In a world where privacy reshapes how we measure and connect, digital advertising is undergoing a quiet but fundamental shift. The traditional approach of identifying users and following them around the web is giving way to something more precise, more respectful, and ultimately more effective: targeting intention.

At the center of this evolution are custom intent audiences. Unlike demographic or interest-based segments, these audiences are built around real-time purpose. They don't rely on who users are, but on what users are trying to do in the moment. For advertisers navigating a post-cookie landscape and rising expectations for both relevance and privacy, the change to intent-based marketing couldn't be more timely.

Custom Intent Audiences vs. Traditional Segments: What Makes the Difference?

Traditional audience segments work by assigning users to broad groups based on past behavior or assumed interests. A user who once browsed for electric cars might continue receiving related ads for weeks, even if their focus has shifted elsewhere. Custom intent audiences, by contrast, start with the present. They identify users based on the content they are actively consuming (articles, product comparisons, search behaviors…) and match that with the user’s likely intention.

This present-tense approach makes custom intent audiences inherently more relevant. It’s the difference between assuming someone is interested in fitness because they follow a sports brand on social media, and recognizing they’re ready to buy running shoes because they’re comparing prices on review pages.

The outcome? More qualified impressions, less wasted spend, and a clearer path to performance.

From Identity to Intention: Privacy-First Precision

One of the main advantages of custom intent audiences is how well they fit within today’s privacy-first advertising landscape.

Unlike identity-based models that depend on cookies or device IDs, intention-based targeting doesn’t need to know who a user is. It only needs to understand what they’re doing.

This difference isn’t just technical but philosophical. Instead of building user profiles based on long-term surveillance, advertisers are focusing on real-time, in-the-moment signals. These include the depth and structure of content, the tone and sentiment of the page, and the user’s position in their decision-making journey.

By using AI models trained to read these signals in real time, advertisers can detect not just interest, but readiness. And they can do so without compromising user privacy.

The Metrics That Matter

Performance is still the goal. And here, the numbers speak for themselves. When global automotive brand Nissan adopted Seedtag’s intention-based strategy to promote its C-SUV category, the results were substantial:

• A 67% reduction in Cost Per Qualified Visit (CPQV).
• A 34% drop in Cost Per Lead (CPL).
• A threefold increase in qualified visits against target.
  
  

What made the difference was simple. Ads were served only when users were showing active signals of intent; not just reading about cars, but comparing models, evaluating financing, or locating dealerships. In doing so, Nissan avoided mid-funnel waste and focused their investment where it had the most impact.

Across industries, similar results are emerging. Campaigns that embrace custom intent audiences consistently outperform those relying on static segments, particularly when it comes to mid- and lower-funnel outcomes.

Aligning Creative with Intention

Reaching the right user in the right moment is only part of the equation. The creative needs to match that moment too. When advertisers target based on intention, the creative strategy must follow suit. Messaging that resonates in a high-intent context looks different than messaging designed for awareness or passive browsing.

For example, a user reading general reviews about smartphones might respond well to informative, value-driven creative. But a user comparing two models side-by-side, looking at specifications or price breakdowns, is further along the journey. In that context, the ad should be clear, action-oriented, and directly aligned with the decision at hand.

This is where Seedtag’s AI intention models play a dual role. Not only do they assess the user’s intent, they also measure how relevant the campaign’s messaging is for that moment. By calculating both an Intention Score and a Campaign Relevance Score, Seedtag’s system ensures that ads appear not just when users are ready to act, but when the message is most likely to land.

This alignment between content, mindset, and creative is what turns impressions into outcomes.

The Case for Rethinking Audience Strategy

Marketers have spent years optimizing for attention by measuring viewability, maximizing impressions, and expanding reach. But attention alone doesn’t drive performance. Without intention, attention is passive. It doesn’t necessarily signal interest, and it rarely signals readiness.

Custom intent audiences offer a way to bridge that gap. They allow advertisers to:

• Replace volume with precision.
• Move beyond proxy metrics to actionable insight.
• Increase qualified engagement without relying on personal data.

In short, they bring intentionality into targeting, in a move that’s both ethically sound and commercially effective.

Where to Begin: Turning Strategy Into Skill

Custom intent audiences are a feature that represents a mindset shift. One that requires new ways of planning, measuring, and creating. For advertisers looking to make that shift, knowledge is the first step.

That’s why Seedtag has launched a new certification through Seedtag Academy: “Targeting Intention.” This program unpacks everything from the foundations of intent-based targeting to the role of AI in modeling user mindset in real time.

It’s designed for marketers who want to:

• Understand how intention works across the funnel.
• Activate privacy-first strategies that don’t sacrifice performance.
• Build creative that speaks to purpose, not just persona.

Whether you're in strategy, media, creative, or analytics, the course provides a practical framework for applying intention in real campaigns.

You’ll learn why performance now depends on understanding not who users are, but what they’re trying to do,  and how you can meet them there.

Enroll in the Seedtag Academy Certification

If you're ready to move beyond identity and start targeting real-time purpose, the new Seedtag Academy certification on “Targeting Intention” is now open. Learn how to activate campaigns that perform better, cost less, and respect your audience. Discover the full course and enroll today.

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Back to School is more than the return of classes and new supplies. It is a cultural reset that sparks consumer activity across fashion, technology, nutrition, and family life. Each year, this season mobilizes millions of students, parents, and educators who are making purchase decisions with purpose and intention.

For advertisers, 2025 offers a unique opportunity to connect with audiences who are actively shaping their routines and priorities. The season reflects a blend of family needs, digital-first lifestyles, and rising expectations for health, comfort, and value.

Seedtag’s neuro-contextual AI, Liz, decodes how people think, engage, and decide, empowering advertisers to truly understand audience behavior and prepare for back-to-school campaigns that feel timely, meaningful, and impactful.

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The Audiences Defining Back-to-School 2025

Back-to-School is powered by four core groups, each with unique mindsets and behaviors:

• Parents: Seek tech and healthy products for their children, along with solutions to enhance family comfort and well-being.
• Grade School Students: Interested in technology, video games, fashion, and sports, looking for ways to express themselves and have fun.
• University Students: Prioritize tech for productivity, comfortable fashion, and focus on mental health and well-being.
• Education Leaders: Seek educational tools to improve teaching and solutions for managing workload and well-being.
  
  

This audience segmentation reveals that 52% of parents are influenced by online promotions and 45% of university students engage with seasonal offers.

Key Insight: Each group interacts with content differently, and these numbers highlight just how active and responsive Back-to-School audiences are when it comes to digital engagement.

Why This Back to School Trends Matters for Brands:

Campaigns that are personalized will perform best. A neuro-contextual approach allows advertisers to target based on real interests, emotions, and intent—rather than assumptions.

What Content Really Matters This Season?

Seedtag’s analysis of back-to-school engagement reveals four major content clusters dominating attention. Each cluster signals a space where advertisers can activate with impact.

Healthy Snacks and Nutrition

Parents are prioritizing well-being, making nutrition one of the strongest back-to-school themes.

• Interest peaks around cereals, fruit, oat snacks, dairy alternatives, and wellness drinks.
• Major players: Kellogg’s, Coca-Cola, Tesco, Aldi, Danone, Innocent, Alpro, Nestlé, PepsiCo, Tropicana.

Why This Matters for Brands

Healthy snacking has become a back-to-school essential. FMCG brands can leverage this momentum with contextual activations that align with family-focused lifestyles.

EdTech and Devices

Tech and learning go hand in hand, with students and parents seeking devices that enhance productivity and entertainment.

• Hot topics include laptops, smartphones, headphones, and gaming consoles.
• Major players: Microsoft, Google, HP, Lenovo, Logitech, Samsung, Apple.

Why This Matters for Brands

With digital habits firmly established, the Back-to-School moment is prime time for tech brands to position their products as must-haves for both education and leisure.

School Clothing and Footwear

Fashion continues to define identity during Back to School.

• Strong engagement with uniforms, sportswear, sustainable apparel, and footwear.
• Major players: Adidas, Nike, Puma, Primark, Asda, Next, Marks & Spencer.

Why This Back to School Trends Matters for Brands

Students and parents look for comfort, affordability, and style. Brands that highlight cultural relevance and emotional connection will earn stronger loyalty.

Family Cars

Back to School influences mobility decisions, with parents looking for cars that deliver safety, convenience, and sustainability.

• Strong engagement around SUVs, hybrid cars, and electric models.
• Major players: Volkswagen, Toyota, BMW, Kia, Ford, Hyundai, Renault, Peugeot.

Why This Matters for Advertisers

Automotive advertisers can tap into the family conversation by linking campaigns to everyday school runs, safe travel, and eco-conscious choices.

From Insight to Connection: The Role of Neuro-Contextual Advertising

The back-to-school season is one of the most competitive moments of the year, with countless brands vying for attention. What cuts through the noise is not louder messaging, but smarter relevance.

Seedtag’s neuro-contextual AI, Liz, does not simply classify articles or surface keywords. It interprets cognitive signals in real time, decoding user interests, intentions, and emotions.

