“Content is king” remains one of the most widely accepted ideas in SEO. Not everyone has agreed. Different schools of thought have always existed, with some practitioners prioritizing backlinks and others focusing on technical SEO.
Content is often treated as the primary driver of search visibility. I’m not arguing that.
My point is simpler: if you’ve relied on content to drive results — and earn a living — you should start doubling down on distribution.
With AI search changing the game, creating great content (and, yes, building some backlinks) is no longer enough to get it seen. The more important question may no longer be “What should I write next?” but “Where should I push this next?”
AI tools are further fragmenting search
Content distribution has become far more important in recent years, especially as audiences spread across more online spaces. In many teams, this job was usually outsourced to someone other than SEOs:
Social media managers.
Community managers.
PR specialists.
Various assistants and interns.
Sure, distribution held some value to SEO, but it was generally considered more beneficial to other functions.
Thanks to AI search, it’s finally landed squarely on our plate. Since AI models have fragmented search to an unprecedented level, distribution is now key to meaningful SEO outcomes.
There are three key drivers behind this change:
Different tools have different sourcing logic.
AI tools source differently from traditional search.
Their logic is changeable.
If this all sounds a bit abstract, let’s briefly dig into the evidence and explain what’s really going on.
Different tools have different sourcing logic
Search is fragmenting as people use a wider range of tools. Ideally, one strategy would work everywhere, but research shows that’s not the case.
AI search tools cite different sources, a 2025 Search Atlas study found. Some show significantly more overlap with the SERPs than others. This indicates that different tools follow different sourcing logic. And as long as that’s true, optimizing for one won’t necessarily boost visibility on another.
The whole thing is even trickier because users seem more open to switching tools than before. Gemini may soon surpass formerly unrivaled ChatGPT in traffic share, according to Similarweb. That could change again quickly.
Thinking there’s a single clear winner, like Google used to be, would be wrong. Focusing on the most popular tool at the moment isn’t a guaranteed strategy.
To maximize visibility, we need to consider how multiple AI tools source their information, which implies our distribution strategy needs to be broad.
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AI search uses different logic from traditional search
The Search Atlas study showed that some AI search tools overlap with Google more than others — but in all cases, the overlap is pretty low. Perplexity ranked the highest at 43%, while ChatGPT barely hit 21%.
Characterizing Web Search in The Age of Generative AI (PDF) explicitly finds that AI search tools draw from a much wider pool of sources and are more likely to cite sites with fewer visits than traditional search engines.
This shows us that fragmentation is compounding. The pool of potential sources is wider, with little overlap among AI tools or between AI and traditional search.
The sourcing logic is changeable
The most problematic factor out of all, though, is that the sourcing logic of one tool can and often does change, too. This leads to different domains getting cited for the same prompts at different points in time — a phenomenon called citation drift.
Citation drift is more frequent than we might assume. Over the course of just a month, for instance, AI tools change approximately 40-60% of the domains they cite for the same prompt, according to Profound.
In other words, one domain could appear several times in a single response, then disappear completely the following month. This flip-flopping gets even worse over longer periods. For example, Profound’s study also showed that, from January to July, as many as 70% to 90% of the domains cited for the same prompt had changed.
Search is fragmented across tools and time. As cited domains change more frequently, users see more sources, making it even harder for you to push your brand to the front.
So, what can we do about it? How should we approach this increasing fragmentation of search?
While this might change as new tools and strategies emerge, the best answer we have so far is this: focus on broad, multi-channel distribution.
When you can’t reliably predict which sources will be used, the best strategy is to widen your footprint. This creates more potential entry points into AI systems’ training and retrieval layers.
This will require some serious shifts in how many SEOs approach their work. Here are a few you can implement right away.
1. Get good at collaborating
You’re unlikely to win fragmented AI search on your own. Optimizing for it now takes a much broader approach than before, pulling in digital PR, social media, community management, and other functions.
Those areas require skills many SEOs don’t have. Those who do still have only 24 hours in a day, so spreading that work across multiple disciplines isn’t realistic.
This only works with a team. You might hate that idea, especially because it means giving up full control of your projects and results. I get it, but that’s the reality right now. You’ll have to let some things go, trust others to handle them, and divide responsibilities. In other words, you’ll need to collaborate efficiently.
Even if you let experts handle certain tasks, you’ll still need at least a surface-level understanding of other disciplines becoming central to search.
SEOs will still own at least parts of distribution, whether that means handling the high-level strategy or downright executing it on specific channels.
In either case, doing this well requires skills you may not have used much before. So now’s the time to develop them.
That could mean learning more about digital PR, partnerships, thought leadership, syndication, community presence, or something else. With so many possibilities, it helps to start with the area you feel most comfortable with or most drawn to at the moment.
3. Shift your mindset from ranking to presence
You also need to change how you think about SEO, and then translate that shift into actual workflows. Google is still a major traffic driver, and rankings still matter. But for a fragmented, AI-driven search, obsessing over rank won’t cut it.
Instead of asking, “How do I get this content to rank?” You now need to ask, “How do I get this content into as many places as possible?”
