Oct 23 2025
Measuring When AI Assistants And Search Engines Disagree via @sejournal, @DuaneForrester

Before you get started, it’s important to heed this warning: There is math ahead! If doing math and learning equations makes your head swim, or makes you want to sit down and eat a whole cake, prepare yourself (or grab a cake). But if you like math, if you enjoy equations, and you really do believe that k=N (you sadist!), oh, this article is going to thrill you as we explore hybrid search in a bit more depth.

(Image Credit: Duane Forrester)

For years (decades), SEO lived inside a single feedback loop. We optimized, ranked, and tracked. Everything made sense because Google gave us the scoreboard. (I’m oversimplifying, but you get the point.)

Now, AI assistants sit above that layer. They summarize, cite, and answer questions before a click ever happens. Your content can be surfaced, paraphrased, or ignored, and none of it shows in analytics.

That doesn’t make SEO obsolete. It means a new kind of visibility now runs parallel to it. This article shows ideas of how to measure that visibility without code, special access, or a developer, and how to stay grounded in what we actually know.

Why This Matters

Search engines still drive almost all measurable traffic. Google alone handles almost 4 billion searches per day. By comparison, Perplexity’s reported total annual query volume is roughly 10 billion.

So yes, assistants are still small by comparison. But they’re shaping how information gets interpreted. You can already see it when ChatGPT Search or Perplexity answers a question and links to its sources. Those citations reveal which content blocks (chunks) and domains the models currently trust.

The challenge is that marketers have no native dashboard to show how often that happens. Google recently added AI Mode performance data into Search Console. According to Google’s documentation, AI Mode impressions, clicks, and positions are now included in the overall “Web” search type.

That inclusion matters, but it’s blended in. There’s currently no way to isolate AI Mode traffic. The data is there, just folded into the larger bucket. No percentage split. No trend line. Not yet.

Until that visibility improves, I’m suggesting we can use a proxy test to understand where assistants and search agree and where they diverge.

Two Retrieval Systems, Two Ways To Be Found

Traditional search engines use lexical retrieval, where they match words and phrases directly. The dominant algorithm, BM25, has powered solutions like Elasticsearch and similar systems for years. It’s also in use in today’s common search engines.

AI assistants rely on semantic retrieval. Instead of exact words, they map meaning through embeddings, the mathematical fingerprints of text. This lets them find conceptually related passages even when the exact words differ.

Each system makes different mistakes. Lexical retrieval misses synonyms. Semantic retrieval can connect unrelated ideas. But when combined, they produce better results.

Inside most hybrid retrieval systems, the two methods are fused using a rule called Reciprocal Rank Fusion (RRF). You don’t have to be able to run it, but understanding the concept helps you interpret what you’ll measure later.

RRF In Plain English

Hybrid retrieval merges multiple ranked lists into one balanced list. The math behind that fusion is RRF.

The formula is simple: score equals one divided by k plus rank. This is written as 1 ÷ (k + rank). If an item appears in several lists, you add those scores together.

Here, “rank” means the item’s position in that list, starting with 1 as the top. “k” is a constant that smooths the difference between top and mid-ranked items. Most systems typically use something near 60, but each may tune it differently.

It’s worth remembering that a vector model doesn’t rank results by counting word matches. It measures how close each document’s embedding is to the query’s embedding in multi-dimensional space. The system then sorts those similarity scores from highest to lowest, effectively creating a ranked list. It looks like a search engine ranking, but it’s driven by distance math, not term frequency.

(Image Credit: Duane Forrester)

Let’s make it tangible with small numbers and two ranked lists. One from BM25 (keyword relevance) and one from a vector model (semantic relevance). We’ll use k = 10 for clarity.

Document A is ranked number 1 in BM25 and number 3 in the vector list.
From BM25: 1 ÷ (10 + 1) = 1 ÷ 11 = 0.0909.
From the vector list: 1 ÷ (10 + 3) = 1 ÷ 13 = 0.0769.
Add them together: 0.0909 + 0.0769 = 0.1678.

Document B is ranked number 2 in BM25 and number 1 in the vector list.
From BM25: 1 ÷ (10 + 2) = 1 ÷ 12 = 0.0833.
From the vector list: 1 ÷ (10 + 1) = 1 ÷ 11 = 0.0909.
Add them: 0.0833 + 0.0909 = 0.1742.

Document C is ranked number 3 in BM25 and number 2 in the vector list.
From BM25: 1 ÷ (10 + 3) = 1 ÷ 13 = 0.0769.
From the vector list: 1 ÷ (10 + 2) = 1 ÷ 12 = 0.0833.
Add them: 0.0769 + 0.0833 = 0.1602.

Document B wins here as it ranks high in both lists. If you raise k to 60, the differences shrink, producing a smoother, less top-heavy blend.

This example is purely illustrative. Every platform adjusts parameters differently, and no public documentation confirms which k values any engine uses. Think of it as an analogy for how multiple signals get averaged together.

Where This Math Actually Lives

You’ll never need to code it yourself as RRF is already part of modern search stacks. Here are examples of this type of system from their foundational providers. If you read through all of these, you’ll have a deeper understanding of how platforms like Perplexity do what they do:

All of them follow the same basic process: Retrieve with BM25, retrieve with vectors, score with RRF, and merge. The math above explains the concept, not the literal formula inside every product.

Observing Hybrid Retrieval In The Wild

Marketers can’t see those internal lists, but we can observe how systems behave at the surface. The trick is comparing what Google ranks with what an assistant cites, then measuring overlap, novelty, and consistency. This external math is a heuristic, a proxy for visibility. It’s not the same math the platforms calculate internally.

Step 1. Gather The Data

Pick 10 queries that matter to your business.

For each query:

  1. Run it in Google Search and copy the top 10 organic URLs.
  2. Run it in an assistant that shows citations, such as Perplexity or ChatGPT Search, and copy every cited URL or domain.

Now you have two lists per query: Google Top 10 and Assistant Citations.

(Be aware that not every assistant shows full citations, and not every query triggers them. Some assistants may summarize without listing sources at all. When that happens, skip that query as it simply can’t be measured this way.)

Step 2. Count Three Things

  1. Intersection (I): how many URLs or domains appear in both lists.
  2. Novelty (N): how many assistant citations do not appear in Google’s top 10.
    If the assistant has six citations and three overlap, N = 6 − 3 = 3.
  3. Frequency (F): how often each domain appears across all 10 queries.

Step 3. Turn Counts Into Quick Metrics

For each query set:

Shared Visibility Rate (SVR) = I ÷ 10.
This measures how much of Google’s top 10 also appears in the assistant’s citations.

Unique Assistant Visibility Rate (UAVR) = N ÷ total assistant citations for that query.
This shows how much new material the assistant introduces.

Repeat Citation Count (RCC) = (sum of F for each domain) ÷ number of queries.
This reflects how consistently a domain is cited across different answers.

Example:

Google top 10 = 10 URLs. Assistant citations = 6. Three overlap.
I = 3, N = 3, F (for example.com) = 4 (appears in four assistant answers).
SVR = 3 ÷ 10 = 0.30.
UAVR = 3 ÷ 6 = 0.50.
RCC = 4 ÷ 10 = 0.40.

You now have a numeric snapshot of how closely assistants mirror or diverge from search.

