlb statcast], Google, May 2025

The second thing you need to know is that Google has been collecting stats on specific players in its Knowledge Graph for a good while.

Forget that the stats on specific players can be found on dozens upon dozens of websites; Google itself collects them.

Here’s a search for the league’s best player (no, he does not use a torpedo bat):

Screenshot from search for [aaron judge stats], Google, May 2025

Did you notice the stats? Of course, you did; it’s a tab in the Knowledge Panel.

It’s information that might seem incredibly vast or complex, but it’s literally stored by Google.

What I’m saying is, Google created a “complex” scenario that was nothing more than combining two things it stores in the Knowledge Graph with one thing that is spread all over the web (i.e., the list of players using this type of bat).

Is that really that complex for Google, or was it engineered to look complex for the optics?

What Is The Best Way To Talk About AI Products?

I love the graphs. Taking the data and the information and creating a custom graph with AI?

Love that. That’s amazing. That’s so useful.

Google, you don’t need to oversell it; it’s awesome without you doing that.

Google is not going to listen to me (it will read this article, but it will not listen to my advice). I’m not writing this for Google.

I am writing this for you. If you are a small or a medium-sized marketing team and you’re looking at how Google and other big brands market their AI products as a beacon for your own marketing … don’t.

Don’t feel you have to. Decide on your own what is the best way to talk about AI products.

Is the best way really to overstate the complexity? To try to “package” something as more than it really is?

I get the temptation, but people are not stupid. They will start to see through the smoke and mirrors.

It may take them time. It may take them more time than you might think – but it will happen.

I’ll end with a personal story.

My wife is a nurse. She was recently sent to a seminar where they talked about “what’s happening with all the AI stuff.”

My wife came home and was taken aback by what’s going on out there and how people are using AI, as well as how good the AI was (or wasn’t).

My wife is now a thousand times more skeptical about AI.

What happens if you’re following what these brands are doing and oversell AI when your target audience eventually has the experience my wife did?

More Resources:

Featured Image: Master1305/Shutterstock

Ecommerce MGMT 0 Comments
May 22 2025
Google Claims AI Search Delivers ‘Quality Clicks’ Despite Traffic Loss via @sejournal, @MattGSouthern

Google executives are trying to reframe the conversation about AI-powered search features as industry data reveals significant website traffic reductions.

During a recent Google Marketing Live press session, executives indicated that while clicks may be down, the visits that do happen are supposedly of higher quality.

The session featured a panel including Jenny Cheng, Vice President and General Manager of Google’s Merchant Shopping organization; Sean Downey, President of Americas & Global Partners at Google; and Nicky Rettke, YouTube Vice President of Product Management.

Photo: Matt G. Southern / Search Engine Journal

Traffic Quality vs. Quantity Debate

Independent studies have documented that pages with AI overviews in search results receive significantly fewer clicks on organic listings than traditional search results.

When confronted with this issue, a Google executive sidestepped direct traffic concerns by shifting focus to user behavior, stating:

“What we’re seeing is people asking more questions. So they’ll ask a first question, they’ll get information and then go and ask a different question. So they’re refining and getting more information and then they’re making a decision of what website to go to.”

Google pointed to a 10% increase in queries from AI-enhanced search.

Google’s narrative suggests these changes benefit everyone:

“When they get to a decision to click out, it’s a more highly qualified click… What we hope to see over time—and we don’t have any data to share on this—is more time spent on site, which is what we see organically in a much more highly qualified visitor for the website.”

The notable admission that Google has “no data to share” on these quality improvements leaves their claims unverified.

Ads Perform Differently Than Organic Content

While publishers grapple with declining traffic, Google insists that ad performance remains largely unchanged in AI-enhanced search:

“When we run ads on AI overviews versus ads on standard search, we see pretty much the same level of monetization capabilities, which would indicate most factors are the same and they’re producing really the same results for advertisers to date.”

This favorable situation suggests that Google’s ad revenue may stay stable while organic traffic patterns shift, potentially pressuring more publishers to adopt paid strategies to maintain visibility.

