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has published hundreds of videos across various series, from Search for Beginners and Ask Googlebot
to Search Console Training and Search Off the Record. While our series structure works well for
deep dives and episodic updates, we know that finding a clear, sequential learning path from scratch can sometimes be challenging.
PR budgets could double by 2027 as brands adapt to AI-powered search.
AI systems increasingly rely on authoritative third-party sources when generating answers and recommendations.
Digital PR can help brands build visibility and credibility beyond their own websites.
Consistent earned media coverage can strengthen a brand’s authority over time.
Brands should connect digital PR with SEO and AI visibility rather than treating them as separate strategies.
For years, public relations and SEO have operated in neighboring but often separate parts of the marketing organization. AI search is beginning to change that relationship.
As AI systems become a larger part of how people discover companies, products, and services, the sources those systems rely on are becoming increasingly important. Third-party coverage from trusted publications can provide information that helps establish a brand’s credibility and authority. That shift is changing how organizations think about digital PR. New industry projections suggest PR budgets could double by 2027 as brands respond to the growing importance of AI-powered search. The underlying reason is straightforward: brands need to be visible in the places AI systems use to understand and evaluate them. For marketers, that makes earned media more than a traditional awareness channel. Consistent third-party coverage can contribute to a brand’s broader search presence while strengthening the information available about it across the web.
Why AI Search Is Increasing the Value of Digital PR
AI search changes how people encounter information. Instead of receiving a list of links and deciding which ones to visit, users can receive a synthesized answer based on information gathered from multiple sources.
AI systems need information they can retrieve and use when generating answers.
A company’s own website is an important source, but it isn’t the only one. News publications, industry websites, reviews, research organizations, and other third-party sources can provide additional context about a brand. Academic databases are a great example.
This is where earned media becomes valuable.
A company that is consistently mentioned by credible publications creates a broader information footprint than one that relies primarily on its own website. Those mentions can help reinforce what the company does, who its experts are, and where it has authority.
That doesn’t guarantee an AI system will cite any particular article. It does create more opportunities for a brand’s information to exist within the sources AI systems may encounter.
Third-Party Coverage Can Build Brand Authority
Authority is difficult to establish through a single piece of coverage.
One major publication can create awareness, but its effect can fade. A sustained presence across relevant publications creates a different type of signal.
Repeated coverage can reinforce the same core facts about a business across multiple sources. Over time, this can contribute to stronger brand recognition and a clearer understanding of the company’s expertise.
For AI visibility, that consistency matters because AI systems are constantly processing information from across the web.
Why One Media Win Isn’t Enough
The temptation in PR has often been to measure success by the biggest placement a campaign can generate. A marquee publication can certainly be valuable, but it shouldn’t be the only objective.
Credibility Compounds Over Time
A single article can introduce a company to a new audience. An ongoing stream of relevant coverage can keep reinforcing the brand.
This is particularly important for companies competing in categories where many businesses make similar claims.
Regular coverage gives journalists, customers, search engines, and AI systems more opportunities to encounter consistent information about the brand.
That makes digital PR a long-term investment rather than a series of isolated campaigns.
Consistency Creates a Stronger Information Footprint
Imagine two companies competing for visibility in the same industry.
One receives a major feature once a year. The other is regularly covered by relevant publications for research, commentary, product developments, and industry expertise.
The second company has a much larger body of third-party information surrounding it.
That doesn’t mean quantity automatically creates authority. Coverage needs to be relevant and credible. But a consistent stream of meaningful mentions gives a brand more opportunities to establish expertise and reinforce its identity.
What This Means for SEO and AI Visibility
The growing importance of digital PR creates an opportunity for SEO teams and PR teams to work more closely together.
Digital PR and SEO Should Work Together
SEO has traditionally focused heavily on a brand’s own website. Digital PR extends that strategy into the broader web.
A strong PR campaign can generate coverage that supports brand awareness, creates referral opportunities, and contributes to the overall authority surrounding a company.
At the same time, SEO research can help identify topics, publications, and audiences that are strategically important to the brand.
Combining these approaches creates a more connected approach to organic visibility.
AI Visibility Depends on More Than Owned Content
Brands have significant control over their own websites. They have much less control over what independent publications say about them.
That makes third-party coverage particularly valuable.
When a brand is consistently described by credible external sources, those sources can provide context beyond what the company says about itself. This can be especially useful when AI systems are trying to determine which businesses, products, or experts are relevant to a user’s question.
The goal isn’t to manufacture mentions. It is to build a legitimate reputation that is reflected across the web.
How Brands Can Build an Always-On Digital PR Strategy
If digital PR is becoming more important to AI visibility, brands need a strategy that goes beyond occasional campaigns.
Create a Consistent Flow of Newsworthy Ideas
An always-on program starts with having something worth talking about.
Brands can develop stories around original research, customer trends, proprietary data, expert commentary, product developments, and broader industry changes.
Not every idea needs to become a major campaign. The goal is to consistently identify useful information that journalists and audiences would find valuable.
Build Relationships With Relevant Publications
Distribution alone isn’t enough.
Brands should identify publications and journalists who regularly cover their industry and develop relationships with them over time.
This makes it easier to provide useful expertise when relevant stories emerge rather than approaching media outlets only when the brand has something it wants promoted.
Measure the Cumulative Impact
An always-on strategy should be evaluated differently from a campaign built around one major placement.
Track the growth of relevant coverage over time alongside brand visibility, branded search, referral traffic, and other meaningful indicators.
For AI visibility, also monitor how frequently the brand appears in relevant AI answers where measurement is possible.
The objective is to understand the cumulative effect of sustained authority building.
PR Investment Is Becoming an AI Visibility Investment
The growing importance of AI search is changing the role of PR.
Brands still need traditional media coverage because journalists and publications reach real audiences. But those same sources can also contribute to the information ecosystem that AI systems use to understand brands.
That makes earned media increasingly relevant to search strategy.
The strongest competitive advantage in AI search is consistency, not a single media win. Credibility compounds through ongoing earned coverage that reinforces brand authority over time.
The implication for marketers is significant. PR budgets shouldn’t be viewed only through the lens of immediate media impressions. The long-term value of consistent coverage can extend into brand authority, organic visibility, and AI discovery.
How to Prepare for the Shift in PR Investment
Brands don’t necessarily need to abandon existing PR programs. Instead, they should consider how those programs can support broader search and visibility goals.
Connect PR Goals with Search Goals
PR and SEO teams should identify the topics, entities, and areas of expertise that matter most to the brand.
Those priorities can then inform media outreach and content development.
A company trying to establish authority in a particular category, for example, can build an ongoing PR program around the research, expertise, and commentary that supports that position.
Prioritize Quality and Relevance
More coverage isn’t automatically better.
A mention from an irrelevant or low-quality source may contribute little to a brand’s authority. The strongest opportunities are publications and sources that are relevant to the industry and trusted by the audiences the brand wants to reach.
Make Digital PR an Ongoing Investment
The biggest change may be moving away from one-off PR campaigns.
An always-on digital PR program gives brands more opportunities to build relationships, publish useful information, and earn coverage throughout the year.
That sustained activity can create a stronger foundation for organic visibility as search continues to evolve.
The Future of PR Is Connected to Search
AI is changing how brands are discovered, and PR is becoming part of that discovery process.
