14 Things Executives And SEOs Need To Focus On In 2026 via @sejournal, @DuaneForrester

So many people spent 2025 arguing about whether SEO was dying. It was never dying. It was shifting into a new layer. Discovery continues to move from search boxes to AI systems. Answers now come from models that rewrite your work, summarize competitors, blend sources, and shape decisions before a browser window loads. In 2026, this shift becomes visible enough that executives and SEOs can no longer treat it like an edge case; percentages from sources will shift. The search stack that supported the last 20 years is now only one of several layers that shape customer decisions. (I talk about all this in my new book, “The Machine Layer” (non-affiliate link).)

This matters because the companies that win in 2026 will be the ones treating AI systems as new distribution channels. The companies that lose will be the ones waiting for their analytics dashboards to catch up. You no longer optimize for a single front door. You now optimize for many. Each one is powered by models that decide what to show, who to show it to, and how to describe you.

Here are 14 things that will define competitive advantage in 2026. Each one is already visible in real data. Together, they point to a year where discovery becomes more ambient, more conversational, and more dependent on how well a machine can parse and trust you. And at the end of this list is one heck of a prediction that I bet you didn’t see coming for next year! If I’m being honest, I’m sure a few of you did, but to this depth? Realizing it was all so close?

Grab a coffee or tea, find your favorite spot to read, and let’s get started!

1. AI Answer Surfaces Become The New Front Door

ChatGPT, Claude, Gemini, Meta AI, Perplexity, CoPilot, and Apple Intelligence now sit between customers and your website. More and more users ask questions inside these systems before they ever search. And the answers they get are inconsistent. BrightEdge’s analysis showed that AI engines disagree with each other 62% of the time. When engines disagree this much, brand visibility becomes unstable. Executives need reporting that reveals how often their brand appears inside these systems. SEOs need workflows that evaluate chunk retrieval, embedding strength, and citation presence across multiple answer engines.

2. Content Must Be Designed For Machine Retrieval

Microsoft’s 2025 Copilot study analyzed more than 200,000 work sessions. The most common AI-assisted tasks were gathering information, explaining information, and rewriting information. These are the core tasks modern content must support. AI models choose content that is structured, predictable, and easy to embed. If your content lacks clear sectioning, consistent patterns, or explicit definitions, it becomes harder for models to use. This impacts whether you appear in answers. In 2026, your formatting choices become ranking signals for machines.

3. On-Device LLMs Change How People Search

Apple Intelligence runs many tasks locally. It also rewrites queries in more natural conversational patterns. This pushes search activity away from browsers and deeper into the operating system. People will ask their device short, private questions that never hit the web. They will ask follow-up questions inside the OS. They will make decisions without ever visiting a page. This shifts both volume and structure. SEOs will need content designed for lightweight, edge device retrieval.

4. Wearables Start Steering The Discovery Funnel

Meta Ray Bans already support visual queries. The user points at something and asks what it is. Voice and camera replace typing. This increases micro queries tied to real-world context. Expect to see more identify this, what does this do, and how do I fix that queries. Wearables compress the distance between stimulus and search. Executives should invest in image quality, product clarity, and structured metadata. SEOs should treat visual search signals as core inputs.

5. Short-Form Video Becomes A Training Input For AI

Video is now a core training signal for modern multimodal models. V-JEPA 2 from Meta AI is trained on an unknown number of hours of raw video and images, but this still shows that large-scale video learning is becoming foundational for motion understanding, physical prediction, and video question answering. Gemini 2.5 from Google DeepMind explicitly supported video understanding, allowing the model to interpret video clips, extract visual and audio context, and reason over sequences. OpenAI’s Sora research demonstrates that state-of-the-art generative video models learn from diverse video inputs to understand motion, physical interactions, transitions, and real-world dynamics. In 2026, your short-form video becomes part of your broader signal footprint. Not only the transcript. The visuals, pacing, motion, and structure become vectors the model can interpret. When your video output and written content diverge, the model will default to whichever medium communicates more clearly and consistently.

6. Organic Search Signals Shift Toward Trust And Provenance

Traditional algorithms relied on links, keywords, and click patterns. AI systems shift that weight toward provenance and verification. Perplexity describes its model as retrieval-augmented, pulling from authoritative sources like articles, websites, and journals and surfacing citations to show where information comes from. Independent audits support this direction. A 2023 evaluation of generative search engines found that systems like Perplexity favored content that is factual, well-structured, and supported by external evidence when assembling cited answers. This remains true today as well. SEO industry analysis also shows that pages with clear metadata, consistent topical organization, and visible author identity are more likely to be cited. Naturally, all of this changes what trust looks like. Machines prioritize consistency, clarity, and verifiable sourcing. Executives should focus on data governance and content stability. SEOs should focus on structured citations, author attribution, and semantic coherence across their content ecosystem.

