Six months ago, you could spot AI-generated text by its polished grammar, rigid essay structure, suspicious fondness for em dashes – and, of course, the inevitable emoji bullets (🔥🚀✨). The real giveaway, at least to my eye and ear, isn’t the emojis or the punctuation. It’s the cadence.

AI writing has a rhythm problem. The sentences are clipped. Overly dramatic. Split into one-line paragraphs that feel more like infomercials than journalism.

“The truth? This wasn’t SEO causation. It was a stock market correction.”
“They were left behind. They were angry. They weren’t your people.”

On the page, this is nails-on-chalkboard grating. It doesn’t read as conversational. It reads as performative. In my opinion, this is, without a doubt, AI’s most recognizable stylistic fingerprint.

A Brief History Of The AI Cadence

This rhythm predates AI. It has been the language of speechwriters, preachers, and copywriters long before GPT entered the chat. Think Reagan’s addresses, Clinton’s campaign rallies, Obama’s campaign speeches, Churchill’s wartime broadcasts, and Blair’s conference speeches. Each leaned on rhythm and repetition to generate a great deal of emotion out of a speck of substance. Pair that with Captain Kirk’s famously staccato delivery, televangelists’ sermons, or TED Talks built around dramatic pauses, and you see how cadence can make small or mundane ideas feel powerful and deep.

That style used to stay in its lane. Where print valued density and clarity, speech valued brevity and rhythm. Readers could re-read; listeners could not. Editors enforced writing standards and styles and the economics of print rewarded information density over theatrics. As a result, this cadence lived solely in spoken word. It lived in speeches and sales copy, and not in essays and articles.

AI collapsed those boundaries. Because LLMs cannot (or chose to not) differentiate between a stump speech, a YouTube transcript, and a white paper, they overindex patterns designed to persuade aloud and repurpose them for the written page. Now, we are inundated with technical articles that read like motivational talks.

Why AIs Default To This Cadence

The AI cadence is not an accident – it’s a reflection of what models were most heavily trained on. Large language models have been fed a disproportionate amount of spoken-word material: transcripts of speeches, news reports, debates, interviews, webinars, podcasts, and video scripts. These aren’t “written texts” in the traditional sense; they are spoken performances converted into text.

Why so much spoken-word data? Because it’s cheap and plentiful. Back when I was running my ISP, I loved radio and TV for advertising and news mentions because it was far less expensive than buying or winning space in print. Broadcasters had 24 hours a day to fill, and local stations were always desperate for content. Print, on the other hand, is expensive. Every page of a newspaper, magazine, or book costs money to produce, and publishers limit content to what is necessary or affordable. As a result, far more hours of audio and video have been produced than carefully edited prose — and much of that material ends up transcribed. Those transcripts give the models a vast mountain of “written-down speech” compared to a relatively smaller body of curated, edited text.

The difference is subtle but important: a transcript is in a written medium, but it is not writing in a written style. It preserves the cadence of spoken delivery — short bursts, rhetorical pauses, fragments. Models overindex this rhythm because it dominates the dataset.

Even when prompted to avoid it, the models can’t resist drifting back into this rhythm. They might manage a few sentences of varied prose, but the gravitational pull of the AI cadence always drags them back. It’s now the default groove burned into their training.

The Em Dash Problem

That overindexing also explains a related AI tell: the sudden overuse of em dashes. In polished writing, dashes were historically used sparingly for emphasis or interruption. In speech, however, pauses are constant. Transcripts often mark those pauses with dashes. For a model swimming in transcripts, the dash becomes a default punctuation mark, because it functions as the written equivalent of a spoken pause. The result is copy littered with dashes – not because the ideas require them, but because the training data normalized them.

Punctuation As Breath

Punctuation has always been about more than grammar. Periods, commas, and dashes are signals for how we pause and where we breathe. They are like rests in music, telling the reader when to stop, inhale, and reset before continuing. Well-edited prose balances those pauses so the rhythm feels natural.

The AI cadence breaks this balance. When every thought is chopped into fragments, you’re effectively told to breathe after every line. Reading an article like this feels like hyperventilating: shallow breaths, constant interruptions, no sustained flow. It makes everything sound catastrophic, urgent, or world-shattering, even when the subject matter is mundane. Gentle readers, not every sentence or every idea warrants that level of drama.

Where this leaves us is that when models generate text, they parrot back the structures they’ve seen most often: speech rhythms and speech punctuation, presented as though they were the standard for written communication. They are not. They’re salesmanship with line breaks and pauses dressed up as prose.

Why Readers React To It

This cadence feels powerful at first. It mimics natural speech. It creates rhythm. It feels dramatic without requiring depth. That’s why it pops in feeds.

However, the longer it is stretched out, like in long-form content, or the more a reader is exposed to the same cadence over and over and over again, the power you once felt collapses into disdain. This breathy, short-sentence delivery leads to:

• Oversimplification which flattens nuance.
• Repetition that manipulates more than it informs.
• Every line to demand attention ensuring none of them earn it.
• Readers to suspect style is substituting for substance.

Here is the deeper problem: when everything is delivered as if it were earth-shattering, readers begin to doubt the authenticity of the message itself. It’s Syndrome’s hypothesis in The Incredibles: “When everyone is super, no one is.” If every sentence screams urgency, then nothing actually carries weight.

Historically, this kind of relentless, crisis-driven cadence has also been a manipulation tactic. Political demagogues, televangelists, and snake-oil salesmen leaned on hyperbole precisely because they lacked evidence. When AI reproduces that same rhythm on the page, it inherits the credibility problem too. Readers may not articulate it consciously, but they feel it: if you have to shout every line, maybe you don’t have enough substance to stand on quietly.

Just as keyword stuffing once became a hallmark of low-quality SEO, this cadence is already becoming the hallmark of low-quality AI. Readers recognize the rhythm before they absorb the message. When the medium distracts from the message, trust erodes.

A Tale Of Two Paragraphs

AI cadence in practice:

“The algorithm changed.
Sites lost traffic.
Panic spread.
And the industry?
It declared SEO dead – again.”

Now, the same idea written for readers:

“When the algorithm changed, many sites saw a drop in traffic. The panic was predictable. Within days, familiar headlines declared SEO dead once again. The cycle repeats every few years, and every few years it proves wrong.”

The difference here is obvious: one is an infomercial and the other is writing.

How To Spot It

Editors and readers can train themselves to notice:

• Long runs of one-sentence paragraphs.
• Rhetorical questions with no depth (often beginning with conjunctions like And or But…
• Sentence fragments pretending to be profound.
• Sermon-like pacing that seems to expect a chorus of ‘amens’ (or applause, if you’re lucky)…

Simply put, once you have seen it, you cannot unsee it: it is the literary equivalent of a laugh track.

How To Write Like A Human Again

How do we remedy this situation? Short of, I suppose, doing our own writing?

• Vary sentence length instead of defaulting to extremes.
• Use rhetorical questions sparingly – only when they genuinely add depth.
• Group related ideas into paragraphs; readers can handle more than one sentence at a time. Unless you are writing FOR toddlers, do not treat your readers as though they ARE toddlers.
• Prioritize clarity and voice over performative drama. Note here that the goal isn’t to sound casual at all costs, but to sound intentional, rational, and backed by data.

Why It Matters For SEOs And Marketers

AI writing tools are embedded in nearly every workflow. Left unchecked, they will flood the web with copy that reads like an endless sales pitch. Professionals must edit not just for facts but for voice.

That means:

• Training teams to recognize and break the AI cadence.
• Creating style guides that emphasize varied sentence and paragraph structure.
• Editing AI drafts with rhythm in mind, not just keywords.
• Writing for humans who read – not just platforms that skim.

Respecting the reader’s time and intelligence is, in the end, the real optimization.

Is There Ever A Place For This Style?

Yes, of course, but like most things, in moderation. Staccato writing is effective for:

• Ad copy where space is limited.
• Video scripts where pacing drives attention. (Your LinkedIn vertical videos and IG Reels? Have at it. This is where the staccato AI cadence shines.)
• The occasional LinkedIn post engineered for scanning.

However, should this become the default writing style for articles, blogs, or essays? Abso-effing-lutely not. It cheapens the content and undermines credibility.

In Closing

AI has introduced more than just new tools. It has also normalized certain stylistic tics that don’t belong in most forms of writing. Among these, the AI cadence problem is the most recognizable and the most damaging when left unchecked.

Writers, editors, and marketers need to treat the presence of AI cadence in their writings the same way we treated keyword stuffing a decade ago: as a major red flag. The difference between human and AI writing isn’t just factual accuracy. It’s rhythm, intent, and voice.