Third-party coverage gives AI systems additional information about brands while giving consumers independent sources they can use to evaluate a company. Over time, a consistent stream of relevant coverage can help strengthen the brand’s authority across the web.
This doesn’t mean every PR placement will result in an AI citation. It means brands should recognize that the value of earned media can extend beyond the publication’s immediate audience.
The brands that invest consistently in credible coverage will have more opportunities to establish authority as AI search becomes a larger part of how people find information.
FAQs
Why is AI changing PR budgets?
AI search is increasing the importance of authoritative third-party information. As AI systems use external sources to understand and answer questions about brands, organizations are placing greater emphasis on earned media and digital PR as part of their broader visibility strategies.
How does digital PR help with AI visibility?
Digital PR can generate credible third-party coverage that provides additional information about a brand across the web. This can contribute to the broader information ecosystem AI systems use when understanding brands, although coverage doesn’t guarantee an AI citation.
Is earned media becoming more important for SEO?
Earned media can complement SEO by building authority and visibility beyond a company’s own website. Relevant third-party coverage can strengthen brand recognition and provide additional sources of information for search and AI systems.
What is an always-on digital PR strategy?
An always-on strategy continuously identifies opportunities for relevant media coverage throughout the year instead of relying on occasional campaigns. It can include original research, expert commentary, industry insights, and other newsworthy material.
Conclusion
AI search is changing how consumers discover and evaluate brands, and that shift is increasing the strategic value of digital PR.
Third-party coverage can give AI systems additional sources of information while helping consumers validate a brand’s expertise and credibility. A single major placement can create valuable exposure, but consistent coverage can build a much stronger information footprint over time.
For marketers, this means PR and SEO should increasingly work toward the same visibility goals. Building an always-on digital PR program can create a steady stream of earned media that supports brand authority while contributing to a broader AI visibility strategy.
The brands that treat PR as an ongoing investment in credibility will be better positioned as AI search continues to reshape how people discover businesses.
If you want to build an always-on digital PR strategy that supports both traditional search and AI visibility, reach out to the NP Digital team to learn how earned media can fit into your broader organic strategy.
Yoast SEO AI+ for Shopify is live today. The new plan combines Yoast SEO for Shopify with Yoast AI Brand Insights, giving you visibility into how AI systems like ChatGPT, Gemini, Claude, and Perplexity describe your brand, alongside everything you already rely on for search.
Why now
More shoppers are starting their search with AI these days, not just Google. Shopify has seen this firsthand: in Q1 2026, AI-driven traffic to Shopify stores grew 8x year over year, and orders from AI-powered search grew nearly 13x (Shopify, “How Agentic Commerce Works,” 2026).
That doesn’t mean checkout itself has moved to AI, at least not yet. OpenAI closed ChatGPT’s Instant Checkout back in March. What’s actually happening is simpler: AI is helping people discover and compare products, and then they come to your store to buy. AI is becoming a new way for shoppers to find you. Your store is still part of where that relationship builds, alongside social and everything else that shapes how your brand comes across, and it’s still where the sale happens.
That’s why it’s worth knowing what AI is already saying about your brand, before a shopper ever reaches your site. That’s what Yoast SEO AI+ for Shopify helps you do.
Your products have a new audience: AI
See the pitch it’s making. Shape the narrative to your advantage.
Yoast SEO AI+ for Shopify gives you structured data and technical SEO so your products are read correctly, and Yoast AI Brand Insights so you can see how your brand comes across once shoppers arrive.
What’s new
Yoast SEO AI+ for Shopify combines Yoast SEO for Shopify with Yoast AI Brand Insights. Everything you already rely on, product SEO, structured data, technical SEO, is still there. What’s new is visibility into the layer before the sale: how AI systems describe your brand before a shopper ever reaches your store.
With AI Brand Insights, you can:
See your brand’s AI Visibility Index score
Know whether AI speaks positively about you, with sentiment tracking
See how you compare to competitors in AI
See which sources AI is citing when it talks about you
Run scans across ChatGPT, Gemini, Claude, and Perplexity, then decide what to do with what you find. AI Brand Insights shows you the story AI is telling about your brand. It doesn’t write it for you. That part is still yours.
What’s included
Yoast SEO AI+ for Shopify is the complete product visibility plan: everything in Yoast SEO for Shopify, plus AI Brand Insights. You can compare the plans here.
Pricing
Yoast SEO AI+ for Shopify is $45/month or $450/year, with a 14-day free trial.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-10-01 12:30:582026-10-01 12:30:58Introducing Yoast SEO AI+ for Shopify
Is your 2026 SEO strategy actually ready for the next wave of AI search updates?
Between AI-driven search overhauls and constant algorithm tweaks, keeping your site visible can be challenging.
The SEO Update by Yoast brings you the latest insights on algorithm updates, AI-driven search changes, and industry developments, all in one easy-to-follow session.
Join Carolyn Shelby and Alex Moss as they discuss the stories shaping SEO today and share actionable takeaways you can apply right away.
Who should sign up?
This update is ideal if you:
Want expert insight into recent SEO and AI changes and trends
Need help refining or validating your SEO strategy
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-10-01 11:00:242026-10-01 11:00:24The SEO Update by Yoast – October 2026
If you’ve been setting up MCP connections or agentic workflows, this release is for you. You ask an AI tool to check your site’s SEO. It comes back with real findings: a product page that’s telling Google it’s a recipe, a link still pointing to an old test version of your site. Helpful, but useless on its own: you still have to go and fix each one yourself, by hand, in the back end of your website. Now, Yoast SEO enables the same AI tool to make those changes for you.
Yoast SEO Abilities is what makes that possible. It lets your AI tool read what’s wrong with a page’s SEO and update it directly, the same technical settings you’d otherwise have to change by hand. You diagnose and fix in the same place.
AI tools are moving from answering questions to being able to take actions. Abilities gives that shift a front door on your own site, one you control.
What you could do before
Since its release in April, Yoast SEO already registers three abilities with WordPress’s Abilities API: SEO scores, readability scores, and inclusive language scores for your most recently modified posts. Any AI tool could reliably check which posts had a weak SEO score. However, it had no standardized way to read the underlying fields or change them. Someone still had to open wp-admin.
That gap is what this release closes.
Read a post’s full SEO field set
yoast-seo/get-post-seo-data returns the title, meta description, robots flags, schema type, canonical, social previews, and scores.
In addition, you can identify the post by ID, permalink, or title search. As a result, your agent can find the post you mean from a plain-language description in one call, instead of hunting through several.
Fix the technical fields
Core technical settings are now writable from your AI tool: canonical, robots flags, cornerstone, and schema type. These are fields Yoast already manages, so nothing new sits between your content and your site.
Roll back what you change
Any change made through the WordPress Abilities API can be rolled back, as long as your AI tool holds the changed values in context. In practice, read a post’s SEO data before you update it. Then stay in that session while you review the result. Once the context is gone, the previous value goes with it, so treat the read call as part of the write.
Work in bulk
Bulk and batch operations let you apply a get, update, or filter request across multiple posts in one call. You get per-post results back, so you can see exactly what changed and where.
Control where it matters
Your focus keyphrase stays read-only. You always set it yourself, and it is never generated for you. An automatic change there could invalidate content briefs or marketing workflows, and it would skew every assessment that follows. For content workflows, we suggest continuing in the editor, where the content flow lives.