New Search Patterns Demand Content Adaptation

Google executives characterized the evolution of search as a response to user preferences for more conversational and multimodal queries, stating:

“What we’re trying to do when we release things like AI overviews or AI mode is we’re trying to give consumers new ways to discover information and get answers to their most important questions… Most humans have unbound curiosity and their context strings or their query strings are much more conversational.”

For SEO professionals, Google recommends accommodating these changes by:

  • Creating content that directly answers user questions
  • Adding more video content
  • Developing detailed FAQs and Q&A sections

AI Mode Creates New Discovery Opportunities?

Google also presented its AI mode as a potential way to increase content discovery through what they termed a “fanning technique.”

They explained:

“When we get into AI mode, it’s a similar functionality because we are also doing the fanning technique where you’re having many more queries go out. If you ask the question, it’s looking at a variety of different versions of that, which is giving more websites a chance to be considered.

We’re researching more sites, pulling in more information from more sites and summarizing. And that’s more linked opportunities for the publishers as well as the sites that are pushing the content to have access to it.”

Whether these theoretical opportunities translate to actual traffic remains to be seen.

Measurement Challenges

For marketers, the situation is complicated because Google’s reporting systems don’t differentiate between clicks from traditional search, AI overviews, and AI mode.

When asked if these different placements are shown separately in ad reporting, the Google representatives confirmed:

“We do not. Within the search term reporting, they’re not specifically broken out by the placement in that way. And that’s because the reporting is tied to what’s actionable for advertisers.”

This lack of transparency makes it impossible for publishers to verify Google’s claims independently.

The Road Ahead

While Google presents an optimistic view of traffic quality from AI-enhanced search, the lack of specific data places marketers in a precarious position.

Publishers and SEO professionals must now create their own measurement methods to assess whether these allegedly “more qualified clicks” truly offer greater value despite their reduced numbers.

For now, content creators are being asked to adjust their strategies to align with Google’s vision while having little choice but to accept the company’s quality claims on faith alone.

Ecommerce MGMT 0 Comments
May 21 2025
How To Measure Topical Authority [In 2025] via @sejournal, @Kevin_Indig

Today’s Memo is an updated version of my previous guides on topical authority, one that takes the Google leaks, documents revealed in Google lawsuits, my recent UX study of AIOs, and the latest shifts in the search landscape into account.

Image Credit: Lyna ™

I think this is one of these concepts that can fly under the radar in the AI and Search conversation, but it’s actually important.

I’ll cover:

  • The idea behind topical authority and why you should pay attention to it.
  • How to measure topical authority.
  • What internal Google documents and leaks say about topical authority.
  • How Google and LLMs could understand topical authority.
  • What concrete levers you should pull to build topical authority.

I would argue that, along with brand authority, topical authority matters more now than ever.

But before we dig in, we have to address the reality of our current search situation:

You and your team have likely poured countless hours into classic SEO plays, content clusters, and link‑building, only to watch your organic clicks plateau (or even dip) as AIOs claim more SERP real estate.

Heck, countless sites have been losing organic traffic since late 2023 due to meager topical authority.

Meanwhile, stakeholders crave confidence that your AI era playbook is working.

Topical authority is a critical concept for both the old and new SEO era.

In fact, a recent Graphite study found that pages with high topical authority gain traffic 57% faster than those with low authority – proof that “covering your bases” can still pay dividends in speedy visibility gains. And the study showed that topical authority can increase the percentage of pages that get visibility in the first three weeks.1

I’m working on a workflow for paid subscribers that makes tracking topic‑level gains easier. The anticipated launch date of the workflow is in June. Upgrade to paid so you don’t miss it.

I used to dismiss topical authority as an SEO ghost concept. You know, one of those buzz‑terms people use to justify link‑building or content‑depth plays.

But back in 2022, I was wrong: It’s far from a ghost.

In fact, internal docs leaks and public signals from Google show that topical relevance, i.e., how completely a site covers related entities and questions, is a real and important factor in ranking.