The real divide isn’t human versus machine. It’s generic versus intentional. Intentional writing that is structured for clarity, rooted in substance, and respectful of the reader will always stand out.

More Resources:

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Featured Image: N Universe/Shutterstock

https://www.searchenginejournal.com/and-the-truth-this-writing-style-screams-ai/555854/

Ahrefs’ Tim Soulo recently posted that AI is making publishing evergreen content obsolete and no longer worth the investment because AI summaries leave fewer clicks for publishers.  He posits that it may be more profitable to focus on trending topics, calling it Fast SEO.  Is publishing evergreen content no longer a viable content strategy?

The Reason For Evergreen Content

Evergreen content can be a basic topic that generally doesn’t change much from year to year. For example, the answer to how to change a tire will generally always be the same.

The promise of evergreen content was that it represents a steady source of traffic. Once a web page is ranking for evergreen topics, publishers basically just have to make sure that it’s updated if the topic has changed in some way.

Does AI Break The Evergreen Content Promise?

Tim Soulo is suggesting that evergreen content, which can be easy to answer with a summary, is less likely to send a click because AI summarizes the answer and satisfies the user, who may not need to visit a website.

Soulo tweeted:

“The era of “evergreen SEO content” is over. We’re entering the era of “fast SEO.”

There’s little point in writing yet another “Ultimate Guide To ___.” Most evergreen topics have already been covered to death and turned into common knowledge. Google is therefore happy to give an AI answer, and searchers are fine with that.

Instead, the real opportunity lies in spotting and covering new trends — or even setting them yourself.”

Is Fast SEO The Future Of Publishing?

Fast SEO is another way of describing trending topics. Trending topics have always been around; it’s why Google invented the freshness algorithm, to satisfy users with up-to-date content when a “query deserves freshness.”

Soulo’s idea is that trending topics are not the kind of content that AI summarizes. Perplexity is the exception; it has an entire content discovery section called Perplexity Discover that’s dedicated to showing trending news articles.

Fast SEO is about spotting and seizing short-lived content opportunities. These can be new developments, shifts in the industry or perceptions, or cultural moments.

His tweet captures the current feeling within the SEO and publishing communities that AI is the reason for diminishing traffic from Google.

The Evergreen Content Situation Is Worse Than Imagined

A technical issue that Soulo didn’t mention but is relevant here is that it’s challenging to create an “Ultimate Guide To X, Y, Z” or the “Definitive Guide To Bla, Bla, Bla” and expect it to be fresh and different from what is already published.

The barrier to entry for evergreen content is higher now than it’s ever been for several reasons:

• There are more people publishing content.
• People are consuming multiple forms of content (text, audio, and video).
• Search algorithms are focused on quality, which shuts out those who focus harder on SEO than they do on people.
• User behavior signals are more reliable than traditional link signals, and SEOs still haven’t caught on to this, making it harder to rank.
• Query Fan-Out is causing a huge disruption in SEO.

Why Query Fan-Out Is A Disruption

Evergreen content is an uphill struggle, compounded by the seeming inevitability that AI will summarize the content and, because of Query Fan-Out, possibly send the click to another website that is cited because it offers the answer to a follow-up question to the initial search query.

Query Fan-Out displays answers to the initial query and to follow-up questions to the initial search query. If the user is happy with the summary to the initial query, they may become interested in one of the follow-up queries, and one of those will get the click, not the initial query.

This completely changes what it means to target a search query. How does an SEO target a follow-up question? Maybe, instead of targeting the main high-traffic query, it may make sense to target the follow-up queries with evergreen content.

Evergreen Content Publishing Still Has Life

There is another side to this story, and it’s about user demand. Foundational questions stick around for a long time. People will always search “how to tie a bowtie” or “how to set up WordPress.” Many users prefer the stability of an established guide that has been reviewed and updated by a trusted brand. It’s not about being a brand; it’s about being the kind of site that is trusted, well-liked, and recommended.

A strong resource can become the canonical source for a topic, ranking for years and generating the kind of user behavior signals that reinforce its authority and signal the quality of being trusted.

Trend-driven content, by contrast, often delivers only a brief spike before fading. A newsroom model is difficult to maintain because it requires constant work to be first and be the best.

The Third Way: Do It All

The choice between producing evergreen content and trending topics doesn’t have to be binary; there’s a third option where you can do it all. Evergreen and trending topics can complement each other because each side provides opportunities for driving traffic to the other. Fresh, trend-driven content can link back to the evergreen, and this can be reversed to send readers to fresh content from the evergreen.

Trend-driven content sometimes becomes evergreen itself. But in general, creating evergreen content requires deep planning, quality execution, and marketing. Somebody’s going to get the click from evergreen content, it might as well be you.

Featured Image by Shutterstock/Stokkete

https://www.searchenginejournal.com/are-ai-search-summaries-making-evergreen-articles-obsolete/556721/

Here’s what I’m covering this week: How to get the most out of personas in your day-to-day work across SEO, content, and the broader org.

Because in the AI-search era, personas built from organic queries and prompts have value for every touchpoint: ad copy, sales scripts, support docs, product messaging.

They carry the unfiltered language of your audience (their fears, hesitations, and demands) straight into the hands of the teams shaping your funnel.

If you’re not operationalizing search-data-based personas across departments, you’re missing one of the few forms of market intelligence that scale across SEO, marketing, sales, and product.

Personas shouldn’t live stagnantly in a slide deck. I’ll show you how to make them pull their weight across the org.

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Last week, I showed you how to create search personas based on data you already have available, along with how to use an LLM-ready persona card to extract custom insights.

But the best persona in the world doesn’t help if it collects dust in your Google Drive.

This week, I’m digging into how to make these search persona insights actionable – not only across your SEO processes and production, but also across broader teams that SEO work touches.

However, before we dive in, I want to share a few notable perspectives on search personas that came up in conversation on this LinkedIn thread:

Malte Landwehr, CPO & CMO at Peec AI, gave this visual example in the thread (with additional context) that resonated strongly. From his own research and testing, he shared a visual detailing LLM visibility for various headphones based on prompts for personas and use cases.

The findings? LLMs recommended different brands/products based on different persona-based prompts.

And below, David Melamed brings up an interesting and important question below.

I agree with David: The more personalized search results are, the less you can segment or generalize across a group.

But if you check out our conversation in the comments, David absolutely gets it, and his concerns are valid.

He shares that “more long tail content and citations across more unique niches, scenarios and comparisons should beat out persona driven content” and that “looking at questions, related searches in search console, and Google and Microsoft ads search term reports… [along with] experience and other voice of customer research (listening to calls, analyzing reviews, reddit threads, complaints, etc..)” would be a helpful approach.

And that’s what I tackled last week in Personas are critical for AI search (part 1 on the persona topic): To succeed with user personas for SEO – and make them valuable and usable – the goal is to build custom, unique search personas from your actual in-house data and long-tail Google Search Console.

So, David brought up a valid point, one that’s aligned with how we should be building useful search personas for today.

Lastly, Elisa Daniela Montanari sums up how a lot of us feel about the shift toward qualitative research (along with mentioning her goals to upskill as an SEO by diving into user research tactics):

And with these conversations in mind…

I’d argue that high-quality, customer-centered SEO research captures unfiltered questions, painpoints, and intents at scale, across the entire journey – and that makes it one of the most versatile forms of market intelligence that you can use across your brand as a whole.

So if organic query and prompt research is so valuable and versatile, how do you ensure they’re actually used?

Because all strategists everywhere have had that stupidly challenging moment: After doing all the labor-intensive data-gathering of building user personas for SEO, it’s time to get your team or clients to use those insights regularly across SEO production.

You need to prep your findings so they’re not left gathering cobwebs in the dark corners of the cloud.

1. Create An Internal Knowledge Hub For Core Search Personas

Not another slide deck or spreadsheet that gathers dust. A simple, easily-accessible hub that is a living, breathing document.

Translate data into the formats your team and stakeholders already use: dashboards, one-page briefs, funnel visualizations.

Think Notion, Airtable, Asana, Google Sheets, Slack Canvas – wherever your team is already working and discussing production.

Key contributors need to have access to fluidly comment and update as organic questions and pain points surface across your audience.

2. Build A Clear Narrative Around How And Why Using These Personas Is Valuable

Position SEO research/persona use as a “horizontal competency” that makes every department smarter.

Kick off persona use with a short session showing:

• Real queries from your personas.
• How those queries reveal pain points, objections, or jobs-to-be-done.
• Where competitors are (or aren’t) meeting those needs.
• Inform the team on how users are interacting with AI-based search results (see Trust Still Lives in Blue Links for details on the four AIO intent patterns).

