Every week, the internet tells you what's trending. It rarely tells you how any of it felt.

Open any trends page, any weekly recap, any "most talked about" list, and the logic is the same. Volume wins. Whatever generated the most clicks, shares, or mentions gets ranked first, regardless of what that attention actually meant. A blockbuster movie premiere and a last-minute playoff upset can sit side by side on the same list, indistinguishable by the metric used to rank them.

But attention and emotion are not the same thing. A story can dominate headlines and still leave people cold. Another can spike quietly in wonder, anger, or admiration without ever cracking the top of a trends page. Volume tells you where people looked. It says nothing about what they felt while looking.

That gap between the content people consume and how they actually feel about it is exactly where Neuro-Contextual advertising begins.

From Signal to Spotlight: Introducing the Seedtag Emotion Quotient

Today, we're putting Liz, our Neuro-Contextual AI, to work with the launch of the Seedtag Emotion Quotient, a weekly ranking of the five cultural moments that generated the strongest measured emotional response, not the ones simply generating the most volume. 

Each moment is scored against its own emotional baseline. Take The Odyssey: admiration and anticipation for Nolan's epic were real, but off-screen romance between its stars ended up generating the strongest emotional signal of all, stronger than the film itself. And sometimes, what stands out is how differently the same event can be felt. At this year’s US Open, coverage around Carlos Alcaraz drove admiration to 3.93x its usual baseline, while stories around Novak Djokovic's early exit pushed sadness to 2.37x. Same tournament, same week, but two very different ways of experiencing it. For advertisers, that's an important distinction. Knowing that the US Open is capturing attention is one thing. Understanding whether that attention is being shaped by admiration or sadness gives brands a much more precise read on the moment and how their message can fit within it.

That's the point of the Emotion Quotient. It's the same intelligence behind Liz, our Neuro-Contextual AI, made visible: reading not just what a moment is about, but how it's genuinely felt.

Key Takeaways

• The Seedtag Emotion Quotient turns that same Neuro-Contextual analysis into a public, weekly view of the five cultural moments the internet felt the most.
• Liz, our Neuro-Contextual AI, reads interest, emotion, and intent across content in real time, without relying on personal data.
• Emotional signals rarely appear alone. Liz identifies the multiple emotions layered within a single piece of content.
• Traditional contextual targeting can identify what a piece of content is about. Neuro-Contextual advertising reads why it matters, understanding what sets one piece of content apart from another and how it's likely to land with the person engaging with it.
• Understanding why, not just what, is what makes Neuro-Contextual advertising fully resonant, and it's the foundation of full-funnel outcomes across the open web and CTV.

It's Not About Who Someone Is. It's About What They're Passionate About, In This Moment. 

What Is Neuro-Contextual Advertising?

Contextual targeting has been part of digital advertising for years. At its most basic, it matches ads to content using keywords and categories. A travel article gets travel ads. A sports recap gets sports ads. It works, but it reaches its limit there. Classification is as far as it goes.

More advanced systems now use vector embeddings, numerical representations that place related concepts closer together in meaning, even when they share no keywords at all. That's a real improvement in how machines read content. But embeddings alone only summarize meaning. They don't explain why a moment matters to the person reading it.

That's the gap Neuro-Contextual advertising is built to close.

Neuro-Contextual Advertising, our approach at Seedtag, adds a purpose-built layer of business logic on top of that foundation, one built to interpret interest, emotion, and intent within content itself. 

Two articles can sit in the exact same category and still mean entirely different things. Someone reading a spoiler-filled recap of a finale and someone reading a spoiler-free review are both talking about the same show, but their intent runs in opposite directions: one wants closure, the other wants to be convinced to watch. Knowing what the content is about can't tell them apart. Understanding why each reader is there can.

The same holds for emotion. A product launch event and a product recall story can both sit under "tech news," yet one carries excitement and anticipation, the other frustration and concern. That same underlying intelligence, reading interest, emotion, and intent together, is what powers everything we build, the Seedtag Emotion Quotient included.