Again, the goal is to create multiple entry points across AI systems, platforms, and audiences, increasing the chances of your content getting discovered, cited, and surfaced.
That’s why it’s important to start thinking more about overall presence across ecosystems rather than just positions in specific search engines.
4. Redesign your workflow
If you’ve successfully shifted your mindset from ranking to presence, it’s time to build a workflow that reflects that change.
I know firsthand how easy it is to forget about distribution, especially if it wasn’t part of your process before. To make it stick, you need to redesign your workflow to position distribution at the core.
A good place to start is by adding a launch phase, where content is distributed immediately upon publishing. After that, you could include a recurring phase every few months to ensure you regularly refresh and redistribute content.
Define reusable details upfront, like which channels you’ll consistently target and who owns each one. That way, you’ll minimize planning from scratch and make sure nothing falls through the cracks.
5. Start with these easy-to-implement best practices
Finally, if you want some easy tactics to immediately add to your to-do list, consider these:
Pilot content partnerships, starting where it’s easiest. Usually, that implies reaching out to existing business partners first.
Proactively distribute your content on third-party sites, whether that means syndicating it or repurposing it for Quora and LinkedIn.
Pay attention to where AI tools already pull from. While sourcing logic changes constantly, you may still notice recurring patterns worth leveraging.
Give a special push to your existing, older content to counteract the pitfalls of citation drift. Reintroduce it on new channels, or work to get it referenced in new places.
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Rethinking SEO processes for fragmented AI search
The shifts are large enough that you’ll need to rethink how you do SEO. As search fragments, the work itself will have to evolve.
The approaches and workflows you relied on in the past won’t translate cleanly into a landscape shaped by multiple AI tools, changing sourcing logic, and constantly shifting citations.
These processes will also become more complex because they require closer collaboration with other teams. Distribution now intersects with digital PR, social media, partnerships, and community management, making cross-team coordination more important than before.
There’s a long road ahead. The best way to keep your sanity is to start small: focus on manageable steps, take them one at a time, and build from there.
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If you’ve been in marketing long enough, you’ve probably lived through a few identity crises. First, we were channel experts. Then, we became integrated marketers, growth marketers, and performance marketers. Somewhere along the way, someone added “AI” to everyone’s job description and called it a day.
Now, we’re entering the era of the full-stack marketer. From where I sit — particularly as a media leader — the role is starting to look a lot like product management.
This doesn’t mean you need to start writing Jira tickets for fun (though some of you already do). It means that tomorrow’s most effective media leaders won’t just optimize campaigns. They’ll own outcomes, connect dots across teams, and think holistically about the entire user experience, from first impression to final conversion (and beyond).
I’ve seen this shift most clearly in industries with long consideration cycles, multiple stakeholders, and rising acquisition costs — where marketing performance is inseparable from the experience itself.
Let’s break down what’s driving the rise of the full-stack marketer, what it really means to “think like a product manager,” and why this mindset is becoming non-negotiable for media leaders.
What is a full-stack marketer, anyway?
A full-stack marketer isn’t someone who does everything (burnout isn’t a job requirement). Instead, it’s someone who understands how everything works together.
Over the course of my career, I’ve learned that the most impactful media decisions rarely come from being the deepest expert in one area. They come from having working fluency across many:
Media and channels: Paid search, paid social, programmatic, CTV, SEO, email, SMS, and whatever new acronym launches next quarter.
Creative and messaging: Knowing what resonates, where, and why.
Data and analytics: Not just reading dashboards, but asking better questions of the data.
UX and CRO: Understanding friction, intent, and user behavior.
Technology and platforms: CRMs, CMSs, marketing automation, and attribution tools.
The full-stack marketer doesn’t need to be the deepest expert in every area, but they do need to know enough to connect insights, spot gaps, and make informed trade-offs. In practice, this means constantly zooming out to see the system and zooming back in when something breaks.
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Why media leaders are evolving into product thinkers
Earlier in my career, media leadership was often defined by questions like:
Are we hitting CPA targets?
Which channels are driving the most conversions?
How do we allocate budget more efficiently?
Those questions still matter. I ask them all the time. But over the years, I’ve learned they’re no longer sufficient on their own. Today’s environment forces media leaders to grapple with bigger, messier questions:
Why are conversion rates declining even when traffic is strong?
Where are prospects dropping out of the funnel, and why?
How does media performance change when the application experience changes?
What happens after the lead submits?
These are product questions. Product managers obsess over the end-to-end experience: the user journey, friction points, trade-offs, and outcomes. Media leaders who adopt this mindset stop seeing campaigns as isolated efforts and start seeing them as inputs into a broader system.
In many of the industries I’ve worked in, that system is anything but simple.
Marketing performance rarely exists in isolation. In many industries (especially those with longer decision cycles), a click is just the beginning, not the win.
Whether you’re selling financial services, healthcare, or education, prospects move through nonlinear journeys influenced by multiple touchpoints, stakeholders, and moments of friction. This is where full-stack thinking becomes critical.
Example 1: When media isn’t the problem, the experience is
I’ve lost count of how many times I’ve heard this reaction when performance starts slipping: “The platform is getting more expensive.”