Access follows native WordPress capability checks. There is no separate permission system to learn. Therefore, you can audit access the same way you audit any other admin-level access.
Built on WordPress standards
These abilities use WordPress’s Abilities API. Once you connect the WordPress MCP Adapter, they become callable MCP tools for any MCP-aware client, for example Claude Desktop, Claude Code, Cursor, or VS Code. Therefore no custom integration is needed.
Typed input and output schemas spell out what to send and what to expect back. For anyone building dashboards or reporting tools on top, that predictability matters more than any single feature.
For agencies, it also means one standard setup across every WordPress build, with no manual per-site reconfiguration.
What you need to get started
Connect your own LLM subscription to Yoast first. After that, read access, core SEO field updates, and bulk operations are all included as part of Yoast SEO. The developer documentation covers the full ability list, the typed input and output schemas, and how to connect the WordPress MCP Adapter.
See it in your dashboard
Once you’ve connected your LLM subscription, head to Yoast SEO, then Integrations. We’ve also reorganized this page: instead of one long list, integrations are now grouped by topic, so they’re easier to scan. Look for the new AI & agentic group. For further information, read the help article on how to set up Abilities.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-29 07:12:012026-09-29 07:12:01Turn your AI tools from spectators into collaborators with Yoast
AI systems are more likely to reference brands that present original research.
Research suggests that where information appears on a page can influence its chances of receiving an AI citation.
Clear formatting makes original findings easier for AI systems to identify and extract.
Brands can apply this approach to blog posts as well as product and service pages.
Proprietary data can strengthen a brand’s authority while creating new opportunities for AI visibility.
Original research gives brands something valuable in an increasingly crowded search landscape: information that isn’t available everywhere else.
That advantage may become even more important as AI search changes how people discover information. A recent Search Engine Land analysis of AI citations found that proprietary data can be a strong differentiator for brands seeking visibility in AI-generated answers.
The research also points to an important qualification: Unique information alone isn’t enough. AI systems were more likely to cite information when it appeared earlier on the page and was presented in a format that made it easy to identify and extract.
For marketers, this changes how original research should be approached. The value lies in both what you publish and how you present it.
What the Research Found About AI Citations
The research highlights two factors that can work together when brands publish original information. The first is having something unique to contribute. The second is making that information easy to find.
Original Data Gives AI Systems Something Unique to Cite
AI systems have access to an enormous amount of information. When the same statistics, explanations, and observations appear across numerous websites, individual pages can have a difficult time standing out.
Proprietary data gives brands an opportunity to offer something different. This can take the shape of:
Original surveys (e.g., asking 1,000 homeowners about their home-buying journey)
Audience research (e.g., most and least common homebuyer demographics, according to your CRM data)
Internal studies (e.g., analysis of lead form data to present trends in average home loan amount requested over the last 20 years)
Industry statistics (e.g., home loan interest rate trends during times of economic uncertainty)
When an AI system needs to answer a question related to that subject, unique findings give it a source that offers information beyond the standard answers already circulating online.
For content teams, this makes original research worth considering during the planning process. A page that contributes a new finding can offer more value than one that simply summarizes information already available on competing sites.
Where Information Appears Can Affect Its Visibility
The research also found that information appearing earlier on a page was more likely to receive an AI citation.
That finding gives marketers another factor to consider when organizing content. If an important statistic is buried several sections into an article, an AI system may have a harder time identifying it as one of the page’s key contributions.
This doesn’t mean every page should follow a rigid formula. It does suggest that important findings deserve prominent placement.
When a piece of original research is central to a page, introducing the finding earlier can make the information easier for both AI systems and readers to locate.
Why Content Structure Matters for AI Visibility
Publishing unique information is only part of the opportunity. The research also highlights the role that page structure plays in making that information accessible.
An AI system needs to identify and interpret information before it can use that information in an answer. Clear, well-organized writing can make that process easier.
Consider a page containing an original statistic. Instead of making readers work through several paragraphs before reaching the finding, state the result clearly under a relevant heading. Then explain what the number represents, how it was collected, and why it matters.
This gives the statistic enough context to stand on its own.
The goal isn’t to create content specifically for machines. Good structure makes important information easier for everyone, especially your human readers, to understand.
Put Your Strongest Insights Near the Top
The findings also support a practical recommendation for content teams: consider bringing your strongest original insights closer to the beginning of the page.
NP Digital recommends surfacing important original statistics within roughly the first third of educational and commercial pages. This helps crawlers and readers find the most critical information early on.
The exact placement will depend on the page. A product page may need introductory information before presenting a research finding, while an educational article might be able to lead with the data.
The important point is to avoid hiding the information that makes your content distinctive.
How to Make Original Data More Visible in AI Search
The research provides a useful direction for content teams, but putting it into practice starts with looking at the information a brand already has. If we look at the examples below, we can see how proprietary data appears both on a brand site and in an AI overview citing it.
Start by auditing existing content for topics that could benefit from original research. Look for pages that touch on trends, make claims, or target keywords that include phrases like “how many” or “how much.”
Customer research, surveys, internal studies, and product usage data can all provide useful fodder. Some of these insights may already exist within an organization but have never been published publicly.
There may also be opportunities to conduct original research to create new content. A study focused around an important industry question can produce findings that other websites don’t have.
The goal is to contribute information that adds value to the topic. Adding statistics simply to make a page appear more authoritative won’t create the same benefit.
Structure Data for Humans and AI Systems
Once you identify original information, look at how it is presented.
Use descriptive headings that tell readers what a section contains. State important findings clearly instead of making readers search for them. When a statistic needs additional context, provide that explanation close to the finding.
Schema markup gives AI systems another way to interpret your content beyond the visible text. Dataset schema helps identify original research as structured data. FAQ schema helps format question-and-answer content so AI systems can extract it directly.
Comparison tables also give AI systems a clear structure to work with. Instead of describing several data points across multiple paragraphs, a table presents categories, numbers, and time periods in one place. This makes the information easier to scan for readers and easier to extract for AI systems.
FAQs create another opportunity for direct citation. Framing a key finding as a question and answer offers a self-contained unit that AI systems can reference without interpreting surrounding paragraphs. Adding FAQ schema reinforces that structure at the code level, not just in the visible layout.
These practices support AI search while improving the reading experience at the same time. Readers can scan the page more easily, and AI systems have clearer information to interpret.
Apply the Strategy Beyond Blog Content
Original data doesn’t need to live exclusively in blog posts.
Product pages can incorporate relevant customer or usage research. Service pages can include industry findings that demonstrate expertise. Commercial pages can use proprietary statistics to provide evidence for the claims they make.
This is particularly important for pages that contribute directly to business goals. Hard numbers that prove the effectiveness of your product are much more likely to get someone to convert than a bit of clever copywriting.
As AI search becomes part of the discovery process, brands should consider whether their most valuable pages contain information that gives AI systems a reason to reference them.
Original Data Is Becoming an SEO Advantage
AI search is changing the value of information on the web.
AI systems can summarize information that has been published many times before. Original findings give brands an opportunity to contribute something less easily replicated.
That makes proprietary research a potentially valuable asset for both traditional SEO and emerging AI experiences. But the research discussed here suggests that publishing unique information is only part of the equation. The information also needs to be accessible.