Step 4. Interpret

These scores are not industry benchmarks by any means, simply suggested starting points for you. Feel free to adjust as you feel the need:

  • High SVR (> 0.6) means your content aligns with both systems. Lexical and semantic relevance are in sync.
  • Moderate SVR (0.3 – 0.6) with high RCC suggests your pages are semantically trusted but need clearer markup or stronger linking.
  • Low SVR (< 0.3) with high UAVR shows assistants trust other sources. That often signals structure or clarity issues.
  • High RCC for competitors indicates the model repeatedly cites their domains, so it’s worth studying for schema or content design cues.

Step 5. Act

If SVR is low, improve headings, clarity, and crawlability. If RCC is low for your brand, standardize author fields, schema, and timestamps. If UAVR is high, track those new domains as they may already hold semantic trust in your niche.

(This approach won’t always work exactly as outlined. Some assistants limit the number of citations or vary them regionally. Results can differ by geography and query type. Treat it as an observational exercise, not a rigid framework.)

Why This Math Is Important

This math gives marketers a way to quantify agreement and disagreement between two retrieval systems. It’s diagnostic math, not ranking math. It doesn’t tell you why the assistant chose a source; it tells you that it did, and how consistently.

That pattern is the visible edge of the invisible hybrid logic operating behind the scenes. Think of it like watching the weather by looking at tree movement. You’re not simulating the atmosphere, just reading its effects.

On-Page Work That Helps Hybrid Retrieval

Once you see how overlap and novelty play out, the next step is tightening structure and clarity.

  • Write in short claim-and-evidence blocks of 200-300 words.
  • Use clear headings, bullets, and stable anchors so BM25 can find exact terms.
  • Add structured data (FAQ, HowTo, Product, TechArticle) so vectors and assistants understand context.
  • Keep canonical URLs stable and timestamp content updates.
  • Publish canonical PDF versions for high-trust topics; assistants often cite fixed, verifiable formats first.

These steps support both crawlers and LLMs as they share the language of structure.

Reporting And Executive Framing

Executives don’t care about BM25 or embeddings nearly as much as they care about visibility and trust.

Your new metrics (SVR, UAVR, and RCC) can help translate the abstract into something measurable: how much of your existing SEO presence carries into AI discovery, and where competitors are cited instead.

Pair those findings with Search Console’s AI Mode performance totals, but remember: You can’t currently separate AI Mode data from regular web clicks, so treat any AI-specific estimate as directional, not definitive. Also worth noting that there may still be regional limits on data availability.

These limits don’t make the math less useful, however. They help keep expectations realistic while giving you a concrete way to talk about AI-driven visibility with leadership.

Summing Up

The gap between search and assistants isn’t a wall. It’s more of a signal difference. Search engines rank pages after the answer is known. Assistants retrieve chunks before the answer exists.

The math in this article is an idea of how to observe that transition without developer tools. It’s not the platform’s math; it’s a marketer’s proxy that helps make the invisible visible.

In the end, the fundamentals stay the same. You still optimize for clarity, structure, and authority.

Now you can measure how that authority travels between ranking systems and retrieval systems, and do it with realistic expectations.

That visibility, counted and contextualized, is how modern SEO stays anchored in reality.

More Resources:

This post was originally published on Duane Forrester Decodes. 

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Oct 23 2025
Time We Actually Start To Measure Relevancy When We Talk About “Relevant Traffic” via @sejournal, @TaylorDanRW

Every SEO strategy claims to drive “relevant traffic.” It is one of the industry’s most overused phrases, and one of the least examined. We celebrate growth in organic sessions and point to conversions as proof that our efforts are working.

Yet the metric we often use to prove “relevance” – last-click revenue or leads – tells us nothing about why those visits mattered, or how they contributed to the user’s journey.

If we want to mature SEO measurement, we need to redefine what relevance means and start measuring it directly, not infer it from transactional outcome.

With AI disrupting the user journey, and a lack of data and visibility from these platforms and Google’s latest Search additions (AI Mode and AI Overviews), now is the perfect time to redefine what SEO success looks like for the new, modern Search era – and for me, this starts with defining “relevant traffic.”

The Illusion Of Relevance

In most performance reports, “relevant traffic” is shorthand for “traffic that converts.”

But this definition is structurally flawed. Conversion metrics reward the final interaction, not the fit between user intent and content. They measure commercial efficiency, not contextual alignment.

A visitor could land on a blog post, spend five minutes reading, bookmark it, and return two weeks later via paid search to convert. In most attribution models, that organic session adds no measurable value to SEO. Yet that same session might have been the most relevant interaction in the entire funnel – the moment the brand aligned with the user’s need.

In Universal Analytics, we had some insights into this as we were able to view assisted conversion paths, but with Google Analytics 4, viewing conversion path reports is only available in the Advertising section.

Even when we had visibility on the conversion paths, we didn’t always consider the attribution touchpoints that Organic had on conversions with last-click attribution to other channels.

When we define relevance only through monetary endpoints, we constrain SEO to a transactional role and undervalue its strategic contribution: shaping how users discover, interpret, and trust a brand.

The Problem With Last-Click Thinking

Last-click attribution still dominates SEO reporting, even as marketers acknowledge its limitations.

It persists not because it is accurate, but because it is easy. It allows for simple narratives: “Organic drove X in revenue this month.” But simplicity comes at the cost of understanding.

User journeys are no longer linear; Search is firmly establishing itself as multimodal, which has been a shift happening over the past decade and is being further enabled by improvements in hardware, and AI.

Search is iterative, fragmented, and increasingly mediated by AI summarization and recommendation layers. A single decision may involve dozens of micro-moments, queries that refine, pivot, or explore tangents. Measuring “relevant traffic” through the lens of last-click attribution is like judging a novel by its final paragraph.

The more we compress SEO’s role into the conversion event, the more we disconnect it from how users actually experience relevance: as a sequence of signals that build familiarity, context, and trust.

What Relevance Really Measures

Actual relevance exists at the intersection of three dimensions: intent alignment, experience quality, and journey contribution.

1. Intent Alignment

  • Does the content match what the user sought to understand or achieve?
  • Are we solving the user’s actual problem, not just matching their keywords?
  • Relevance begins when the user’s context meets the brand’s competence.

2. Experience Quality

  • How well does the content facilitate progress, not just consumption?
  • Do users explore related content, complete micro-interactions, or return later?
  • Engagement depth, scroll behavior, and path continuation are not vanity metrics; they are proxies for satisfaction.

3. Journey Contribution

  • What role does the interaction play in the broader decision arc?
  • Did it inform, influence, or reassure, even if it did not close?
  • Assisted conversions, repeat session value, and brand recall metrics can capture this more effectively than revenue alone.

These dimensions demand a shift from output metrics (traffic, conversions) to outcome metrics (user progress, decision confidence, and informational completeness).

In other words, from “how much” to “how well.”

Measuring Relevance Beyond The Click

If we accept that relevance is not synonymous with revenue, then new measurement frameworks are needed. These might include:

  • Experience fit indices: Using behavioral data (scroll depth, dwell time, secondary navigation) to quantify whether users engage as expected given the intent type.
    Example: informational queries that lead to exploration and bookmarking score high on relevance, even if they do not convert immediately.
  • Query progression analysis: Tracking whether users continue refining their query after visiting your page. If they stop searching or pivot to branded terms, that is evidence of resolved intent.
  • Session contribution mapping: Modeling the cumulative influence of organic visits across multiple sessions and touchpoints. Tools like GA4’s data-driven attribution can be extended to show assist depth rather than last-touch value.
  • Experience-level segmentation: Grouping traffic by user purpose (for example, research, comparison, decision) and benchmarking engagement outcomes against expected behaviors for that intent.