7. Real-Time Cohort Creation Replaces Static Personas

LLMs build temporary cohorts by clustering people with similar intent patterns. These clusters can form in seconds and dissolve just as fast. They are not tied to demographics or personas. They are based on what someone is trying to do right now. This is the basis of the experiential cohort concept. Marketers have not caught up yet. In 2026, cohort-based targeting will shift toward intent embeddings and away from persona documents. SEOs should tune content for intent patterns, not identity attributes.

8. Agent-To-Agent Commerce Becomes Real

Agents will schedule appointments, book travel, reorder supplies, compare providers, and negotiate simple agreements. Your content becomes instructions for another machine. To support that, it must be unambiguous. It must be explicit about requirements, constraints, availability, pricing rules, and exceptions. If you want an agent to pick your business, you need a content model that feeds the agent’s decision tree. Executives should map the top 10 agent-mediated tasks in their industry. SEOs should design content that makes those tasks easy for a machine to interpret.

9. Hardware Acceleration Pushes AI Into Every Routine

NVIDIA, Apple, and Qualcomm are all building hardware optimized for on-device and low-latency AI inference. These chips reduce friction, which increases the number of everyday questions people ask without ever opening a browser. NVIDIA’s data center inference platforms show how much compute is moving toward real-time model execution. Qualcomm’s AI Hub highlights how modern phones can run complex models locally, shrinking the gap between thought and action. Apple’s M-series chips include Neural Engines that support local model execution inside Apple Intelligence. Lower friction means people will ask more small, immediate questions as they move through their day instead of grouping everything into one session. SEOs should plan for discovery happening across many short, assistant-driven interactions rather than a single focused search moment.

10. Query Volume Expands As Voice And Camera Take Over

Voice input grows the long tail. Camera input grows contextual queries. The Microsoft Work Trend Index shows rising AI usage across everyday task categories, including personal information gathering. People ask more questions because speaking is easier than typing. The shape of demand widens, which increases ambiguity. SEOs need stronger intent classification workflows and a better understanding of how retrieval models cluster similar questions.

11. Brand Authority Becomes Machine Measurable

Models determine authority by measuring consistency across your content. They look for stable terminology, clear entity relationships, and patterns in how third parties reference you. They look for alignment between what you publish and how the rest of the web describes your work. This is not the old human quality framework. It is a statistical confidence score. Executives should invest in knowledge graphs. SEOs should map their entity network and tune the language around each entity for stability.

12. Zero-click Environments Become Your Primary Competitor

Answer engines pull from multiple sources and give the user a single synthesized answer. This reduces visits but increases influence. In 2026, the dominant competitors for organic attention are ChatGPT, Perplexity, Gemini, CoPilot, Meta AI, and Apple Intelligence. You do not win by resisting zero click. You win by being the source the engine prefers. Executives must adopt new performance metrics that reflect answer presence. SEOs should run monthly audits of brand visibility across all major platforms, tracking citations, mentions, paraphrases, and omissions.

13. Competitive Intelligence Shifts Into Prompt Space

Your competitors now live inside AI answers, whether they want to or not. Their content becomes part of the same retrieval memory that models use to answer your queries. In 2026, SEOs will evaluate competitor visibility by studying how platforms describe them. You will ask models to summarize competitors, benchmark capabilities, and compare offerings. The insights you get will shape strategy. This becomes a new research channel that executives can use for positioning and differentiation.

14. Your Website Becomes A Training Corpus

AI systems will digest your content many times before a human does. That means your site is now a data repository. It must be structured, stable, and consistent. Publishing sloppy structure or unaligned phrasing creates noise inside retrieval models. Executives should treat their content like a data pipeline. SEOs should think like information architects. The question shifts from how do we rank to how do we become the preferred reference source for a model.

The companies that succeed in 2026 will be the ones that understand this shift early. Visibility now lives in many places at once. Authority is measured by machines, not just people. Trust is earned through structure, clarity, and consistency. The winners will build for a world where discovery is ambient, and answers are synthesized. The losers will cling to dashboards built for a past that is not coming back.

Now, if you’ve read this far, thank you, and I have a surprise – an actual prediction for 2026! I think it’s a big, important one, so buckle up!

I’m calling this Latent Choice Signals, or these, I suppose, as it’s a grouping of signals that paint a picture for the platforms. From the consumer’s POV, this is the essential mental map they’re following: “I saw it, I felt something about it, and I decided not to continue.” This is the core. The user’s mind is making a choice, even if they never articulate it or click anything. That behavior generates meaning. And the system can interpret that meaning at scale. Let’s dig in…

The Prediction No One Sees Coming

By the end of 2026, AI systems will begin optimizing decisions around the patterns users never articulate. Not the queries they type. Not the questions they ask. But the choices they avoid.