By mirroring the sophistication of human thought, Liz ensures campaigns are delivered in high-quality, privacy-first environments across premium CTV, video, and the open web. Instead of being limited to top-funnel exposure, Liz aligns brand messaging with every stage of the journey, from awareness to consideration to purchase intent.

The result is intelligence that empowers advertisers to scale relevance while respecting user privacy, enabling them to anticipate audience behavior and prepare for back-to-school campaigns that feel timely, meaningful, and impactful.

The Playbook for Back-to-School Success

• Align with healthy habits: Nutrition-driven activations will resonate with families prioritizing well-being.
• Own the EdTech conversation: Position devices and platforms as essential for productivity and lifestyle.
• Blend fashion with culture: From uniforms to sneakers, connect with audiences through identity and expression.
• Drive family relevance: Automotive brands can link mobility, safety, and sustainability to the Back-to-School journey.

From Trends to Action

Back to School 2025 is more than a seasonal spike. It is a cultural touchpoint that reflects shifting priorities around health, technology, identity, and family life.

Brands that align with these conversations and leverage neuro-contextual AI to deliver campaigns in the right moments will go beyond visibility. They will earn relevance at the very moment when audiences are most open to engagement.x

Ready to maximize your back-to-school campaigns? Discover how Seedtag’s can help you deliver impact where it matters most. Get in touch!

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At a time when marketing teams are under increasing pressure to justify every dollar spent, the gap between brand marketing and performance marketing is narrowing. What used to be considered two distinct disciplines, upper-funnel storytelling and lower-funnel conversion, are now in constant conversation.

But how can marketers truly unite both approaches to build long-term brand equity without sacrificing short-term returns?

That’s the question at the heart of the latest episode of Seedtag’s AdTech Heroes podcast. Host Dal Singh sits down with Louise Owen, Chief Performance Officer at UM, for a candid discussion about how brand and performance teams can align around a shared goal: delivering results that matter.

From Louise’s unconventional career path in engineering to her experience leading strategies across multiple global markets, the conversation moves seamlessly between frameworks, culture, and measurement.

The takeaway? Full-funnel marketing success doesn’t come from choosing between brand marketing and performance marketing. It comes from understanding how they work together.

“Brands are asked to demonstrate performance… especially for above-the-line channels where measurement takes time.”, Louise Owen, Chief Performance Officer at UM

From Engineering to AdTech: A Global Perspective

Louise’s path into media was far from linear. With a background in civil and industrial engineering, she initially focused on data analysis and optimization. That technical foundation eventually led her into search trading, where she became curious not just about how campaigns were being optimized, but why.

That curiosity fueled a career that took her to GroupM roles in the US, Colombia, Singapore, Australia, and France, before ultimately bringing her to London. The result is a uniquely global perspective on how media strategies evolve across different markets and what holds them together.

“I was always interested in how things fit together,” Louise explains. “Understanding the mechanics of media was only part of the job. I wanted to know what was driving decisions across the full brand and campaign lifecycle.”

Why Now: The Need for a Chief Performance Officer

So why introduce a Chief Performance Officer role at a network known for its branding strength? As Louise puts it, “Brands are asked to demonstrate performance… especially for above-the-line channels where measurement takes time.”

With financial pressures mounting and marketing budgets under scrutiny, CMOs are being asked to show real, measurable impact across every channel. And while branding efforts might deliver over the long term, stakeholders want visibility now. That tension is what her role aims to address, and bridging the gap between strategic vision and operational impact.

Her work focuses on helping brands understand how to mature digitally, regardless of whether they identify as performance-driven or brand-led. That means showing how each part of a media plan contributes to outcomes and how collaboration between teams can sharpen both sides of the funnel.

How Brand Marketing and Performance Marketing Drive Each Other

For Louise, the divide between brand marketing and performance marketing is largely an internal construct. “To consumers, every touchpoint is a brand experience,” she says. Whether it’s a product video, a display ad, or a sponsored post on social media, each moment contributes to perception and engagement.

This shift is forcing teams to rethink silos. Tools like unified planning platforms and shared audience insights are helping brands take a more integrated approach - one where strategy, audience segmentation, and measurement are designed from the ground up to serve both awareness and conversion goals.

She offers a simple example: search. While often viewed as a performance channel, it also serves as a visibility tool. Being discoverable at the right moment reflects how well a brand has established itself. A strong brand presence enhances search results. A clear search signal helps refine brand messaging. The two are inseparable.

This integration is about more than campaign design. It’s also about shifting measurement goals. Rather than segmenting success by tactic, brands are now starting to ask broader questions: Which audiences are engaging? What content is resonating? Where is value being created?

Audience Understanding Comes First

Behind every successful full-funnel campaign is one central factor: knowing your audience.

Louise emphasizes that aligning on target segments (real, reachable, addressable audiences) is what allows brand and performance teams to work in sync.

It starts with the basics: Who are you trying to reach? What are their behaviors, their needs, their intentions? This is where intent-based marketing powered by data plays a crucial role.

Louise points out that brand planners and performance marketers often use different data sets, which can create disconnects in messaging and targeting. Integrating those perspectives allows for smarter segmentation, more relevant messaging, and better outcomes.

Retail media, she notes, is one of the spaces where this convergence is playing out most clearly. By combining emotional engagement with direct access to purchase behaviors, retail environments offer a snapshot of how upper and lower funnel dynamics are colliding in real time.

Local Nuances, Global Lessons

Having worked across five continents, Louise has a deep appreciation for local nuance. In countries like Australia, for example, centralized infrastructure and detailed consumer research enable advanced cross-channel campaigns. In contrast, regions with more complex supply chains or data regulations require more adaptive planning.

She highlights the challenges global brands face when trying to unify their ad tech and martech stacks across regions. What works in the UK may not translate to Poland or India, due to legal constraints, supply chain issues, or market fragmentation.

Yet these differences are also opportunities and are consistently pushing brands to rethink how and where they collect data, how they define success, and how they adapt creative to local needs.

Can Brand and Performance Teams Drive Full-Funnel Success?

The answer, for Louise, is an emphatic yes but only if organizations are willing to shift how they work, not just how they plan.

She shares examples of brands using performance data to improve brand targeting, and vice versa. One case involved a brand with overlapping audiences across multiple products. By examining performance insights, that is how people engaged with different campaigns and moved between products, then the brand was able to reallocate spend, refine messaging, and reduce internal duplication.

Instead of managing campaigns in isolation, they began planning them as part of a shared ecosystem, where performance results could inform brand direction and brand signals could optimize conversion.

It’s this kind of feedback loop that Louise sees as essential to future success. And it’s why she believes that AI, if deployed correctly, could finally unlock better measurement across the board, offering real-time insights that reflect how audiences actually behave, not just how they’re expected to.

Measurement and the Road Ahead

One of Louise’s biggest hopes for the industry is smarter, more customizable measurement. As she notes, legacy approaches like marketing mix models often struggle to keep up with fast-changing digital behavior. She believes AI will play a major role in evolving these systems while helping brands understand which channels actually drive growth, and why.

She’s also candid about the role that data infrastructure plays. Too often, companies are held back by fragmented systems and years of unstructured information. If she could go back in time and give brands one piece of advice, it would be to unify their data from the beginning.

“Unification of data and signals is really what powers insights,” she says. “You need a clean dataset to build scenarios and make good decisions.”

What It Means for Marketers Today

Ultimately, Louise’s insights point to a simple but often overlooked truth: real marketing impact comes from alignment. When brand marketing vs performance marketing is seen as a choice, teams work in opposition. But when they’re aligned, from segmentation to creative to measurement, the result is smarter campaigns, more relevant experiences, and stronger business outcomes.

The challenge now is less about building new capabilities and more about connecting existing ones. For marketers, that means investing in shared tools, fostering cross-team collaboration, and reframing measurement in terms of real-world results.

Brand marketing vs performance marketing isn’t a debate. It’s a relationship. And as Louise makes clear in this conversation, the most successful brands are the ones that treat it that way.

Listen to the Full Episode
Want to dive deeper into this conversation? Listen to Louise Owen on AdTech Heroes: When Branding Meets Performance: Insights from Kinesso and hear how brands can unite storytelling and strategy for full-funnel success.

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Passions Over Profiles: How AI Sees the Real Me… and My Bees

On paper, I’m the definition of a demographic stereotype: a suburban white male in his late 40s who likes sports and wood-fired cooking. Statistically, I fit the mold—over 80% of U.S. men consider themselves sports fans, and surveys show grilling is still seen as a male-dominated activity.

But numbers only tell a fraction of my story. Beyond the smoker and team sports, I’m passionate about health, fitness, and well-being. I love creating meals from fresh, organic ingredients sourced directly from farms. Cycling is central to my lifestyle, taking me across New York State in search of the best trails and vegetable stands. That love for the outdoors and healthy eating led me to gardening, and eventually, to beekeeping—an uncommon pursuit for someone with my demographic profile.