Sometimes that’s true. But a product-minded media leader asks deeper questions:
Has the conversion experience changed recently?
Did we add steps, fields, or requirements?
Are we driving mobile traffic to a hostile desktop experience?
Across industries, I’ve repeatedly seen strong intent at the keyword or audience level, healthy CTRs, and solid landing-page engagement followed by a steep drop-off at the point of conversion. It’s a product experience problem.
In higher ed, this often shows up when high-intent program traffic is routed to lengthy or confusing application flows, generic inquiry forms, or experiences that don’t match the promise of the ad, especially on mobile. Prospective students signal strong intent, only to hit friction that has nothing to do with media and everything to do with the experience they’re asked to navigate.
A full-stack marketer doesn’t just flag this: they bring data, partner cross-functionally, and help prioritize fixes based on impact.
Example 2: Different audiences, different ‘products’
One of the most important product principles is that not all users are the same, and they shouldn’t be treated that way.
Many organizations market to multiple audiences at once, each with different motivations, risk tolerance, and timelines. Treating them as if they’re buying the same “thing” is a fast track to average results.
A product-minded media leader understands that:
The value proposition changes by audience.
The conversion event may be different.
The decision timeline is almost certainly different.
I’ve seen this clearly in healthcare, where patients, caregivers, and referring providers evaluate the same organization through entirely different lenses. Financial services presents a similar challenge, with banking, investment, and insurance decisions varying dramatically by life stage and goals.
Full-stack marketers adapt media strategy accordingly, from channel mix to messaging to measurement. This is because they understand product-market fit, not just audience targeting.
Example 3: What happens after the conversion
One of the biggest blind spots in media strategy is what happens after someone converts. Product thinkers ask:
How quickly does someone follow up?
Is the first touch personalized or generic?
Does the message align with the promise of the ad?
I’ve seen performance improve without changing media at all, simply by improving speed-to-lead or aligning follow-up messaging with campaign intent.
Healthcare offers especially clear examples of this dynamic due to intake workflows, appointment scheduling, and care coordination, but the principle is universal: media doesn’t end at the form fill. The full-stack marketer is accountable for conversions and outcomes.
Another hallmark of product management is roadmap thinking: prioritizing initiatives based on impact, effort, and sequencing. Full-stack media leaders bring this same approach to marketing:
Short-term wins versus long-term bets.
Testing frameworks instead of one-off experiments.
Phase 3: Layer in audience-based creative and messaging.
Instead of chasing the “next shiny channel,” full-stack marketers focus on compounding gains.
Data fluency: Asking better questions
Product managers don’t just look at metrics. They interrogate them. The same should be true for media leaders. Instead of asking, “What’s the CPA?” I’ve learned to ask:
“Which segments are converting efficiently, and which aren’t?”
“How does performance differ by device, geography, or life stage?”
“What signals indicate readiness vs. research?”
In higher ed, this might mean:
Separating brand vs. non-brand intent.
Looking at assisted conversions.
Evaluating performance by program.
Data becomes a tool for decision-making.
Collaboration is the new superpower
Full-stack marketers are inherently collaborative because they have to be. In higher ed, success often requires alignment across:
Admissions.
Enrollment marketing.
IT and web teams.
Academic leadership.
External partners.
Media leaders who think like product managers don’t just execute requests. They help stakeholders understand trade-offs, prioritize initiatives, and rally around shared goals. They also translate data into stories people can act on.
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So, what does this mean for tomorrow’s media leaders?
The rise of the full-stack marketer doesn’t mean specialization is dead. It means seeing the entire system matters more than optimizing any single piece of it.
From my perspective, tomorrow’s strongest media leaders will:
Understand the business behind the campaign.
Think beyond their channel.
Advocate for the user experience.
Use data to inform and influence.
Embrace ambiguity (and occasionally chaos).
In categories where trust, timing, and transformation are at the core of the “product,” this mindset is no longer optional.
At its heart, marketing here is more than campaigns. It’s guiding life-changing choices. If you’re a media leader feeling like your role is expanding faster than your job description — congratulations! You’re not losing focus. You’re evolving.
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Buying AI capabilities to drive marketing is easy. Enabling marketing teams to actually use it independently, decisively, and at scale is far harder.
The main culprit? Humans.
Marketing teams have always had the same elusive goal: to move at the pace of the consumer. Responding to each customer’s needs in real time, delivering the relevant message at the right moment, and optimizing customer lifetime value to drive loyalty and ROI. The goal is not new.
What is perpetually new are the AI technologies available to analyze consumer data and generate instant, personalized messaging at scale. But while technology evolves rapidly, the ability of marketing teams to harness it independently and decisively has not kept pace. The main obstacle is organizational: most marketing teams have not structured themselves to extract full value from the technology they already have.
This is not to say that there is no progress. There is. Marketing teams that have crossed that chasm are seeing extraordinary results.
One case in point is Caesars Entertainment that reduced campaign execution time from five days to five minutes. Asadul Shah, vice president of player revenue Strategy, called it “a massive game changer.”