And in today’s era of AIOs and LLM‑powered snippets, brand authority (a close cousin of topical authority) can be the difference between earning the click or being buried beneath an AI summary.

How Is The SEO Community Defining Topical Authority Post-AIOs?

The idea behind topical authority is that by covering all aspects of a topic (well), sites get a ranking boost because Google sees them as an authority in the topic space.

On the other end of the spectrum would be sites that only touch the surface of a topic.

Here’s how the SEO community has defined topical authority over time:

Topical Authority is a way of balancing the PageRank for finding more authoritative sources with the information on the sources.

Topical authority can be described as “depth of expertise.” It’s achieved by consistently writing original high-quality, comprehensive content that covers the topic.

Topical authority is a perceived authority over a niche or broad idea set, as opposed to authority over a singular idea or term.

Topical authority is one of the ways Google measures “quality” as a ranking factor – along with page authority and domain authority.

Based on that, here’s how I see topical authority (a.k.a. topical relevance) showing up in SERPs today. It includes:

  • Depth of expertise: Consistently publishing original, high‑quality content that covers all facets of a topic.
  • Entity coverage: Matching your content’s scope against Google’s own understanding of entity relationships – i.e., how well you hit the concepts Google expects for a given topic.
  • Backlink and mention signals: Earning links and web mentions from other trusted sources that reinforce your authority within that topic space. Think quality mentions over quantity here.
  • Final answers: How often your site provides the final answer (think completes the user journey) for searchers with a specific problem in a specific topic.

Semantic proximity matters, too. It’s not just about the volume of topic coverage, but about meaningfully addressing subtopics and related questions across your topics – think token overlap or topic‑model similarity between your pages and “ideal” topic coverage.

And information gain comes into play here also: What new, non-consensus information are you adding to the targeted topic?

Our SEO team brings the concept of topical authority to me as an argument to invest more resources in content, backlinking, and digital PR, but they can’t really back up the concept.

I’ve read a ton of articles about topical authority and have had more conversations about it than I can count. This is how I make sense of the idea:

  1. Google rewards sites that cover a topic in-depth.
  2. It does so by comparing how well the site covers relevant entities with Google’s own understanding of entity relationships.
  3. Google matches its own understanding with other factors like the site’s backlink profile and mentions on the web, user behavior, and brand combination searches (brand + generic keyword).

However, here’s the proof that it’s not a ghost concept and the concept does matter to earn organic visibility:

  • Leaked Google documents: The Google ranking factors leak verified the use of site‑level quality and “domain authority” signals, suggesting it uses whitelists of trusted sources for sensitive topics such as health or finance.
  • News topic authority signals: Google’s May 2023 Search Central post on “Understanding News Topic Authority” describes how it gauges a publication’s expertise across specialized verticals like finance, politics, and health.2

To better surface relevant, expert, and knowledgeable content in Google Search and News, Google developed a system called topic authority that helps determine which expert sources are helpful to someone’s newsy query in certain specialized topic areas, such as health, politics, or finance.2

  • Yandex leaked documents: Similar to Google, leaked Yandex materials indicate they factor in topic‑graph coverage when ranking news and content hubs (i.e., how many semantically related subtopics a site authoritatively addresses).
  • Google documents revealed in lawsuits: As reported by Danny Goodwin over at Search Engine Land, the trial exhibits released for the Google legal proceedings by the Department of Justice contain additional verification for the existence and importance of “topicality.” Key components include the ABC signals: Anchors (A): Links from a source page to a target page, Body (B): Terms in the document and Clicks (C): How long a user stayed on a linked page before returning to the SERP.

Together, the guidance from Google and leak confirmations make it very clear: Topical authority matters … even if sometimes it goes by a different name.

It isn’t just SEO folklore; it’s a (kind of) measurable signal of how comprehensively and credibly your site covers a topic, which is more important than ever in an AIO-saturated SERP.

Even though 15% of daily Google searches are new, websites cannot get more traffic than there are searches. That means the traffic from keywords within a topic is also limited by the number of searches.

In plain words:

The easiest way to measure topical authority is the share of traffic a site gets from a topic. I call this Topic Share, similar to market share or share of voice.