The same logic applies to people, not just pages. Two readers can share every demographic box, same age, same income, same zip code, and still care about completely different things. Demographics describe who someone is on paper. They say nothing about what actually moves that person in the moment. Neuro-Contextual advertising shifts the question from "who is this person?" to "what are they passionate about right now?", and content itself is where that answer shows up first.

The Tech Behind the Ranking

Behind every score in the ranking is Liz, our Neuro-Contextual AI, reading content at scale and in real time. Liz scans more than 100 million URLs every day, with native understanding across 10+ languages and access to inventory from more than 30,000 premium media publishers globally, spanning the open web, video, and CTV.

That scale matters because the Seedtag Emotion Quotient isn't built from isolated pieces of content. Liz understands the connections between content across this broader ecosystem, identifying signals of interest, emotion, and intent as they emerge. The result is a view of cultural moments grounded not in a snapshot, but in what is happening across the content universe around us.

From there, understanding goes deeper than keywords. Liz maps the relationships between topics using vector embeddings grounded in neuroscience, which is how the ranking can isolate the specific terms driving a spike, not just name the general subject behind it.

Because human reactions are rarely singular, Liz reads emotion in layers rather than assigning one flat label. A single moment can carry curiosity and excitement at once, or admiration alongside real tension, and the ranking is built to reflect that complexity.

All of this happens without relying on personal data. Liz reads the content environment itself, not the individual people engaging with it, which is what keeps the ranking privacy-first by design.

Each week, the Seedtag Emotion Quotient publishes a new read on the five moments that generated the strongest measured emotional response over the week prior. It's built to be explored, not just read: every entry breaks down the specific emotions behind it, the story driving the spike, and how that moment's emotional profile compares to its usual baseline, a starting point for thinking through which emotional signals are worth aligning a campaign with, and when.

Why Does Measuring Emotion Matter for Advertisers?

The Seedtag Emotion Quotient makes something visible that advertisers should care about directly: emotion is measurable, and it predicts performance.

That's not just true for the moments in this ranking. It holds inside real campaigns too.

The same Neuro-Contextual intelligence behind the ranking already drives stronger performance where it counts most: inside live campaigns. Liz, our Neuro-Contextual AI, is the same engine behind Seedtag NeuroX, our Neuro-Contextual Exchange, powering both from the same underlying intelligence. NeuroX connects that intelligence at scale across 30,000+ premium publishers and broadcasters, making every impression understood whether or not identity is present in the bidstream.

Working with Professor Moran Cerf from Columbia University, our neuroscience research measured real-time brain responses and found that Neuro-Contextual ads drive 3.5 times higher neural engagement than non-contextual ads, and a 26% stronger emotional response than standard contextual advertising. Full-funnel outcomes, from awareness to conversion, are consistently stronger when a message aligns with the emotional state of the moment it appears in.

Is Neuro-Contextual advertising the future of targeting? As identity signals keep fragmenting across the open web, understanding the moment becomes the only foundation that holds.

It's not about knowing what content people consume. It's about understanding how that content makes them feel.

The Seedtag Emotion Quotient is live now. Explore this week's ranking.

The industry has always chased one promise: delivering the right message to the right person at the right time. The surprising part of advertising automation in 2026 is that the creative side of that promise is largely solved. Dynamic creative platforms, modern ad servers, and workflow automation have brought the industry much closer to making personalization at scale a reality.

What hasn't caught up is measurement.

That gap between what advertising automation can execute and what teams can actually measure is where many agencies still struggle. Closing it requires more than adopting another AI tool. It demands smarter workflows, disciplined testing, and connected data that turns execution into measurable business outcomes.

In this episode of AdTech Heroes, I sat down with Lisa Markou, Executive Vice President, Platforms at Publicis Collective, to explore what advertising automation actually looks like inside a modern agency, why measurement remains the industry's biggest challenge, and how leading teams are deciding what to automate first.

Key Takeaways

• Creative automation has made personalized advertising at scale possible, but measurement and attribution have yet to keep pace.
• Advertising automation in 2026 is less about adding new AI tools and more about simplifying technology stacks and eliminating inefficient workflows.
• The biggest advertising automation benefits often come from workflow automation, replacing manual processes that slow teams down and introduce errors.
• The most successful automation strategies begin with small, focused tests before expanding across larger marketing campaigns.
• Connected data—not disconnected platforms—is what allows automation to deliver meaningful business outcomes.