Beekeeping isn’t just a hobby; it’s a reflection of my values: sustainability, environmental stewardship, and a handcrafted connection to nature. It’s proof that broad labels like “sports” or “barbecue” can branch into highly specific, deeply personal passions—unique combinations of interests, behaviors, and values that no demographic snapshot can reveal.

Why Seedtag’s AI Liz Makes the Difference: AI for Advertising​

This is where Seedtag’s Neural-Contextual AI, Liz, stands apart. Liz doesn’t pigeonhole me by age or gender. Instead, she understands the full context—maybe I’m browsing cycling routes, then organic cooking guides, then articles about pollinators or sustainable farming. She connects these signals to understand who I truly am, delivering relevance instead of stereotypes.

Traditional demographic targeting would drop me into a “male, 45–54, suburban” box and push sports cars or generic gadgets. Liz’s neuro-contextual targeting sees the chef, the cyclist, and the environmental steward—and serves me organic food products, sustainable gear, boutique travel experiences, and brands that share my values.

So while many people who look like me may be sports fans, that doesn’t define the whole person. Like minded affinities can reveal deeper values—care for artisanal craftsmanship, our environment and experiences. Seedtag’s AI Liz bridges these unique interests, replacing broad demographic assumptions with rich, colorfully informed context.

When brands connect with me through my passions, they’re not just serving ads; they’re starting conversations that feel relevant, personal, and worth engaging with. That’s the power of advertising in context: it speaks to who I am, not just what I look like on paper.

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Earlier this year, we introduced Neuro-Contextual Advertising, Seedtag’s new evolution in marketing innovation that combines neuroscience principles with Agentic AI to interpret interest, emotion, and intent in real time. This represents a decisive evolution from traditional contextual targeting, moving beyond reading pages to truly understanding people and what drives them.

At the heart of this transformation is Liz, our proprietary, fully in-house Neuro-Contextual AI. Liz mirrors the sophistication of human thought by interpreting cognitive signals in real time and delivering high-quality, privacy-first, full-funnel advertising across premium CTV, video, and the open web. Building on this neuro-contextual understanding, Seedtag utilizes the Liz Agent, powered by the latest advancements in Agentic AI, to autonomously activate Liz’s intelligence through an intuitive, conversational interface. Because Liz is developed entirely in-house, we have full control over its evolution, ensuring our technology stays ahead in the privacy-first era without relying on third-party tracking or personal data.

Now, this vision is being recognized on a global stage. eMarketer’s “Tech Trends H1 2025” report has named Neuro-Contextual Advertising as the number one trend at the intersection of AI and neuroscience, and placed Seedtag at the forefront of this transformation. For us, this is more than industry recognition, it is validation of a shift we have been driving for years, and a signpost for where digital advertising is headed next.

Why This Recognition Matters

As eMarketer states in its Tech Trends H1 2025 report, “AI-powered neuro-contextual advertising is revolutionizing how brands target consumers. Companies like Seedtag are combining neuroscience research with real-time emotional state detection.”

eMarketer’s recognition of neuro-contextual advertising as the top trend highlights its growing importance for brands, agencies, and publishers navigating the evolving advertising landscape. The future of advertising lies not in static keywords or third-party data, but in understanding the human psyche in a privacy-first way.

The report emphasizes several key differentiators of Seedtag’s approach:

• Real-time emotional intelligence: analyzing context and viewing patterns to detect emotional states as they happen.
• Dynamic creative alignment: adjusting ad tone from upbeat creative during high-energy moments to more subdued messaging in reflective contexts.
• Privacy-first delivery: achieving precision targeting without cookies or user tracking, aligning with the expectations of a privacy-first era.

From Marketing Innovation to Impact

When we launched Neuro-Contextual advertising, we set out to answer a critical need. We wanted to deliver advertising that feels timely, resonates emotionally, and drives measurable outcomes while respecting privacy.

Our approach is built on three cognitive pillars:

• Interest: Liz connects real-time content signals to broader user interests, helping brands reach people based on what truly matters to them.
• Emotion: Liz detects the emotional tone of content to deliver ads that align with how users feel, creating deeper and more human connections.
• Intent: By analyzing context, Liz anticipates the goal a person has in mind when engaging with content, whether they are browsing, researching, or ready to convert.

Liz is both the intelligence that extracts audience insights on interest, intent, and emotion, and the AI Agent that activates those insights to create, customize, and optimize campaigns in real time. This integration ensures campaigns are continuously optimized, and remain relevant at every stage of the funnel.

A Turning Point for the Industry

In a crowded adtech landscape, external validation from a respected source like eMarketer matters. It signals that neuro-contextual is not a niche experiment; it is a defining trend shaping the future of advertising.

As the report notes, “Companies like Seedtag are pioneering technology that understands viewer mindset… Using neuroscience-trained AI, Seedtag’s platform intuits interests, emotions, and purchase intent by analyzing context and viewing patterns.”

Advertising has always aimed to connect brands with their audiences in the right place and at the right time. Historically, digital advertising intelligence relied heavily on methods like keyword matching, URL targeting, and basic content categorization. While these tools had their place, they offered only a surface-level understanding of audience context.

Traditional contextual targeting could tell you that a user was reading an article about electric cars, but not whether the article was a glowing review or a critical takedown — or whether the reader was simply curious, seriously considering a purchase, or already decided against it. As privacy regulations tightened and consumer expectations grew, this gap became a critical weakness.

Neuro-contextual advertising closes that gap. By combining neuroscience principles with advanced AI, it moves from simply identifying “what” content is about to understanding “why” a person is engaging with it, and “how” they are likely to feel and act next.

For brands and agencies, this means

• Access to full-funnel outcomes across premium CTV, video, and open web.
• Audience intelligence that is dynamic, scalable, and privacy-first.
• A competitive advantage in engaging consumers in moments that matter most.

Looking Ahead Marketing Innovation

Recognition from eMarketer is just the beginning. We are continuing to refine Liz’s capabilities, expand the functionality of the Liz Agent, and explore partnerships in neuroscience to advance the scientific expertise at the core of our approach.

Our mission remains the same: to help brands win their audiences by tapping into their interests, emotions, and intentions, and to do so in a way that is respectful, relevant, and results-driven.

The future of advertising is not just about being seen; it is about being understood. And with neuro-contextual advertising at the forefront, that future is already here.

Source: eMarketer, Tech Trends H1 2025.

Discover how Seedtag’s Neuro-Contextual Advertising can help your brand win audiences through their interests, emotions, and intentions. Learn more about this marketing innovation trend here.

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Amid accelerating deployment of programmatic technology in TV, the openRTB Content Object has become an essential ingredient for buying and selling television media in real-time.

As the television and programmatic advertising ecosystems converge, several interesting applications and technical specifications are playing a more prominent role in how TV ads are bought and sold.

Innovative addressable TV specs are unlocking 1:1 advertising opportunities across broadcast television inventory. Competitive separation and deduplication rules, which have long been table stakes for highly-curated commercial breaks, are becoming commonplace for dynamically-constructed ad pods. And traditional creative review processes, once a tedious (yet necessary) task for programmers, are being automated at scale — just to name a few examples.  

While many of these evolving solutions are playing a pivotal role in the convergent TV arena, one critical, but often-overlooked, specification is the Content Object. Part of the IAB Tech Lab’s openRTB protocol, the Content Object helps bring powerful contextual data to programmatic marketplaces, enabling media sellers and buyers to seamlessly transact in real time off key information that historically has been used to inform direct TV buys.

As the leading convergent TV advertising platform, we often find ourselves speaking with media sellers and buyers about the opportunities surrounding the Content Object, and how it can best be deployed. Below, we’ve answered some of the most common questions we hear, hoping to shed some more light on why the Content Object is so key for programmatic TV:

What is the IAB oRTB Content Object?

The Content Object is one part of the IAB’s openRTB standard, which is a widely-adopted transaction protocol used for the programmatic buying and selling of media (in real-time). The oRTB protocol has a number of different object specifications for both bid requests and responses, including metadata like geography, users, devices, and more.

As a bid request specification, the Content Object is a set of standardized information shared by media sellers that is specific to the actual content or program in which an ad opportunity is available, rather than the app or bundle. Exchanging this type of information enables media sellers and buyers to transact off highly-valuable content metadata, such as a TV show’s name, rating, or genre.

What are the types of metadata included and exchanged via the Content Object?

The Content Object includes a wealth of contextual metadata, spanning 25 available fields in total. The metadata supported by the spec includes information common in episodic television, such as the specific series, show, episode, genre, and rating. Other information like production quality, program language, whether or not the opportunity lives within a livestream, and so forth, is also supported via the Content Object — a full version of which can be found in section 3.2.16, here.

How are programmatic media sellers and buyers using the Content Object, and what are the associated benefits?

Media sellers use the Content Object to automatically provide prospective media buyers with valuable information on the context or program in which their ad may appear (again, as part of the bid request). This is critical within programmatic marketplaces specifically, as advertisers are increasingly seeking more flexibility and transparency into their campaigns from both a content targeting and ad delivery perspective.