Before that transformation, Caesars marketers manually built targeting lists across disconnected systems, coordinated across multiple tools and waited on engineers, analysts and creative teams before anything could go out. The result was an operation too slow to target players with the precision and timing the market demanded.
Caesars worked with Optimove to consolidate data, orchestration and execution in one platform. Shah noted the transformation made marketing “not just more efficient; it is more responsive to what our players actually need in the moment.”
What made it work was not technology alone. Caesars implemented Positionless Marketing, a framework that frees marketing teams from fixed roles, giving every marketer the power to execute any task instantly and independently. Optimove provided the platform. Caesars built the team structure to make it real. Technology and human ingenuity working together making Positionless Marketing possible.
Any organization achieving this kind of transformation is doing what McKinsey calls “organizing to value,” a fundamental rethink of structure, decision-making and accountability that turns a marketing team into an operation built to drive value continuously. For marketing, that means becoming a Positionless team that optimizes customer lifetime value, drives loyalty and delivers measurable ROI.Below, we use McKinsey’s Organize to Value framework to outline the pitfalls that block Positionless Marketing and the blueprint to build teams that can execute any marketing task, instantly and independently.
The six pitfalls inhibiting the transition to Positionless Marketing
McKinsey has identified six core problems preventing marketing teams from successfully evolving into the Positionless model. Of these, only one is about technology. All the others are about how leaders and teams are getting in their own way.
Unclear objectives push teams toward activity metrics instead of outcomes. When marketing goals are vague, execution defaults to roles and handoffs rather than impact.
Misaligned governance creates approval layers that add days to decisions that should be faster. In marketing, excessive controls directly conflict with the speed required to deliver customer value.
Uncommitted leaders manage through silos rather than enabling autonomy, preventing marketing teams from evolving past role-based dependency.
Stagnant marketing culture resists experimentation even when the right tools are in place, slowing execution regardless of technology investment.
Muddled marketing execution, with unclear process ownership, leaves no single person accountable for results, and performance erodes accordingly.
Disconnected technology reinforces data compartmentalization and separation of tasks among sub-teams, making strategic alignment and agile responses virtually impossible.
These are the realities of assembly-line marketing operations — not Positionless ones. Insights live with analysts. Creativity lives with designers. Activation lives with engineers. Value disappears in the spaces between them.The assembly line was built for control. It was never built to deliver value.
How McKinsey’s Blueprint helps build positionless marketing teams (and why the effort pays off)
McKinsey’s “Organize to Value” blueprint proposes a fundamental shift: design organizations around value creation, clear outcomes, impact over job titles and minimal friction execution. It provides the foundation to become Positionless and build the conditions for marketing teams to keep customers for life.
To make Positionless Marketing a reality, marketing leaders should focus on pragmatic application and the aspects that most influence marketing execution.
Start with purpose and behavior. Make explicit why actions are taken, alongside what is delivered. A shared sense of purpose allows teams to make fast decisions without waiting for approval on each one.
Restructure work around outcomes and accountability. Map current processes and identify where approvals slow execution without adding value. Build cross-functional flexibility over time rather than reorganizing overnight.
Leadership and processes. Establish a clear decision-to-execution flow and set explicit expectations for how fast each part of the marketing process should move. Processes should enable flow, not control.
Governance, technology and talent. Effective governance ensures consistency without slowing execution. Technology and AI should unlock new value, not just automate existing processes. And talent should be deployed based on what the work requires, not what a title suggests.
Empower marketers to act beyond their role. Once purpose, accountability, process and technology are aligned, marketers should be free to step across traditional job functions and execute independently as Positionless Marketers. The measure of success is not role compliance; it is value delivery.
These changes require sustained commitment. But the alternative (an assembly-line structure that was never built to deliver customer value) is far costlier than the transformation itself.
The results speak for themselves. In addition to Caesars:
FDJ United implemented Positionless Marketing to eliminate overlapping platforms, remove reliance on other teams wherever possible and enable continuous improvement through real-time measurement. Campaign time was slashed from six weeks to hours, with end-to-end campaigns now executed by one marketer from ideation to analysis.
A major retailer achieved a 16.1x increase in purchase rates while saving 300 working hours per year with the same team size. The shift to Positionless Marketing allowed the team to scale personalization and impact without adding headcount… demonstrating that the framework’s value is not just speed of execution, but the ability to do fundamentally more with what you already have.
The window to act is narrowing
The technology and AI tools are here and ever evolving. Today, AI generates infinite creative variants. Data platforms surface real-time behavioral signals. Decisioning engines coordinate across channels instantly.
But technology layered on top of an assembly-line structure creates the illusion of progress. The same handoffs happen. The same approvals add the same delays. Speed arrives at the edge; the bottleneck stays in the middle.
External pressures are accelerating. Customers expect personalization and the best experience across all channels. Competition is rising and growing more complex.
Marketing leaders who wait for transformation will find their competitors have already made it. The ones moving first are pulling ahead.
McKinsey confirms what the best marketing teams already know: the right structure and technology unleash human potential — and vice versa. Smart people trapped in the wrong system will still underperform. The best AI tools in the world won’t deliver results when constrained by the wrong organization.
McKinsey’s blueprint is pointing out the way. Positionless Marketing is the destination.