These models do not replace commercial key performance indicators (KPIs); they contextualize them. They help organizations distinguish between traffic that sells and traffic that shapes future sales.

This isn’t to say that SEO activities shouldn’t be tied to commercial KPIs, but the role of SEO has evolved in the wider web ecosystem, and our understanding of value should also evolve with it.

Why This Matters Now

AI-driven search interfaces, from Google’s AI Overviews to ChatGPT and Perplexity, are forcing marketers to confront a new reality – relevance is being interpreted algorithmically.

Users are no longer exposed to 10 blue links and maybe some static SERP features, but to synthesized, conversational results. In this environment, content must not only rank; it must earn inclusion through semantic and experiential alignment.

This makes relevance an operational imperative. Brands that measure relevance effectively will understand how users perceive and progress through discovery in both traditional and AI-mediated ecosystems. Those who continue to equate relevance with conversion will misallocate resources toward transactional content at the expense of influence and visibility.

The next generation of SEO measurement should ask:

Does this content help the user make a better decision, faster? Not just, Did it make us money?

From Performance Marketing To Performance Understanding

The shift from measuring revenue to measuring relevance parallels the broader evolution of marketing itself, from performance marketing to performance understanding.

For years, the goal has been attribution: assigning value to touchpoints. But attribution without understanding is accounting, not insight.

Measuring relevance reintroduces meaning into the equation. It bridges brand and performance, showing not just what worked, but why it mattered.

This mindset reframes SEO as an experience design function, not merely a traffic acquisition channel. It also creates a more sustainable way to defend SEO investment by proving how organic experiences improve user outcomes and brand perception, not just immediate sales.

Redefining “Relevant Traffic” For The Next Era Of Search

It is time to retire the phrase “relevant traffic” as a catch-all justification for SEO success. Relevance cannot be declared; it must be demonstrated through evidence of user progress and alignment.

A modern SEO report should read less like a sales ledger and more like an experience diagnostic:

  • What intents did we serve best?
  • Which content formats drive confidence?
  • Where does our relevance break down?

Only then can we claim, with integrity, that our traffic is genuinely relevant.

Final Thought

Relevance is not measured at the checkout page. It is estimated that now a user feels understood.

Until we start measuring that, “relevant traffic” remains a slogan, not a strategy.

More Resources:

Featured Image: Master1305/Shutterstock

Ecommerce MGMT 0 Comments
Oct 23 2025
Google’s New BlockRank Democratizes Advanced Semantic Search via @sejournal, @martinibuster

A new research paper from Google DeepMind  proposes a new AI search ranking algorithm called BlockRank that works so well it puts advanced semantic search ranking within reach of individuals and organizations. The researchers conclude that it “can democratize access to powerful information discovery tools.”

In-Context Ranking (ICR)

The research paper describes the breakthrough of using In-Context Ranking (ICR), a way to rank web pages using a large language model’s contextual understanding abilities.

It prompts the model with:

  1. Instructions for the task (for example, “rank these web pages”)
  2. Candidate documents (the pages to rank)
  3. And the search query.

ICR is a relatively new approach first explored by researchers from Google DeepMind and Google Research in 2024 (Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? PDF). That earlier study showed that ICR could match the performance of retrieval systems built specifically for search.

But that improvement came with a downside in that it requires escalating computing power as the number of pages to be ranked are increased.

When a large language model (LLM) compares multiple documents to decide which are most relevant to a query, it has to “pay attention” to every word in every document and how each word relates to all others. This attention process gets much slower as more documents are added because the work grows exponentially.

The new research solves that efficiency problem, which is why the research paper is called, Scalable In-context Ranking with Generative Models, because it shows how to scale In-context Ranking (ICR) with what they call BlockRank.

How BlockRank Was Developed

The researchers examined how the model actually uses attention during In-Context Retrieval and found two patterns:

  • Inter-document block sparsity:
    The researchers found that when the model reads a group of documents, it tends to focus mainly on each document separately instead of comparing them all to each other. They call this “block sparsity,” meaning there’s little direct comparison between different documents. Building on that insight, they changed how the model reads the input so that it reviews each document on its own but still compares all of them against the question being asked. This keeps the part that matters, matching the documents to the query, while skipping the unnecessary document-to-document comparisons. The result is a system that runs much faster without losing accuracy.
  • Query-document block relevance:
    When the LLM reads the query, it doesn’t treat every word in that question as equally important. Some parts of the question, like specific keywords or punctuation that signal intent, help the model decide which document deserves more attention. The researchers found that the model’s internal attention patterns, particularly how certain words in the query focus on specific documents, often align with which documents are relevant. This behavior, which they call “query-document block relevance,” became something the researchers could train the model to use more effectively.

The researchers identified these two attention patterns and then designed a new approach informed by what they learned. The first pattern, inter-document block sparsity, revealed that the model was wasting computation by comparing documents to each other when that information wasn’t useful. The second pattern, query-document block relevance, showed that certain parts of a question already point toward the right document.

Based on these insights, they redesigned how the model handles attention and how it is trained. The result is BlockRank, a more efficient form of In-Context Retrieval that cuts unnecessary comparisons and teaches the model to focus on what truly signals relevance.

Benchmarking Accuracy Of BlockRank

The researchers tested BlockRank for how well it ranks documents on three major benchmarks:

  • BEIR
    A collection of many different search and question-answering tasks used to test how well a system can find and rank relevant information across a wide range of topics.
  • MS MARCO
    A large dataset of real Bing search queries and passages, used to measure how accurately a system can rank passages that best answer a user’s question.
  • Natural Questions (NQ)
    A benchmark built from real Google search questions, designed to test whether a system can identify and rank the passages from Wikipedia that directly answer those questions.

They used a 7-billion-parameter Mistral LLM and compared BlockRank to other strong ranking models, including FIRST, RankZephyr, RankVicuna, and a fully fine-tuned Mistral baseline.

BlockRank performed as well as or better than those systems on all three benchmarks, matching the results on MS MARCO and Natural Questions and doing slightly better on BEIR.

The researchers explained the results:

“Experiments on MSMarco and NQ show BlockRank (Mistral-7B) matches or surpasses standard fine-tuning effectiveness while being significantly more efficient at inference and training. This offers a scalable and effective approach for LLM-based ICR.”

They also acknowledged that they didn’t test multiple LLMs and that these results are specific to Mistral 7B.

Is BlockRank Used By Google?

The research paper says nothing about it being used in a live environment. So it’s purely conjecture to say that it might be used. Also, it’s natural to try to identify where BlockRank fits into AI Mode or AI Overviews but the descriptions of how AI Mode’s FastSearch and RankEmbed work are vastly different from what BlockRank does. So it’s unlikely that BlockRank is related to FastSearch or RankEmbed.

Why BlockRank Is A Breakthrough

What the research paper does say is that this is a breakthrough technology that puts an advanced ranking system within reach of individuals and organizations that wouldn’t normally be able to have this kind of high quality ranking technology.