This is the shift almost everyone misses, and you can see the edges of it forming across three different fields. When you pull them together, the picture becomes clearer.

First, operating system-level AI is already learning from behavior that is not explicitly expressed. Apple Intelligence is described as a personal intelligence layer that blends generative models with on device personal context to prioritize messages, summarize notifications, and suggest actions across apps. Apple built this for convenience and privacy, but it created something more important. The system must learn over time which suggestions people accept and which they quietly ignore. It sees which notifications get swiped away, which app actions never get used, and which prompts are abandoned. It does not need to read your mind. It only needs to see which proposed actions never earn a tap. Those patterns are already part of how it ranks what to surface next.

Second, recommender systems already treat non-actions as meaningful signals. You see it every time you skip a YouTube video, swipe past a TikTok in under a second, or close Netflix when the row of suggestions feels wrong. These platforms do not publish their exact mechanics, but implicit feedback is a well-established concept in the research world. Classic work on collaborative filtering for implicit feedback datasets shows how systems use viewing, skipping, and browsing behavior to model preference, even when users never rate anything directly. Newer work continues to refine how clicks, views, and avoidance patterns feed recommendation models at scale. It is reasonable to expect LLM-driven assistants to borrow from the same logic. The pattern is too useful to ignore. When you close an assistant, rephrase a question to avoid a certain brand, or scroll past a suggestion without engaging, that is data about what you did not want.

Third, alignment research already trains models to follow what humans prefer, not just what text predicts. OpenAI’s “Learning to summarize with human feedback” work shows how models can be tuned using human comparisons between outputs, with a reward model that learns which responses people think are better. This has been in play for years now. This kind of reinforcement learning from human feedback was built for tasks like summarization and style, but the underlying principle matters here. Models can be optimized around patterns of acceptance and rejection. Over time, conversational systems can extend this to live settings, where corrections, rewrites, and abandonments are treated as signals about what the user did not want, even when they never spell that out.

Put these three domains together, and a larger pattern emerges. As AI systems move into glasses, phones, laptops, cars, and operating systems, they will gain precise visibility into the choices people avoid. These avoidance patterns will become signals that inform how assistants rank options, choose providers, and recommend products.

This will not feel like surveillance. The model is not peeking into your private life. It is watching your interaction patterns with the system itself. It sees where you hesitate, which suggestions you skip, which tasks you hand off, which providers create follow-up questions, which prices cause users to pause, which explanations reduce confidence, and which interfaces break the chain of intent. These are all first-party behavioral signals the assistant is already allowed to use. And that platforms see these signals on a global scale.

In 2026, these Latent Choice Signals will become strong enough that they form a new optimization layer. A silent ranking system built around friction. If your brand generates hesitation, the assistant will reduce your visibility long before your analytics flag a problem. If your content creates confusion during synthesis, it will be bypassed during retrieval. If your policies trigger too many follow-up questions, the model will favor a competitor with clearer flows. The user will never know why. All they will see is the assistant presenting a different option.

This is the layer that will blindside executives. Dashboards will look normal. Rankings may appear stable. Traffic may hold steady. Yet conversions inside AI-mediated decisions will drift. Customers will stop choosing you, not because you lost traditional ranking signals, but because you introduced cognitive friction the model can detect and optimize against.

The winners will be the companies that treat avoidance as a measurable signal. They will analyze which parts of their product and content cause hesitation. They will refine policies to reduce ambiguity. They will simplify offerings. They will align explanations with how models process uncertainty. They will build experiences that reduce agent-level friction and improve confidence inside a retrieval sequence.

By late 2026, negative intent signals may become one of the strongest competitive filters in digital business. Not because users say anything, but because their silence now has structure the model can learn from. Anyone watching today’s data can see this shift forming, but almost no one is naming it. Yet the early indicators are already here, hiding between the interactions users never get far enough to complete.

This is the prediction that will define the next phase of AI-driven discovery. And the companies that understand it early will be the ones the assistants prefer.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Collagery/Shutterstock

Scoring My 2025 Predictions via @sejournal, @Kevin_Indig

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Every year, I hold myself accountable for my previous predictions by scoring them.

This year, I got three misses, two mixed results, and five hits:

1. Agentic LLM Models Reach +100 Million Users

Score: Miss

Thought Process: When I made this prediction, I assumed that once models improved at reasoning, usage would shift from chat to action, and agents would become the obvious next step.

Reality: While general LLM usage (like Gemini and ChatGPT) cleared +800 million weekly users, true autonomous agent adoption, where the AI performs complex actions like “buying a product” without oversight, remained a niche power-user feature with very wonky performance.