In the AI era, unique data creates authority, but only structured content turns that authority into visibility.
The opportunity for marketers is to bring these ideas together. Find information your organization can uniquely contribute, then make those findings clear and easy to locate on the page.
That approach can give original research a longer life as search continues to change.
FAQs
What is proprietary data in SEO?
Proprietary data is information a brand has collected or produced itself that isn’t readily available from competing sources. It can include original research, customer insights, surveys, internal studies, and company-specific statistics.
Can original data improve AI citations?
Original data can give AI systems information that is difficult to find elsewhere. Research discussed by Search Engine Land found that unique information was more likely to receive AI citations when it appeared early on the page and was presented clearly.
Where should original data appear on a page?
Important findings should generally be placed where they are easy to find, particularly when they are central to the page’s purpose. NP Digital recommends considering the first third of educational and commercial pages for significant original statistics.
How should I structure content for AI search?
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Make important findings easy to identify and understand. Use clear headings, state statistics directly, and provide relevant context close to the information. These practices also make pages easier for people to scan and navigate.
Conclusion
Original data gives brands an opportunity to contribute something unique to AI search. But having proprietary information doesn’t guarantee that it will be discovered or cited.
The research suggests that presentation matters. Important findings should be easy to locate, clearly explained, and placed prominently on the page.
Brands can start by auditing existing content for first-party insights and original research, then revisiting the structure of their highest-value pages. As AI search continues to influence discovery, combining proprietary data with clear content structure can give brands another way to build AI visibility while providing more useful information to their audiences.
If you want to strengthen your visibility in AI search, reach out to the NP Digital team to learn how original data, content strategy, and AI search optimization can work together to support your long-term growth.
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Conversion rate drives more revenue impact than traffic volume, but most teams still spend their effort chasing clicks instead of fixing the funnel.
More impressions don’t guarantee more clicks, more sessions don’t guarantee more conversions, and more leads don’t guarantee more revenue.
Conversion loss usually traces back to four sources: messaging, friction, trust, and experience problems.
A four-step CRO framework (research, prioritize, test, scale) keeps testing systematic instead of reactive.
People convert when uncertainty gets removed, not when a page simply looks nicer.
Have you been pouring more budget into traffic, only to watch revenue stay flat? You’re not alone, and it’s not a traffic problem. According to NP Digital research, for businesses generating at least $10 million in annual revenue that invest in both conversion rate optimization and traffic generation, conversion rate accounted for 68 percent with website traffic just 32 percent. Yet 93 percent of teams still put most of their effort into driving more traffic, and only 7 percent focus on conversion rate.
Most companies don’t have a traffic problem. They have a conversion problem they haven’t identified yet, and that gap is exactly where competitors are gaining ground.
The Traffic Trap
It’s easy to fall into over-fixation on traffic. Often, more traffic feels like progress. Impressions climb, sessions go up, and the dashboard looks busy. None of that guarantees revenue follows, though.
NP Digital’s research found three assumptions that consistently trip up growth teams: more impressions doesn’t mean more clicks, more sessions doesn’t mean more conversions, and more leads doesn’t mean more revenue.
Each step in that chain has its own leak point, and pushing more traffic into the top of the funnel just sends more visitors through the same broken experience.
Meanwhile, the cost of acquiring that traffic keeps climbing. Cost per click is rising across every major ad platform, competition for the same high-intent keywords keeps intensifying, and the number of touchpoints a buyer needs before converting keeps growing. Organic click-through rates are dropping as AI answers replace traditional search results, and attention spans on landing pages keep shrinking. Growth is getting harder the more you spend, which is exactly why the acquisition side of your funnel has a ceiling that conversion optimization doesn’t.
Why Conversion Is the Highest-Leverage Layer
At some point, acquisition spend hits a ceiling. Costs climb, and the extra traffic you’re buying tends to skew toward lower lead quality, so you end up with diminishing returns on the surplus. Conversion doesn’t have that same ceiling.
NP Digital data backs this up. Companies running fewer than five tests per quarter saw 2.7 percent revenue growth. Companies running 11 to 15 tests per quarter saw 5.6 percent, more than double. That dip points to a sweet spot of roughly four tests a month. Beyond that, teams don’t have enough time to review results and build on what they learn before the next test launches, so the extra volume stops paying off.
The reason is structural. You can only bid so high on a keyword or an audience before the math stops working. A landing page, a checkout flow, or a lead form has no such ceiling. Every percentage point of improvement compounds across every channel already sending you traffic, which is why testing your way to a higher conversion rate tends to outperform simply increasing spend.
The Four Main Sources of Conversion Loss
Most conversion problems aren’t hidden. Teams just aren’t measuring the right things. When NP Digital audits funnels across industries, the drop-off almost always traces back to one of four categories.
Messaging problems. Visitors land on a page and can’t quickly answer three questions: What do you do? Why are you different? Why should I trust you? If a visitor has to dig for the answer, they leave before finding it.
Friction problems. Long forms, too many steps, slow page speed, and complicated checkout flows all add resistance at exactly the moment a visitor is deciding whether to commit.
Trust problems. Reviews, testimonials, case studies, and social proof do the work of convincing a stranger to hand over their money or their information. Skip this step and you’re asking for a decision the visitor doesn’t have enough evidence to make.
Experience problems. Poor mobile UX, weak navigation, and confusing journeys pull visitors away from the path you built for them, even when your messaging and trust signals are solid.
NP Digital data found B2B teams lose the most ground between leads and marketing-qualified leads, at a 59.4 percent drop-off, while B2C teams lose the most between add-to-cart and checkout, at 32.8 percent.
Average conversion rates vary wildly by page type. Checkout pages convert at 26.8 percent on average, lead generation pages at 4.1 percent, product pages at 2.0 percent, contact pages at 1.6 percent, and homepages at just 0.02 percent.
That split is more telling than it looks. Product and contact pages should, in theory, convert well, since the visitors landing there already want a specific product or want to talk to someone. Instead they’re among the lowest-converting page types on the list. And while a 26.8 percent checkout conversion rate sounds strong on its own, it also means nearly 75 percent of visitors who reach checkout still abandon their order, so something in that final step is working against you.
In our findings, the biggest gains from testing come from the middle of the funnel, at 49 percent for B2B and 43 percent for B2C, not the bottom.
That’s a useful correction. Most teams focus their testing energy on the bottom of the funnel, since that’s where the purchase happens. But the conversion problem showing up at the bottom is frequently created higher up, in the messaging and targeting that brought the visitor there in the first place.
Conversion Is a Human Problem Before It’s a Marketing Problem
Most CRO conversations focus on buttons, colors, layouts, and A/B tests. But people don’t convert because of a design change, but because a barrier to their decision disappeared.
Four drivers move people to act:
Clarity: what you do, who it’s for, and why it matters.
Relevance: different landing pages by audience and different messaging by funnel stage.
Trust: reviews, case studies, and brand authority.
Risk reduction: guarantees, free trials, flexible contracts, and transparent pricing.
Most visitors don’t buy because they’re convinced. They buy because uncertainty has been removed.
That gap is bigger than most brands realize. In one NP Digital study, only 18 percent of visitors agree they feel a sense of trust landing on a site, while 46 percent of companies believe visitors feel that trust. A visitor who doesn’t trust what they’re reading won’t convert, no matter how strong the offer is underneath it.