The researchers explain:

“The BlockRank methodology, by enhancing the efficiency and scalability of In-context Retrieval (ICR) in Large Language Models (LLMs), makes advanced semantic retrieval more computationally tractable and can democratize access to powerful information discovery tools. This could accelerate research, improve educational outcomes by providing more relevant information quickly, and empower individuals and organizations with better decision-making capabilities.

Furthermore, the increased efficiency directly translates to reduced energy consumption for retrieval-intensive LLM applications, contributing to more environmentally sustainable AI development and deployment.

By enabling effective ICR on potentially smaller or more optimized models, BlockRank could also broaden the reach of these technologies in resource-constrained environments.”

SEOs and publishers are free to their opinions of whether or not this could be used by Google. I don’t think there’s evidence of that but it would be interesting to ask a Googler about it.

Google appears to be in the process of making BlockRank available on GitHub, but it doesn’t appear to have any code available there yet.

Read about BlockRank here:
Scalable In-context Ranking with Generative Models

Featured Image by Shutterstock/Nithid

Ecommerce MGMT 0 Comments
Oct 22 2025
How And Why Google Rewrites Your Hard-Earned Headlines

TL;DR

  1. Google can and does rewrite headlines and titles frequently. Almost anything on your page could be used.
  2. The title is not all that matters. The entirety of your page – from the title to the on-page content – should remove ambiguity.
  3. The title tag is the most important term. Stick to 12 words and 600 pixels to avoid truncation and maximize value from each word.
  4. Google uses three rough concepts – Semantic title and content alignment, satisfactory click behavior, and searcher intent alignment – for this.
Image Credit: Harry Clarkson-Bennett

This is based on the Google leak documentation and Shaun Anderson’s excellent article on title tag rewriting. I’ve jazzed it to make it more news and publisher-specific.

“On average, five times as many people read the headline as read the body copy.”
David Ogilvy

No idea if that’s true or not.

I’m sure it’s some old-age advertising BS. But alongside the featured image, it is our shop window. Headlines are the gatekeepers. They need to be clickable, work for humans and machines, and prioritize clarity.

So, when you’ve spent a long time crafting a headline for your own story, why-oh-why does Google mess you around?

I’m sure you get a ton of questions from other people in the SEO team and the wider newsroom (or the legal team) about this.

Something like:

Why is our on-page headline being pulled into the SERP?

Or

We can just have the same on-page headline and title tag, can’t we? Why does it matter?

You could rinse and repeat this conversation and theory for almost anything. Meta descriptions are the most obvious situation, where some research shows they’re rewritten 70% of the time. The answer will, unfortunately, always be that, because Google can and does do what it wants.

But it helps to know the what and the why when having these conversations.

Mark Williams-Cook and team did some research to show that up to 80% of meta descriptions were being rewritten and the rewriting increased traffic. Maybe the machine knows best after all.

Why Does Google Rewrite Title Tags?

The search giant uses document understanding, query matching, content rewriting, and user engagement data to determine when a title or H1 should be changed in SERPs.

It rewrites them because it knows what is best satisfying users in real time. An area of search where we as publishers are at the bleeding edge. When you have access to that much data and you take a share of ad revenue, it would be a little obtuse not to optimize for clicks in real-time.

Image Credit: Harry Clarkson-Bennett

Does Length Matter?

No innuendos, please; this is a professional newsletter.

Google’s official documentation doesn’t define a limit for title tags. I think it’s just based on the title becoming truncated. Given Google now rewrites so much, longer, more keyword-rich and descriptive titles, longer titles could help with ranking in Top Stories and traditional search results.

According to Gary Illyes, there is real value in having longer title tags:

“The title tag (length), is an externally made-up metric. Technically there’s a limit, but it’s not a small number…

Try to keep it precise to the page, but I wouldn’t think about whether it’s long enough…”

Sara Taher ran some interesting analysis (albeit on evergreen content only) that showed the average title length falls between 42-46 characters. If titles are too long, Google will probably cut them off or rewrite them. Precision matters for evergreen search.

What Are The Key Determinants?

Based on the Google leak and Shaun’s analysis, I’d say there are three concepts at play Google uses to determine whether a title should be rewritten. I have made this up, by the way, so feel free to use your own.

  • Semantic title and content alignment.
  • Satisfactory click behavior.
  • Searcher intent alignment.

Semantic Title And Content Alignment

This is undoubtedly the most prominent section. Your on-page content and title/headline have to align.

This is why clickbait content and content written directly for Google Discover is so risky. Because you’re writing a cheque that you can’t cash. Create content specifically for a platform like Discover, and you will erode your quality signals over time.

Image Credit: Harry Clarkson-Bennett

The titlematchScore, h1ContentScore, and spammyTitleDetection review the base quality of a headline based on the page’s content and query intent. Mismatched titles, headlines, and keyword-stuffed versions are, at best, rewritten.

At worst, they downgrade the quality of your site algorithmically.

The titleMatchAnchorText ensures our title tags and header(s) are compared to internal and external anchors and evaluated in comparison to the hierarchy of the page (the headingHierarchyScore).

Finally, the “best” title is chosen from on-page elements via the snippetTitleExtraction. While Google primarily uses the title or H1 tag, any visible element can be used if it “best represents the page.”

Satisfactory Click Behavior

Much more straightforward. Exactly how Google uses user engagement signals (think of Navboost’s good vs bad click signals) to best cultivate a SERP for a particular term and cohort of people.

Image Credit: Harry Clarkson-Bennett

The titleClickSatisfaction metric combines click data at a query level with on-page engagement data (think scroll depth, time on page, on-page interactions, pogo-sticking).

So, ranking adjustments are made if Google believes the title used in the SERP is underperforming against your prior performance and the competition. So, the title you see could be one of many tests happening simultaneously, I suspect.

For those unfamiliar with Navboost, it is one of Google’s primary ranking engines. It’s based on user interaction signals, like clicks, hovers, scrolls, and swipes, over 13 months to refine rankings.

For news publishers, Glue helps rank content in real time for fresh, real-time events. Source and page level authority. It’s a fundamental part of how news SEO really works.

Searcher Intent Alignment

Searcher intent really matters when it comes to page titles. And Google knows this far better than we do. So, if the content on your page (headings, paragraphs, images, et al.) and search intent isn’t reflected by your page title, it’s gone.

Image Credit: Harry Clarkson-Bennett

Once a page title has been identified as not fit for purpose, the pageTitleRewriter metric is designed to rewrite “unhelpful or misleading page titles.”

And page titles are rewritten at a query level. The queryIntentTitleAlignment measures how the page title aligns with searcher intent. Once this is established, the page alignment and query intent are reviewed to ensure the title best reflects the page at a query level.

Then the queryDependentTitleSelection adjusts the title based on the specifics of the search and searcher. Primarily at the query and location-level. The best contextual match is picked.