A three-minute Loom video can do wonders.

Use the data you have (Google Search Console, Semrush, Ahrefs, LLM prompt monitoring tools) to back up the importance of use.

At the end of this memo, I have a slide deck template for premium subscribers that will help you build this narrative and guide effective persona implementation across teams.

3. Train Contributors On How Personas Will Be Used Across Production – And Follow Through

Train your SEO/content contributors that personas don’t just shape blog posts – they inform all communication touchpoints in the customer journey.

If you’re also using search personas to inform your sales and customer care team interactions (and you should – more on that below), create examples of how to use personas across all communication channels.

Highlight missed opportunities (e.g., ad copy vs. organic messaging mismatch, customer support docs hidden from search, sales scripts that could benefit).

And although this means extra work for leaders, managers, or editors, this part is crucial: Let your team know that briefs that don’t specify personas will be rejected or sent back for revision. That also goes for drafts that don’t speak directly to defined personas and their search behaviors/needs.

Yes, it’s an added step on an often-already-overloaded plate of a marketer, but this is how you ensure they’re successfully implemented across your work over time.

Here’s where your personas stop being a strategy deck or training session and start shaping what users experience.

1. Incorporate Persona Data Into Every Content Brief

Your search persona data is there to help you direct every brief beyond target queries and products/services features to mention.

Use it to inform your content producers of the following:

• Unique, data-backed pain points.
• Real customer/lead questions that need answering.
• Proof points needed to reduce hesitation.
• What authority signals resonate with your target reader.
• Behaviors that impact interactions with the page.
• Copy on the page.

In every content brief, flag actual language from queries, call transcripts, or reviews that should be used on the page. Create a copy bank that’s tagged into your content briefs that your writers, editors, and LLMs can pull from.

For example, if your persona says “integration headaches,” don’t water it down to “implementation challenges.” Use their words.

2. Use Search Persona Data To Inform Page Structure

Match the flow of the page to how specific personas are likely to consume information.

Some personas need trust-driven validation upfront (editorial quality signals, branded logos, stats, testimonials). Others need efficiency first, then a CTA.

Here’s a practical way to estimate what each of your search personas needs on the page:

• Follow guidance (and use the regex) provided in Personas are critical for AI search to extract GSC long-tail queries that can contain indicators of specific search personas.
• Select a specific URL or page that comes up for multiple long-tails for a consistent search persona type.
• Examine on-page user scrolling and clicking behavior via your heatmap tool.
• Look for places users pause, scroll past, or toggle back and forth between information. Strong behavioral patterns (skips, hesitations, long-tread times) point to places to better optimize page structure based on search persona type.

Once you’re done gathering information based on user behavioral patterns, audit your on-page modules, formats, and design capabilities to ensure you have all pieces needed to create pages that fulfill those specific needs.

Enlist your product and/or web design team to create what’s needed to serve a better on-page experience.

Then, include direction in each brief of what sort of modules and information structuring is needed based on search persona type.

3. Map To Topic Clusters In The Brief

Specific search personas naturally gravitate toward certain topics or proof points.

A searcher who uses technical language for their queries may cluster around integrations and APIs and need to see clear documentation is available for how to use them, while a user with economic or decision-making intent may cluster around ROI topics.

Build semantically related internal linking paths that explicitly connect those journeys for your SEO personas. Use your topic map (if you’ve built one) and revisit your keyword universe as needed.

4. Personas Should Inform Your AI-Assisted Workflows

Use search persona details as inputs to LLM prompts and/or incorporate them into your AI-assisted content generation, like AirOps workflows.

Instead of “write an article about X with the search intent of Y,” frame it as “write for a skeptical buyer evaluating vendors – include comparisons and third-party validation.”

Or better yet? Use your persona cards (see Personas Are Crucial for AI Search for a detailed guide) to help guide additional prompts personas might use in LLMs when attempting to solve queries related to your brand.

Below, take a look at how this could work in practice, using the four distinct AIO intent patterns from the additional analysis of the UX study of AIOs found in Trust Still Lives in Blue Links:

1. Efficiency-first validations that reward clean, extractable facts (accepting of AIOs).
2. Trust-driven validations that convert only with credibility (validate AIOs).
3. Comparative validations that use AIOs but compare with multiple sources.
4. Skeptical rejections that automatically distrust AIOs for high-stakes queries.

Let’s say you work for a fintech startup that provides easy-to-use business insurance for small to midsize businesses.

Here’s how you might use personas to inform content production for efficiency-first and trust-driven search behaviors:

Example 1: Junior operations coordinator at a 20-person marketing agency → accepting of AIOs (efficiency-first) → queries “What’s the average cost of business insurance for a 20-person company?” → Likely to validate range via the AIO → Takeaway for your brand: Create content geared to businesses with small teams and/or junior learners that includes straightforward facts and ranges that are easily extractable, so it’s cited in AIOs. Make your pricing explanations scannable and structured. Internally link to other knowledge guides for project managers or operations leads at small to midsize businesses.

Example 2: Small business owner in healthcare services → validate AIOs with second-clicks (trust-driven) → queries “Do I need business insurance for HIPAA compliance?” → Likely to read the AIO but won’t act until they see credible signals → citations from legal/insurance authorities → Takeaway for your brand: Position your content with authoritative references (link to .gov or .org sources) and highlight compliance expertise so your page is validated by trust; include case studies and/or social proof of authority; Internally link to other guides for healthcare service businesses.

How To Know Search Persona Implementation Is Working

Watch for these signals:

• Higher engagement time and more downstream actions on the page.
• Lower bounce rates on persona-driven pages.
• More citations and visibility in AIOs and LLM outputs (your copy matches how users ask questions).
• Increased assisted conversions: Pages designed for a specific persona show up more often in multi-touch journeys or are incorporated strategically and/or organically into follow-up communications by sales/customer teams.
• Sales/Customer service team feedback loop: Fewer “this didn’t answer my question” moments.

Amanda jumping in here: In March of this last year, I led one of my clients to pivot hard to persona-focused content. Not only have we seen an increase in AIO inclusion, AI Mode citations, and LLM visibility for these niche terms, but we’ve also experienced a boost in visits to our core guides that were geared toward our broader audience. After this pivot, we’re seeing anywhere between a 20-60% month-over-month increase in organic visits from ChatGPT, and a ~40% increase month-over-month in visible AIO inclusion, to include our older core content as well. Although some of this growth is likely due to increased overall ChatGPT adoption and increase in Google’s use of AIOs across queries, here’s the takeaway (and my hypothesis): As you create niche content for personas, it’s possible you could also see a lift in your core content as it’s served to these specific groups of searchers – based on what these tools know about (1) the end user and (2) who your brand serves best. But only time (and more experiments) will truly tell.

The reality is, no matter how well you implement search personas into your SEO and content production, SEO and growth marketing teams can’t win on their own.

Search personas have the real opportunity to contribute to results when the rest of the org picks them up and runs with them throughout lead and customer touchpoints.

The trick is to make it dead-simple for every team to see why personas matter for their work and how to apply them.

Plus, a big advantage of bringing other teams on board is that SEO-driven personas – built from real search queries, prompts, social chatter, and call transcripts – arm everyone with the exact language customers use.

That means you can reduce hesitations, preemptively answer questions, and build trust across every channel of communication.

Below, here’s a quick list of guidance to help you collaborate with other teams on how to use search persona data.

And in the next section, I’ll jump into how to create intentional feedback loops so your personas stay fresh, useful, and relevant.

Email Marketing

• Work with email teams to trigger sequences based on persona signals (query intent by pages visited, topics visited).
• Example: If someone hits three pricing-related pages, route them into a nurture path designed for a search-data-informed persona that includes supportive content often visited by those users.
• Benefit: Aligns your SEO insights with lifecycle marketing, reducing drop-off between discovery and conversion.

Paid Media And Advertising

• Lift search-persona informed language directly into ad copy → track if it increases CTR because you’re speaking the way customers search.
• Map objections to creatives: For example, run ads that emphasize compliance and audits if you have search data illustrating a segment of users who have detailed questions about security of your software.
• Test messaging by persona to learn faster which angles convert.
• Benefit: SEO persona research de-risks your paid spend by validating copy before it goes live.

Social And Community

• Translate persona pain points into campaign themes and engagement prompts.
• Highlight UGC that shows peers solving the same persona pain point = social proof!
• Build Reddit or forum campaigns where you provide helpful answers framed through persona lenses.
• Benefit: Social teams stop guessing what will resonate – they get ready-made hooks from organic customer query data and in-house transcript research.