By transacting off the Content Object, media sellers earn premiums for their inventory by making it more transparent and enticing for buyers, which helps to drive up demand density (e.g. the number of brands bidding on their inventory). Media buyers meanwhile benefit from greater contextual targeting insights for premium TV programming, ensuring brand safety against key client criteria while allowing for more relevant and impactful advertising on the big screen.

More broadly, why is the oRTB Content Object so important as programmatic and TV converge?

As programmatic technology and oRTB protocols are increasingly deployed in both connected and traditional television, it becomes critical that consistent parameters and taxonomies are established to inform the buying and selling of TV media.

Historically, episodic TV inventory has been sold directly against a combination of audience ratings and show-level information. Ensuring the latter of these two (valuable content data) is exchanged consistently between media sellers and buyers in an automated fashion via programmatic is critical to helping all parties accomplish key objectives, from either a yield optimization or an advertising impact standpoint.

This is all the more important as privacy regulations evolve and as viewing behaviors proliferate across cable, broadcast, and connected TV. Establishing platform-agnostic and privacy-conscious transaction standards, such as via the Content Object, is key for building both a sustainable advertising ecosystem and interoperability across systems.

What should media sellers and buyers be doing in order to take full advantage of the Content Object?

If you’re a media seller, configure your bid requests to pass all relevant information within the Content Object. This ensures your inventory is made available to all relevant advertisers who are interested in buying against specific shows or genres, which ultimately helps you drive greater demand density and yield.

For media buyers, configure your advertising platform — whether that be a DSP or an internal trading desk — to accept and read the Content Object. This is a crucial first step that will allow you and your end brand clients to unlock highly-valuable contextual metadata for campaign targeting and private marketplace (PMP) curation.

To learn more about the Content Object, and how you can capitalize on all that it enables from a yield or advertising impact standpoint, reach out to us here:

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In Episode 22 of The PubWay podcast, hosts Tina Iannacchino and Mike Villalobos welcome Brian Lin, SVP of Product Management, Advertising at TelevisaUnivision. The episode dives deep into the state of CTV measurement and the broader challenges facing programmatic advertising today.

Brian, who leads advanced advertising strategy for the world's leading Spanish-language media company, shares timely insights on first-party data, evolving consumer behavior, and what publishers and advertisers need to get right if they want to improve performance across CTV environments.

The conversation touches on everything from co-viewing dynamics and data match rates to the role of AI in scaling measurement. Below, we break down the most critical takeaways from the episode.

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CTV Measurement at a Crossroads

With connected TV approaching mass adoption, advertisers and publishers alike are trying to understand how to measure campaign performance accurately. One of Brian’s early points underscores the magnitude of this shift:

“CTV is almost at the point in which it's getting close to 50% of total video consumption.”

This growth brings both opportunity and complexity. While digital tools make CTV inherently more measurable than traditional linear television, many advertisers still struggle to capture the full picture of their campaigns. One reason? Data fragmentation.

Advertisers often rely on disparate data sets stitched together through intermediaries, introducing gaps and reducing match rates. “There’s always a tradeoff between data quality and scale,” Brian explains. “You want a high match rate, but not at the expense of accuracy.”

In CTV, where brands look to measure outcomes like cost per completed view (CPCV), return on ad spend (ROAS), and unique viewer reach, missing signals can undermine performance and accountability. Improving CTV measurement starts with improving the quality and interoperability of the data itself.

Closing the Gaps with First-Party Data

For publishers and advertisers, closing the measurement gap means building stronger, more privacy-conscious data infrastructure. Brian points to TelevisaUnivision’s own first-party data strategy as a blueprint.

By building a household graph that aggregates signals from across local live events, streaming content, linear television, and audio platforms, the company now reaches 95% of US Hispanics. “It’s a game changer,” Brian notes, especially in a landscape where third-party data still struggles to identify Spanish-speaking audiences accurately.

“Some third-party datasets show only about 40% accuracy in identifying Hispanic consumers,” he explains. “That’s a huge miss for advertisers with the right intent.”

Clean rooms are emerging as an effective solution to connect first-party data from publishers and advertisers. These environments allow datasets to be combined securely, enabling granular CTV measurement while respecting privacy standards.

Measuring CTV in Multi-Viewer Environments

Traditional measurement models were built for one-to-one devices like laptops and mobile phones. But with CTV, viewers gather in living rooms, often watching together. This creates a multiplier effect on impression value and a measurement blind spot for brands focused solely on device-level data.

“Co-viewing is still one of the biggest opportunities in CTV,” Brian says.

While general market co-viewing rates hover around 1.5 to 1.7 viewers per screen, that figure rises to 2.6 to 3 for US Hispanic households.

What this means in practical terms is that a CTV ad served to one device might actually be reaching three people. Adjusting measurement frameworks to account for co-viewing can dramatically improve perceived campaign performance, especially in family-oriented or multicultural households.

But to do so, publishers must be willing to share more metadata and log-level data with advertisers. “It’s a receipt,” Brian explains. “Advertisers should know what content their ads ran against if we want them to measure and come back.”

The Role of AI in Real-Time CTV Optimization

AI is already playing a supporting role in content classification, sentiment analysis, and targeting. But its true potential lies in making CTV measurement more dynamic and adaptive.

Take metadata, for instance. In the past, CTV inventory was often sold in bulk, with little transparency about the content it would appear alongside. But as AI tools improve, publishers can now categorize programming with greater precision, identifying not just genres, but tone, emotion, and thematic context.

This opens the door for more sophisticated brand safety controls and targeting strategies. For example, an advertiser promoting family products might want to align with upbeat, co-viewed programming but avoid more intense or adult-themed content.

At TelevisaUnivision, AI is also being applied in creative ways. During the Latin Grammys, the network partnered with ShopSense and Walmart to create a second-screen experience: as celebrities walked the red carpet, viewers could scan a QR code to shop similar outfits in real time. It’s a small but tangible example of how CTV advertising can evolve beyond traditional ad pods.

Looking Ahead: What’s Next for CTV Measurement?

As the podcast wraps, Brian offers a glimpse into a future shaped by both AI and, surprisingly, quantum computing.

With current cloud infrastructure, many platforms sample data rather than process it all, limiting the granularity of insights. But new breakthroughs in quantum hardware could allow real-time analysis of massive data sets without the tradeoffs publishers face today.

“Most programmatic partners don’t look at every opportunity in the bid stream because the cost is too high,” Brian explains. “With quantum computing, that could change.”

More immediately, publishers need to rethink how they define and share content metadata. While some hesitate to expose too much information for fear of cherry-picking, withholding it entirely limits advertisers’ ability to measure outcomes, target appropriately, and ensure brand safety.

The industry will likely move toward more transparency over time, driven by advertiser demand, technology improvements, and the increasing sophistication of AI tools that can enrich CTV metadata automatically.

Realigning Expectations Around Performance

With so many variables at play, CTV advertisers often ask a simple but important question: what’s the benchmark? Did my campaign deliver what it promised?

Today, many of those benchmarks are still being written. From completion rate to exposed audience to brand lift, CTV measurement still lacks the standardization of linear television. But progress is being made.

By embracing innovations like clean rooms, metadata enrichment, and cross-platform data graphs, publishers can offer advertisers the clarity they need. And when that happens, the entire CTV ecosystem becomes more efficient, accountable, and resilient.

As Brian puts it, “When advertisers get access to the right data, and can prove effectiveness, they come back.”

Tune In to the Full Episode

For a deeper dive into data quality, CTV campaign performance, and how publishers like TelevisaUnivision are shaping the future of digital video, listen to Episode 22 of The PubWay: Navigating Data Quality & CTV Measurement.

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In the rapidly shifting landscape of digital advertising, one question is dominating every strategy conversation: What is contextual advertising and why is it central to the future of media?

Today’s advertising environment looks nothing like it did ten years ago. What was once a channel dominated by third-party cookies and behavioral data is now being reshaped by stricter privacy regulations, growing user awareness, and changing consumption habits. Advertisers are being asked to do more with less and to do it while respecting users' privacy expectations.

The answer lies in context. As traditional tracking tools phase out and reliance on personal data becomes increasingly problematic, brands and publishers need new ways to serve relevant, effective ads that drive results.

This is where contextual advertising comes in, and where our new Mastering Contextual Advertising Guide delivers the insights needed to navigate this new reality with confidence.

Understanding Contextual Advertising

At its core, contextual advertising is about delivering relevant ads based on the content a user is actively engaging with - not their personal data or browsing history.

It differs from behavioral advertising in a few key ways:

• Targeting method: While behavioral ads rely on tracking users across websites to build profiles, contextual ads are based on the actual content of the page being viewed.
• Privacy: Contextual advertising does not require cookies or invasive tracking. It's a privacy-first solution, built for a landscape where user consent and transparency are non-negotiables.
• Relevance: Because contextual ads match the environment they appear in, they tend to feel more organic, and drive stronger engagement.
• User experience: With no intrusive data collection or off-base assumptions, contextual ads offer a smoother, more user-centric experience.