Suggestions For Publishers

I’ll try to do this (in a vague order of precedence):

  1. Make your title stand out. Be clickable. Front-load entities. Use power words, numbers, or punctuation where applicable.
  2. Stick to 12 words and 600 pixels to avoid truncation and maximize value from each word.
  3. Your title tag better represent the content on your page effectively for people and machines.
  4. Avoid keyword stuffing. Entities in headlines = good. Search revolves around entities. People, places, and organizations are the bedrock of search and news in particular. Just don’t overdo it.
  5. Do not lean too heavily into clickbait headlines. There’s a temptation to do more for Discover at the minute. The headlines on that platform tend to sail a little too close to the clickbait wind.
  6. Make sure your title best reflects the user intent and keep things simple. The benefit of search is that people are directly looking for an answer. Titles don’t always have to be wildly clicky, especially with evergreen content. Simple, direct language helps pass titleLanguageClarity checks and reduces truncation
  7. Utilize secondary (H2s) and tertiary (H3s) headings on your page. This has multiple benefits. A well broken-up page encourages quality user engagement. It increases the chances of your article ranking for longer-tail queries. And, it helps provide the relevant context to your page for Google.
  8. Monitor CTR and run headline testing on-site. If you have the capacity to run headline testing in real-time, fantastic. If not, I suggest taking headline and CTR data at scale and building a model that helps you understand what makes a headline clickable at a subfolder or topic level. Do emotional, first-person headlines with a front-loaded entity perform best in /politics, for example?
  9. Control your internal anchor text. Particularly important for evergreen content. But even with news, there are five headlines to pay attention to. And internal links (and their anchors) are a pivotal one. The matching anchor text reinforces trust in the topic.

If you are looking into developing your Discover profile, I would recommend testing the OG title if you want to test “clickier” headlines that aren’t visible on page.

Final Thoughts

So, the goal isn’t just to have a well-crafted headline. The goal is to have a brilliant set of titles – clickable, entity and keyword rich, highly relevant. As Shaun says, it’s to create a constellation of signals – the , the </p> <h1>, the URL, the intro paragraph – that remove all ambiguity.</h1> <p>

As ever, clicks are an immensely powerful signal. Google has more data points than I’ve had hot dinners, so had a pretty good ideas what will do well. But real clicks can override this. The goldmineNavboostFactor is proof that click behavior influences which title is displayed.

The title tag is the most important headline on the page when it comes to search. More so than the

. But they have to work together. To draw people in and engage them instantly.

But it all matters. Removing ambiguity is always a good thing. Particularly in a world of AI slop.

More Resources: 

This post was originally published on Leadership In SEO.

Featured Image: Billion Photos/Shutterstock

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Oct 22 2025
SEO Is Not A Tactic. It’s Infrastructure For Growth via @sejournal, @billhunt

In the age of AI, many companies still treat SEO as a bolt-on tactic, something to patch in after the website is designed, the content is written, and the campaigns are launched. As I explored in “Why Your SEO Isn’t Working – And It’s Not the Team’s Fault,” the real obstacles aren’t a lack of knowledge or talent. They’re embedded in how companies structure ownership, prioritize resources, and treat SEO as a tactic. It’s infrastructure. And unless it’s treated as such, most organizations will never realize their full growth potential.

Search is no longer about reacting to keywords; it’s about structuring your entire digital presence to be discoverable, interpretable, and aligned with the customer journey. When done right, SEO becomes the connective tissue across content, product, and performance marketing.

Effectively Engage Intent-Driven Prospects

As I first argued in my 1994 business school thesis, and still believe today, search is the best opportunity companies have to engage “interest-driven” prospects. These are people actively declaring their needs, preferences, and intentions via a search interface. All we have to do is listen and nurture them in their journey.

When organizations structure content and infrastructure to meet that demand, they not only reduce friction – they unlock scalable demand capture.

Search:

  • Works across the funnel: awareness, consideration, conversion.
  • Reduces customer acquisition cost (CAC) by meeting customers on their terms.
  • Surfaces unmet demand signals that never show up in customer relationship management (CRM).
  • Reveals how people describe, evaluate, and compare products.
  • Can be a cost-effective tactic for removing friction by matching sales and marketing content precisely with the needs of the person seeking it.

In short, SEO gives you real-time visibility into what people want and how to serve them better. But only if the business treats it as a growth engine – not a last-minute add-on.

Case In Point: Search Left Out Of The Business

In one engagement, we analyzed 2.8 million keywords for a large enterprise with a $50 million PPC budget. The goal? Understand how well they were showing up across the full buying journey. This was a significant data and mathematical problem. For each product or service, we identified the buyer’s journey from awareness to support. We then created a series of rules to develop and classify queries representing searchers in each phase.

We could easily see the query chains of users from their first discovery query all the way through the buy cycle until they were looking for support information. It wasn’t perfect, but it did capture over 100 patterns of content types sought in different phases. By monitoring these pages and user paths, we were better able to satisfy their information needs and convert them into customers.

We checked organic rank: If the page wasn’t in the top five or had a paid ad, we counted it as having no exposure. Once we had the full picture, we saw the dysfunction clearly:

  • In the critical early non-branded discovery phase, we had no presence for nearly 400 million queries related to technologies the company sold.
  • Even more shocking, we missed 93% of 130 million queries tied to implementation-specific searches – like power specs, BTU requirements, or images for engineering diagrams.

The content existed, but it was buried in PDFs or trapped in crawl-unfriendly support sections. These were highly motivated searchers building proposals or writing budget justifications. We were making it hard for them to find what they needed.

To build our business case for change, we took all of these queries and layered in marketing qualified lead (MQL) and sales qualified lead (SQL) metrics to quantify the potential missed opportunity. Using conservative assumptions to avoid executive panic, we demonstrated that this gap represented over $580 million in unrealized revenue.

This wasn’t a content gap – it was a mindset and infrastructure failure. Search wasn’t seen as a system. It wasn’t connected to growth.

SEO As Strategic Growth Infrastructure

But what we uncovered wasn’t just a content gap but a mindset and infrastructure failure. Search wasn’t seen as a system. It wasn’t connected to growth. Organic search had been siloed into a tactical role, and paid search was framed as an acquisition driver, both disconnected from each other and from how the business grows. The result? A website optimized for internal org charts, not for how customers think, search, and decide. This is where the true value of SEO as infrastructure comes into focus. It’s not just about saving money on media; it’s about building systems that align with the full buyer journey.

When SEO is embedded into product planning, content creation, and experience design, you don’t just show up more often. You present the right content at the right time to advance the user to the next step, whether that’s deeper research, a sales inquiry, or successful onboarding. This isn’t about creating more content. It’s about orchestrating a connected, intent-responsive experience that nurtures buyers across every phase of the journey. That’s the shift from SEO-as-tactic to SEO-as-infrastructure. When treated as infrastructure, SEO provides a high-leverage system that reveals market opportunities, drives persistent visibility, and reduces acquisition costs over time.

Done right, SEO delivers:

  • Scalable, evergreen visibility across product lines and geographies.
  • Lower marginal acquisition costs as rankings compound.
  • Faster adaptation to evolving user needs and market trends.
  • Systemic alignment between product, content, and experience.

Just like investing in cloud infrastructure enables engineering agility, investing in SEO infrastructure enables commercial agility, giving product, marketing, and sales teams the insight and systems to execute faster and smarter. I believe AI search results will act as a system-wide health check: It reveals messaging gaps, content blind spots, unclear product positioning, and even operational issues that frustrate customers. It’s the clearest signal you’ll ever get about what customers want and whether you’re delivering.

And as digital maturity rises, functions once seen as tactical, like SEO, are now key contributors to:

  • Operational leverage.
  • Customer acquisition.
  • Digital product-market fit.
  • Margin protection at scale.