• Google’s “Project Mariner” and OpenAI’s agent features only entered broad public beta in mid-2025.
• Most consumers still use AI for information (chat) rather than action (agents).

I over-weighted the speed of productization and user trust, and under-weighted how slow people are to let software spend their money or act without supervision.

2. More AI Victims

Score: Hit

Thought Process: Here, I zoomed out from early signals like Chegg and Stack Overflow and treated them as the first visible cracks in a broader margin collapse. My bet was that whenever AI sits between buyers and a labor-intensive industry, the middle layer will feel the pain first.

Reality: By Q3 2025, major call center outsourcing firms faced a crisis as enterprise clients switched to “AI Voice First” support layers. Translation services continued to shrink as browser-based, real-time AI translation became native to OS updates.

• In RWS Holdings’ (translation services) 2025 half-year report, Adjusted EBITDA plummeted 41%, and profit before tax fell nearly 60%.
• Chegg continues to fall apart.
• In September 2025, Concentrix shares dropped ~9% in a single day after missing earnings expectations and cutting its full-year guidance. Enterprise clients aggressively switched to “AI Voice First” layers. Instead of hiring 100 agents for a support queue, a client might hire 10 agents for complex issues and use an AI voice agent for the rest. This destroyed the traditional “per-seat” billing model that BPOs rely on.

3. AI Automation Becomes The Default For Marketing Teams

Score: Hit

Thought Process: This call came from watching clients quietly stitch together AirOps, Make, Zapier, and custom scripts while headcount stayed flat or shrank. I expected economic pressure plus better tooling to push marketing toward “systems thinking,” where workflows matter more than channels.

Reality: “System building” became the primary skill on marketing job descriptions in 2025.

• Recruitment data from Ashdown Group (2025 Marketing Job Market Report) showed that roles involving campaign automation and AI tool integration commanded a 7-9% salary premium over generalist marketing roles.
• A 2025 HubSpot State of Marketing report found that 78% of B2B organizations had shifted to relying on marketing automation as their primary infrastructure.

With marketing budgets remaining tight, the reliance on “Team of One” structures powered by automation chains (Make, Zapier, custom AI workflows) became the industry standard.

4. AI Overviews Evolve

Score: Hit

Thought Process: I read AI Overviews as an experiment, not a finished product, and assumed Google would iterate toward more personalized, richer SERP formats once multimodal models matured. The underlying belief was that Google had to change the page itself to defend its moat against standalone LLMs.

Reality: Google tested its Web Guide SERP layout extensively as of November. For me (opted into SGE, Search Generative Experience), it’s still the default. Instead of a standard list of blue links or a single AI answer at the top, “Web Guide” breaks the entire search result page into AI-generated “buckets” or headlines.

• In June 2025, Google began embedding YouTube Shorts and timestamped video clips directly into AI Overviews.
• In December, Google started integrating AI Mode deeper into AI Overviews.

5. Reddit Becomes Part Of The Default Channel Mix

Score: Hit

Thought Process: I assumed that once Reddit showed up for everything from product searches to troubleshooting, marketers would have no choice but to treat it like a core performance channel, not a side project. When you compare ad revenue from Meta and Alphabet platforms with how visible they are in Search, Reddit’s upside becomes clear.

Reality: Reddit had a banner year in 2025. Ad revenue grew 61% YoY Q1 2025 – and then another +68% in Q3 (to $585M). With Google Search continuing to prioritize forum discussions, Reddit became an unavoidable placement for advertisers seeking high-intent traffic.

• Daily Active Users (DAUs): Reached 108.1 million in Q1 and climbed to 116 million by Q3 2025.
• Reddit is the second largest site on Google by visibility (only Wikipedia is larger).
• Reddit finally launched true ecommerce catalog ads (Dynamic Product Ads → DPA). Early beta tests in Q1 2025 showed these ads delivered a 2x higher return on ad spend (ROAS) compared to previous formats, making Reddit viable for “performance” marketers, not just “brand awareness” teams.
• Of course, Reddit remains the most cited platform for most LLMs.

6. More Sites Cloak For LLMs

Score: Miss

Thought Process: I expected especially B2B sites to move tactically and fast by feeding bots a cleaner, more structured version of their sites once it became clear LLMs rewarded that pattern. Underneath sat the assumption that people would quietly bend the rules if there was upside and no obvious penalty.

Reality: The “Bot-Only Web” did not emerge via cloaking; it emerged via APIs and paywalls. Instead of creating optimized versions for bots (cloaking), most major publishers aggressively blocked bots via robots.txt and lawsuits (e.g., The New York Times vs. OpenAI continuing saga).