Creative Automation Has Solved What Measurement Still Can't

Creative technology has advanced rapidly over the past few years. According to Markou, the technology behind creative automation, including dynamic creative platforms and modern ad-serving capabilities, has finally reached a point where that vision is achievable.

Measurement, however, remains the missing piece.

Connecting metadata, taxonomy, creative assets, and media signals across multiple platforms continues to be one of advertising's biggest operational challenges. While renewed attention on brand metrics and engagement signals can help marketers understand campaign performance sooner, accurately attributing business outcomes still requires significant work behind the scenes.

For marketers, this means the next competitive advantage won't come from delivering more personalized creative alone. It will come from proving which creative actually drives results.

What Is Advertising Automation, and How Are Agencies Using It in 2026?

Ask five people to define advertising automation, and you'll probably get five different answers. For Markou, the definition extends far beyond AI or generative tools.

Her teams apply workflow automation to the tasks that historically consumed the most time, from dashboard creation and reporting to media planning and operational processes that once relied on manual spreadsheets. The objective isn't simply greater efficiency. It's freeing the marketing team to focus on strategy instead of repetitive work.

At the same time, not every legacy system needs replacing. Sometimes a manual workaround is more effective than forcing a new marketing automation platform into an existing workflow. The real challenge is knowing when consolidation genuinely improves performance, and when it simply adds complexity.

Rather than asking, "What new tool should we adopt?", agencies are increasingly asking, "Which process no longer deserves to exist?"

The Best Way to Test AI-Driven Automation Before Scaling

One of the biggest mistakes organizations make is trying to automate everything at once.

Markou advocates for a different approach: start small. Whether it's a single campaign, a tentpole event, or one portion of the media budget, focused testing allows teams to understand the trade-offs before investing in a broader rollout.

Just as importantly, every test needs a clear objective. Without defining what success looks like upfront, even promising automation pilots generate little actionable learning.

Breaking automation into smaller experiments reduces risk, builds internal confidence, and gives leadership the evidence needed to scale successful initiatives across future marketing campaigns.

The Biggest Advertising Automation Benefits Start With Better Workflows

Throughout the conversation, one theme kept resurfacing: agencies often overcomplicate automation by focusing on what new technology to add rather than what unnecessary process they can remove.

Markou described how outdated spreadsheets, redundant platforms, and legacy approvals frequently remain in place simply because replacing them feels disruptive. Yet the long-term cost of maintaining inefficient processes often outweighs the temporary effort required to redesign them.

The biggest advertising automation benefits rarely come from the newest AI feature. They come from reducing manual work, improving collaboration, and building automated workflows that help teams move faster with fewer errors.

Connected Data Is the Foundation of Modern Advertising Automation

Even the most sophisticated automation depends on one thing: connected data.

Markou explained that aligning taxonomy, naming conventions, customer data, and measurement across vendors and platforms remains one of the industry's most complex challenges. Add evolving privacy regulations, regional differences in first-party data onboarding, and increasingly fragmented marketing channels, and it's easy to see why automation alone isn't enough.

As the saying goes: good data in leads to good decisions out. Without a strong data foundation, even the most advanced marketing automation software struggles to deliver meaningful results.

For marketers, success increasingly depends on connecting systems, not simply adding more of them.

Where Advertising Automation Goes From Here

Advertising automation is no longer a side experiment. It is quickly becoming the operating standard for modern media organizations.

The agencies moving ahead aren't necessarily the ones adopting every new AI capability first. They're the ones willing to rethink outdated workflows, test new approaches deliberately, and build connected systems that turn data into better decisions.

As automation continues to evolve, competitive advantage won't come from adding more technology. It will come from understanding which processes to simplify, which experiments to scale, and how to connect every part of the marketing process into a more measurable, efficient whole.

For a deeper look at how leading agencies are approaching automation, connected data, and modern media operations, watch the full conversation with Lisa Markou in AdTech Heroes, Episode 60.