Technical infrastructure is a key enabler of this shift. Sites that embed SEO principles into their content management system (CMS), development workflows, and indexing architecture aren’t just faster, they’re more findable, interpretable, and durable in an AI-shaped ecosystem. It’s the technical foundation that powers business visibility.

SEO is no longer just about rankings. It’s:

  • A lens into unmet customer demand.
  • A framework for reducing acquisition costs.
  • A lever for improving digital experiences.
  • A driver of compounding traffic and long-term growth.

This mirrors the broader theme in “Closing the Digital Performance Gap” – where we argue that digital systems like SEO must be treated as capital investments, not just marketing tactics. When commissioned correctly, SEO becomes an accelerant, not a dependency. Without that mindset shift at the executive level, web performance remains fragmented.

But Isn’t SEO Dead? Let’s Clear That Up

Yes, zero-click results are rising, especially for simple facts and generic queries. But that’s not where business growth happens. Most high-value customer journeys, especially in B2B, enterprise, or considered purchases, don’t end with a snippet. They involve exploration, comparison, and validation. They require depth. They demand trust. And they often result in a click. This is even more critical with AI search providing richer information.

The users who do click after scanning AI results are often more intent-driven, more informed, and further along in the buying process. That makes it more critical – not less – to ensure your site is structured to show up, be interpreted correctly, and deliver value when it matters most. SEO isn’t dead. Lazy SEO is. The fundamentals haven’t changed: Show up when it matters, deliver what people need, and reduce friction at every touchpoint. That’s not going away – no matter how AI evolves.

Final Thought

In “From Line Item to Leverage,” we made the case that digital infrastructure, when aligned to strategy, drives measurable shareholder impact. SEO is a prime example: It compounds over time, improves capital efficiency, and scales without inflating costs. To win in today’s environment, SEO must be commissioned like infrastructure: planned early, engineered with purpose, and connected to business strategy. Because the most significant growth levers are rarely flashy – they’re usually buried under decades of organizational neglect, waiting to be unlocked as a competitive advantage.

To achieve this, organizations must move beyond silos and recognize the chain reaction between searcher needs and business outcomes. That means understanding what potential customers want, ensuring that content exists in the correct format and mode, and making it discoverable and indexable.

Search marketing can be a cost-effective tactic for removing friction by matching sales and marketing content precisely with the needs of the person seeking it. In today’s AI-first environment, search becomes even more vital. It’s your early detection system for what customers care about – and the most capital-efficient lever you have to meet them there.

More Resources:

Featured Image: Master1305/Shutterstock

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Oct 22 2025
Why Some Brands Win in AI Overviews While Others Get Ignored [Webinar] via @sejournal, @hethr_campbell

Turn Reviews Into Real Visibility, Trust, and Conversions

Reviews are no longer just stars on a page. They are key trust signals that influence both humans and AI. With AI increasingly shaping which brands consumers trust, it is critical to know the review tactics that drive visibility, loyalty, and ROI.

Join our November 5, 2025 webinar to get a research-backed playbook that turns reviews and AI into measurable gains in search visibility, conversions, and credibility.

What You Will Learn

  • How trust signals like recency, authenticity, and response style influence rankings and conversions.
  • Where consumers are reading, leaving, and acting on reviews across Google, social media, and other platforms.
  • Proven frameworks for responding to reviews that build credibility, mitigate risks, and increase loyalty.

Why You Cannot Miss This Webinar

Based on a study of over 1,000 U.S. consumers, this session translates those insights into actionable frameworks to prove ROI, protect reputation, and strengthen client retention.

Register now to learn the latest AI and review tactics that help your brand get chosen and trusted.

🛑 Can’t make it live? Sign up anyway, and we will send you the on-demand recording.

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Oct 21 2025
Surfer SEO Acquired By Positive Group via @sejournal, @martinibuster

The French technology group Positive acquired Surfer, the popular content optimization tool. The acquisition helps Positive create a “full-funnel” brand visibility solution together with its marketing and CRM tools.

The acquisition of Surfer extends Positive’s reach from marketing software to AI-based brand visibility. Positive described the deal as part of a European AI strategy that supports jobs and protects data. Positive’s revenue has grown fivefold in the past five years, rising from €50 million to an expected €70 million in 2025.

Surfer SEO

Founded in 2017, Surfer developed SEO tools based on language models that help marketers improve visibility on both search engines and AI assistants, which have become a growing source of website traffic and customers.

Sign Of Broader Industry Trends

The acquisition shows that search optimization continues to be an important part of business marketing as AI search and chat play a larger role in how consumers learn about products, services, and brands. This deal enables Positive to offer AI-based visibility solutions alongside its CRM and automation products, expanding its technology portfolio.

What Acquisition Means For Customers

Positive Group, based in France, is a technology solutions company that develops digital tools for marketing, CRM, automation, and data management. It operates through several divisions: User (marketing and CRM), Signitic (email signatures), and now Surfer (AI search optimization). The company is majority-owned by its executives, employs about 400 people, and keeps its servers in France and Germany. Surfer, based in Poland, brings experience in AI content optimization and a strong presence in North America. Together, they combine infrastructure, market knowledge, and product development within one technology-focused group.

Lucjan Suski, CEO and co-founder of Surfer, commented:

“SEO is evolving fast, and it matters more than ever before. We help marketers win the AI SEO era. Positive helps them grow across every other part of their digital strategy. Together, we’ll give marketers the complete toolkit to lead across AI search, email marketing automation, and beyond.”

According to Mathieu Tarnus, Positive’s founding president, and Paul de Fombelle, its CEO:

“Artificial intelligence is at the heart of our value proposition. With the acquisition of Surfer, our customers are moving from optimizing their traditional SEO positioning to optimizing their brand presence in the responses provided by conversational AI assistants. Surfer stands out from established market players by directly integrating AI into content creation and optimization.”

The acquisition adds Surfer’s AI optimization capabilities to Positive’s product ecosystem, helping customers improve visibility in AI-generated answers. For both companies, the deal is an opportunity to expand their capabilities in AI-based brand visibility.

Featured Image by Shutterstock/GhoST RideR 98

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Oct 21 2025
Google Announces A New Era For Voice Search via @sejournal, @martinibuster

Google announced an update to its voice search, which changes how voice search queries are processed and then ranked. The new AI model uses speech as input for the search and ranking process, completely bypassing the stage where voice is converted to text.

The old system was called Cascade ASR, where a voice query is converted into text and then put through the normal ranking process. The problem with that method is that it’s prone to mistakes. The audio-to-text conversion process can lose some of the contextual cues, which can then introduce an error.

The new system is called Speech-to-Retrieval (S2R). It’s a neural network-based machine-learning model trained on large datasets of paired audio queries and documents. This training enables it to process spoken search queries (without converting them into text) and match them directly to relevant documents.

Dual-Encoder Model: Two Neural Networks

The system uses two neural networks:

  1. One of the neural networks, called the audio encoder, converts spoken queries into a vector-space representation of their meaning.
  2. The second network, the document encoder, represents written information in the same kind of vector format.

The two encoders learn to map spoken queries and text documents into a shared semantic space so that related audio and text documents end up close together according to their semantic similarity.

Audio Encoder

Speech-to-Retrieval (S2R) takes the audio of someone’s voice query and transforms it into a vector (numbers) that represents the semantic meaning of what the person is asking for.