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Google Ads is set to enhance the viewer experience of Performance Max video ads with an innovative asset optimization feature. Leveraging advanced AI voice models, this update aims to infuse video ads with realistic voice-overs, ultimately enhancing user engagement and ad performance.
Why we care. Advertisers who don’t actively opt out by March 20, will have their video ads automatically enhanced with Google’s AI voice models, changing how their ads sound to viewers without requiring any creative production work.
How it works.
The feature only activates on videos that don’t already contain a voice track
Google’s AI selects text from advertiser-provided headlines and descriptions, then generates a realistic voice-over from that copy
The voice-over is layered onto the existing base video and saved as a new video asset
The catch. This is opt-out, not opt-in. The default setting means ads will be automatically eligible for voice enhancement unless advertisers proactively disable it.
Key dates. Advertisers can choose to exclude their ads from this feature until March 20th. To do so, they must opt out of the video enhancement control. After the opt-out period, all ads with video enhancement control enabled will automatically be eligible for voice-enhanced versions.
Action steps for advertisers. Advertisers can adjust their video settings by visiting their ads in Google Ads.
First seen. This update was shared by Paid Search expert Arpan Banerjee who shared the update on LinkedIn.
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Version 4.6 of Yoast Duplicate Post is here, and it’s all about making your editing experience feel more natural in WordPress’s Block Editor, and making sure “Rewrite & Republish” works reliably every time you need it.
A more modern editing experience
Everything where you’d expect it. The Duplicate Post controls now sit in the Block Editor’s sidebar, right alongside WordPress’s own settings, no more hunting around. If you’re still on the Classic Editor, nothing changes for you.
Buttons that look the part. The “Copy to a new draft” and “Rewrite & Republish” actions are now proper bordered buttons, consistent with the rest of the WordPress interface. Cleaner, clearer, and easier to use.
Built for the future. Under the hood improvements ensure Duplicate Post stays stable and compatible as WordPress continues to evolve, so you don’t have to think about it.
Yoast Duplicate Post has always been about reliability. While the plugin has served millions of you faithfully since our last release, we’re excited to bring you version 4.6. This update is packed with long-awaited fixes and thoughtful interface refinements that ensure the plugin stays modern, stable, and ready for the future of WordPress.
Enrico Battocchi – Plugin team lead and creator of Duplicate Post
More reliable “Rewrite & Republish” workflows
Your posts won’t get stuck. If something goes wrong mid-process, like a redirect being interrupted, the plugin now handles it gracefully and cleans up automatically. Your content will never be left in a stuck state.
Attachments copied completely. All attachment metadata, including captions and descriptions, is fully preserved when you duplicate a post. Nothing gets left behind.
International & security improvements
The right words, in your language. Buttons and notices in the Block Editor are now correctly translated across all languages, with none of the behind-the-scenes errors that some locales were seeing.
Consistent styling, always. Buttons display correctly regardless of your admin configuration, including when the WordPress admin bar is turned off.
Version 4.6 is available now. As always, we recommend testing in a staging environment before updating your live site.
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58% of consumers now use GenAI tools instead of traditional search to find products.
Imagine your customer runs a simple query in Google’s AI Mode: “Winter jackets for women.”
Instead of a long list of links, they get direct product recommendations — alongside:
Descriptions of features and best use cases
Ratings and reviews
Editorial sites that mention the product
Direct comparisons with top competitors
All in one response.
Which raises an obvious question:
Why do some products show up, while others are ignored entirely?
Many factors influence AI recommendations.
But one of the most important — and most controllable — is your product pages.
In basic terms, AI needs to understand what your product is and who it’s for.
When that information is clear, structured, and specific, your products have a much better chance of appearing in AI results.
In this guide, we’ll break down how AI evaluates product pages, and which elements matter most.
Plus, we’ll see how leading ecommerce brands structure their pages to get recommended.
Free checklist: To get a head start, download our Product Page AI Optimization Checklist. It includes everything you need to get more product mentions in AI platforms.
How AI Models “Think” About Product Pages
Ever wondered how large language models (LLMs) choose which products to surface in answers?
While there’s a lot at play, you can basically narrow it down to two factors:
Consistency: Information about your brand and products matches across your website and third-party sites
Consensus: Multiple reputable sources validate your product’s quality, use cases, and performance. This includes reviews on your product pages and third-party sites.
For LLMs to confidently cite a product page, they need consistent, up-to-date information.
AI models analyze product pages to pull details that help them answer user queries.
Remember, AI queries don’t look like a regular search.
Prompts are often highly specific requests for products that fit a clear use case or situation.
Example: What are the best women’s road racing shoes for a 10K in Ireland?
AI looks for product pages that clearly communicate:
What the product is
What it’s used for
Who uses it
In what situations it can be used
This helps the system understand your product in the context of user queries.
Take this Nike road racing shoe product page, for example.
AI systems understand when and how to recommend this product because it contains details like:
What the product is: “Women’s Road Racing Shoes”
Who should use it and when: Racing-related language like “marathon” and “race day shoe” makes it clear this product is for racing
When I searched “best road racing shoes for women” in AI Mode, it recommended Nike’s Alphafly.