The best CRO programs rely on testing, and they run that testing through four repeatable phases.
Research. Pull analytics insights, heatmaps, session recordings, and customer feedback to see how visitors actually move through your site, not how you assume they move through it.
Prioritize. Rank opportunities by highest-traffic pages, largest funnel drop-offs, and effort versus impact. Fix the leak that costs you the most, not the one that’s easiest to notice.
Test. Run experiments on headlines, offers, CTAs, form length, layouts, and pricing presentation. Test one variable at a time so you know exactly what moved the number.
Scale. Deploy winning experiences across the rest of the funnel, not just the page where you tested them. A winning headline on one landing page is often a winning headline on five more.
The organizational barriers that stall this framework are consistent across companies: resources (not enough time, budget, or headcount), followed by lack of stakeholder buy-in and lack of in-house testing expertise. Most CRO programs not necessarily due to a bad framework, but because the organization hasn’t agreed on who owns it or why it matters.
A test isn’t automatically valid just because it hit statistical significance. Before you trust a result enough to scale it, check five things: whether bot or AI-agent traffic skewed the numbers, whether engagement with the test matched the intended experience, whether users actually followed the path to conversion you expected, whether the variant increased abandonment anywhere else in the funnel, and whether the projected revenue lift justifies the time it took to build and launch. Skipping this check is how teams end up scaling a “winner” that isn’t actually one.
Measuring CRO Success Beyond Conversion Rate
A higher conversion rate doesn’t always mean a better business. Two of the biggest mistakes here are focusing only on conversion rate and optimizing for volume instead of value.
A test that lifts conversion rate by adding a discount code field, for instance, might also lower average order value or attract lower-quality leads that never close. The scorecard that actually protects revenue includes four metrics: revenue per visitor, customer acquisition cost, lead quality, and customer lifetime value.
The more of these metrics you track, the better you can tell whether a win in testing is a win for the business. Focus only on front-end conversions and you’ll optimize yourself into more leads that cost more to close and stick around for less time.
The 60-Day CRO Growth Plan
Most companies need a structured plan to remove friction and improve conversion funnel optimization rather than a fancy website redesign. Our recommended 60-day plan runs in four phases.
Days 1 to 15 (Diagnose): Review landing pages, product pages, pricing pages, lead forms, and checkout experiences. Evaluate heatmaps, session recordings, scroll depth, and funnel abandonment data. Identify your highest- and lowest-converting channels.
Days 16 to 30 (Prioritize and test): Focus on your highest-traffic pages, highest-revenue pages, and largest drop-off points. Test headlines, value propositions, CTAs, form length, and social proof. Align marketing, product, design, and analytics around a shared testing calendar.
Days 31 to 45 (Scale): Apply winning elements to additional landing pages, product pages, lead-generation flows, and checkout experiences. Build variants by traffic source, funnel stage, customer intent, and visitor type.
Days 46 to 60 (Operationalize): Establish a monthly experiment calendar, a testing backlog, and a prioritization framework so testing continues past the initial 60 days. Track revenue per visitor, conversion rate, lead quality, pipeline contribution, and CAC improvements.
Teams that build this system early move faster in every quarter that follows, because they’re refining a working process instead of starting from scratch each time.
FAQs
What is CRO in marketing?
Conversion rate optimization (CRO) is the practice of increasing the percentage of website visitors who complete a desired action, such as making a purchase, submitting a lead form, or starting a trial. It works by identifying friction points in a funnel and testing changes that remove them.
Why isn’t more traffic fixing my conversion rate?
Traffic and conversion rate solve different problems. If your funnel has messaging, friction, trust, or experience issues, sending more visitors through it just means more people encountering the same drop-off points. Fixing the funnel itself typically produces more revenue than adding volume on top of it.
What causes the most conversion loss?
Conversion loss typically comes from four sources: unclear messaging, friction in forms or checkout flows, insufficient trust signals, and poor site experience. B2B teams tend to lose the most between the lead and marketing-qualified-lead stage, while B2C teams tend to lose the most between add-to-cart and checkout.
How often should I run CRO tests?
Companies running 11 to 15 tests per quarter saw the strongest revenue growth in NP Digital’s 2026 survey. The right cadence depends on your traffic volume, since you need enough visitors per variant to reach statistically significant results.
Conclusion
More traffic doesn’t fix a broken funnel, it just sends more visitors through the same leak.
The organizations that win at growth treat CRO as an ongoing discipline.
They combine process, psychology, and measurement, and they build testing programs that compound instead of resetting every quarter. You don’t need twice the traffic, it’s better to focus on more value from the traffic you already have. If you want help finding where your funnel is leaking, talk to a CRO team that audits funnels for a living.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-23 16:00:002026-09-23 16:00:00Why More Traffic Won’t Fix Your Growth Problem (But This Will)
LinkedIn is reportedly developing several new ways for creators to monetize their audiences.
The roadmap includes subscriptions, creator funding, brand partnerships, paid experiences, and ticketed events.
The expansion could make LinkedIn a more important destination for B2B creators.
Brands may gain more opportunities to partner with industry experts and professional influencers.
Marketers should start thinking about LinkedIn creators as strategic partners, not simply another social distribution channel.
LinkedIn has long been a place for professionals to build their networks and share industry expertise. Now, the platform appears to be preparing to give creators more ways to turn that expertise into a business.
Leaked internal documents reportedly reveal plans for a broader LinkedIn creator monetization ecosystem, including paid subscriptions, a creator fund, a marketplace for sponsored partnerships, paid one-on-one experiences, and ticketed events.
The roadmap would build on programs LinkedIn already offers, including BrandLink and Thought Leader Ads.
If these plans move forward, they could change the role LinkedIn plays in the creator economy. Creators would have more ways to generate revenue directly from their audiences, while brands could gain new opportunities to work with trusted voices in professional communities.
The research also points to an important qualification. Unique information alone isn’t enough. AI systems were more likely to cite information when it appeared earlier on the page and was presented in a format that made it easy to identify and extract.
What LinkedIn’s Creator Monetization Plans Include
The reported roadmap points to a much broader creator ecosystem than LinkedIn has offered historically.
Paid Subscriptions and Creator Funding
One part of the reported roadmap involves giving creators additional ways to generate direct revenue from their audiences.
Paid subscriptions, similar to the existing Instagram Creator Subscriptions, could allow creators to offer exclusive content or experiences to followers willing to pay for access. A creator fund would provide another potential source of financial support for people producing content on the platform.
Together, these tools could make it easier for LinkedIn creators to invest more time in building audiences on the platform.
Brand Partnerships and Paid Experiences
The plans also reportedly include a brand marketplace designed to connect creators with companies interested in sponsored partnerships. This is likely to be similar to tools like TikTok Creator Marketplace and YouTube Creator Partnerships – which is built directly into Google Ads and YouTube Studio.
This could expand on LinkedIn’s existing BrandLink and Thought Leader Ads programs by creating more opportunities for brands to work directly with professional creators.
The roadmap reportedly goes beyond sponsored content. Paid one-on-one experiences and ticketed creator events could give experts additional ways to monetize their knowledge and relationships.
Why LinkedIn Is Investing in Creators
LinkedIn’s reported plans reflect the growing importance of creators to the platform’s broader ecosystem.