The announcement uses the example of the famous painting The Scream by Edvard Munch. In this example, the spoken phrase “the scream painting” becomes a point in the vector space near information about Edvard Munch’s The Scream (such as the museum it’s at, etc.).

Document Encoder

The document encoder does a similar thing with text documents like web pages, turning them into their own vectors that represent what those documents are about.

During model training, both encoders learn together so that vectors for matching audio queries and documents end up near each other, while unrelated ones are far apart in the vector space.

Rich Vector Representation

Google’s announcement says that the encoders transform the audio and text into “rich vector representations.” A rich vector representation is an embedding that encodes meaning and context from the audio and the text. It’s called “rich” because it contains the intent and context.

For S2R, this means the system doesn’t rely on keyword matching; it “understands” conceptually what the user is asking for. So even if someone says “show me Munch’s screaming face painting,” the vector representation of that query will still end up near documents about The Scream.

According to Google’s announcement:

“The key to this model is how it is trained. Using a large dataset of paired audio queries and relevant documents, the system learns to adjust the parameters of both encoders simultaneously.

The training objective ensures that the vector for an audio query is geometrically close to the vectors of its corresponding documents in the representation space. This architecture allows the model to learn something closer to the essential intent required for retrieval directly from the audio, bypassing the fragile intermediate step of transcribing every word, which is the principal weakness of the cascade design.”

Ranking Layer

S2R has a ranking process, just like regular text-based search. When someone speaks a query, the audio is first processed by the pre-trained audio encoder, which converts it into a numerical form (vector) that captures what the person means. That vector is then compared to Google’s index to find pages whose meanings are most similar to the spoken request.

For example, if someone says “the scream painting,” the model turns that phrase into a vector that represents its meaning. The system then looks through its document index and finds pages that have vectors with a close match, such as information about Edvard Munch’s The Scream.

Once those likely matches are identified, a separate ranking stage takes over. This part of the system combines the similarity scores from the first stage with hundreds of other ranking signals for relevance and quality in order to decide which pages should be ranked first.

Benchmarking

Google tested the new system against Cascade ASR and against a perfect-scoring version of Cascade ASR called Cascade Groundtruth. S2R beat Cascade ASR and very nearly matched Cascade Groundtruth. Google concluded that the performance is promising but that there is room for additional improvement.

Voice Search Is Live

Although the benchmarking revealed that there is some room for improvement, Google announced that the new system is live and in use in multiple languages, calling it a new era in search. The system is presumably used in English.

Google explains:

“Voice Search is now powered by our new Speech-to-Retrieval engine, which gets answers straight from your spoken query without having to convert it to text first, resulting in a faster, more reliable search for everyone.”

Read more:

​​Speech-to-Retrieval (S2R): A new approach to voice search

Featured Image by Shutterstock/ViDI Studio

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Oct 20 2025
Review Of AEO/GEO Tactics Leads To A Surprising SEO Insight via @sejournal, @martinibuster

GEO/AEO is criticized by SEOs who claim that it’s just SEO at best and unsupported lies at worst. Are SEOs right, or are they just defending their turf? Bing recently published a guide to AI search visibility that provides a perfect opportunity to test whether optimization for AI answers recommendations is distinct from traditional SEO practices.

Chunking Content

Some AEO/GEO optimizers are saying that it’s important to write content in chunks because that’s how AI and LLMs break up a pages of content, into chunks of content. Bing’s guide to answer engine optimization, written by Krishna Madhavan, Principal Product Manager at Bing, echoes the concept of chunking.

Bing’s Madhavan writes:

“AI assistants don’t read a page top to bottom like a person would. They break content into smaller, usable pieces — a process called parsing. These modular pieces are what get ranked and assembled into answers.”

The thing that some SEOs tend to forget is that chunking content is not new. It’s been around for at least five years. Google introduced their passage ranking algorithm back in 2020. The passages algorithm breaks up a web page into sections to understand how the page and a section of it is relevant to a search query.

Google says:

“Passage ranking is an AI system we use to identify individual sections or “passages” of a web page to better understand how relevant a page is to a search.”

Google’s 2020 announcement described passage ranking in these terms:

“Very specific searches can be the hardest to get right, since sometimes the single sentence that answers your question might be buried deep in a web page. We’ve recently made a breakthrough in ranking and are now able to better understand the relevancy of specific passages. By understanding passages in addition to the relevancy of the overall page, we can find that needle-in-a-haystack information you’re looking for. This technology will improve 7 percent of search queries across all languages as we roll it out globally.”

As far as chunking is concerned, any SEO who has optimized content for Google’s Featured Snippets can attest to the importance of creating passages that directly answer questions. It’s been a fundamental part of SEO since at least 2014, when Google introduced Featured Snippets.

Titles, Descriptions, and H1s

The Bing guide to ranking in AI also states that descriptions, headings, and titles are important signals to AI systems.

I don’t think I need to belabor the point that descriptions, headings, and titles are fundamental elements of SEO. So again, there is nothing her to differentiate AEO/GEO from SEO.

Lists and Tables

Bing recommends bulleted lists and tables as a way to easily communicate complex information to users and search engines. This approach to organizing data is similar to an advanced SEO method called disambiguation. Disambiguation is about making the meaning and purpose of a web page as clear as possible, to make it less ambiguous.

Making a page less ambiguous can incorporate semantic HTML to clearly delineate which part of a web page is the main content (MC in the parlance of Google’s third-party quality rater guidelines) and which part of the web page is just advertisements, navigation, a sidebar, or the footer.

Another form of disambiguation is through the proper use of HTML elements like ordered lists (OL) and the use of tables to communicate tabular data such as product comparisons or a schedule of dates and times for an event.

The use of HTML elements (like H, OL, and UL) give structure to on-page information, which is why it’s called structured information. Structured information and structured data are two different things. Structured information is on the page and is seen in the browser and by crawlers. Structured data is meta data that only a bot will see.

There are studies that structured information helps AI Agents make sense of a web page, so I have to concede that structured information is something that is particularly helpful to AI Agents in a unique way.

Question And Answer Pairs

Bing recommends Q&A’s, which are question and answer pairs that an AI can use directly. Bing’s Madhavan writes:

“Direct questions with clear answers mirror the way people search. Assistants can often lift these pairs word for word into AI-generated responses.”

This is a mix of passage ranking and the SEO practice of writing for featured snippets, where you pose a question and give the answer. It’s a risky approach to create an entire page of questions and answers but if it feels useful and helpful then it may be worth doing.

Something to keep in mind is that Google’s systems consider content lacking in unique insight on the same level of spam. Google also considers content created specifically for search engines as low quality as well.

Anyone considering writing questions and answers on a web page for the purpose of AI SEO should first consider the whether it’s useful for people and think deeply about the quality of the question and answer pairs. Otherwise it’s just a page of rote made for search engine content.

Be Precise With Semantic Clarity

Bing also recommends semantic clarity. This is also important for SEO. Madhavan writes:

  • “Write for intent, not just keywords. Use phrasing that directly answers the questions users ask.
  • Avoid vague language. Terms like innovative or eco mean little without specifics. Instead, anchor claims in measurable facts.
  • Add context. A product page should say “42 dB dishwasher designed for open-concept kitchens” instead of just “quiet dishwasher.”
  • Use synonyms and related terms. This reinforces meaning and helps AI connect concepts (quiet, noise level, sound rating).”