And where did the information it quoted come from?
Nike’s own product page.
AI models also look for consensus signals on product pages.
This includes customer reviews and ratings.
When AI analyzes reviews, it looks for patterns. This includes repeated mentions of specific use cases, features, or product benefits.
For example, the Nike Alphafly is highly rated with plenty of reviews on the Nike website.
Among other benefits, this improves its chances of being recommended by AI platforms.
But AI doesn’t rely solely on product pages.
It cross-references independent sources to back up claims about your products.
In a similar search for racing shoes, I found that AI Mode cites various third-party sources to support its recommendations.
Like this one, that includes a review of Nike shoes, complete with product details.
Product pages are one piece of the AI visibility puzzle.
But they create the foundation AI systems need to confidently recommend your products.
6 Essential Elements of a Product Page for AI Visibility
You likely already have some (or all) of the elements below on your product pages.
But for AI visibility, having them isn’t enough.
What matters is clarity, specificity, and structure.
Note: These elements aren’t in any particular order: all are important for AI visibility.
1. Clear Product Descriptions with Semantic Language
A clear product description explains more than what your product is. It spells out what it does, who it’s for, and why someone would choose it.
This matters for AI visibility because LLMs rely heavily on semantic retrieval.
In other words, AI understands the intent and meaning behind queries. Not just exact-match keywords.
For example, when someone searches for “vacuum for pet hair,” AI doesn’t just look for that phrase.
It also looks for semantically related terms. Things like “stubborn hair,” “carpets,” “pet odors,” and “allergens.”
These terms help AI infer use cases, surface the right features, and decide when your product is a good fit.
Including them on product pages improves your chances of appearing in AI-generated answers.
So, how do you find these terms?
First, read forums, reviews, and social media conversations.
Learn how people talk about the problems they’re facing and the products they’re using.
Using our vacuum example, I dove into r/VacuumCleaners. There, I found recurring phrases around weight, clogging, tangles, and flooring-specific concerns.
Next, conduct keyword research on related terms.
This shows you how people actually phrase their searches.
A tool like Semrush’s Keyword Magic Tool is great for this task.
This feature brings buy-in-chat functionality to eligible product recommendations in AI Mode and Gemini.
But what if you don’t use a product feed or API?
LLMs can still find product information on public webpages. But it may be outdated.
And that’s a problem.
AI platforms evaluate recency and consistency.
Mismatched prices or outdated stock can hurt your AI visibility. In part, because it leads to a poor customer experience.
To see how this plays out in practice, I tested ChatGPT’s “Shopping research” mode.
The AI asks questions to narrow results, including how much you want to spend.
I told ChatGPT I was looking for a new couch. I specified both my budget and need for delivery to Massachusetts.
ChatGPT returned five options, all of which fit my budget and availability requirements.
The “Best overall” option even highlighted that it was “in stock for fast delivery” to my state.
To further test how price affects results, I asked if any of the recommended couches were on sale.
It narrowed down my options and provided sale pricing.
ChatGPT only mentioned one couch as being on sale.
To find out why, I reviewed the product pages for each recommendation. But only one clearly highlighted both the original and sale price.
Walmart’s product pages boldly showcase the previous price versus the discount.
In its response, ChatGPT specifically mentioned that Walmart displays this info on its product page.
Walmart also submits its product feeds to platforms like Google Merchant Center.
So its pricing (both sale and original) is clear and current across platforms.
Product feeds and APIs keep your price and inventory fresh.
When AI systems have access to this data, they can recommend your products when users narrow options by price, availability, or discounts.
3. Ratings and Reviews
Many AI systems display ratings and reviews in product recommendations.
In AI Mode, you can click a product recommendation and see reviews directly in the sidebar.
ChatGPT also includes information from reviews.
It often surfaces them as part of the response:
But LLMs do more than show you reviews. They also weigh reviews and ratings when choosing recommendations.
ChatGPT often includes labels like “Budget-friendly” or “Most popular” based on reviews.
OpenAI has confirmed that answers may include summaries of the themes most commonly mentioned in reviews.
That could mean pros, cons, and use cases pulled directly from reviews.
Here’s how that looks in practice when I search for warm winter hiking boots:
Ultimately, reviews on your product page don’t just affect whether your product appears in AI search.
They can also influence how it’s positioned.
When AI systems analyze reviews, they look for consistency:
Repeated mentions of specific use cases
Commonly praised features
Patterns in star ratings
Shared language around benefits or problems
The more clearly those patterns emerge, the easier it is for AI to confidently recommend — and describe — your product.
This applies to reviews on your own product pages and on third-party sites.
When I asked AI Mode for a hydrating cleanser for sensitive skin, the first recommendation was a product from CeraVe.
Interestingly, the product description itself doesn’t explicitly emphasize “sensitive skin.”
But the reviews on CeraVe’s product page do.
Here’s what I noticed:
Reviews are tagged with commonly mentioned phrases
One of the most prominent tags is “sensitive skin”
There are over 100 reviews referencing sensitive skin — most of them positive
Having reviews on every product page is a best practice that increases trust and authority.