Every summer, the back-to-school season sneaks up faster than expected. Families start planning while the last day of school is still weeks away, and by the time September arrives, most of the shopping decisions have already been made. 

For marketers, this creates a familiar challenge: the back-to-school marketing window opens earlier every year, and audiences are far more layered than a simple parent-buying-supplies narrative suggests.

This season is no longer just about pencils and backpacks. It stretches across education, food, community, technology, and fashion, often overlapping in ways brands don't expect. Understanding that complexity is the real key to a stronger back-to-school ad strategy.

Below, we break down the trends, timing, and audience behaviors shaping back-to-school marketing this year,  along with what these shifts mean for seasonal marketing overall. 

Key Takeaways

• Back-to-school marketing now spans far more than school supplies, touching food, technology, sports, and fashion content.
• Education-related content leads the conversation, with food and lunch prep following close behind.
• Liz, our Neuro-Contextual AI, uncovered 14 distinct contextual neighborhoods within the season, each reflecting a different shopper motivation.
• Shoppers fall into distinct interest groups, from test prep researchers to homeschooling parents to sports families.
• Some of the most valuable back-to-school trends this year come from unexpected overlaps between categories.

What Are the Key Back-to-School Marketing Trends for This Year?

One of the clearest back-to-school trends this year is just how education-driven the season has become. Education leads the back-to-school content universe at 67% of mentions, covering everything from test prep to classroom learning tools.

Food and drink content follows as the second biggest driver, with a strong focus on lunchbox packaging and baking. This shows how deeply back-to-school planning is tied to daily routines rather than a single shopping trip.

Beyond these two leading categories, Liz, our Neuro-Contextual AI, uncovered 14 custom contextual neighborhoods within the back-to-school content universe, connecting topics like homeschooling, online learning, athletics, and craft making. 

This fragmentation is exactly why generic back-to-school advertising ideas tend to underperform. The audience isn't one shopper. It's many shoppers with very different needs happening at the same time.

When Should Brands Start Their Back-to-School Marketing Campaign?

Timing is one of the most common questions marketers ask heading into the school season. 

Back-to-school planning clearly starts well before the first day of class: the interest clusters we identified span everything from early supply shopping to major sales moments, with events like Black Friday, Cyber, and Prime Day appearing within the same shopping conversations.

This overlap between back-to-school shopping and broader seasonal discount periods suggests that families are planning and comparing prices across an extended window, not making a single last-minute purchase. School marketing campaigns built around one moment risk missing the earlier stages of that planning process.

Who Are Today's Back-to-School Shoppers?

Back-to-school shoppers are not a single audience. The season breaks into six core interest areas, each shaped by different motivations and content habits:

1. Books and Study: Families researching test prep (ACT, PSAT, college admission), elementary reading tools, and classic books and authors.
2. Supplies and Shopping: Parents focused on preschool learning materials, seasonal crafts, classroom tools, and teacher appreciation gifts.
3. Snacking and Food: Households planning lunchbox staples, homemade cookie recipes, and weekly grocery prep tied to the new routine.
4. Community: Families engaging with school sports, school trips and events, and school support topics like PTA and staffing.
5. Technology: Shoppers exploring online learning tools, college and career paths, teacher digital resources, and homeschooling programs.
6. Gear and Fashion: Consumers shopping for school day essentials, trusted clothing brands, and smart shopping strategies around sales.

This range shows why a single back-to-school marketing message rarely works across the board. School shoppers move between these interest areas depending on their household needs.

What Surprising Behaviors Are Shaping Back-to-School Shopping?

Some of the most interesting back-to-school trends come from where these categories unexpectedly connect. Within the Books and Study neighborhood, we identified conversations blending literature curriculum mapping with classic authors like Jane Austen, meaning academic test prep and nostalgic reading discussions live side by side.

Homeschooling shows a similar pattern. A neighborhood cluster where homeschooling intersects directly with technology interests, particularly among parents sharing digital learning resources with one another.

Within the Community pillar, school sports conversations sit closely alongside smaller school milestones like book fairs, showing that the same emotional energy fueling student athletics also extends to everyday school events.