And thanks to AI, contextual ads have become smarter than ever. With the ability to analyze not just keywords but entire articles, visuals, and video content, Contextual AI delivers human-like understanding at scale enabling advertisers to place messages that truly align with the moment.

Why Contextual Is Back

While contextual advertising isn’t new, its return marks a shift in priorities for advertisers.

In the early 2000s, contextual ads were widely used in search and display formats. But with the rise of behavioral tracking in the 2010s, they faded into the background. Now, with increasing regulation and consumer demand for data protection, contextual has not only returned but it’s evolved.

The latest generation of contextual tools:

• Analyze page content in real time using machine learning and semantic models.
• Place ads based on relevance rather than identity.
• Avoid the pitfalls of demographic and behavioral bias.
• Offer campaign performance without sacrificing user trust.

It’s this combination of relevance, scale, and privacy that makes contextual the most future-ready approach in digital advertising today.

The Benefits: What’s In For Advertisers

Relevance That Drives Results

Contextual ads meet users in the moment, serving messages that align with what they’re reading, watching, or listening to. Whether it's a cooking ad on a recipe site or a fitness brand on a health article, the match feels intuitive and delivers stronger click-through rates and conversions.

Higher Engagement, Lower Intrusion

Ads that reflect the user’s current interests are less likely to disrupt their experience. This means more attention, less ad fatigue, and a more positive perception of the brand.

Non-Biased Targeting

Because contextual advertising doesn’t rely on personal identifiers, it avoids the ethical concerns and stereotyping risks that can come with behavioral targeting. This results in more inclusive reach and a fairer experience for all users.

Privacy Compliance by Design

As privacy laws evolve, contextual targeting remains fully compliant, helping advertisers future-proof their strategies without compromising performance.

Powered by AI: The New Era of Contextual

Today’s contextual solutions go far beyond keyword matching. With Contextual AI, brands can understand and respond to page content in real time, factoring in everything from tone to visual elements.

This enhanced precision means:

• Better ad placements
• Higher relevancy scores
• Stronger ROI

Contextual AI also unlocks creative optimization that helps brands test, adapt, and personalize ad content depending on the page or platform it appears on.

And with formats like display, video, and even CTV now context-enabled, the reach and flexibility of contextual campaigns are wider than ever.

Ready To Launch Your Own Strategies? Start Here

In our new Mastering Contextual Advertising Guide, we cover everything you need to get started, scale up, or refine your contextual strategy.

What’s inside:

• A deeper look at how contextual advertising works
• 5 steps to developing a privacy-first, high-performance strategy
• Targeting methods explained: keywords, topics, categories
• Insights into AI-powered contextual tools
• Tips for designing creatives that resonate with the content around them
• Guidance on tracking the right KPIs to optimize for success

We also explore how contextual is expanding across channels from the open web to CTV, in-app environments, digital audio, and more.

Contextual advertising isn’t just an alternative, it’s the future. As digital privacy becomes non-negotiable, advertisers need strategies that can perform without personal data.

By aligning with content, not identities, contextual advertising builds relevance that users welcome and results that marketers can measure. And at Seedtag we have taken it to the next level with our new category, Neuro-Contextual advertising.

Want to master the strategy that’s reshaping the advertising landscape?

Download the Mastering Contextual Advertising Guide now and discover how to build smarter campaigns that resonate, perform, and respect privacy from the start.

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The Pub Way Podcast returns with an in-depth look at CTV advertising, focusing on demand side platforms (DSPs). Hosted by Tina Iannacchino (VP of Publisher Partnerships North America at Seedtag) and Mike Villalobos (SVP of Strategy and Commercial Operations, North America at Seedtag), Episode 13 welcomes Keith Gooberman, CEO and Co-Founder of Pontiac Intelligence, to discuss how DSPs are adapting to evolving data policies and the new opportunities CTV brings for publishers. Here we bring you all you publishers need to know about how DSPs are changing connected TV buys, why privacy remains central, and what it all means for publisher revenue.

Why DSPs Matter: A Crash Course For Publishers

A demand side platform (DSP) is the digital interface that enables advertisers to purchase inventory across channels (desktop, mobile, and especially CTV advertising) in a unified manner.

Historically, DSPs relied on cookie-based data for granular targeting. Now, with privacy concerns reshaping online advertising, DSPs are turning to private marketplace (PMP) deals, prioritizing direct collaboration with content owners.

Publishers stand to benefit. PMP deals typically command higher CPMs and more transparent data-sharing. As Keith observes, tomorrow’s programmatic environment will feature deeper partnerships between DSPs and content owners, creating unique revenue streams for publishers who can provide distinctive inventory or advanced targeting signals.

Shifting From Cookies To Context

Global privacy regulations are forcing a move away from broad data collection. While Google’s cookie plans fluctuate, the overall direction remains privacy-first. In a CTV context, cookies are largely irrelevant, so targeting methods must evolve. DSPs are adapting by focusing on content signals, forging direct relationships with streaming services, and negotiating PMPs that bypass the open exchange.

This is good news for publishers: those who excel at packaging content and user engagement data without compromising confidentiality will be well-positioned. Although sharing log-level data can be sensitive, it often reassures advertisers that they’re buying premium inventory, encouraging greater spend.

CTV Advertising & Log-Level Data: Striking A Balance

Advertisers increasingly seek transparency. They want show-level insights (e.g., “Which program did my ad appear in?”) to confirm brand suitability and measure effectiveness. Yet publishers understandably guard their data, worried about undercutting direct deals or exposing proprietary information.

Keith explains that with a tailored DSP approach (built around PMPs) publishers can negotiate exactly what to share. This selective data release can elevate CPMs, especially when unique audience contexts or exclusive programming is on offer.

The key is clear communication: publishers who help advertisers understand the content environment can attract stronger campaign commitments.

The New Wave Of Contextual Targeting In CTV

Traditional contextual targeting (based on keywords or page categories) now faces its biggest test in CTV advertising, where video content dominates. AI tools can parse shows at a deeper level beyond mere categories, recognizing mood, dialogue, or plot themes. This refined approach offers advertisers a better sense of what’s on screen, ensuring relevant ad placements without relying on personal data.

For publishers, robust AI-driven context elevates value. If you can detail the emotional tone or specific segments of your videos, you stand out in a crowded market. DSPs want premium signals to differentiate one CTV channel from another, and sophisticated content analytics can deliver that competitive edge.

Evolving Identity Strategies

Despite Google’s shifting timeline on cookies, the days of unrestricted data collection are numbered. Many CTV environments rely on device or IP-based identifiers rather than cookies. DSPs address this by blending partial user details with broader contextual cues and PMP agreements. Publishers with strong first-party data or advanced audience insights can fill that gap, commanding higher prices if they maintain user trust.

Key Takeaways For Publishers

1. Demand Side Platforms (DSPs) Are Central To CTV
   Publishers should recognize that DSPs are the gateway to expanding CTV ad buys. By accommodating PMP deals, you can secure premium revenue while retaining more control.
2. Privacy Shifts Ad Buying Toward Context
   As personal identifiers phase out, brand alignment rests on deeper content signals. Publishers who refine their program data and present it in user-friendly ways will see sustained interest.
3. Log-Level Data Requires Careful Sharing
   Advertisers crave transparency. Selective data disclosures like show title, genre, location, can raise advertiser confidence. Clear boundaries protect publisher advantage.
4. AI Enhances Contextual Value
   Automated tools can interpret video scenes and sentiment. Publishers who incorporate AI-based insights can stand apart and deliver more targeted inventory to advertisers.
5. Direct Communication Builds Better Deals
   Flexible PMP relationships allow publishers to define how data is shared. DSPs often welcome these refined deals, provided they gain reliable insights into inventory quality.

DSP technology once revolved around open exchanges and third-party data; now it’s pivoting to direct deals, granular content analysis, and privacy-friendly user signals. Publishers who adapt to these market realities and offer a mix of audience clarity, brand safety, and strong contextual data are poised for long-term success.

Want the full story? Tune in to Episode 13, featuring Pontiac Intelligence’s Keith Gooberman, to hear firsthand how DSPs operate, where CTV advertising is headed, and how publishers can thrive in a shifting ecosystem.

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Advertising has always aimed to connect brands with their audience, ideally in the right place and at the right time. Historically, digital advertising intelligence relied heavily on traditional methods such as keyword matching, URL targeting, or content categories. Yet, as consumer expectations and privacy regulations tighten, brands demand more nuanced solutions that understand not just what people consume, but how they think, feel and decide.

Enter neuro-contextual advertising, Seedtag’s new category that leverages neuroscience principles alongside advanced AI to interpret interest, emotion and intent in real time. This shift moves marketing from rigid, content-based triggers to a human-centric model that mirrors the brain’s information processing in order to deliver high-quality, privacy-first, full-funnel advertising across premium CTV, video and the open web.

Here, we explore how this new approach fundamentally transforms advertising strategies, shifting from simply reading pages to truly understanding people and what moves them.