7. The Current Google Shopping Tab Will Become The Default

Score: Miss

Thought Process: I treated the Shopping tab as Google’s sandbox for a future Amazon competitor, similar to how past tab experiments leaked into the main SERP. The core belief was that Google would push harder on shoppable, personalized results in the face of AI pressure and ecommerce growth needs.

Reality: Google kept the main search tab distinct. While the Shopping experience became more personalized and AI-driven (often resembling a feed), Google did not replace the default search experience with the Shopping tab interface for commercial queries.

8. AI-Generated Audio And Video Hits Mass Adoption

Score: Hit

Thought Process: Here, I connected the dots between rapidly improving generative tools and the constant pressure on creators to ship more content with the same or less budget. I assumed that once tools like Sora, Veo, and ElevenLabs crossed a basic quality bar, they would seep into production pipelines even if audiences could still tell something was synthetic.

Reality: 2025 was the year of the “Synthetic Creator.” YouTube had to update its partner program policies in July 2025 specifically to manage the flood of AI-generated content. Despite the crackdown on “AI slop,” high-quality AI-generated B-roll and voiceovers became standard for millions of creators.

• On July 15, 2025, YouTube officially updated its Partner Program (YPP) eligibility terms to include a specific clause against “Mass-Produced & Repetitious Synthetic Content.”
• The policy explicitly demonetized channels that used “templated, programmatic generation” (slop) but protected creators who used AI tools for “production assistance” (B-roll, voiceovers, scripting) as long as there was “clear editorial oversight.”
• 87% of creators now use AI in their workflows.

9. Google And Apple Divorce

Score: Mixed

Thought Process: I read the DOJ case as a structural threat to Google’s distribution deals and assumed judges would eventually push on the default search arrangements. My framing was that even a partial unwind of exclusivity could shift how search power is negotiated, without instantly changing who “wins” search. Judge Mehta’s first conclusion sounded a lot like he would take a hard stance on remedies.

Reality: The DOJ remedy ruling in September 2025 was disappointing. A toothless tiger. The court prohibited Google from paying for default exclusivity on browsers (Chrome/Safari) and devices, but:

• Google can still pay Apple to be the default, but the contract cannot say “Apple must not use anyone else.”
• The judge did not enforce a “choice screen” like the European Union, which leaves the door open for Apple to voluntarily implement choice screens or offer alternatives (like ChatGPT or Perplexity) without losing Google’s payments entirely.
• Also, Apple is licensing Gemini for Siri.

So, was it really a divorce or a forced transition to an open marriage?

10. Apple Or OpenAI Announces Smart Glasses

Score: Mixed

Thought Process: This prediction came from treating smart glasses as the logical next hardware surface for AI assistants, especially with Meta gaining traction. I assumed that OpenAI plus Jony Ive, or Apple’s need for a new device story, would pull a prototype into the public eye even if real adoption stayed years out.

Reality: On November 24, 2025, OpenAI CEO Sam Altman and designer Jony Ive officially confirmed that their joint hardware venture (under the startup “LoveFrom”) had a finished prototype during an interview hosted by Laurene Powell Jobs. The device was described as “screen-free” and “less intrusive than a phone,” aligning with the “smart glasses” or “AI Pin” form factor predictions.

However, it’s not yet proven that OpenAI will publish glasses. It might also be some sort of necklace device. Meanwhile, Apple did not announce a new product and is dealing with leadership issues instead. And then, just before I hit publish on this Memo, Google announced new smart glasses for 2026.

My Conclusion: This Is The Year “AI Deployment” Began

For those of us in tech and digital marketing, we’re going to remember 2025 as the year the AI-driven “pilot programs” ended and official “deployment” began.

Not only internally across our teams, workflows, and tech stacks, but we also watched classic search habits (informed by decades of human + search engine behavior) transform right in front of us.

We didn’t get the sci-fi future of agents buying our groceries (Pred No. 1) or widespread smart glasses (Pred No. 10) just yet.

Instead, we got something more pragmatically disruptive: A world where marketing teams are half the size but twice as technical, where BPO industries (business process outsourcing) are collapsing, and where “Googling it” increasingly means “Reading a Reddit thread summarized by an AI.”

Featured Image: Paulo Bobita/Search Engine Journal

Long-Tail SEO in an AI World

In 2006, Wired magazine editor Chris Anderson famously described the availability of niche products online as the “long tail.” Search optimizers adopted the term, calling queries of three words or more “long-tail keywords.”

Optimizing for long-tail searches has multiple benefits. Consumers searching on extended keywords tend to know what they want, and longer queries typically have less keyword competition. Yet the biggest benefit could now be AI visibility: Generative AI platforms such as ChatGPT fan out using multiword queries to answer user prompts.