They also advise to not use abstract words like “next-gen” or “cutting edge” because it doesn’t really say anything. This is a big, big issue with AI-generated content because it tends to use abstract words that can completely be removed and not change the meaning of the sentence or paragraph.

Lastly, they advise to not use decorative symbols, which is good a tip. Decorative symbols like the arrow → symbol don’t really communicate anything semantically.

All of this advice is good. It’s good for SEO, good for AI, and like all the other AI SEO practices, there is nothing about it that is specific to AI.

Bing Acknowledges Traditional SEO

The funny thing about Bing’s guide to ranking better for AI is that it explicitly acknowledges that traditional SEO is what matters.

Bing’s Madhavan writes:

“Whether you call it GEO, AIO, or SEO, one thing hasn’t changed: visibility is everything. In today’s world of AI search, it’s not just about being found, it’s about being selected. And that starts with content.

…traditional SEO fundamentals still matter.”

AI Search Optimization = SEO

Google and Bing have incorporated AI into traditional search for about a decade. AI Search ranking is not new. So it should not be surprising that SEO best practices align with ranking for AI answers. The same considerations also parallel with considerations about users and how they interact with content.

Many SEOs are still stuck in the decades-old keyword optimization paradigm and maybe for them these methods of disambiguation and precision are new to them. So perhaps it’s a good thing that the broader SEO industry catches up with many of these concepts for optimizing content and to recognize that there is no AEO/GEO, it’s still just SEO.

Featured Image by Shutterstock/Roman Samborskyi

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Oct 17 2025
Google Says What Content Gets Clicked On AI Overviews via @sejournal, @martinibuster

Google’s Liz Reid, Vice President of Search, recently said that AI Overviews shows what kind of content makes people click through to visit a site. She also said that Google expanded the concept of spam to include content that does not bring the creator’s perspective and depth.

People’s Preferences Drives What Search Shows

Liz Reid affirmed that user behavior tells them what kinds of content people want to see, like short-form videos and so on. That behavior causes Google to want to show that to them and that the system itself will begin to learn and adjust to the kinds of content (forums, text, video, etc.) that they prefer.

She said:

“…we do have to respond to who users want to hear from, right? Like, we are in the business of both giving them high quality information, but information that they seek out. And so we have over time adjusted our ranking to surface more of this content in response to what we’ve heard from users.

…You see it from users, right? Like we do everything from user research to we run an experiment. And so you take feedback from what you hear, from research about what users want, you then test it out, and then you see how users actually act. And then based on how users act, the system then starts to learn and adjust as well.”

The important insight is that user preferences play an active role in shaping what appears in AI search results. Google’s ranking systems are designed to respond not just to quality but to the types of content users seek out and engage with. This means that shifts in user behavior related to content preferences directly influence what is surfaced. The system continuously adapts based on real-world feedback. The takeaway here is that SEOs and creators should actively gauge what kind of content users are engaging with and be ready to pivot in response to changes.

The conversation is building up toward where Reid says exactly what kinds of content engages users, based on the feedback they get through user behavior.

AI-Generated Is Not Always Spam

Liz next affirms that AI-generated content where she essentially confirms that the bar they’re using to decide what’s high and low quality is agnostic to whether the content is created by a human or an AI.

She said:

“Now, AI generated content doesn’t necessarily equal spam.

But oftentimes when people are referring to it, they’re referring to the spam version of it, right? Or the phrase AI slop, right? This content that feels extremely low value across, okay? And we really want to make an effort that that doesn’t surface.”

Her point is pretty clear that all content is judged by the same standard. So if content is judged to be low quality, it’s judged based on the merits of the content, not by the origin.

People Click On Rich Content

At this point in the interview Reid stops talking about low quality content and turns to discussing the kind of content that makes people click through to a website. She said that user behavior tells them that users don’t want superficial content and that the click patterns shows that more people click through to content that has depth, expresses a unique perspective that does not mirror what everyone else is saying and that these kinds of content engages users. This is the kind of content that gets clicks on AI search.

Reid explained:

“But what we see is people want content from that human perspective. They want that sense of like, what’s the unique thing you bring to it, okay? And actually what we see on what people click on, on AI Overviews, is content that is richer and deeper, okay?

That surface-level AI generated content, people don’t want that because if they click on that, they don’t actually learn that much more than they previously got. They don’t trust the result anymore.

So what we see with AI Overviews is that we surface these sites and get fewer what we call bounce clicks. A bounce click is like you click on your site, Yeah, I didn’t want that, and you go back.

AI Overviews gives some content, and then we get to surface deeper, richer content, and we’ll look to continue to do that over time so that we really do get that creator content and not the AI generated.”

Reid’s comments indicate that click patterns indicate content offering a distinct perspective or insight derived from experience performs better than low-effort content. This indicates that there is intention within AI Overviews to not amplify generic output and to uprank content that demonstrates a firm knowledge of the topic.

Google’s Ranking Weights

Here’s an interesting part that explains what gets up-ranked and down-ranked, expressed in a way I’ve not seen before. Reid said that they’ve extended the concept of spam to also include content that repeats what’s already well known. She also said that they are giving more ranking weight to content that brings a unique perspective or expertise to the content.

Here Reid explains the downranking:

“Now, it is hard work, but we spend a lot of time and we have a lot of expertise built on this such that we’ve been able to take the spam rate of what actually shows up, down.

And as well as we’ve sort of expanded beyond this concept of spam to sort of low-value content, right? This content that doesn’t add very much, kind of tells you what everybody else knows, it doesn’t bring it…”

And this is the part where she says Google is giving more ranking weight to content that contains expertise:

“…and tried to up-weight more and more content specifically from someone who really went in and brought their perspective or brought their expertise, put real time and craft into the work.”

Takeaways

How To Get More Upranked On AI Overviews

1. Create “Richer and Deeper” Content

Reid said, “people want content from that human perspective. They want that sense of like, what’s the unique thing you bring to it, okay? And actually what we see on what people click on, on AI Overviews, is content that is richer and deeper, okay?”

Takeaway:
Publish content that shows original thought, unique insights, and depth rather than echoing what’s already widely said. In my opinion, using software that analyzes the content that’s already ranking or using a skyscraper/10x content strategy is setting yourself up for doing exactly the opposite of what Liz Reid is recommending. A creator will never express a unique insight by echoing what a competitor has already done.

2. Reflect Human Perspective

Reid said, “people want content from that human perspective. They want that sense of like, what’s the unique thing you bring to it.”

Takeaway: Incorporate your own analysis, experiences, or firsthand understanding so that the content is authentic and expresses expertise.

3. Demonstrate Expertise and Craft

Reid shared that Google is trying “to up-weight more and more content specifically from someone who really went in and brought their perspective or brought their expertise, put real time and craft into the work.”

Takeaway:
Effort, originality, and subject-matter knowledge are the qualities that Google is up-weighting to perform better within AI Overviews.

Reid draws a clear distinction between content that repeats what is already widely known and content that adds unique value through perspective or expertise. Google treats superficial content like spam and lowers the weights of the rankings to reduce its visibility, while actively “upweighting” content that demonstrates effort and insight, what she termed as the craft. Craft means skill and expertise, mastery of something. The message here is that originality and actual expertise are important for ranking well, particularly in AI Overviews and I would think the same applies for AI Mode.

Watch the interview from about the 18 minute mark:

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