Contextual Advertising: From Classification to Understanding

When programmatic advertising first emerged, contextual targeting offered a privacy-friendly way to reach audiences by matching ad placements to page-level topics. Early systems relied on static taxonomies, predefined categories such as “automotive” or “travel.” At its best, this method ensured ads appeared alongside relevant content, but it also introduced two key challenges:

1. Surface-Level Relevance
   Matching ads to pages based on keywords or tags alone often failed to capture nuance. A single article titled “The Future of Electric Cars” might garner targeted auto advertisers, yet within that page, a negative review of a specific model could mean the reader is unlikely to buy. Traditional contextual simply could not distinguish between promotional enthusiasm and critical analysis.
2. Upper-Funnel Focus
   By design, keyword- or URL-based systems excel at generating awareness while driving clicks when audiences present broad interest. However, as marketers pushed for measurable conversions, the limitations became clear. Without insight into audience intent (the stage in the buying journey), media spend risked being wasted on readers who lacked the inclination to act.

In short, traditional contextual approaches were good at placing ads adjacent to relevant content, but less adept at understanding the underlying motivations and emotions driving user behavior.

Advances in artificial intelligence advertising as part of the AI revolution in advertising improved on basic keyword matching. Modern contextual AI platforms began employing natural language processing (NLP) and computer vision to parse page structure, sentiment, tone and images. This allowed systems to identify whether an article was positive, negative or neutral about a topic, and to understand whether images depicted aspirational lifestyles or technical specifications.

Still, these algorithms primarily focused on content recognition and classifying each page into thematic buckets such as “Luxury Vehicles” or “Fuel Efficiency.” While more accurate than earlier methods, the approach remained fundamentally aligned with “What is on this page?” rather than “What does this page say about the user’s mindset?” In other words, it still lacked a true understanding of interest, emotion and intention - the three pillars of human decision-making.

Introducing Neuro-Contextual Advertising

Neuro-contextual advertising represents a fundamental leap: it mirrors how the human brain processes information, creating a more cohesive model of audience engagement. Instead of surface-level content analysis, neuro-contextual integrates neuroscience insights into artificial intelligence advertising. By interpreting interest, intent, and emotion in real-time, Seedtag’s proprietary AI, Liz, mirrors human cognitive processes, enabling highly precise, responsive, and scalable ad delivery.

What sets neuro-contextual apart? It comprehends not just the "what," but also the "why" behind user behavior, delivering advertising that resonates on deeper, cognitive levels. This translates directly into improved outcomes at every funnel stage, making neuro-contextual an essential evolution for marketers who demand more from their ad spend.

Understanding Interest: Capturing Attention

From neuroscience, we know that human brains respond more favorably to familiar, context-congruent stimuli, processing them faster and more efficiently. Neuro-contextual technology leverages these insights to position advertising precisely within moments of heightened relevance.

For instance, when a user engages with content around sustainability, Liz immediately understands their genuine interest based on intention based targeting and AI intention models. Rather than serving generic environmental ads, neuro-contextual recognizes subtle patterns, such as emotional tone, narrative focus, and visual elements… to identify deeper resonance. As a result, the advertising aligns seamlessly with the user’s attention, driving significantly higher engagement.

Decoding Emotion: Enhancing Recall and Affinity

Emotions play a central role in decision-making processes. Neuro-contextual goes beyond traditional analytical approaches by actively interpreting emotional signals within digital content, from sentiment and imagery to narrative style.

Consider an advertising campaign for luxury travel. Traditional contextual might align this campaign with content containing keywords like "travel" or "vacation." Neuro-contextual, however, evaluates the emotional nuances, placing ads within content reflecting aspiration, relaxation, or indulgence - all emotions that directly align with the luxury traveler’s mindset. This refined alignment drives deeper emotional engagement, enhancing brand recall and affinity.

Identifying Intention: Driving Action

At its core, neuro-contextual advertising is designed to not only understand user intent but also act upon it dynamically. By analyzing deeper cognitive signals, Seedtag's Liz detects when a user's engagement indicates readiness to act.

Take the example of financial products: a user reading detailed comparative content about investment options indicates a much stronger intention than someone casually exploring financial news. Neuro-contextual recognizes this intent in real-time, adjusting campaign delivery to prioritize highly specific, action-driven messaging. This precise targeting of user intention significantly enhances conversion rates, optimizing ad spend for measurable outcomes.

Neuroscience Principles in Neuro-Contextual AI

Cognitive Fluency and Emotional Encoding

At the heart of neuro-contextual lies the principle of cognitive fluency: the ease with which our brains process familiar, context-congruent stimuli. When an advertisement appears alongside content that aligns with the user’s interests or emotional state, it is processed more readily and regarded more favorably. Neuroscientific research confirms that positive emotional context enhances memory encoding which makes users more likely to recall both the content and the brand message.

Seedtag applies this by identifying emotionally charged intersections, that is pages where audience interest and sentiment peak. In each case, neuro-contextual AI analyses audio sentiment, facial expressions, color palettes and even pacing to gauge emotional intensity. Ads served in these windows benefit from the emotional resonance, translating into higher attention, recall and eventual action.

Interest, Attention and Memory

Interest acts as the gateway to attention; without curiosity or relevance, information is ignored. Neuroscience shows that attention is a limited resource as stimuli must compete for cognitive prioritisation. By matching ads to content that already captures genuine interest, neuro-contextual AI ensures that brand messages earn that scarce attention.

Furthermore, episodic and semantic memory processes work in tandem when we engage with emotionally-charged, interest-driven content. By aligning ads to these rich, multi-layered experiences, Seedtag’s technology capitalises on both types of memory encoding and thus helping brands remain top-of-mind when users transition from exploration to decision-making.

The Role of Neuroscience for Full-Funnel Outcomes

Neuro-contextual advertising moves Seedtag decisively beyond traditional contextual limitations. While conventional contextual targeting excels in awareness, Seedtag’s neuro-contextual approach supports full-funnel marketing outcomes, from initial attention through mid-funnel interest and emotional resonance, down to lower-funnel conversions.

Neuro-contextual ensures advertising remains relevant across premium CTV, video, and open web environments, with advanced embedding technologies analyzing content and context across vast digital ecosystems. Instead of pre-defined segments, Seedtag’s neuro-contextual advertising dynamically creates custom, scalable audiences based on real-time cognitive signals. This cross-environment synergy ensures marketing efforts remain aligned with consumer states of mind - no matter where or how audiences consume content.

Real-Time Optimization: From Predictive to Agentic Intelligence

Central to Seedtag’s approach is the combination of advanced neuroscience insights with sophisticated AI algorithms. Unlike predictive analytics that merely forecast outcomes, neuro-contextual applies real-time cognitive intelligence, adjusting ad delivery dynamically.

Seedtag’s Liz Agent remains an enabler, transforming Liz’s deep insights into actionable campaign configurations via a conversational interface. The agent layer interprets natural-language prompts and retrieves relevant segments and automates bid adjustments across channels. The Liz Agent plays a crucial role in realising neuro-contextual intelligence in real time, blurring the line between strategy and execution.

This is transformative for marketing strategies. Campaigns leveraging neuro-contextual advertising no longer remain static post-launch. Instead, they evolve continuously, optimizing at unprecedented speed and precision. The result? High-quality engagements precisely attuned to real-time user interest, emotion, and intent.

Connecting the Dots: Neuroscience and Digital Advertising Intelligence

The adoption of neuroscience principles within digital advertising intelligence underscores a broader trend in marketing: a shift toward understanding the holistic human experience.

The advertising landscape has outgrown the label of “contextual targeting.” Seedtag's evolution from contextual to neuro-contextual represents a decisive shift for the advertising industry. No longer confined to simple classification or upper-funnel objectives, neuro-contextual technology introduces the sophisticated cognitive insights marketers have long needed.

In an advertising ecosystem challenged by consumer privacy, evolving regulations, and increasing demand for meaningful engagement, neuro-contextual advertising presents not merely an upgrade but a necessary evolution. It reshapes digital advertising intelligence, moving from mere keyword recognition to real-time understanding of how audiences think, engage, and make decisions.

This groundbreaking approach signifies more than technological advancement as it signals a fundamental redefinition of advertising itself at every stage of the funnel.

Win Your Audience: Tap into Interests, Emotions and Intentions

Learn more about Seedtag's neuro-contextual advertising and explore how your brand can leverage cutting-edge neuroscience and artificial intelligence to deliver superior marketing outcomes.

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At this year’s Cannes Lions International Festival of Creativity, amid sun-soaked terraces and industry-wide discussions about the future of advertising, one theme overtook all conversations at the Croisette: redefining relevance through deeper user understanding. For Seedtag, Cannes 2025 marked the opportunity to return to the French Riviera and bring forward the future of artificial intelligence for marketing: neuro-contextual advertising.

Built on the idea that context is no longer enough, neuro-contextual advertising moves beyond static classifications and towards cognitive intelligence - interpreting real-time signals of interest, emotion, and intent to connect with people in more meaningful, privacy-first ways.

Defining the New Era Through Emotion, Intention, and Intelligence

Seedtag recently unveiled its new positioning by championing a model that resonates far beyond keywords or audiences. Focusing on more than just data, but on better understanding on how people feel, why they care, and what drives their decisions in the moment.