Long-Tail Queries

A seed term plus modifiers

Any long-tail query consists of a seed term and one or more modifiers. For example, “shoes” is a seed term, and potential modifiers are:

• “for women,”
• “red,”
• “near me,”
• “on-sale.”

Combining the seed term and modifiers — “red shoes for women,” “on sale near me” — yields narrow queries that describe searchers’ needs, such as gender, color, location, and price.

Modifiers reflect the searcher’s intent and stage in a buying journey, from exploration to purchase. Thus, keyword research is the process of extending a core term with modifiers to optimize a site for buying journeys.

The more modifiers, the more specific the intent and, typically, the lesser the volume and clicks. Conversely, more modifiers improve the likelihood of conversions, provided the content of the landing page follows closely from that phrase. A query of “red shoes for women” should link to a page with women wearing red shoes.

Types of modifiers

A core term can have many modifiers, such as:

• Location,
• Description (“red”),
• Price (typically from searchers eager to buy),
• Brand,
• Age and gender,
• Questions (“how to clean shoes”).

Long-Tail Opportunities

Keyword research tools

Grouping keywords by modifier type can reveal your audience’s search patterns. Keyword research tools such as Semrush and others can filter lists by modifiers to reveal the most popular.

Semrush’s Keyword Magic Tool reveals the most popular modifiers for “shoes.”

Adjust Semrush’s “Advanced filters” to see queries that contain more words.

“Advanced filters” reveal queries that contain more words.

Search Console

Regular expressions (regex) in Search Console can identify longer queries, such as fan-out searches from ChatGPT and other genAI platforms. In Search Console, go to “Performance,” click “Add filter,” choose “Query,” and “Custom (regex).”

Then type:

([^” “]*s){10,}?

This regex filters queries to those with more than 10 words. Change “10” to “5” or “25” to find queries longer than 5 or 25 words, respectively.

Regex in Search Console can identify longer queries, such as fan-out searches from ChatGPT and other genAI platforms.

Keyword Dos and Don’ts

Search engines no longer match queries to exact word strings on web pages, focusing instead on the searcher’s intent or meaning. Hence a query for “red shoes for women” could produce an organic listing for “maroon slippers for busy moms.”

Keyword optimization circa 2025 reflects this evolution.

• Avoid stuffing a page with keywords. Instead, enrich content with synonyms and related phrases.

• Don’t create a page with variations of a single keyword. Group pages by modifiers and optimize for the entire group.

• Include the main keyword in the page title and the H1 heading. Google could use either of those to create the all-important search snippet.

• Assign products to only one category. Don’t confuse Google (and genAI platforms) by creating multiple categories for the same item to target different keywords.

• Search Google (and genAI platforms) for your target query and study the results. Are there other opportunities, such as images and videos?

• Don’t force an exact match keyword if it’s awkward or grammatically incorrect. Ask yourself, “How would I search for this item?” In other words, write for people, not search engines.

So You Want To Paywall?

There are three inevitabilities in life. Death, taxes, and big tech companies dumping on the little guy. As zero-click searches reach an all-time high and content is stolen and repurposed for the gain of the almighty tech loser, there’s only one viable solution.

To paywall.

To create a value exchange that reduces reliance on third-party platforms. To become as self-sufficient as possible. Like an off-grid cabin or your mum’s basement, a paywall gives you a sense of security you just cannot put a price on.

As we’re all finding, any kind of reliance on these guys doesn’t put us in a good position. They do not want to send us traffic.

TL;DR

1. Subscriber revenue is intrinsically more valuable to a business because it is predictable. Subscription and advertiser revenue are not created equal.
2. Don’t paywall everything. Use dynamic/metered paywalls and leave high-reach, generally lower-quality platforms like Google Discover free for email signups.
3. Subscription success relies on your USP – whether that’s exclusive data, deep, niche insights, or a certain vibe – you have to stand out.
4. The customer experience and understanding of your audience matter. Create habit-forming connections and products. Become an essential part of their life.

But What About Our Traffic?

Your traffic will decline. But guess what? You’re already hemorrhaging clicks and have been for some time. And traffic doesn’t pay the bills.

The only way to sustain rankings over time is with high-quality engagement data. Navboost stores and uses 13 months of data to identify good vs bad clicks, click quality, the last longest click, and on-page interactions to establish the most relevant content. All at a query level.

Paywalls are not your friend when it comes to user engagement. Not for the masses. But for a small cohort of people who like you enough to pay, your engagement data will be excellent.

In an ultra-personalized world, you will still do well to the people who really matter.

We have data that pretty perfectly highlights the impact of a paywall on rankings. Over the course of three to four months in traditional search, your rankings start to steadily drop before settling in severe mediocrity. You’ve got to fight for every click. With great content, marketing, savviness. Everything.