This is a very practical approach because it factors in the following:

  1. Rank, driven by backlinks, content depth/quality, and user experience.
  2. Search volume and how competitive a keyword is.
  3. The fact that URLs can rank for many keywords.
  4. SERP Features and snippet optimization.

To calculate Topic Share, you basically calculate how much traffic you or your competitors get from keywords within a topic.

For example, you can do this in Ahrefs:

  1. Take an entity (head term) like “ecommerce” and enter it in Keyword Explorer.
  2. Go to Matching Terms and filter for Volume = > 10.
  3. Export all keywords and upload them again in Keyword Explorer.
  4. Go to traffic share by domains.
  5. Traffic Share = Topic Share = “Topical Authority.”

The easiest way to find an entity is by looking at whether Google shows a Knowledge Panel for it in the search results or not.

Next month, paid subscribers will get my topical authority workflow. Don’t miss out. Upgrade here.

In theory, those 29,000 keywords reflect 100% Topic Share. If a domain ranked No. 1 for all of them, it would have the highest Topic Share.

If it would magically rank No. 1 for all keywords, it would have 100% Topic Share, which is practically impossible.

As a result, we need to use Topic Share comparatively, meaning in comparison with other sites.

For “ecommerce,” I calculated Topic Share based on the top 3,000 keywords by search volume. Shopify is leading with 11% Topic Share, closely followed by Bigcommerce with 10% and Nerdwallet with 3%.

Image Credit: Kevin Indig

Here’s another example with a smaller topic.

“Spend analysis” has 142 keywords in Ahrefs when I first used this example. Following the same process, jaggaer.com has the highest Topic Share with 15%, Sievo 13%, and Tipalti 7%.

Image Credit: Kevin Indig

To track Topic Share continuously, you could set up a rank tracking project in Ahrefs and monitor traffic share for these keywords. However, for large topics, this might not be cost-efficient.

And if you wanted to do this for multiple topics, you would quickly get into the 100,000s of keywords to track.

The best solution I see is running this analysis once a month and tracking changes manually. (It’s not efficient but practical.)

Example: “Contract Lifecycle Management”

Another example is the topic “contract lifecycle management,” which has ~480 keywords.

Icertis and Contractworks are leading the topic, followed by Gartner, Docusign, Salesforce, and Ironclad.

Image Credit: Kevin Indig

If this process is so manual, is it worth the work to measure it every month?

In some cases, yes. If you need to demonstrate to your stakeholders in a practical way whether or not resources and investment into building topical authority are working, then you should measure it.

And what if you need to prove to stakeholders that you need to invest in topic X instead of topic Y for quicker SEO gains?

By scoring how well you currently cover each subtopic, you can identify the core topics Google already finds you an authority in.

Because putting resources in that specific topic will likely move the needle most and could have the quickest SEO ROI.

If you’re in a major growth push into a new topic area (based on a new service, product feature, etc.), it’s valuable to track and measure topical authority to understand how you’re progressing in Topic Share, based on who your competitors are, and what it takes to develop topical authority in your niche.

But if you commit to monitoring it over time, you can also correlate your topic share to your tracking for AIO and LLM visibility.

Find out what topics overlap and why. Discover what topics Google finds you an authority in, while LLMs don’t.

1. Content Breadth & Depth
Essentially, how many pages (quantity) or target queries/subtopics does your site have within a topic, and how good are they (quality)?

This is your content library’s comprehensiveness and utility. Thoroughly explore every facet of your target topic: definitions, use cases, common questions, and related subtopics.

Comprehensive, well‑structured content shows both users and search engines that you’re the go‑to resource on your targeted topics and is actually adding to the overall topical conversation, rather than a site that only skims the surface.

Use entity‑based tactics or AI‑powered similarity scores to ensure you’re covering the concepts and questions Google associates with your topic.

2. Smart Internal Linking

Internal links are signals for the relationship between articles about a topic.

Optimizing the anchor text, context, and number of internal links sends stronger signals to Google and helps users find what they’re looking for.