From the stage to the shoreline, our team helped define this new era of contextual advertising - one powered by neuroscience principles and made scalable by Agentic AI. Seedtag’s neuro-contextual intelligence connects not only content and creativity, but also emotion and cognition, bringing brands closer to the real drivers of consumer behavior.

In a marketplace seeking relevance without compromise, Seedtag’s approach stands as one built for privacy, designed for outcomes and powered by understanding.

A Crossover to Tune Into: AdTech Heroes x The Pub Way

One of the highlights of the week was the special crossover episode of our two flagship podcasts: AdTech Heroes x The Pub Way – Winning Audiences in a New Era of Engagement. Hosted live at The Drum’s podcast studio, the session brought together voices from across the ecosystem to answer a timely question: how can brands and publishers use real-time context and AI to engage audiences more effectively, while prioritizing passions over profiles, and meaning over assumption?

Moderated by Seedtag’s Tina Iannacchino and Marko Johns, the conversation featured Jamie Dunlop, Managing Partner at MediaPlus UK, and Tony Gemma, VP Global Head of Creative at Yahoo. Together, they unpacked the shifts in strategy required to meet audiences where they are, not demographically but behaviorally and emotionally.

Jamie unpacked how MediaPlus moved beyond demographic targeting to focus on real human behavior, arguing that knowing how people think and feel is more important than knowing who they are. “Demographics treat Prince Charles and Ozzy Osbourne as the same person,” he said. “They’re not.”

Tony, from Yahoo, made the case that the creative side of programmatic has long been neglected. “Programmatic forgot to bring its creative friend along,” he noted. The group agreed that while media and data have advanced, creativity often lags behind. The takeaway? Brands that succeed are the ones reuniting data, creative, and context while treating creative as a measurable driver of outcomes, not just a finishing touch.

From personalization at scale to creativity that aligns with intent, the conversation reflected an industry yearning for a system that doesn’t just automate targeting but understands people and delivers campaigns that connect.

Elevating Brands with Purposeful Technology

AI was examined with depth, especially in terms of its role in shaping more intelligent, ethical advertising. As Mike Villalobos, SVP of Strategy North America, noted during the AI in Action panel hosted by Sigma Software, AWS, and Ipsos:

“AI isn't a feature, but rather a core part of our foundation to accelerate and sustain our growth.”

That sentiment was echoed across the festival. The conversation has clearly shifted from curiosity around AI to a firm expectation that it delivers measurable value. The bar is no longer automation. Neuro-contextual delivers on that expectation by integrating neuroscience insights with real-time emotional understanding. It’s not just about being faster. It’s about being smarter and enabling marketers to activate campaigns that align with how people actually feel and think, in the moment.

This theme of thoughtful progress was also front and center in Cannes Truth Be Told, a panel exploring the monetization of journalism hosted by Unplugged Collective and Beeler.Tech. Representing Seedtag, Tina Iannacchino, VP of Publisher Partnerships North America, addressed the challenge of balancing brand safety with media responsibility.

“In today’s ad tech ecosystem, quality journalism is often caught in the crossfire of rigid brand safety measures.”

Keyword blocklists, while designed to protect brands, frequently end up demonetizing essential reporting on politics, conflict, or climate. That creates a disconnect where high-value editorial content is excluded from media plans, while sensational or low-quality content remains monetized.

Advertisers must move beyond blanket controls and toward more intelligent, context-aware solutions. Only then can we ensure brand safety without undermining trusted journalism.

And in a forward-looking conversation hosted by VaynerX, Seedtag’s Global Chief Revenue Officer Brian Danzis, joined executives from VaynerMedia and Digiday’s Editor-in-Chief Jim Cooper, to explore how AI is reshaping the media ecosystem. The panel challenged the industry to cut through the noise and focus on real use cases that are actively changing how content is created, distributed, and monetized.

The group discussed how AI is helping companies rethink how they reach audiences, make decisions, and define success. One example shared during the session was United Airlines’ recent campaign with Seedtag. By combining contextual AI with high-impact formats, the campaign reached premium audiences in brand-safe environments, without relying on personal data. The result? A measurable boost in attention and engagement, proving that relevance and performance can go hand in hand in a privacy-first world.

As Brian highlighted:

“Relevance drives cognition, interest, emotion and intent. That is what neuro-contextual means, and it is leading us into the new era of advertising.”

Together, these conversations reflected a broader shift at Cannes Lions this year: toward solutions that not only perform, but reflect the values and complexity of the audiences they aim to serve.

Seedtag in the Spotlight: A Full Week of Industry Impact

From private roundtables on brand safety and media integrity, to our collaboration with HUMAN for the “Collective Apéro,” Seedtag showed up to Cannes ready to share how brands, agencies and publishers worldwide can embrace the new era of digital advertising.

Whether joining VaynerMedia on stage for a sunset conversation on AI’s future in media, or exploring how to balance personalization with accountability on Île Sainte-Marguerite, our team helped shape the conversations that will guide the next wave of marketing strategy.

While the industry has long relied on contextual targeting for privacy-first reach, Seedtag’s conversations at Cannes showed how far we’ve come. Today’s marketers are looking for relevance that adapts, moment by moment, to the emotional and cognitive state of the consumer.

Seedtag’s neuro-contextual system is built to do just that. Through dynamic semantic embeddings and continuous network-level analysis, our proprietary AI Liz delivers scalable understanding of what moves people. Whether in a premium CTV environment or across the open web, campaigns adapt in real time to changing intent, interest, and emotional tone.

It’s strategic evolution - meeting the moment when creativity, audience mindset, and media come together.

From launching our latest Contextual TV capabilities to showcasing our AI Intention Models and real-time contextual insights platform, Seedtag’s presence reflected our ambition: to make advertising work better by understanding more deeply.

Engagement, Intelligence, and the Industry We Want

As Cannes Lions 2025 came to a close, one thing was clear: the most effective campaigns are no longer the ones that shout the loudest, but the ones that understand the audience most deeply.

If AI was the buzzword of the week, emotional relevance was the quiet headline underneath. And neuro-contextual advertising is where those two forces meet: scalable intelligence grounded in how people actually think, feel, and decide.

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It is a well-known truth that summer always brings with it a certain expectation. Longer days, lighter routines, and moments we try to make last just a little longer. But for advertisers, the season is more than a backdrop. It's a dynamic series of moments where context makes the difference in your summer campaign hits.

From road trips to national holidays, sports dominating all conversations, and to back-to-school planning, consumers engage with summer in ways that are both emotional and practical. The question for brands is no longer “What should we say this summer?” but “What are people already thinking about… and how can we show up meaningfully in those moments?”

This post explores the summer marketing ideas that emerge when brands listen more closely to seasonal behaviors, and how Seedtag’s neuro-contextual technology helps decode not just the what, but the why behind each interaction.

What Summer Trends Should Advertisers Watch to Craft Impactful and Timely Summer Marketing Ideas?

One of the most striking findings from Seedtag’s analysis of more than 2,400 summer-related topics is just how many dimensions the season contains. What appears on the surface to be a linear journey from Memorial Day to Labor Day is actually a layered narrative of shifting priorities, emotions, and buying signals.

Memorial Day: A Moment of Remembrance, Gathering, and Everyday Traditions

For many, Memorial Day marks the beginning of summer. But beyond the grill and three-day weekend, it’s also a deeply personal and reflective holiday.

Seedtag’s insights show consumers gravitate toward content centered around honoring veterans, visiting national landmarks, and engaging in symbolic activities that carry emotional weight. At the same time, there’s a distinct uptick in planning for seasonal cookouts and outdoor events buffalo chicken, caprese salads, and summer drinks top search patterns.

Campaigns that recognize both the reverence and everyday togetherness of this holiday are far more likely to resonate. For automotive brands, it’s an opportunity to speak to travel and mobility. For CPG and food retailers, it’s a chance to help families set the table, literally and figuratively.

Juneteenth: A Growing Space for Culture, Community, and Creativity

As Juneteenth grows in visibility, so too does the way consumers engage with it. People are looking not only to learn but to celebrate through music, food, history, and shared identity.

From Beyoncé and Kendrick Lamar to heritage travel and soul food recipes, interest around this holiday cuts across multiple categories. The content reflects pride, creativity, and exploration whether it's through music playlists, recipes, or tributes to trailblazing Black figures in history.

For advertisers, Juneteenth is a moment to show up with intention and cultural clarity. It’s not about running “holiday creative.” It’s about aligning with stories already being told and doing so in a way that feels both timely and considered.

Road Trips: The Great American Reset

There may be nothing more quintessentially summer than packing up the car and heading somewhere (let’s be honest, just anywhere) with the windows down and snacks within reach. According to Seedtag’s analysis, road trips remain the season’s single largest area of consumer interest.

Topics range from dream destinations like Yosemite and Big Sur to checklists about rental car options, tire safety, and navigation apps. But beneath that lies something deeper: a collective craving for freedom, spontaneity, and being in control of the journey.