Sales

• Use personas to shape sales scripts to reduce organic hesitations, along with your follow-up email templates.
• Provide a list of key characteristics or organic phrases discovered in your SEO user persona research for sales to easily pick up on what scripts or content to use.
• Equip reps with content “proof kits” (case studies, calculators, benchmarks) that map to persona objections.
• Example: Lead comes in from organic content around “integration headaches.” Sales can immediately address hesitations with comparison docs + customer proof.
• Benefit: SEO insights close the loop. Your leads feel heard because the same language follows them from organic query to sales call.

Customer Support

• Build FAQs, hub pages, and documentation around persona pain points and natural language so customers can self-serve faster.
• Train reps on marketing and educational language developed for personas to keep communication consistent across the lifecycle.
• Feed recurring support questions back to SEO/content as new opportunities.
• Benefit: Less friction for customers, more organic opportunities uncovered for SEO.

Product And/Or Product Marketing

• Tie persona insights to feature positioning: “Which persona is this release for?”
• Test messaging against persona objections to see what sticks before launch.
• Document frameworks: “For Persona A, highlight speed. For Persona B, highlight compliance.”
• Benefit: SEO personas become market intelligence, not just marketing intel. This helps product teams ship smarter. Unanswered questions or unsolved organic problems are great opportunities for new features.

One of the biggest pitfalls with doing the work to create search personas is then treating them like static, lifeless relics afterward.

A 2015 B2B study conducted by Cintell found that 71% of companies who exceeded revenue goals had documented personas – and nearly two-thirds of those orgs had updated them within the last six months.

(Listen, I am well aware 2015 is approximately 47 internet years ago – but I’d argue core human decision-making behavior takes much longer to change than a decade.)

No matter the study’s age, the message rings true today: Marketing and user personas win when they’re kept alive.

SEO personas make this easier than traditional personas because they’re rooted in fluid signals, like real search queries, prompts, and customer language that evolve as quickly as the market and trends do.

If you’re closely monitoring GSC data, Semrush, or AIO/LLM interactions, you’ll see shifts in questions and pain points before most competitors.

How to operationalize a persona freshness feedback loop across your team:

• Employ direct communication channels: Create dedicated Slack channels, a shared CRM note hub, or monthly syncs where Sales, Customers, and Marketing can drop fresh objections, questions, or hesitations they’re hearing. If you’ve got power users or partners who can drop in routine feedback and thoughts, even better.
• Develop a regular review cadence: Run a quarterly refresh of persona pain points, objections, and query patterns. Layer in branded search trends, referral data, and AIO/LLM interactions to validate updates.
• Create an escalation path: Set up a clear process for when a “new pain point” surfaces. Sales hears it first → SEO/content teams get it next → new content or updates ship fast → implement/inform across marketing channels. How do you make room for organic escalations in your SEO/content production systems?
• Do hesitation check-ins: Bi-weekly or monthly cross-team reviews (Support + Sales + SEO) where you identify the top organic customer/lead hesitations and assign assets to resolve them: case studies, how-to videos, tools and calculators, testimonials/reviews, community feedback on social channels.
• Hold a regular retro: Tie shipped assets back to KPIs. Which persona-driven pages moved the needle? Which didn’t? Prune or upgrade pages that aren’t solving the problem.

The big takeaway here is search personas are never one-and-done.

They’re a dynamic, qualitative and quantitative data-based operating system for your marketing, sales, and product teams … and if you keep the feedback loop tight, they’ll keep paying dividends.

---

Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/making-seo-personas-actionable-across-teams/556618/

Google is hiring a new anti-scraping czar, whose job will be to analyze search traffic to identify the patterns of search scrapers, assess the impact, and work with engineering teams to develop new anti-scraping models for improving anti-scraping defenses.

Search Results Scraping

SEOs rely on SERP tracking companies to provide search results data for understanding search ranking trends, enabling competitive intelligence, and other keyword-related research and analysis.

Many of these companies conduct massive amounts of automated crawling of Google’s search results to take a snapshot of ranking positions and data related to search features triggered by keyword phrases. This scraping is suspected of causing significant changes to what’s reported in Google Search Console.

In the early days of SEO, there used to be a free keyword data source via Yahoo’s Overture, their PPC service. Many SEOs used to search on Yahoo so often that their searches would unintentionally inflate the keyword volume. Smart SEOs would know better to not optimize for those keyword phrases.

I have suspected that some SEOs may also have intentionally scraped Yahoo’s search results using fake keyword phrases in order to generate keyword volumes for those queries, in order to mislead competitors into optimizing for phantom search queries.

&num=100 Results Parameter

There is a growing suspicion backed by Google Search Console data that search result scraping may have inflated the official keyword impression data and that it may be the reason why Search Console Data appears to show that AI Search results aren’t sending traffic while Google’s internal data shows the opposite.

This suspicion is based on falling keyword impressions that correlate with Google’s recent action to block generating 100 search results with one search query, a technique used by various keyword tracking tools.

Google Anti-Scraping Engineering Analyst

Jamie Indigo posted that Google is looking to hire an Engineering Analyst focused on combatting search scraping.

The responsibilities for the job are:

• “Investigate and analyze patterns of abuse on Google Search, utilizing data-motivated insights to develop countermeasures and enhance platform security.
  Analyze datasets to identify trends, patterns, and anomalies that may indicate abuse within Google Search.
• Develop and track metrics to measure scraper impact and the effectiveness of anti-scraping defenses. Collaborate with engineering teams to design, test, and launch new anti-scraper rules, models, and system enhancements.
• Investigate proof-of-concept attacks and research reports that identify blind spots and guide the engineering team’s development priorities. Evaluate the effectiveness of existing and proposed detection mechanisms, understanding the impact on scrapers and real users.
• Contribute to the development of signals and features for machine learning models to detect abusive behavior. Develop and maintain threat intelligence on scraper actors, motivations, tactics and the scraper ecosystem.”

What Does It Mean?

There hasn’t been an official statement from Google but it’s fairly apparent that Google may be putting a stop to search results scrapers. This should result in more accurate Search Console data, so that’s a plus.

Featured Image by Shutterstock/DIMAS WINDU

https://www.searchenginejournal.com/google-is-hiring-an-anti-scraping-engineering-analyst/556262/

AIOs, LLMs & the New Rules of SEO

AI Overviews are changing everything.

Your impressions might be up, but the traffic isn’t following. Competitors are showing up in AI search while your brand remains invisible.

How do you measure success when ChatGPT or Gemini doesn’t show traditional rankings? How do you define “winning” in a world where every query can produce a different answer?

Learn the SEO & GEO strategies enterprise brands are using to secure visibility in AI Overviews and large language models.

AI Mode is growing fast. Millions of users are turning to AI engines for answers, and brand visibility is now the single most important metric. 

In this webinar, Tom Capper, Sr. Search Scientist at STAT Search Analytics, will guide you through how enterprise SEOs can adapt, measure, and thrive in this new environment.

You’ll Learn:

• How verticals and user intents are shifting under AI Overviews and where SERP visibility and traffic opportunities still exist.
• Practical ways to leverage traditional SEO while optimizing for generative engines.
• How to bridge the gap between SEO and GEO with actionable strategies for enterprise brands.
• How to measure success in AI search when impressions and rankings no longer tell the full story.

Register now to gain the latest, data-driven insights on maintaining visibility across AI Overviews, ChatGPT, Gemini, and more.

🛑 Can’t attend live? Sign up anyway, and we’ll send you the recording.

https://www.searchenginejournal.com/win-brand-visibility-in-ai-search/555017/

New data from the Audience Key content marketing platform indicates that Amazon’s visibility has suffered a significant drop. The decline follows two changes Amazon made to its presence in Google Shopping, although it is uncertain whether those changes are direct or indirect causes.

The first change was the discontinuation of its paid Shopping ads, and the second was the consolidation of its three merchant store names (Amazon, Amazon.com, and Amazon.com – Seller) into a single store identity, “Amazon.” These changes appear to have had a measurable effect on how often Amazon product cards appear in Google’s organic Shopping results.

Audience Key is a content marketing platform that fills a gap in competitive intelligence by tracking and reporting on Google’s organic product grid rankings at scale. This is a new product that has recently rolled out.

According to Audience Key:

“Across 79,000+ keywords, Audience Key’s first-of-its-kind tracking showed the effects of Amazon’s changes to its merchant feed — the approach initially wiped out 31% of its organic product card rankings. Weeks later, Amazon has now disappeared completely — creating a seismic shift that is immediately reshaping e-commerce SERPs and freeing up prime shelf space for rivals.”Tom Rusling, founder of Audience Key notified me today that Amazon has subsequently completely dropped out of the organic search results, beginning on August 18th.