These overlaps matter because they reveal opportunities that a single-category campaign would miss entirely.

Back-to-School Marketing Tips for Brands

Based on these patterns, a few principles stand out for building a stronger school marketing campaign this year:

Speak to more than one interest area at once. Since families move fluidly between categories like Books and Study, Snacking and Food, and Gear and Fashion, campaigns that reflect more than one of these moments tend to feel more relevant than single-category messaging.

Recognize that back-to-school shopping overlaps with sales culture. Because gear and fashion conversations already connect with major discount periods, back-to-school promotions can align naturally with broader seasonal sale moments rather than competing against them.

Don't overlook homeschooling and technology audiences. This growing segment blends digital learning tools with community-driven resource sharing, representing a distinct content environment from traditional classroom shopping.

Match tone to the moment within each pillar, whether that's the practical urgency of school supplies, the routine-driven nature of lunch planning, or the community spirit around school events and sports.

From Supplies to Sentiment: Why Context Matters More Than Ever

As back-to-school shopping spreads across six distinct interest areas and 14 contextual neighborhoods, relevance becomes harder to achieve through guesswork alone. 

This is where our Neuro-Contextual approach becomes valuable. Liz, Seedtag’s proprietary Neuro-Contextual AI, reads signals of interest, emotion, and intent within content itself, identifying these clusters by both definitional meaning and how topics naturally occur together on the page.

Rather than targeting a generic "parent" profile, this approach allows advertising to align with the specific moment a shopper is in, whether that's researching test prep, planning lunches, or browsing backpacks ahead of a sale. And it does this without relying on individual tracking, keeping the experience privacy-first while still feeling personal.

The Bigger Opportunity for Back-to-School Marketing

Back-to-school marketing isn't about reaching one type of shopper. It's about understanding the many overlapping moments that make up the season, from test prep stress to lunchbox planning to Friday night games.

Brands that recognize these connections, rather than treating back-to-school as a single retail event, are better positioned to build campaigns that feel relevant at every stage of the school season.

Want to see the full range of interests, content clusters, and behaviors shaping this year's back-to-school season? Download our Back-to-School Insights report to explore the data behind these trends.

Publisher monetization has entered a new phase. As streaming consumption officially surpasses linear television, advertising budgets are following audience attention into Connected TV (CTV), FAST channels, mobile apps, and other emerging digital environments. That shift is reshaping where publishers generate revenue and how they build sustainable monetization strategies.

For publishers, this is one of the biggest stories in publisher ad revenue news today. Revenue growth no longer depends on expanding inventory alone. It depends on creating advertising experiences that are transparent, measurable, and connected across multiple channels.

At Cannes Lions 2026, Mike and I sat down with Chandra Cirulnick, VP of Global Supply Partnerships at Yahoo DSP, for The Pub Way Podcast to explore what this evolution means for publisher ad management. Our conversation covered everything from CTV and FAST channel monetization to in-app growth, programmatic advertising, and the increasing importance of omnichannel strategies.

While we discussed several emerging formats, one idea connected them all. Publishers that diversify their revenue streams while giving advertisers greater transparency into performance are building stronger foundations for long-term publisher monetization.

Key Takeaways

• Streaming has overtaken linear TV, shifting publisher ad revenue toward CTV and FAST channels.
• New programmatic advertising formats are expanding monetization beyond traditional commercial breaks.
• CTV and FAST support different publisher monetization strategies, each serving unique advertiser needs.
• Transparency around inventory and campaign performance is becoming a competitive advantage.
• Omnichannel ad revenue strategies for digital publishers are creating stronger, more sustainable revenue growth.

How Are Publishers Monetizing CTV and FAST Channels in 2026?

Streaming has fundamentally expanded the number of ways publishers can generate advertising revenue. Instead of relying solely on traditional commercial breaks, publishers now have access to a growing mix of premium formats that can be packaged for different buyers, campaign objectives, and budgets.

Pause ads, home screen placements, shoppable experiences, and live sports sponsorships have all become available through programmatic advertising over the past year. These formats create new opportunities for publishers to diversify revenue streams while giving advertisers more flexible ways to reach audiences.