Professional Creators Are Driving Platform Engagement
LinkedIn has developed a large community of professionals who use the platform to share expertise, build personal brands, and participate in industry conversations.
These creators give users a reason to return beyond traditional networking. Their content can generate conversations around industries, careers, business trends, and professional expertise.
For LinkedIn, supporting these creators can help strengthen the content ecosystem that keeps audiences engaged. Many of the tools we’ve discussed are already available to influencers on other platforms. What’s new and exciting is LinkedIn’s effort to bring these creator opportunities to its own platform.
Creators Can Build Businesses Around Expertise
Professional creators have a particularly valuable asset: specialized knowledge.
A creator discussing marketing, finance, technology, leadership, or another professional field can build an audience around expertise that also has commercial value. We can expect creators who already share career-focused content on other platforms to begin cross-posting to LinkedIn or creating dedicated content specifically for the platform. Monetization tools give those creators more opportunities to turn that audience into a sustainable business.
Existing tools like Patreon are out there that do this, but having a platform-native version streamlines the process and provides visibility to all creators in a place they already are spending their time.
LinkedIn has quietly become one of the fastest-growing creator platforms, and these monetization tools show the company is serious about keeping creators invested.
What This Means for B2B Marketing
LinkedIn’s creator strategy could have implications beyond the creators themselves. Brands may also gain new ways to reach professional audiences through people they already trust.
Thought Leadership Partnerships Could Become More Sophisticated
B2B brands have increasingly turned to executives, industry experts, and subject-matter creators to build credibility with professional audiences. Many of these creators have already built strong followings on LinkedIn, creating an opportunity to engage their existing audiences without directing them to another platform.
A broader creator marketplace could make these relationships easier to develop and manage.
Instead of treating creators primarily as distribution channels, brands can build partnerships around expertise and audience relevance. This can be particularly valuable for companies selling complex products or services that require trust before a purchase. Examples include medical companies, financial services, as well as subscription services or anything that requires a large purchase.
LinkedIn Could Become a Primary Creator Channel
More monetization opportunities could encourage creators to make LinkedIn a larger part of their content strategy.
For some B2B creators, the platform may become a primary destination for their content rather than a place where they repurpose material created elsewhere.
That shift could also change how brands approach LinkedIn influencer marketing. If creators are investing more heavily in the platform, marketers may have more opportunities to build long-term relationships with experts who have established credibility within specific professional communities.
How Brands Can Prepare for LinkedIn’s Creator Economy
The reported tools are still part of a developing roadmap, so brands don’t need to overhaul their influencer strategy today. They can, however, start preparing for a more mature B2B creator economy.
Identify the Experts Your Audience Already Trusts
Start by identifying creators and industry experts who already speak to your target audience on LinkedIn.
Look beyond follower counts. A smaller creator with deep credibility in a specific industry may be more valuable than a large generalist audience.
Build Relationships Before You Need Them
Creator partnerships work better when they aren’t treated as one-off transactions.
Brands can begin building relationships with relevant LinkedIn creators through genuine engagement, collaboration, and conversations around shared areas of expertise.
That groundwork can make future partnerships more authentic when new monetization tools become available.
Think Beyond Sponsored Posts
LinkedIn’s reported roadmap suggests that creator partnerships could eventually include more than sponsored content.
Brands may have opportunities to collaborate on events, educational experiences, exclusive content, or other formats that allow creators to demonstrate their expertise.
That gives B2B marketers more ways to use creator partnerships throughout the customer journey.
LinkedIn Is Becoming More Than a Professional Network
LinkedIn’s reported creator monetization plans point to a broader evolution of the platform.
Professional expertise has become a form of content with its own audience and commercial value. By giving creators more ways to earn from that expertise, LinkedIn could encourage more professionals to build their presence on the platform.
For brands, the opportunity is equally significant. A stronger creator ecosystem could provide access to trusted voices with established relationships inside specific professional communities.
The companies that understand those relationships early may have an advantage as LinkedIn influencer marketing becomes a more established part of B2B strategy.
FAQs
What is LinkedIn creator monetization?
LinkedIn creator monetization refers to tools directly on the platform that allow creators to generate revenue from their audiences and expertise. Reported plans include subscriptions, creator funding, brand partnerships, paid experiences, and ticketed events.
Is LinkedIn launching a creator fund?
Leaked internal documents reportedly show that LinkedIn is developing a creator fund as part of a broader monetization roadmap. Similar programs have previously been introduced by TikTok, YouTube, Meta, Snapchat, and Pinterest. However, the plans outlined in these documents have not been officially confirmed or launched by LinkedIn.
How could LinkedIn’s creator tools affect B2B marketing?
A broader creator ecosystem could give B2B brands more ways to partner with industry experts and thought leaders. This could expand opportunities for sponsored content, educational experiences, events, and other forms of creator collaboration.
Should brands invest in LinkedIn creators now?
Brands can begin identifying relevant creators and building relationships before new monetization tools become available. The strongest partnerships are likely to come from creators with genuine expertise and influence within a brand’s target industry.
Conclusion
LinkedIn’s reported creator monetization roadmap suggests the platform sees professional creators as an increasingly important part of its future.
Subscriptions, creator funding, brand partnerships, and paid experiences could give experts more reasons to build their businesses on LinkedIn. For B2B brands, that could create new opportunities to work with trusted voices and reach professional audiences through content that feels more credible and relevant.
The tools themselves are still developing, but marketers can prepare now by identifying the creators their audiences trust and building relationships with them. As LinkedIn’s creator economy matures, those relationships could become an increasingly valuable part of B2B marketing.
If you want to build a stronger LinkedIn influencer marketing strategy, reach out to the NP Digital team to explore how creator partnerships and thought leadership can support your broader B2B marketing goals.
All four major AI engines share a similar training recipe (pretraining, instruction tuning, and preference optimization), but each one is tuned toward a different outcome.
Public preference-training datasets show a documented shift from a single accept-or-reject judgment to a five-axis grading rubric, and that newer rubric appears to reward more structured, list-shaped answers.
Perplexity leans on live retrieval and citations. Claude’s edge is depth and long-form reasoning, while ChatGPT covers the broadest set of use cases and Gemini’s advantage comes from native access to Google’s own ecosystem.
Third-party benchmark testing changes every few months, so treat any single comparison as a snapshot, not a permanent ranking.
Measuring AI visibility means watching citation frequency, brand mentions, and source diversity, not just keyword rank.
You already know what GEO is. You’ve read the definitions, sat through the LLMO comparisons, and seen the “AI search is changing everything” takes. What’s still missing from most of that content is GEO by platform: the actual, mechanical differences in how ChatGPT, Claude, Gemini, and Perplexity decide what to surface. Clients are asking this question directly, and most of the guidance out there treats “optimize for AI search” as one strategy instead of four.
Here’s the ground rule before we go further: nobody outside OpenAI, Anthropic, Google, and Perplexity knows the current, exact formula any of these engines uses to rank or select content. Those systems are proprietary, and they shift on a rolling basis. What we can do is walk through the documented mechanisms these systems share, where they genuinely diverge in practice, and what that means for your tactics and measurement, platform by platform.