Encourage customers to leave detailed feedback by:
Prompting for use cases in review forms
Asking follow-up questions after purchase
Offering light incentives (like a coupon) in exchange for honest reviews
Note: The most important thing is that these reviews are real. Fake or AI-generated reviews may temporarily improve your brand’s visibility in AI search. But they are never worth the long-term risk to your reputation.
4. Contextual Use Cases
AI search looks for explicit connections between what a product is and why someone needs it.
So, your entire product page should explain when, why, and in what situations a product makes sense.
This requires a shift in how you think about product marketing.
Instead of asking, “What can this product do?”
Ask, “In what specific scenario would someone actively look for this?”
Start by identifying who buys your product and what triggers that purchase. If you don’t already have this insight, customer interviews are your fastest path.
Look for:
The situation that prompted the search
The alternatives they considered
The constraint that mattered most (travel, space, safety, performance, etc.)
Once you have this, choose one or two clear, specific use cases to feature on each product page.
Don’t just list all the possible ways your product can be used.
AI isn’t great at matching vague versatility.
Instead, focus on the use cases that come up repeatedly in customer conversations. That way, AI can match your product to a specific intent.
Let’s look at an example for an electronics brand.
This product page for Anker’s 3-in-1 mobile charger states it’s “ultra compact and travel friendly.”
When I search for travel-friendly chargers on ChatGPT, Anker’s 3-in-1 device is the top recommended product.
Obviously, this little charger is a great option for more than just travel.
But by calling out that use case on the product page, it makes it easier for LLMs to recommend it in related queries.
5. Awards and Certifications
LLMs prioritize trustworthy, verifiable information when recommending products.
One of the strongest ways to demonstrate that trust is to feature third-party validation on your product pages.
This includes:
Industry awards and “best of” recognitions
Third-party testing results
Safety and quality certifications
Sustainability or ethical production badges
To see how much awards affect AI visibility, I analyzed 50 ecommerce brands in Semrush’s AI Visibility Overview tool.
This included Samsung, Patagonia, Everlane, Caraway, and others.
First, I identified brands with high AI Visibility scores.
This is a Semrush metric that measures how often brands appear in AI-generated answers.
I focused on brands scoring above their industry average. (This varies by industry, but is generally between 60 to 90.)
Next, I looked at how many of the top-ranking brands feature awards and certifications on their product pages.
And I found something very interesting:
82% of the brands with medium to high AI visibility prominently feature awards and certifications on their product pages.
For example, Samsung has an AI Visibility score of 90.
And its product pages feature multiple awards.
Like being “rated #1 in camera quality” by the American Customer Satisfaction Index.
And winning “Best Phone Camera” by Consumer Reports:
When I asked Claude which phone has the best camera quality, the Samsung Galaxy was one of its top recommendations:
BabyBjorn has an AI Visibility score of 67.
A quick look at its product pages reveals certificates and awards on every product page.
Like this one that references a “Best Bouncer” award from Parents Magazine:
When I asked ChatGPT to recommend the “best and safest baby bouncer,” BabyBjorn was the #1 pick:
Now, this is correlation, not necessarily causation. And awards and certifications are not the only factor.
But they can make a difference for product page visibility in LLM search.
If you already have awards and certifications, showcase them prominently on your product pages.
If you don’t, create a strategy to earn them.
Target industry-specific certifications (safety, quality, sustainability) and awards from reputable organizations.
This includes relevant certifications and “best” awards through PR outreach.
6. Structured Attributes and Schema Markup
Structured attributes are pieces of product information that machines can easily understand.
This includes things like:
Price
Dimensions
Materials
Ratings
Availability
Color
Size
Warranty details
These attributes are vital components of a product page.
Use tables, bullet lists, or specification sections to clearly structure them for machines and customers.
They should also be in your structured data and product feeds.
For example, health company Vitamix features a “Specifications” section on its product pages:
We can’t say definitively that schema affects LLM visibility (yet).
But major AI search engines confirm they rely on structured attributes to understand and recommend products.
What OpenAI says: “When determining which products to surface, ChatGPT considers structured metadata from first-party and third-party providers (e.g., price, product description).
Depending on your needs, some of these factors will be more relevant than others. For example, if you specify a budget of $30, ChatGPT will focus more on price, whereas if price isn’t mentioned, it may focus on other aspects instead.”
Plus, it’s no secret that structured data helps products appear on Google’s main page and Shopping tab.
It’s what allows users to refine results, see ratings, and check prices right on the first page of Google.
But here’s where it gets interesting.
When I conducted a search in AI Mode, Google’s own shopping cards were the main sources.
Clicking into one of those sources, I saw even more of that search-friendly structured data.
And where does all this information come from?
You guessed it: the original product page.
That same structure is what enables Google’s AI responses to display live pricing, availability, sales, and comparisons.
Clear, consistent schema simply gives search engines and LLMs more to work with.
That context helps AI more confidently recommend your product in related queries.
AI Visibility Essentials for Product Pages (By Industry)
The elements above matter on every product page.
But AI evaluates product pages differently depending on the category.
In this section, we’ll break down the category-specific product page details that AI looks for across six common ecommerce industries.