This evolution is also changing how inventory is sold.

Historically, television advertising centered around large upfront commitments, where buyers purchased broad audience reach months in advance. While those agreements remain important, they now exist alongside a growing programmatic marketplace that allows advertisers to buy inventory with far greater precision.

Instead of purchasing an entire market, brands can target audiences based on household counts and campaign objectives. That means advertisers that may not have the budgets for traditional national television campaigns can now reach highly relevant audiences through CTV, making premium inventory accessible to a much broader range of buyers.

For publishers, this creates far greater flexibility in publisher ad management.

Rather than relying on a single sales model, they can package premium inventory for large brand advertisers while simultaneously making additional inventory available through programmatic channels. This balance allows publishers to maximize advertising revenue without sacrificing the premium value of their content.

More importantly, it reflects a broader shift taking place across the industry.

Publisher monetization is no longer built around individual channels. It is increasingly built around connected audience experiences, where CTV, FAST channels, mobile apps, and other digital environments work together to create more valuable opportunities for both publishers and advertisers.

The Difference Between CTV and FAST Channel Monetization

Although they are often grouped together, CTV and FAST channel monetization solve different challenges for publishers.

Connected TV, particularly across premium streaming services, is typically valued for its broad reach, premium content, and household-level addressability. Advertisers use CTV to build large-scale awareness while benefiting from more precise targeting and measurement than traditional television has historically offered.

FAST channels, on the other hand, create value in a different way.

Rather than competing on audience size alone, they allow publishers to build highly engaged communities around specific interests, genres, or cultural moments. Whether it's motorsports, entertainment, news, or lifestyle programming, FAST channels give advertisers the opportunity to reach audiences who have intentionally chosen that content.

During our conversation, Formula 1 emerged as a great example of this shift. While traditional broadcast inventory around the sport may be limited, dedicated FAST programming creates entirely new opportunities for publishers to monetize highly engaged viewers who are actively seeking that content.

That distinction matters.

CTV often delivers scale and premium reach. FAST channels deliver contextual relevance and deeper audience engagement. Together, they give publishers more flexibility to package inventory based on different advertiser objectives instead of relying on a single monetization model.

As streaming ecosystems continue to evolve, understanding the difference between CTV and FAST channel monetization will become increasingly important for publishers looking to diversify advertising revenue while maximizing the value of every impression.

Why Transparency Is the New Currency of Ad Revenue Growth

As new inventory becomes available, advertisers are asking for something beyond scale. They want transparency.

Today, publishers are expected to answer two fundamental questions before media budgets are committed: 

1. Where will my ads appear?
2. How did the campaign actually perform?

The first question is about content transparency. Buyers want to understand the environments where their ads will run before activation, whether that's a premium streaming service, a specific FAST channel, or a curated portfolio of content.

The second is about performance transparency. Advertisers increasingly expect clear measurement that demonstrates how campaigns contributed to business outcomes, not simply how many impressions were delivered.

Publishers are responding by offering different levels of transparency based on campaign objectives.

Portfolio-based or genre-based packages provide advertisers with greater flexibility and broader reach, often at more accessible price points. Show-level sponsorships, live events, and premium programming offer greater certainty around placement, allowing publishers to command higher premiums for inventory that delivers stronger contextual alignment.

This approach creates opportunities across multiple buyer types.

Brands focused on efficiency can access quality inventory through broader packages, while advertisers seeking highly controlled environments can invest in premium placements with full visibility into where their campaigns appear.

For publisher ad management teams, transparency is no longer simply a reporting feature. It has become an important driver of publisher monetization, helping strengthen advertiser confidence while supporting long-term revenue growth.

Omnichannel Ad Revenue Strategies for Digital Publishers

Perhaps the biggest takeaway from our conversation was that publisher monetization is no longer defined by individual channels.

The next stage of growth is being built around connected audience experiences.

Instead of viewing CTV, FAST, mobile apps, podcasts, and streaming as separate revenue opportunities, publishers are beginning to package them as complementary environments that reflect how audiences actually consume media throughout the day.