Anecdotally, I’ve seen Amazon completely dropped out of Google’s organic product grids, including for search queries I know for certain they used to rank for and are now completely gone from the search engine results pages (SERPs).

Overall Impact

The most immediate change was the overall scale of Amazon’s presence. Before July 25, Amazon’s listings appeared in 428,984 organic product cards. After the change, that presence dropped to 294,983.

• Before July 25: 428,984 product cards
• After July 25: 294,983 product cards

Net change: -134,001 cards (31% decline)

This shows that Amazon’s move was not just a brand consolidation but also a large reduction in visibility. It is possible that the brand consolidation triggered a temporary drop in visibility because it’s such a wide-scale change.

Category-Level Changes

The reduction was not spread evenly. Some product categories were hit harder than others. Apparel had the steepest losses, while categories like Home Goods and Laptop Computers also fell sharply.

Smaller categories such as Tires and Indoor Decor declined more moderately, but all showed the same downward trend.

Apparel Category Experiences The Largest Declines

Apparel stands out as the category where Amazon saw the steepest reductions, with its presence cut by more than half across several tracked segments.

Below is the data I currently have, I’m waiting for clarification from Audience Key about whether the following apparel categories are more specific:

• Apparel: 4,571 → 1,804 (-60%)
• Apparel: 4,503 → 1,859 (-59%)
• Apparel: 31,852 → 13,632 (-57%)
• Apparel: 6,932 → 3,029 (-56%)

Several Other Major Categories Affected

The losses were also large in high-volume categories. Home Goods, Laptop Computers, and Outdoor Furnishings all saw reductions, while Business Supplies and Technology products also suffered visibility declines.

• Business Supplies: 12,510 → 9,786 (-22%)
• Home Goods: 133,717 → 73,833 (-45%)
• Laptop Computers: 30,520 → 19,615 (-36%)
• Outdoor Furnishings: 58,416 → 41,995 (-28%)
• Scientific and Technology: 58,880 → 50,666 (-14%)

Smaller Categories Also Affected

Even niche verticals were affected, though the percentage losses were less severe than in Apparel or Home Goods. These declines show Amazon’s reductions were spread across both major and smaller categories.

• Structures: 6,241 → 4,229 (-32%)
• Tires: 3,063 → 2,609 (-15%)
• Indoor Decor: 23,634 → 19,789 (-16%)
• Indoor Decor (variant): 6,626 → 5,926 (-11%)

Merchant Store Consolidation

Another change came from how Amazon presented itself in Shopping results. Before July 25, the company appeared under three names: Amazon, Amazon.com, and Amazon.com – Seller. Afterward, only the unified “Amazon” label remained.

• Total before consolidation (all three names): 428,984 product cards
• After consolidation (single “Amazon”): 294,980 product cards

This simplified Amazon’s presence by unifying it under one name, but it also coincided with a decline in overall coverage.

Where Amazon Is At Today?

Even with the July drops in visibility, Amazon remained the most visible merchant in Google Shopping, with smaller visibility than before. But that’s not longer the case, the situation for Amazon appears to have worsened.

Audience Key speculated on what is going on:

“We thought the first chapter of this story was complete, but just as we prepared this study for publication, everything changed. Again. Our latest U.S. search data reveals a stunning shift: Amazon vanished from the organic product grids.

Whether this is a short-term anomaly or a more permanent new normal, only time will tell. We will continue to monitor and report on our findings. The sudden removal leaves us — and the industry — asking one big question: WHY???

That is certainly a topic for speculation.”

Audience Key speculates that Amazon may be withholding their product feed from Google or that this is a technical or strategic change on Amazon’s part.

One thing that we know about Google organic search is that large-scale changes can have a dramatic impact on search visibility. Audience Key has a unique product that is focused on tracking Google’s product grid, something that many ecommerce companies may find useful. They are apparently well-positioned to notice this kind of change.

Read Audience Key’s blog post about these changes:

Beyond Paid: The Hidden Organic Shockwave from Amazon’s Google Shopping Exit

Featured Image by Shutterstock/Sergei Elagin

https://www.searchenginejournal.com/amazon-experiences-drop-in-google-search-visibility/555729/

Google has announced that the CrUX Dashboard, the Looker Studio-based visualization tool for CrUX data, will be retired at the end of November 2025. The reason given for the deprecation is that it was not designed for “wide-scale” use and that Google has developed more scalable alternatives.

Why The CrUX Dashboard Is Being Retired

The CrUX Dashboard was built in Looker Studio to summarize monthly CrUX data. It gained popularity as Core Web Vitals became the de facto standard for how developers and SEOs measured performance.

Behind the scenes, however, the tool struggled to keep up with demand. According to the official Chrome announcement, it suffered “frequent outages, especially around the second Tuesday of each month when new data was published.”

The Chrome team concluded that while the dashboard showed the value of CrUX data, it was not built on the right technology.

Transition To Better Alternatives

To address these issues, Google launched the CrUX History API, which delivered weekly instead of monthly data, allowing more frequent monitoring of trends. The History API was faster and more scalable, leading to adoption by third-party tools.

In 2024, Google introduced CrUX Vis, which was more scalable and faster. Today, in 2025, CrUX Vis receives four to five times more users than the CrUX Dashboard, showing that users are increasingly moving to the newer tool.

What the Change Means for Users

Chrome will shut down the CrUX Connector to BigQuery in late November 2025. When this connector is removed, dashboards that depend on it will stop updating. Users who want to keep the old dashboard will need to connect directly to BigQuery with their own credentials. The announcement explains that the CrUX Connector infrastructure is unreliable and requires too much monitoring to maintain, which is why investment has shifted to the History API and CrUX Vis.

Some users have asked Google to postpone the shutdown until 2026, but the announcement makes it clear that this is not an option. Although the dashboard and its connector will be retired, the underlying BigQuery dataset will continue to be updated and supported. Google stated that it sees BigQuery as a valuable, longer-term public dataset.

Check out the CrUIX Vis tool here.

Read the original announcement:

CrUX Dashboard deprecation

https://www.searchenginejournal.com/google-retiring-core-web-vitals-dashboard/555714/

I’ve been extremely antsy to publish this study. Consider it the AIO Usability study 1.5, with new insights. You also want to stay tuned for our first AI Mode usability study! It’s coming in a few weeks (make sure to subscribe not to miss it).

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Since March, everyone’s been asking the same question: “Are AI Overviews killing our conversions?”

Our 2025 usability study gives a clearer answer than the hot takes you’ll see on LinkedIn and X (Twitter).

In May 2025, I published significant findings from the first comprehensive UX study of AI Overviews (AIOs). Today, I’m presenting you with new insights from that study based on a cutting-edge RAG system that analyzed over 100,000 words of transcription.

The most significant, stand-out finding from that study: People use AI Overviews to get oriented and save time.

Then, for any search that involves a transaction or high-stakes decision-making, searchers validate outside Google, usually with trusted brands or authority domains.

Net-net: AIO is a preview layer. Blue links still close. Before we dive in, you need to hear these insights from Garrett French, CEO of Xofu, who financed this study:

“What lit me up most from this latest work from Kevin: We have direct insight now into an “anchor pattern” of AIO behavior.

In this usability study, we discovered that users rarely voice distrust of AI Overviews directly – instead they hesitate, refine, or click out.

Therefore, hesitation itself is the loudest signal to us.

We see the same in complex, transition-enabling purchase-committee buying (B2B and B2C): Procurement stalls without lifecycle clarity, engineer stall without specs, IT stalls without validation.

These aren’t complaints. They’re unresolved, unanswered, and even unknown questions that have NEVER shown themselves in KW demand.

As content marketers, we have never held ourselves systematically accountable to answering them.

Customer service logs – as an example of one surface for discovering friction – expose the same hesitations in traceable form through repeated chats, escalations, deployment blocks, etc.

Customer service logs are one surface; AIOs are another.

But the real source of truth is always contextual audience friction.

Answering these “friction-inducing, unasked latent questions give us a way to read those signals and design content that truly moves decisions forward.

What The Study Actually Found:

• Organic results are the most trusted and most consistently successful destination across tasks.
• Sponsored results are noticed but actively skipped due to low trust.
• In-SERP answers quickly resolved roughly 85% of straightforward factual questions.
• Users often use AIO as a preview or shortcut, then click out to finish or validate (on brand sites, YouTube, coupon portals, and the like).
• Shopping carousels aid discovery more than closure. Expect reassessment clicks.
• Trust splits by stakes: Low-stakes search journeys often end in the AIO, while finance or health pushes people to known authorities like PayPal, NIH, or Mayo Clinic.
• Age and device matter. Younger users, especially on smartphones, accept AIOs faster; older cohorts favor blue links and authority domains.
• When the AIO is wrong or feels generic, people bail. We logged 12 unique “AIO is misleading/wrong” flags in higher-stakes contexts.