Why AI Engines Aren’t the Same
Every major AI engine on the market runs a version of the same recipe: pretraining on a massive amount of text, instruction tuning to make the model follow directions, preference optimization to shape its behavior, and product-level decisions about what to retrieve and rank when someone asks it something. That shared foundation is exactly why a lot of GEO optimization by platform advice sounds interchangeable. It’s also why that advice tends to underperform once you actually test it against a specific engine.
Each engine bends that shared recipe toward a different job. Perplexity built its product around source-based retrieval, so its answers read closer to a research assistant than a chatbot: citation-heavy, and grounded in whatever’s currently live on the web. Claude was tuned to go deep, reasoning through multi-step problems, holding context across long documents, and working well inside agent-style workflows where accuracy compounds over many steps. ChatGPT covers the widest surface area of the group, with the largest install base and voice, vision, and multimodal features layered on top of a general-purpose core. Gemini’s biggest edge shows up the moment a task touches anything inside Google’s own ecosystem, whether that’s Docs, Sheets, YouTube, or Workspace data the other three simply can’t see.
None of that comes down to taste. A single, generic “GEO best practices” checklist misses this entirely, because the differences between these systems come from how each one was built and what it’s optimized to reward, not from surface-level style choices.
How GenAI Actually Decides What to Say
Decision-making inside these systems happens in three layers, and understanding them explains most of what looks like unpredictable behavior from the outside.
Training-time shaping comes first. A model reads an enormous amount of text during pretraining, then goes through instruction tuning to learn to follow directions, then preference optimization, commonly RLHF or RLAIF, to learn which of two possible answers a human grader preferred. This is the stage where a model’s default habits get set: how much it hedges, how much detail it volunteers, how polite it sounds by default.
Inference-time selection happens next, every time you send a prompt. The model scores and weights candidate responses, usually through a reward model or alignment layer trained on those same human preference judgments from the step above.
Product-time retrieval is the layer that varies most visibly by platform. Some engines pull in outside sources at the moment you ask a question, a process called RAG, or retrieval-augmented generation, while others rely more on patterns already baked into the model’s weights through fine-tuning. This layer explains a lot of why some products feel more like a search engine and others feel more like a conversation.
Two public datasets make the preference-tuning layer concrete instead of theoretical. Anthropic published hh-rlhf in 2022: 169,000 rows, each one a binary call on which of two responses a human grader preferred. Nvidia’s HelpSteer2, published in 2024, grades responses across five separate axes instead of one: helpfulness, correctness, coherence, complexity, and verbosity.
An independent analysis of both files found that under HelpSteer2’s five-axis rubric, more list-shaped, enumerated answers scored higher in a majority of the pairs examined. That’s a plausible partial explanation for why AI-generated answers so often default to bullets and numbered steps, but it’s worth treating as an informed inference rather than a settled fact. The researcher who ran the analysis was careful to frame it the same way.
One caveat matters more than the data itself: neither file reflects how Anthropic or Nvidia trains models today. They’re historical snapshots from a specific year at a specific lab, not a live map of any current engine’s ranking logic. Treat them as a useful, citable window into how preference tuning has worked in at least these documented cases, not a spec sheet for what’s happening right now.
What Changes by Engine
Perplexity: Built For Sourced Answers
Perplexity’s whole product is oriented around citations and fresh retrieval, and that shows up in the output. Independent testing that runs ChatGPT vs. Claude vs. Gemini vs. Perplexity through identical prompt batches consistently ranks Perplexity ahead on citation accuracy and real-time grounding, which tracks given it’s pulling from the live web at query time instead of relying mostly on training data. Where it falls short is anything creative or long-form. Ask it to draft a full article, and the output tends to read functional rather than polished.
Claude: Built For Depth
Claude tends to show up strongest on tasks that require holding a lot of context and reasoning through it carefully. Testing rounds that track calibration, meaning how often a model’s confidence matches whether it’s actually right, have repeatedly put Claude ahead of the field, particularly on claims where being wrong actually matters. That combination of depth and caution is a big reason teams lean on Claude for long-form content and multi-step agent workflows rather than quick answers.
ChatGPT: Built For Breadth
ChatGPT still carries the largest install base of the group and the widest feature set, voice, vision, image generation, browsing, and a long list of plugins layered onto a general-purpose core. That breadth is a real advantage. Several independent testers also note the output quality swings more than the other three without detailed prompting. It’s the most flexible tool here, and flexibility cuts both ways.
Gemini: Built For The Google Ecosystem
Gemini’s advantage rarely shows up in raw model quality alone. It shows up the moment a task touches Gmail, Docs, Sheets, or YouTube, where Gemini can actually read and act on your own data instead of talking about it in the abstract. For teams already living inside Google Workspace, that access matters more day to day than a benchmark score.
Keep the bigger takeaway simple: none of these four wins across the board, and most credible testing in this space lands on some version of using more than one tool, matched to the task, rather than crowning a single winner. Revisit that assumption every few months. Standings shift, and last quarter’s leaderboard isn’t this quarter’s.
What Tactics Change by Engine
Here’s where this gets practical. Once you understand the mechanism and positioning differences above, the tactical shifts stop feeling arbitrary. They follow directly from what each engine actually rewards.
Perplexity: Lead With Sources
Because Perplexity leans this hard on retrieval, prioritize factual, source-rich content that matches a query directly. Original research, cited statistics, and clearly attributable claims perform better here than persuasive copy. Structure your content so a system pulling live answers can lift a clean, self-contained statement out of it without needing the surrounding context.
Claude: Lead With Structure And POV
Claude rewards depth over surface-level breadth, so long-form content with strong headings, a clear argument, and an experiential point of view earns more traction here than a shallow listicle covering the same ground. If you have real experience running the strategy you’re writing about, say so directly. That’s the kind of signal this engine’s reasoning tends to weight. [Internal link suggestion: long-form content / E-E-A-T guide]
ChatGPT: Lead With Flexibility
ChatGPT serves answers across the widest range of surfaces, so content that works as both a full explainer and a set of shorter, atomized pieces tends to travel further here. Conversational framing helps too, since a large share of ChatGPT’s traffic comes through follow-up questions rather than one query.
Gemini: Lead With Entities And Structure
Gemini’s advantage comes from the Google ecosystem, so optimize for visibility inside it specifically: clean entity definitions, structured data, and schema markup that helps Google’s own systems understand what your content is actually about. This is less about persuasive writing and more about making your content legible to a system that already has your brand’s data sitting in other Google products.
One more distinction is worth real space here, because it’s one of the most practical takeaways in this piece: these engines don’t weight sources the same way. Some lean more heavily on a brand’s own site content. Others lean more on third-party mentions and citations from outlets a brand doesn’t control. Several reward structured, schema-marked data over plain prose, regardless of who published it. Knowing which lever matters most for a given engine changes where you spend your time: publishing more on your own domain, earning more third-party citations, or investing in structured data that makes your existing content easier to parse.
KPIs for Tracking Performance Across Engines
Traditional rank tracking doesn’t map cleanly onto AI answers, so you need a different measurement set once you’re optimizing for GEO by platform instead of just SEO.
Start with citation frequency: how often your content actually gets cited as a source across these engines, not just mentioned. Track brand mentions separately, since showing up by name in an AI answer still carries value even without a citation attached. Watch whether your content lands inside the direct answer summary or gets pushed to a secondary link a user has to click through to find, because that placement difference matters more here than it ever did in traditional search.