These omnichannel ad revenue strategies for digital publishers create more consistent experiences for advertisers while opening additional revenue streams for publishers.

Mobile apps are a good example.

For years, app advertising was largely associated with gaming. Today, non-gaming publishers are increasingly extending their brands into mobile experiences, creating new inventory that keeps audiences engaged beyond the browser while attracting advertisers looking to diversify their media-buying strategies.

Audio is evolving in much the same way.

Dynamic ad insertion has transformed podcast advertising from a largely manual process into a scalable programmatic channel. At the same time, advances in contextual intelligence allow campaigns to align with individual episode topics instead of entire shows, giving advertisers greater flexibility without sacrificing relevance.

The boundaries between formats are also becoming less defined.

Streaming platforms continue to experiment with video-first podcasts, while traditional audio content increasingly incorporates visual elements. Rather than thinking about video, audio, or mobile as separate channels, publishers are beginning to organize inventory around audience attention and content consumption.

This shift reflects a broader evolution across digital advertising.

Successful publisher monetization no longer depends on maximizing one channel at a time. It depends on creating connected advertising experiences that allow advertisers to engage audiences wherever they choose to consume content.

Understanding the Moment Behind Every Ad Dollar

One theme kept resurfacing throughout our conversation with Chandra.

The future of publisher monetization isn't being shaped by new channels alone. It's being shaped by a better understanding of audience attention.

Whether someone is watching a live sporting event on CTV, exploring a niche FAST channel, listening to a podcast during their commute, or engaging with content in a mobile app, the opportunity isn't simply to place another ad. It's to understand what that person is interested in at that specific moment and deliver advertising that complements the experience.

As media consumption becomes increasingly fragmented, context becomes the common thread connecting every environment. Publishers that understand not only where audiences are spending time, but also the interests, emotions, and intent behind that attention, are better positioned to create advertising experiences that feel relevant for consumers and valuable for advertisers.

This is where AI is beginning to play an increasingly important role.

Rather than relying solely on historical performance or broad audience segments, AI-powered contextual intelligence helps identify the environments where audiences are most receptive to a brand's message. That creates opportunities for publishers to strengthen monetization while helping advertisers make more informed media-buying decisions across multiple channels.

At Seedtag, this philosophy sits at the core of our Neuro-Contextual approach.

Powered by Liz, our proprietary AI built on more than a decade of content understanding at scale, Neuro-Contextual intelligence interprets signals of interest, emotion, and intent directly from content. Instead of relying on personal identifiers, it helps advertisers understand when audiences are most receptive to a message, creating more relevant advertising experiences across CTV, premium publisher environments, mobile, and other emerging formats.

As omnichannel strategies continue to evolve, this deeper understanding of context will become increasingly valuable for both publishers and advertisers looking to build sustainable, privacy-first revenue growth.

The Next Chapter of Publisher Monetization

Publisher ad revenue is no longer being shaped by a single channel or a single transaction. It's being built across connected experiences.

CTV continues to expand premium video opportunities. FAST channels are creating new ways to engage highly passionate communities. Mobile apps and digital audio are opening additional revenue streams that complement traditional web inventory. Together, these environments are transforming how publishers package, measure, and monetize advertising.

The publishers that succeed won't simply add more inventory. They'll create connected strategies that reflect how audiences actually consume media, while giving advertisers greater transparency, stronger measurement, and more meaningful ways to engage consumers across every touchpoint.

For advertisers, that means moving beyond isolated media buys and embracing omnichannel planning that prioritizes relevance alongside reach.

For publishers, it means recognizing that the future of publisher monetization isn't about choosing between CTV, FAST, mobile, or audio. It's about connecting those environments into a strategy that delivers long-term value for both buyers and audiences.

As attention continues to shift, the publishers that thrive will be the ones that understand not just where audiences are, but what captures their attention in the moments that matter most.

If you're exploring new ways to grow publisher ad revenue across CTV, FAST channels, mobile, and other digital environments, our conversation with Chandra Cirulnick offers valuable perspectives on where the industry is heading.

Listen to the full episode of The Pub Way Podcast for the complete discussion.