Fashion Brands
Ask any AI engine for clothing recommendations, and you’ll notice something consistent: the results highlight fit, materials, and comfort.
Clearly, the most important product page elements for fashion brands are:
Clear sizing and conversion charts
Material and care information
Customer fit data
Sustainability certifications and ethical production badges
Fashion queries are also highly specific to the individual shopper.
To see how AI handles these searches, I used Semrush’s AI Visibility Toolkit.
I analyzed the topic “jeans for women” using Semrush’s Prompt Research tool.
What’s revealing is the variety of queries under this topic.
Take “Plus size and curvy women’s jeans” for example.
Even within this niche, searches vary widely:
“Best plus size jeans for big thighs”
“Best curvy fit jeans”
Most comfortable jeans for curvy women”
Across all these queries, the AI responses consistently emphasize the same details:
High-rise styles
Stretch denim
Tummy control
Specific silhouettes like bootcut
These details are pulled directly from product pages and customer reviews:
For AI to match products to these specific queries, it needs structured details on your pages.
This is something Abercrombie & Fitch does well.
They display clear fit guidance and aggregated customer fit feedback prominently on product pages.
Health and Wellness Products
Nothing is more important to health and wellness brands than trust and safety.
That’s why non-negotiables for product pages in this industry include:
Full ingredient composition
Clear dosage and instructions
Contraindications and allergen warnings
Source transparency
Clinical studies or certifications
Searches for health products are often deeply personal and complex.
Many start with a product type and the demographic it’s best for.
For example, the topic of “infant multivitamins” includes these common searches:
“Where can I buy reliable infant multivitamins?”
“How do I choose the best multivitamin for my baby?”
In their responses, AI models pull from ingredient lists, dosage information, and certifications.
Brands that perform well for wellness-related AI queries follow the same pattern.
They provide detailed information about ingredients, sourcing, and production on their product pages.
This is what helps popular health company Thorne get recommended often in AI search results:
Their product pages list ingredients in detail:
They also include dosage instructions and verifications of the product quality.
All in a clear, machine-readable format.
Electronics
When it comes to electronics, AI loves to quote specs.
Battery life, screen resolution, charging speed, refresh rates, and more are all pulled into responses.
So every electronics product page should include the essentials:
Full technical specs
Compatibility information
Setup or installation guides
Safety and efficiency certifications
For example, even a simple search — “best cameras for night photography” — returns spec-heavy recommendations.
Structured specs give AI systems what they need to compare products.
This is important on your own site and third parties.
Brands like Sony excel here.
They ensure their product and retailer pages feature technical details that are consistent and in-depth across platforms.
Home and Furniture Brands
Furniture shopping comes with one big question: Will it fit?
AI knows this, which is why technical details dominate recommendations.
Your home and furniture product pages need:
Clear dimensions and room size recommendations
Assembly requirements (tools, time, difficulty)
Materials and care details
Quality and sustainability certifications
For example, in a search for modular sofas for small apartments, ChatGPT mentions configurations in its answer:
One of its top recommendations is a couch by home brand Burrow.
While many factors go into this, its product page is definitely one of them.
It features different configurations of their modular sofas. Plus, the dimensions of each.
It also contains other vital information that users might ask AI systems, such as detailed materials and fabric care.
Outdoor and Sports Equipment
Customers need to know whether your products will survive their outdoor adventures.
Which is why AI takes these elements into account:
Weather ratings and technical materials
Performance specs (capacity, weight, range)
Use-case scenarios
Safety certifications or features
Let’s say your customers ask about hiking backpacks. They’ll see AI models highlight key features, max load, and materials.
Osprey’s backpacks are regularly recommended by AI.
This is because they clearly state use cases like “week-long backpacking trips”:
They also include features that make it ideal for common use cases: materials, weight, volume, dimensions, and load range.
Baby Products
Baby products trigger some of the most safety-sensitive AI recommendations.
AI models look for structured, verifiable details when recommending anything for infants.
If you sell baby products, here’s what your product page should include:
Age and weight suitability
Safety certifications (like OEKO-TEX, GREENGUARD)
Ergonomic or developmental benefits
Material and care instructions
For example, BabyBjorn includes safety certifications on its product pages.
And goes deep into safety information.
This includes how the fabrics are developed, and the appropriate age and weight for safe use.
When I asked Perplexity for the safest baby carrier on the market for newborns, BabyBjorn was among its top recommendations.
It also specifically mentioned the “hip healthy” certification featured on BabyBjorn’s product page.
Increase Your Product Page Visibility in AI Search
If you want AI to recommend your products, the best place to start is your product pages.
Small improvements compound quickly.
Clear descriptions. Structured data. Real reviews. Verifiable trust signals.
Together, they shape how AI understands — and surfaces — your products.
But product pages are just the start.
First, download the Product Page AI Optimization Checklist. It tells you exactly what to review, update, and add to make your product pages AI-friendly.
Then, learn how to build an AI ecommerce SEO strategy that improves your visibility across the entire buyer journey.
AI visibility is possible for your products. Keep testing, keep tracking, and keep growing.
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