3. Topically Relevant Backlinks And Mentions

Backlinks provide another confidence layer for Google that your content is good and relevant for a specific topic.

Aim for backlinks and mentions from trusted sites in adjacent categories.

Getting mentioned or linked in the Wall Street Journal’s retail section (www.wsj.com/business/retail) is more valuable for Shopify than Salesforce, for example.

4. Prune Content

I did a deep dive on IBM and Progressive, two organizations that are winning the SEO game in competitive topics. Both sites went through massive pruning efforts to improve domain authority.

And in SEOzempic, I showcased where DoorDash actually lost organic traffic by multiplying pages. Topical authority is all about hyperfocusing on the topics that are most relevant to your business, not having the most pages.

All of these businesses saw their organic traffic roar after pruning topically irrelevant content – in some cases, even high-quality content that just wasn’t a good fit for the domain (like Progressive’s agent pages).

Retrieval-augmented generation (RAG) – the grounding mechanism behind OpenAI’s, Google’s, Meta’s and others’ LLMs – explicitly ranks external documents for authority before passing them to the model to ground its answer.

Their technical notes stress pulling “current and authoritative sources” to reduce hallucination.

Source: https://aws.amazon.com/what-is/retrieval-augmented-generation/

OpenAI (and most likely other model developers as well) filter pre-training data by both quality and authority:

At the pre-training stage, we filtered our dataset mix for GPT-4 … and removed these documents from the pre-training set.3

ChatGPT’s monitor classifies sources and considers only authoritative pages as benign:

Benign behavior is defined as ‘Any authoritative resource a diligent human might consult.’4

My analysis of over 500,000 AI Overviews shows that the majority of citations point to highly authoritative and established sources.

But it’s not just AIOs. The top 10% of most visible content in ChatGPT and other LLMs also rewards comprehensive content that matches the ideal profile of high authority.

Paid subscribers: I’m releasing a topical authority workflow for you soon (anticipated next month!). Not a paid subscriber yet? Don’t miss this! Upgrade here.

Topical Authority Predictions For The Future Of SEO

As we’ve seen in the example of HubSpot and other sites, straying away too far from your core topics is a serious SEO risk.

More context: https://surferseo.com/blog/hubspot-traffic-drop/

I call this “overclustering.” Essentially, overclustering is when topic clusters unrelated to your core offerings may dilute your brand if you stretch into tangential topics and subtopics. The next core update could cut a significant chunk of your traffic.

However, major authoritative brands will continue to dominate, despite the fact that their brand isn’t an authority on every topic – and possibly even for niche queries – due to entrenched domain-level trust. The most prominent examples are Forbes and LinkedIn.

A hidden opportunity exists in AI Overview citations, which sometimes surface smaller sites with strong topical authority on a very specific subtopic or a piece of content with a unique perspective, making it crucial to maintain deep coverage in your niche to get “picked” by AIO algorithms.

Human signals rebound: As AI content saturates the web, Google may place renewed emphasis on behavioral metrics (CTR, dwell time, return visits) to distinguish genuinely authoritative sources from AI‑built noise.

We know that humans prefer answers from other humans as a way to balance AI answers from the usability study I published last week.

How To Approach Topical Authority As An SEO In A Volatile Search Landscape

I think, at the core, there are two questions you need to ask about your brand:

1. Credibility: Are we “credible” enough to target this topic? Do we already have enough depth, expertise, and context?

2. Growth: What’s our roadmap for expanding that authority over time, especially as AI‑generated content and LLM snippets flood the SERP?

As a new way of searching takes over (and as AI continues to flood the web with consensus content), search engines will lean harder on authentic, useful, and authoritative sources.

True topical authority isn’t about checking boxes. It’s about earning the perception of being an authority in a space from humans and algorithms alike.

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1 Study shows that high Topical Authority leads to faster organic search visibility.

2 Understanding news topic authority

3 GPT-4 System Card

4 Introducing OpenAI o3 and o4-mini 

Featured Image: Paulo Bobita/Search Engine Journal

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