(Interested in diving deeper into the first findings from this study or need a refresher? Read the first full iteration of the UX study of AIOs.)

Why This Matters For The Bottom Line

In my earlier analysis, I argued that top-of-funnel visibility had more downstream impact than our marketing analytics ever credited. I also argued that demand doesn’t just disappear because clicks shrink.

This study’s behavior patterns support that: AIO satisfies quick lookup intent, but purchase intent still routes through external validation and brand trust – aka clicks. Participants in this study shared thoughts aloud, like:

• “There’s the AI results, but I’d rather go straight to PayPal’s own site.”
• “Mayo Clinic at the top of results, that’s where I’d go. I trust Mayo Clinic more than an AI summary.”

And that preserves downstream conversions (when you show up in the right places and have earned authority).

Deeper Insights: Secondary Findings You Need To See

Recently, I worked with Eric Van Buskirk (the research director of the study) and his team over at Clickstream Solutions to do a deeper analysis of the May 2025 findings.

Using an advanced RAG-driven AI system, we analyzed all 91,559 (!) words of the transcripts from recorded user sessions across 275 task instances.

This is important to understand: We were able to find new insights from this study because Eric has built cutting-edge technology.

Our new RAG system analyzes structured fields like SERP Features, AIO satisfaction, or user reactions from transcriptions and annotations. It creates a retrieval layer and uses ChatGPT-5 for semantic search.

The result is faster, more rigorous, and more transparent research. Every claim can be traced to data rows and transcript quotes, patterns are checked across the full dataset, and visual evidence is a query away.

(To sum that all up in plain language: Eric’s custom-built advanced RAG-driven AI system is wildly cool and extremely effective.)

Practical benefits:

• Auditable insights: Conclusions map back to exact data slices.
• Speed: Test a hypothesis in minutes instead of re-reading sessions.
• Scale: Triangulate transcripts, coded fields, and outcomes across all participants.
• Fit for the AI era: Clean structure and trustworthy signals mirror how retrieval systems pick sources, which aligns with our broader stance on visibility and trust.

Here’s what we found:

1. The data verified four distinct AIO Intent Patterns.
2. Key SERP features drove more engagement than others.
3. Core brands shape trust in AIOs.

About The New RAG System

We rebuilt the analysis on a retrieval-augmented system so answers come from the study data, not model guesswork. The backbone lives on structured fields with full transcripts and annotations, indexed in a lightweight database and paired with bucketed data for cohort filtering and cross-checks.

Core components:

• Dataset ingestion and cleaning.
• Retrieval layer based on hybrid keyword + semantic search.
• Auto-coded sentiment to turn speech into consistent, queryable signals.
• Validation loop to minimize hallucination.

The result is faster, more rigorous, and more transparent research. Every claim can be traced to rows and quotes, patterns are checked across the full dataset, and visual evidence is a query away.

Practical benefits:

• Map conclusions back to exact data slices.
• Test a hypothesis in minutes.
• Triangulate transcripts, coded fields, and outcomes across all participants.
• Clean structure and trustworthy signals.

Which AIO Intent Patterns Were Verified Through The Data

One of the biggest secondary findings from the AIO usability study is that the AIO Intent Patterns aren’t just “gut feelings” anymore – they’re statistically validated, built from measurable behavior.

Before some of you roll your eyes and annoyingly declare “here’s yet another newly created SEO/marketing buzzword,” the patterns we discovered in the data weren’t exactly search personas, and they weren’t exactly search intents, either.

Therefore, we’re using the phrase “AIO Intent Pattern” to distinguish these concepts from one another.

Here’s how I define AIO Intent Patterns: AIO Intent Patterns represent statistically validated clusters of user behavior – like dwell, scroll, refinements, and sentiment – that define how people respond to AIOs. They’re recurring, measurable behaviors that describe how people interact with AI Overviews, whether they accept, validate, compare, or reject them.

And, again, these patterns aren’t exactly search intents or queries, but they’re not exactly user profiles either.

Instead, these patterns represent a set of behaviors (that appeared throughout our data) carried out by users to validate AIOs in different and distinct ways. So that’s why we’ve called the individual behavioral patterns “validations” below.

By running a RAG-driven coding pass across 250+ task instances, we were able to quantify four different behavioral patterns of engagement with AIOs:

1. Efficiency-first validations that reward clean, extractable facts (accepting of AIOs).
2. Trust-driven validations that convert only with credibility (validate AIOs).
3. Comparative validations that use AIOs but compare with multiple sources.
4. Skeptical rejections that automatically distrust AIOs for high-stakes queries.

What matters most here is that these aren’t arbitrary labels.

Statistical tests showed the differences in dwell time, scrolling, and refinements between the four groups were far too large to be random.

To put it plainly: These are real AIO use behavioral segments or AIO use intents you can plan for.

Let’s look at each one.

1. Efficiency-First Validations

These are validations where users intend to seek a shortcut. Users dip into AIOs for fast fact lookups, skim for one answer, and move on.

Efficiency-driven validations thrive on content that’s concise, scannable, and fact-rich. Typical queries that are resolved directly in the AIO include:

• “1 cup in ml”
• “how to take a screenshot on Mac”
• “UTC to CET converter”
• “what is robots.txt”
• “email regex example”

Below, you can check out two examples of “efficiency-first validation” task actions from the study.

“Okay, so I like the summary at the top. And I would go ahead and follow these instructions and only come back to a search if they didn’t work.”

“I just had to go straight to the AI overview… and I liked that answer. It gave me the information I needed, organized and clear. Found it.”

Our data shows an average dwell time of just 14 seconds for this group overall, with almost no scrolling or refinements.

Users that have an efficiency-first intent for their queries have a neutral to positive sentiment toward AIOs – with no hesitation flags – because AIOs scratch the efficiency-intent itch quickly.

For this behavioral pattern, the AIO often is the final answer – especially on mobile – and if they do click, it’s usually the first clear, extractable source.

👉 Optimization tips for this validation group:

• Compress key facts into crisp TLDRs, FAQs, and schema so AIO can surface them.
• Place definitions, checklists, and example blocks near the top of your page.
• Use simple tables and step lists that can be lifted cleanly.
• Ensure brand mentions and key facts appear high on the page for visibility.

2. Trust-Driven Validations

These validations are full of caution. Users with trust-driven intents engage with AIOs but rarely stop there.

They’ll skim the overview, hesitate, and then click out to an authority domain to validate what they saw, like in this example below:

The user shares that “…at the top, it gave me a really good description on how to transfer money. But I still clicked the PayPal link because it was directly from the official site. That’s what I went with – I trust that information to be more accurate.”

Typical queries that trigger this validation pattern include:

• “PayPal buyer protection rules”
• “Mayo Clinic strep symptoms”
• “Is creatine safe long term”
• “Stripe refund timeline”
• “GDPR consent requirements example”

And our data from the study verifies users scroll more (2.7x on average), dwell longer (~57s), and often flag uncertainty in trust-driven mode. What they want is authority.

These users have a high rate of hesitation flags in their search experiments. Their sentiment is mixed – often neutral, sometimes anxious or frustrated – and their confidence is only medium to low.

For these searches, the AIO is a starting point, not the destination. They’ll click out to Mayo Clinic, PayPal, Stripe, or other trusted domains to validate.

👉 Optimization tips for this validation group:

• Reinforce trust scaffolding on your landing pages: expert reviewers, citations, and last-reviewed dates.
• Mirror official terminology and link to primary sources.
• Add “What to do next” boxes that align with authority guidance.
• Build strong E-E-A-T signals since credibility is the conversion lever here.

3. Comparative Validations

This search intent actively leans into the AIO for classic comparative queries (think “Ahrefs vs Semrush for content teams”) to fulfill their search intent OR to compare informational resources to get clarity on the “best” of something; they expand, scroll, refine, and use interactive features – but they don’t stop there.

Instead, they explore across multiple sources, hopping to YouTube reviews, Reddit threads, and vendor sites before making a decision.

Example queries that reveal AIO comparative validation behavior:

• “Notion vs Obsidian for teams”
• “Best mirrorless camera under 1000”
• “How to change a bike tire”
• “Standing desk benefits vs risks”
• “Programmatic SEO examples B2B”
• “How to install a nest thermostat”

Here’s an example using a “how to” search, where the user is comparing sources for the best way to receive the most accurate information:

“The AI Overview gave me clear step-by-step instructions that matched what I expected. But since it was a physical DIY task, I still preferred to branch out to watch a video for confirmation.”