Source diversity is worth tracking at the query level: how many distinct domains a given engine pulls from for a topic you care about, and whether your brand is consistently one of them. Query match quality matters more than keyword match here. Look at how closely your content actually maps to the real prompts people are typing, not just the keywords you targeted.
Content freshness rounds this out, especially for retrieval-heavy engines like Perplexity, where how recently you updated a page can affect whether it gets pulled into an answer at all. None of these metrics replace the KPIs you already track. They sit alongside them, and together they give you a fuller picture of whether your content is showing up where your buyers are actually asking questions.
FAQs
Do ChatGPT, Claude, Gemini, and Perplexity Use the Same Ranking Algorithm?
No. All four share a similar training foundation, pretraining, instruction tuning, and preference optimization, but each product layers different retrieval and ranking choices on top of it. That’s why the same query can produce different answers across platforms.
What Is RLHF, And Why Does It Matter For GEO?
RLHF stands for reinforcement learning from human feedback: human graders rank possible responses, and the model gets tuned to favor the ones graders preferred. It matters for GEO because it shapes what a model considers a “good” answer, including, in at least some documented cases, a preference for more structured, list-style content.
Is GEO Different for Every AI Platform?
Yes, meaningfully so. Each engine optimizes for a different outcome and pulls from different sources, so a single, generic GEO checklist will underperform a platform-specific approach.
Conclusion
The mechanics genuinely differ by engine, and that’s not changing as these products keep evolving. But the goal was never to master four black boxes. It’s to understand the shared foundations well enough to make an informed, platform-specific bet, then revisit that bet as the landscape shifts, because it will.
That’s the kind of work we do at NP Digital: tracking how these engines behave in practice, testing content against real prompts, and adjusting strategy as each platform updates. Start with one platform where your buyers already spend time, apply what’s above, and measure it. Then expand from there.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-09-16 16:00:002026-09-16 16:00:00GEO by Engine: How ChatGPT, Claude, Gemini, and Perplexity Really Decide What to Say
Search isn’t shrinking, but its role in the purchase funnel is changing.
People are searching in fundamentally different ways, using cameras, conversations, and keywords to find what they want. At the same time, discovery is happening before many searches, as recommendation engines curate content and help people develop preferences before they ever type a query.
The searches marketers see may be less about discovering what to buy and more about confirming a decision that’s already taking shape. Those earlier stages are difficult to observe, so the activity that helped someone develop a preference can disappear from the purchase path.
A few weeks ago, I was on a walk in my neighborhood, enjoying a Pacific Northwest summer day. Along my usual route, I noticed a beautiful plant that I loved but didn’t recognize. Just a few years ago, I would have had to return home, type a clumsy description like “purple flower Northwest beautiful” into a search engine, and then scroll through dozens of pictures before finding the plant’s name.
This time, I simply pointed my phone camera at the plant, had Google Lens identify it for me, and then bought one at my local plant store the next day. No ad. No search bar. Just a camera and an answer.
Nothing about my experience is unusual anymore, and it should change how every digital marketer views search’s role in the purchase funnel.
The traditional funnel was often described as a straight line from awareness → interest → desire → action, with search activity doing the most work in the interest stage, when people were still figuring out what they wanted.
Now the funnel looks something like passive exposure → preference development → confirmation searches → purchase. Search is still a critical component of a buying decision, but it plays a smaller role in discovery.
That job has shifted to recommendation engines that can predict user intent and curate personalized content before the first query. By the time searches happen, they’re confirming a decision that’s already been made.
I see this play out in client accounts. The queries that are closest to purchase look more like caveman language, i.e., “brand name product model,” indicating that consumers know exactly what they’re looking for when it’s finally time to buy.
The catch here is that the first two stages of the new funnel are ones we can’t really observe. If someone watches a series of videos on social media, develops an interest in a product, and then conducts a Google search before purchase, that activity will appear to be a branded search conversion. The content that helped them form their decision never appears in the purchase path.
Consequently, most marketers still budget as if search does the discovery work because that’s what their data tells them.
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Examining YouTube’s unique position
As search plays a different role in the funnel, YouTube is taking on a larger role in discovery. Two things make it different: machine trust and human trust.
Machine trust: A study of over 100 million AI citations found that YouTube was the second most-cited social media platform, behind only Reddit. AI answer engines trust YouTube so much because of its massive library of content, including elements like video transcripts, which provide AI models with structured text they can display in answers.
Human trust: Consumers have more confidence in YouTube than other social platforms. Studies show that more than two-thirds of consumers find YouTube “somewhat or very trustworthy” when it comes to making purchase decisions.
Creators on YouTube also supply social proof, which now plays a critical role in purchasing decisions, particularly among younger buyers. Up to 70% of Gen Z follow an influencer, and 44% made a purchase based on an influencer’s recommendation.
YouTube enjoys the unique position of being highly trusted by both consumers and the machines making recommendations, and Google has taken steps to ensure the platform gets credit for its role in discovery. In January, it rolled out the Attributed Branded Search metric, which enables marketers to link YouTube ad exposure to a branded search within a specific timeframe.
ABS only captures exposure to YouTube advertising. The organic layer featuring review videos, product unboxings, and comparison videos remains invisible. If the measurable portion is driving tangible results, imagine what the rest of the channel is doing.
This huge invisible discovery layer extends well beyond YouTube. Much of that discovery happens outside the channels brands control.
AI tools don’t repeat your version of the brand. They repeat what the entire web says about you. Earned media drove 84% of AI citations, according to a Muck Rack review of 25 million ChatGPT, Claude, and Gemini responses across multiple industries.
Those third-party mentions, YouTube review videos, and Reddit threads form the raw material for the AI answers and feed recommendations that drive today’s purchasing decisions.
The practical outcome of this shift is that brand reputation management and search visibility have merged into a single role.
Brands still have an enormous role to play in driving discovery. However, those activities must extend beyond paid advertising. Here are three ways you can influence discovery:
Build a video library that answers questions about your product and your brand regardless of whether you’re running YouTube ads. These will become a database AI answers can draw from.
Data health has become a non-negotiable. The algorithm only knows what you tell it. Consequently, your product feeds should provide the algorithms with as much information as possible. It’s also critical to build brand consistency across the channels you control.
Show up in conversations you don’t own. Reddit threads involving your brand are feeding AI Overviews whether you participate or not.
Providing consistent and complete information across channels will give algorithms and AI tools something reliable to work with.
Don’t abandon search
A reshaped funnel shouldn’t be the reason you pull back from search. Google query volume reached an all-time high in early 2026 even as search’s position in the funnel changed.
The mistake won’t come from overinvesting in search. Instead, the mistake will come from expecting search to accomplish something it’s no longer suited for.
Rather than retreating from search, this is an opportunity to reimagine your marketing mix within a new framework. If search now occupies a different funnel position, how can you allocate your marketing resources to cover the upper-funnel work that still must be done?
The answer to this question will be different for every marketing team, but it’s a conversation that everyone should be having right now.
Search behavior won’t stand still
A few years ago, I never would have imagined that I could search for a plant’s name with a camera built into my phone, much less have a back-and-forth conversation with my computer. But here we are.
Marketers are working on the cutting edge, watching new technology change user behavior right before our eyes. Everything could shift again by this time next year. Nobody knows for sure. But the larger trend is clear: people are increasingly searching to confirm what they’ve already decided.