In Google Discover – a highly personalized, click and engagement driven platform – this is even more pronounced. While Discover’s clickless traffic is lower quality, there will be a small cohort of highly engaged users that develop over time, you can target with a paywall.

Unpaywall for the masses, build your owned channels, and paywall for the highly engaged. The platform will take care of the personalization for you.

So, maximize your value exchange with ads and email signups for most users, but don’t neglect those with a high return rate.

There’s some psychology involved in all of this. When a brand becomes widely known for paywalling, I suspect the likelihood of a click goes down as users know what to expect. Or maybe what not to expect.

This likely perpetuates over time, so you should clarify what articles are free to air.

Is Our Content Good Enough?

To nail SEO bingo, it depends. It depends on what your value is in the market. There is a lot of free stuff out there already. But broadly rubbish. So as long as the bar keeps dropping, we’ll all be fine.

I am old-ish. I like words. Writing great content isn’t easy and is usurped in many cases by richer, more visually striking content. Content that satisfies all types of users. Scanners, deep readers, listeners and get the answer and go-ers.

In some ways, you can satisfy all types of users more effectively than ever. I think you have to hit three of the four Es of content creation. Make it resonate, be consistent, and understand your audience. Whatever you create stands a chance.

But that doesn’t mean creating great stuff is any easier. If you work for a traditional publisher, the chances are you’ve brought a spoon to a gun fight. The war for attention is being fought on all fronts, and straight words are losing.

Fortunately, not every subscription model relies on the quality of the prose. It might be that you have unique data, granular insights into a specific market, or are just a bloody good laugh.

Subscriptions come in all shapes and sizes.

Ultimately, it comes down to your market, marketing, positioning and your USP. You have to know and speak to your audience and you have to stand out. As Barry would say, if you’re forgettable, you’re doomed.

How Do We Know If People Will Pay?

When it comes to paying for news, some markets are far more “advance” than others. The Scandinavian market is light-years ahead of almost everyone else when it comes to paying for news. You have to do your research to understand:

• How many people currently pay for news?
• What demographic of person pays?
• How saturated is the market already?
• What is your niche?

While it doesn’t align perfectly, it’s not surprising that those most likely to pay for news have higher income levels. Higher disposable income tends to create an environment where people buy more stuff.

Shocking, I know.

But that doesn’t tell the whole story. Norwegian news outlets have (apparently) a long history of trust with their audience and have never had access to free multi-day newspapers. Ditto other Scandinavian countries. In an age of rubbish and spin, trust and E-E-A-T are more important than ever.

And while the UK sits in a pretty shocking-looking position, almost 24 million of us pay for a BBC license fee. That is, in essence, paying for news. Insert joke about BBC bias and woke cultural agendas here.

Cultural and societal factors really matter. As does your understanding of the market.

Important to note that according to Richard Reeves (AOP Director), Subscriptions have overtaken display advertising as the core source of digital revenue.

“Most heartening is what this represents as the wider information ecosystem fractures: audiences recognise the value of professional journalism and are willing to pay for it.”

In an era of slop, paying for something good is not a bad thing.

Macro And Micro Factors Are Influential

You can only control what you can control. But you shouldn’t dismiss the wider climate.

In the UK and arguably globally, there is a cost-of-living crisis. Globally, there have been a number of very significant geopolitical issues that affect the wider economy. Money doesn’t go as far as it once did, and most subscriptions are a luxury purchase.

Is a £20 or £30 monthly subscription more valuable than a £10 Netflix one? Or Spotify? These are questions you need to ask. Why would someone subscribe and stick around?

And we aren’t just competing with other publishers. While screen time and content consumption are at an all-time high, video consumption and the creator economy are booming.

It is quite literally a near half a trillion dollar market.

Not strengths for traditional publishers. While there have been some very good success stories in recent times (see Wired turning their journalists into individual subscription machines), legacy publishers need to adapt.

So your pricing strategy, customer service, and overall experience are hugely important. You are almost certainly going to be a nice-to-have. So make sure your customer journey and path to conversion are premium, and your audience feel listened to.

You need to speak to your audience. You don’t have to go into this blind. Forging real connections with people is not impossible and making them feel listened to will go a long way.

You can try to figure out what they really value, how much they’re willing to spend and what’s stopping them.

Should I Paywall Everything?

No. Content is designed to do different things, and not everything is a premium product. Whatever journalists will tell you. If you shut down your site entirely, you become too closed off an ecosystem in my opinion.