On average, searchers looking for comparative validations in the AIO dwell for 45+ seconds, scroll 4-5 times, and often open multiple tabs.

Their AIO sentiment is positive, and their confidence is high, but they still want to compare.

If this feels familiar – like classic transactional or commercial search intents – it’s because it is related.

If you’ve been doing SEO for any time, it’s likely you’ve created some of these “versus” or “comparison” pages. You also have likely created “how to” content with step-by-step how-to guidance, like how to install a flatscreen TV on your wall.

Before AIOs, your target users would find themselves there if you ranked well in search.

But now, the AIO frames the landscape first, and the decision comes after weighing pros and cons across information sources to find the best solution.

👉 Optimization tips for this validation group:

• Publish structured comparison pages with decision tables and use-case breakdowns.
• Pair each page with short demo videos, social proof, and credible community posts to echo your takeaways.
• Include “Who it is for” and “Who it isn’t for” sections to reduce ambiguity.
• Seed content in YouTube and forums that AIOs (and users) can pick up.

4. Skeptical Rejections

Searchers with a make-or-break intent? They’re the outright AIO skeptical rejectors.

When stakes are high – health, finance, or legal … the typical YMYL (Your Money, Your Life) stuff – they don’t trust AIO to get it right.

Users may scan the summary briefly, but they quickly move to authoritative sources like government sites, hospitals, or financial institutions.

Common queries where this rejection pattern shows up:

• “Metformin dosage for PCOS”
• “How to file taxes as a freelancer in Germany”
• “Credit card chargeback rights EU”
• “Infant fever when to go to ER”
• “LLC vs GmbH legal liability”

For this search intent, the dwell time in an AIO is short or nonexistent, and their sentiment often skews negative.

They show determination to bypass the AI layer in favor of direct authority validation.

👉 Optimization tips for this validation group:

• Prioritize citations and mentions from highly trusted domains so AIOs lean on you indirectly.
• Align your pages with the language and categories used by official sources.
• Add explicit disclaimers and clear subheadings to strengthen authority signals.
• For YMYL topics, focus on being cited rather than surfaced as the final answer.

SERP Features That Drove Engagement

Our RAG AI-driven system of the usability data verified that not all SERP features are created equal.

When we cut the data down to only features with meaningful engagement – which our study defined as ≥5 seconds of dwell time across at least 10 instances – only four SERP features findings stood out.

(I’ll give you a moment to take a few wild guesses regarding the outcomes … and then you’ll see if you’re right.)

Drumroll please. 🥁🥁🥁

(Okay, moment over. Here we go.)

1. Organic Results Are Still The Backbone

Whenever our study participants gave the classic blue links more than a passing glance, they almost always found success.

Transcripts from the study make it explicit: Users trusted official sites, government domains, and familiar authority brands, as one participant’s quote demonstrates:

“Mayo Clinic at the top of results, that’s where I’d go. I trust Mayo Clinic more than an AI summary.”

What about social or community sites that showed up in the organic blue-link results?

Reddit and YouTube were the social or community platforms found in the SERP that were mentioned most by study participants.

Reddit had 45 unique mentions across the entire study. Overall, seeing a Reddit result in organic results produces a user sentiment that is mostly positive, with some users feeling neutral toward the inclusion of Reddit in search, and very few negative comments about Reddit results.

YouTube had 20 unique mentions across the entire study. The sentiment toward YouTube inclusion in SERP results was overwhelmingly positive (19 out of 20 of those instances had a positive user sentiment). The emotions flagged from the study participants around YouTube results included happy/satisfied or curious/exploring.

There was a very clear theme across the study that appeared when social or community sites popped up in organic results:

• Reddit was invoked when participants wanted community perspective, usually in comparison tasks. Confidence was high because Reddit validated nuance, but AIO trust was weak (users bypassed AIOs to Reddit instead).
• YouTube was used as a visual validator, especially in product or technical comparison tasks. Users expressed positive sentiment and high satisfaction, even when explicit trust wasn’t verbalized. They treated YouTube as a natural step after the AIOs/organic SERP results.

2. Sponsored Results Barely Register

People saw them, but rarely acted on them. “I don’t like going to sponsored sites” was a common refrain.

High visibility, but low trust.

3. Shopping Carousels Aid Discovery But Not Closure.

Participants clicked into Shopping carousels for product ideas, but often bounced back out to reassess with external sites.

The carousel works as a catalog – not a closer.

4. Featured Snippets Continue To Punch Above Their Weight

For straightforward factual lookups, Snippets had an ~85% success rate of engagement.

They were efficient and final for fact-based queries like [example] and [example].

⚠️ Important note: Even though Google is replacing Featured Snippets with AIOs, it’s clear that this method of receiving information within the SERP has a high engagement. While the SERP feature may be in the process of being discontinued, the data shows users like engaging with snippets. The takeaway here is that if you were often appearing for featured snippets and you’re now often appearing for AIO citations, keep up the good work to continue earning visibility there, because it still matters.

SERP Features x AIO Intent Patterns

When you keep the intent pattern layers in mind with different persona groups, it makes the search behaviors sharper:

• Younger users on mobile leaned heavily on AIO and snippets, often stopping there if the stakes were low. → That’s the hallmark of efficiency-first validations (quick fact lookups) and comparative validations (scrolling, refining, and treating AIO as the main lens).
• Older users consistently bypassed AI elements in favor of organic authority results. → This is classic behavior for trust-driven validations, when users click out to brands like PayPal or the Mayo Clinic, and skeptical rejections, when users distrust AIO altogether for high-stakes tasks.
• Transactional queries – money, health, booking – nearly always pushed people toward trusted brands, regardless of what AIO or ads surfaced. → This connects directly to trust-driven validations (users who need authority reinforcement to fulfill their search intent) and skeptical rejections (users who reject AIO in YMYL contexts because AIOs don’t meet the intent behind the behavior).

What this shows is that, for SEOs, the priority isn’t about chasing every feature and “winning them all.”

Take this as an example:

“The AI overview didn’t pop up, so I used the search results. These were mostly weird websites, but CNBC looked trustworthy. They had a comparison of different platforms like CardCash and GCX, so I went with CNBC because they’re a trusted source.”

Your job is to match intent (as always):

• Earn extractable presence in AIOs for quick facts,
• Reinforce trust scaffolding on authority-driven organic pages, and
• Treat Shopping and Sponsored slots as visibility and awareness plays rather than conversion levers.

Which Brands Shaped Trust In AIOs

AIOs don’t stand on their own; they borrow credibility from the brands they surface – whether you like it or not.

(Google truly seems to be cannibalizing itself while devouring all of us, too.)

When participants validated or rejected an AI answer, it often hinged on whether a familiar or authoritative brand was mentioned.

Our RAG-coded study data surfaced clear winners:

• Institutional authorities like PayPal, NIH, and government sites consistently shaped trust, even without clicks.
• Ecommerce and retail giants (Amazon, Walmart, Groupon) carried positive associations from brand familiarity.
• Financial and tax prep services (H&R Block, Jackson Hewitt, CPA mentions) were trusted anchors in transactional searches.
• Car rental brands (Budget, Avis, Dollar, Kayak, Zipcar, Turo) dominated travel-related tasks.
• Emerging platforms (Raise, CardCash, GameFlip, Kade Pay) gained traction primarily because an AIO surfaced them, not because of prior awareness.

👉 Why it matters: Brand trust is the glue between AIO exposure and user action.

Here’s a quick paraphrase of this user’s exploration: We’re looking for places to sell gift cards for instant payment. Platforms like Raise, Gift Card Granny, or CardCash come up. On CardCash, I tried a $10 7-Eleven card, and the offer was $8.30. So they ‘tax’ you for selling. That’s good to know – but it shows you can sell gift cards for cash, and CardCash is one option.

In this instance, the AIO surfaced CardCash. The user didn’t know about it before this search. They explored it in detail, but trust friction (“they tax you”) shaped whether they’d actually use it.

For SEOs, this means three plays running in tandem:

1. Win mentions in AIOs by ensuring your content is structured, scannable, and extractable.
2. Strengthen authority off-site so when users validate (or reject the AIO), they land on your pages with confidence.
3. Build topical authority in your niche through comprehensive persona-based topic coverage and valuable information gain across your topics. (This can be a powerful entry point or opportunity for teams competing against larger brands.)

What does this all mean for your own tactical optimizations?

But here’s the most crucial thing to take away from this analysis today:

With this information in mind, you can now go to your stakeholders and guide them to look at all your prompts, queries, and topics with fresh eyes.