• Commercial Content: If you have affiliate-led content, paywalling is a questionable decision. It may not be wrong per se, but think about whether the pros outweigh the cons. Typically, it’s a good gateway drug for the rest of your content. And makes some money.
• Content You Can Get Elsewhere: Evergreen content of a comparable quality to what already exists in the wider corpus is not a profitable opportunity. I’d argue that leaving this free-to-air has more pros than cons. You can always unpaywall the 100 best albums of all time, but gate the richer, individual album reviews.
• Lower-Quality Platforms: A user that comes from a platform like Discover is far less likely to convert than someone who comes from organic search. So think about the role each platform plays in your content access ecosystem.
• Paywall Vs. Newsletter signup: It is far easier to convert people to a paying subscriber from a newsletter database than from an on-page paywall. And the user journey is far less interrupted. Building an owned channel is never a bad thing, so think about how engaged users are and whether an email would be a more effective starting point.

The Type Of Paywall Matters (Now More Than Ever)

LLMs do not respect paywalls. As it turns out, neither does Google.

I, for one, am stunned.

As of just a few months ago, the search giant asked that publishers with paywalls change the way they block content to help Google out. The lighter touch paywall solution (a JavaScript-based one) includes the full content in the server response.

“…Some JavaScript paywall solutions include the full content in the server response, then use JavaScript to hide it until subscription status is confirmed.

This isn’t a reliable way to limit access to the content. Make sure your paywall only provides the full content once the subscription status is confirmed.”

According to Google, they are struggling to determine the difference. So the problem is on us, not them. They (and I strongly suspect other LLMs) are ingesting this content and training their models on us whether we like it or not.

For those of you who haven’t heard of Common Crawl, it stores a corpus of open web data accessible to “researchers.” By researchers, we now mean tech bros who don’t want to pay for, surprisingly, anything.

According to their CEO;

“If you didn’t want your content on the internet, you shouldn’t have put your content on the internet.”

It doesn’t stop there either. Even if you block all non-whitelisted bots from accessing your site at a CDN level, you may have syndication partnerships in place. If so, it’s likely your content is making it out into the wider world.

The internet is not exactly a leakproof vessel. If you’re setting one up now, try to implement a server-side option.

What Is The Right Paywall For Me?

I have written about the types of paywall available to you and the pros and cons of each. Generally, I think a metered or dynamic paywall is the best option for most publishers. At the very least, a freemium model. Something that gives people enough to draw them in.

And you can’t exactly draw them in if you just hard paywall everything.

You have to think of this as a full-blown marketing strategy. You need to know where people come from. How much of your content they have consumed. Whether it’s better to show them a newsletter signup as opposed to a paywall.

It is absolutely worth knowing that over time, a strong email database will convert far more effectively than a hard paywall.

So encouraging free signups and taking a longer-term view to conversions (you’ll need a good customer journey here) may be far more effective.

How Can I Set One Up?

There are a number of paywall management options out there for publishers. Leaky Paywall, Zephr, Piano. There are plenty.

The best ones integrate with your existing tech stacks, have excellent personalization and customization options, deploy ad-blocking strategies, and run flexible gating strategies.

Larger publishers tend to go with enterprise-level options with deep analytics and CRM integrations. Smaller publishers can work with lighter touch, cheaper operators. You really just need to scope out what will work best for you.

Particularly when it comes to monthly costs and revenue share options.

How Can I Map The Impact?

You’ll need to establish a few key things:

• The average drop in traffic you expect to see.
• The subsequent loss of existing revenue (probably ad-related, but there may be some knock-on wider commercial impact).
• The average value of a subscription (and the expected conversion rate).
• Your customer LTV.

Focusing on Customer LTV shifts marketing from chasing traffic to profitable, loyal audience relationships. It makes businesses understand that not all audiences or subscriptions are created equal.

You generate more subs through paid media because the net is larger. But lots slip through the net. So you need a quality product (in both a product and marketing sense) alongside UX and customer service that reduces friction.

Search and owned channels are smaller, but far more likely to pay because they have taken an action to find you. In some cases, they actually want you in their inbox. The quality is higher, but the overall returns are lower.

So you just can’t treat everybody the same.

Closing Thoughts

Subscriber revenue is so valuable because it’s predictable. Subscription business models have boomed for that very reason. A pound of subscriber revenue is far more valuable than almost anything else, and it should be the focus of your business.

But that doesn’t mean you put all your eggs in one basket. You can have multiple subscription types on your website, and that can help you become habitual with all types of users. But you need to add value to their lives every day.

Puzzles, recipes, short and long-form videos, et al.

Businesses make money in many ways. A diverse business is resilient. Resilient to macro and micro factors that will decimate some publishers over the next few years. So talk to your audience, trial new ways of adding value, and commit when one works. Become habitual.

And, shock horror, people want to belong to something. So while the digital experience is crucial, making an effort to connect with people IRL matters.

More Resources:

This post was originally published on Leadership in SEO.

Featured Image: beast01/Shutterstock