You need to determine:

• Which of the target queries/topics are quick answers?
• Which of the target queries/topics are instances where people need more trust and assurance?
• When do your ideal users expect to explore more, based on the target queries/topics?

This will help you set expectations accordingly and measure success over time.

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Featured Image: Paulo Bobita/Search Engine Journal

https://www.searchenginejournal.com/trust-still-lives-in-blue-links/555592/

In my previous article, “Closing the Digital Performance Gap,” I made the case that web effectiveness is a business issue, not a marketing metric. The website is no longer just a reflection of your brand – it is your brand. If it’s not delivering measurable business results, that’s a leadership problem, not a team problem.

But there’s a deeper issue underneath that: Who actually owns web performance?

The truth is, many companies don’t have a good answer. Or they think they do until something breaks. The SEO team doesn’t own the infrastructure. The dev team isn’t briefed on platform changes. The content team isn’t looped in until after a redesign. Visibility drops, conversions dip, and someone asks, “Why isn’t our SEO team performing?”

Because they don’t own the full system, no one does.

If we want to close the digital performance gap, we must address this root problem: lack of accountability.

The Fallacy Of Distributed Ownership

The idea that “everyone owns the website” likely stems from early digital transformation initiatives, where cross-functional collaboration was encouraged to break down departmental silos. The intent was to foster shared responsibility across departments – but the unintended consequence was diffused accountability.

It sounds collaborative, but in practice, it often means no one is fully accountable for performance.

Here’s how it typically breaks down:

• IT owns infrastructure and hosting.
• Marketing owns content and campaigns.
• SEO owns visibility – but not implementation.
• UX owns experience – but not findability.
• Legal owns compliance – but limits usability.
• Product owns the content management system (CMS) – but doesn’t track SEO.

Each group is doing its job, often with excellence. But the result? Disconnected execution. Strategy gets lost in translation, and performance stalls.

Case in point: For a global alcohol brand, a site refresh had legal requirements mandating an age verification gate before users could access the site. That was the extent of their specification. IT built the gate exactly to spec: a page with the statement to enter your birthdate and three pull-down options for Month, Day, and Year, and a check of that date to the U.S. legal drinking age. UX and creative delayed launch for weeks while debating the optimal wording, positioning, and color scheme.

Once launched, the website traffic, both direct and organic search, dropped to zero. This was due to several key reasons:

1. Analytics were not set up to track visits before and after the age gate.
2. Search engines can’t input a birthdate, so they were blocked.
3. The age requirement was set to the U.S. standard, rejecting younger, yet legal visitors from other countries.

Because everything was done in silos, no one had considered these critical details.

When we finally got all stakeholders in a room, agreed on the issues, and sorted through them, we redesigned the system:

• Search engines were recognized and bypassed the age requirement.
• The age requirement and date format are adapted to the user’s location.
• UX developed multiple variations and tested abandonment.
• Analytics captured pre- and post-gate performance.
• UX used the data to validate new landing page formats.

The result? A compliant, user-friendly, and search-accessible module that could be reused globally. Visibility, conversions, and compliance all increased exponentially. But we lost months and millions in potential traffic simply because no one owned the whole picture.

Without centralized accountability, the site was optimized in parts but underperforming as a whole.

The AI Era Raises The Stakes

This kind of siloed ownership might have been manageable in the old “10 blue links” era. But in an AI-first world – where Google and other platforms synthesize content into answers, summarize brands, and bypass traditional click paths – every decision across your digital operation impacts your visibility, trust, and conversion.

Search visibility today depends on structured data, crawlable infrastructure, content relevance, and citation-worthiness. If even one of these is out of alignment, you lose shelf space in the AI-driven SERP. And chances are, the team responsible for the weak link doesn’t even know they’re part of the problem.

Why Most SEO Advice Falls Short

I’ve seen well-meaning advice to “improve your SEO strategy” fall flat – because it assumes the SEO team has control over all the necessary elements. They don’t.

• You can’t fix crawl issues if you can’t talk to the dev team.
• You can’t win AI citations if your content team doesn’t structure or enrich their pages.
• You can’t build authority if your legal or PR teams strip bios and outbound references.

What’s needed isn’t better tactics. It’s organizational clarity.

The Case For Centralized Digital Ownership

To create sustained performance, companies need to designate real ownership over web effectiveness. That doesn’t mean centralizing every task – but it does mean centralizing accountability.

Here are three practical approaches:

1. Establish A Digital Center Of Excellence (CoE)

A CoE provides governance, guidance, and support across business units and regions. It ensures that:

• Standards are defined and enforced.
• Platforms are chosen and maintained with shared goals.
• Learnings are captured and distributed.
• Key performance indicators (KPIs) are consistent and comparable.

2. Appoint A Digital Effectiveness Officer (DEO)

Think of this like a Commissioning Authority in construction – a role that ensures every component works together to meet the original performance spec. A DEO:

• Connects the dots between dev, SEO, UX, and content.
• Tracks impact beyond traffic (revenue, leads, brand trust).
• Advocates for platform investment and cross-team prioritization.

3. Build Shared KPIs Across Departments

Most teams optimize for what they’re measured on. If the SEO team is judged on rankings but not revenue, and the content team is judged on output but not visibility, you get misaligned efforts. Create chained KPIs that reflect end-to-end performance.

Characteristics Of A Performance-Driven Model

Companies that close the accountability gap tend to share these traits:

• Unified Taxonomy and Tagging – so content is findable and trackable.
• Structured Governance – clear roles and escalation paths across teams.
• Shared Dashboards – everyone sees the same numbers, not vanity metrics.
• Tech Stack Discipline – fewer, better tools with cross-functional usage.
• Scenario Planning – AI, zero-click SERPs, and platform volatility are modeled, not ignored.

Final Thought: Performance Requires Ownership

If you’re serious about web effectiveness, you need more than skilled people and good tools. You need a system where someone is truly accountable for how the site performs – across traffic, visibility, UX, conversion, and AI resilience.

This doesn’t mean a top-down mandate. It means orchestrated ownership with clear roles, measurable outcomes, and a strategic anchor.

It’s time to stop asking the SEO team to fix what they don’t control.

It’s time to build a framework where the web is everyone’s responsibility – and someone’s job.

Let’s make web performance a leadership priority, not a guessing game.

More Resources:

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Featured Image: SFIO CRACHO/Shutterstock

https://www.searchenginejournal.com/who-owns-web-performance-building-a-framework-for-digital-accountability/552885/

Google removed outdated structured data documentation, but instead of returning a 404 response, they have chosen to redirect the old URLs to a changelog that links to the old URL, thereby causing an infinite loop between the two pages. Although that is technically not a soft 404, it is an interesting use of a 301 redirect for a missing web page and not how SEOs typically handle missing web pages and 404 server responses. Did Google make a mistake?

Google Removed Structured Data Documentation

Google quitely published a changelog note announcing they had removed obsolete structured data documentation. An announcement was made three months ago in June and today they finally removed the obsolete documentation.

The missing pages are for the following structured data that is no longer supported:

• Course info
• Estimated salary
• Learning video
• Special announcement
• Vehicle listing.

Those pages are completely missing. Gone, and likely never coming back. The usual procedure in that kind of situation is to return a 404 Page Not Found server response. But that’s not what is happening.

Instead of a 404 response Google is returning a 301 redirect back to the changelog. What makes this setup somewhat weird is that Google is linking back to the missing web page from the changelog, which then redirects back to the changelog, creating an infinite loop between the two pages.

Screenshot Of Changelog

In the above screenshot I’ve underlined  in red the link to the Course Info structured data.

The words “course info” are a link to this URL:
https://developers.google.com/search/docs/appearance/structured-data/course-info

Which redirects right back to the changelog here:
https://developers.google.com/search/updates#september-2025

Which of course contains the links to the five URLs that  no longer exist, essentially causing an infinite loop.

It’s not a good user experience and it’s not good for crawlers. So the question is, why did Google do that? 

301 redirects are an option for pages that are missing, so Google is technically correct to use a 301 redirect. However, 301 redirects are generally used to point “to a more accurate URL” which generally means a redirect to a replacement page, one that serves the same or similar purpose.

Technically they didn’t create a soft 404. But the way they handled the missing pages creates a loop that sends crawlers back and forth between a missing web page and the changelog. It seems that it would have been a better user and crawler experience to instead link to the June 2025 blog post that explains why these structured data types are no longer supported  rather than create an infinite loop.

I don’t think it’s anything most SEOs or publishers would do, so why does Google think it’s a good idea?

Featured Image by Shutterstock/Kues

https://www.searchenginejournal.com/infinite-redirect-loop/555583/