This is fertile ground for automotive, travel, tech, and lifestyle brands. The best campaigns aren’t about the destination but how you get there. And Liz can help pinpoint exactly when, where, and how those intentions emerge across the open web.

Independence Day: A Celebration of Taste, Color, and Connection

Independence Day content shows clear engagement with themes like creative desserts, themed decorations, and festive drinks. It’s a holiday where the visual and sensory elements come to life and where shareability soars.

This is also one of the few summer moments where user-generated content naturally thrives. Recipes, party setups, backyard tables, and homemade cocktails become canvases for expression.

Brands that find smart, light-touch ways to invite participation (think branded ingredients, color-themed collections, or interactive social challenges) can tap into both emotional connection and cultural momentum.

Labor Day: Where Endings Create Urgency

Labor Day signals more than a long weekend. It’s the point in the summer when consumers begin shifting mentally toward what comes next from fall wardrobes to routines and school schedules.

Content patterns reflect this duality: on one hand, people are still looking for summer meal inspiration and weekend getaways. On the other, searches spike for productivity tips, sales events, and seasonal transitions.

Retailers that time their campaigns to this emotional pivot, and build urgency around it, can outperform the typical “seasonal clearance” approach. Think of messaging that acknowledges the shift, while offering a final invitation to enjoy what’s left.

Back to School: One of the Year’s Most Predictable (and Personal) Journeys

If Labor Day is the pivot point, then Back to School is the plunge. Engagement here is widespread and diverse, ranging from parents planning for first grade to students decorating dorm rooms.

Seedtag’s insights show four clear categories: school supplies, crafts and projects, educational content, and college prep. It’s one of the few times of year when content is not only seasonally relevant but emotionally sticky.

Brands that show up with empathy, solutions, and timing aligned to regional school calendars can gain meaningful traction, especially if they understand when those searches begin and how they evolve from mid-July through early September.

How Can Brands Develop Summer Marketing Ideas That Are Directly Inspired by Emerging Summer Trends?

There’s no shortage of summer campaign ideas. The real challenge is making sure those ideas land in the right moment, in the right environment, with the right message. That’s where contextual intelligence becomes the creative starting point, not just the targeting layer.

Here’s how brands can begin to shift their thinking:

Start with Moments, Not Months

Instead of framing campaigns around quarterly plans or static timelines, start with behavior. Memorial Day cookout planning doesn’t start on May 25 but actually starts in early May. College dorm checklists trend long before move-in week.

Seedtag’s neuro-contextual AI, Liz, reads these shifts as they happen. That means brands can build campaigns around actual audience intention, not calendar assumptions.

Use Content as a Creative Brief

If people are engaging with themed drink recipes, start from the content itself. What emotion does it evoke? What intention does it reflect?

Contextual creative can reflect the same tone and cues consumers are already responding to. Liz helps uncover not only what topics are spiking, but how people are reacting to them, whether that’s with curiosity, nostalgia, or inspiration.

Think Across Categories

Summer marketing ideas don’t need to stay in one lane. A road trip kit can feature snacks, car care, and sunglasses. A dorm prep campaign can pair storage solutions with coffee makers and motivational posters.

Liz doesn’t just see individual topics but she maps how they overlap across real-life behavior. Brands that embrace this interconnectedness can create richer, more layered messaging.

Plan for Participation

Summer is social, and not just on social media. It’s a season of shared experiences, with recipes swapped, playlists created, photos taken and plans made throughout the season.

Brands that make it easy for people to participate, be it through branded content, contests, or creator partnerships - all can tap into the momentum of the moment without needing to dominate it.

From Context to Connection: The Role of Neuro-Contextual Intelligence

What enables all of this is not just technology but actually truly understanding the content and what it conveys. Seedtag’s neuro-contextual AI, Liz, doesn’t simply classify articles or surface keywords. It interprets the full spectrum of user attention by combining neuroscience principles with advanced contextual signals.

Rather than relying on identity or outdated segments, Liz reads each moment of engagement in order to decode not just the topic, but the intent, emotion, and mindset behind it. This means advertisers can show up in the right frame of mind, not just on the right page.

While contextual targeting has often been viewed as a top-funnel solution, Liz delivers across the funnel - matching brand messaging with awareness, consideration, and even purchase signals as they emerge in real time.

In a summer marketing landscape that’s becoming more fluid, more expressive, and more diverse, this kind of intelligence isn’t optional. It’s what enables brands to scale relevance without sacrificing nuance.

Final Thoughts: Embrace Summer Stories

The real shift for marketers in 2025 is recognizing that summer isn’t a campaign but a conversation. One that evolves from celebration to reflection to transition. One where road trips and recipes, milestones and markdowns, all coexist.

Seedtag’s contextual insights help map this journey not just by what’s trending, but by how people are feeling and what they’re planning for next. And with Liz’s neuro-contextual intelligence at the center, advertisers can respond in the most human way possible, with empathy, relevance, and intention.

Want to learn more about how Seedtag can help shape your summer campaigns?
Discover the full picture and all of our curated contextual insights here.

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Bringing elements of traditional TV to programmatic media buying and selling, ad pods bridge the gap between disparate viewing environments and help to deliver optimal ad experiences for CTV viewers.

The migration of advertising budgets from linear TV to connected TV (CTV) is closely following increases in ad-supported streaming. According to eMarketer, CTV advertising is forecasted to reach over $35 billion in spending in the U.S. by 2025. In the midst of this significant growth, media buyers and sellers alike are seeking to enhance the way in which CTV ads are bought and sold. And thus, a TV-centric concept – ad pods – has remerged to the forefront of industry conversation.

If you aren’t familiar with their history, ad pods were first formally introduced to the digital advertising ecosystem in 2012 via the IAB Tech Lab’s VAST 3.0 specification. At this time, streaming services were still nascent and lacked the capabilities needed to manage key considerations such as competitive separation, ad duplication, and frequency.

With the CTV ad market growing considerably within the last decade, and more recently within the last two years, the industry has become more focused on driving efficiency and delivering pristine viewer ad experiences in CTV. To support this endeavor, the IAB Tech Lab earlier this year released OpenRTB 2.6, a transaction protocol that includes features to support CTV buying and selling, including – you guessed it – new attributes and guides for ad pods.

As the sell-side ad server built for Convergent TV, we often receive questions about ad pods, how they can be best deployed, and the advantages they offer. Below, we’ve answered some of the most common questions we receive, to provide insight on how the adoption of the IAB Tech Lab’s oRTB 2.6 technical specification can help CTV publishers, advertisers, and viewers alike.

What are ad pods?

Ad pods are a sequenced group of ads that play back-to-back within an ad break, most often in CTV and streaming environments. Using ad pods, programmers and publishers can receive multiple contiguous ads from a single ad request. Scheduled in pre-, mid-, and post-roll environments, ad pods resemble the type of commercial break you’d see in traditional TV, or hear within a radio structure.

Related to ad pods, pod bidding signals are an important feature of the oRTB 2.6 spec that allow key information about the pod and its impression opportunities to be shared among media buyers and sellers. This, for example, includes the sequence of ad impressions, total pod length, maximum number of ads within the pod, and more.

What are the different types of ad pods?

The OpenRTB 2.6 spec outlines three main types of ad pods for CTV environments – structured, dynamic, and hybrid.

Within a structured ad pod, the media seller fully defines the number of ad slots, position in the pod, and respective duration. This gives the publisher full control over every aspect of the pod, but it is largely manual and can be somewhat time-consuming to construct.

Dynamic ad pods, by comparison, are more flexible. While the total duration and maximum number of ads are constrained within a dynamic ad pod, the number of ads and duration of each are indeterminate. This offers publishers more flexibility and efficiency in optimizing their selection of ads in order to fill a pod.

A hybrid ad pod is exactly what it sounds like, combining elements of both structured and dynamic pods. Hybrid ad pods are composed of slots with predetermined durations, along with undefined numbers and durations of individual ads.

What benefits do ad pods provide to media buyers and sellers?

Ad pods afford both publishers and advertisers more strategic and transparent ways of transacting in CTV.

For media sellers, ad pods and pod bidding signals allows for more strategic monetization, including the ability to:

• Optimize inventory yield based on metrics like CPM per second.
• Reduce latency in CTV auctions.
• Provision slot-based targeting and pricing.
• Uphold key brand rules including competitive separation.
• Deliver premium viewer ad experiences by accounting for ad duplication and frequency.

Conversely, for media buyers, ad pods can unlock opportunities to:

• Increase win rates.
• Reduce infrastructure costs and trading volume.
• Execute key business rules, including competitive separation.
• Provision strategic buying opportunities to advertisers, including purchasing of specific slots.

Above all else, what's most important is that ad pods can enable more pristine ad experiences in CTV, which benefits publishers, advertisers, and viewers alike.

Why are ad pods so important in Convergent TV advertising?

As traditional and connected TV converge, ad pods – when paired with necessary ad serving technology – play an essential role in bridging the gap between disparate viewing environments. In addition to the benefits previously mentioned, ad pods and pod bidding signals make the real-time buying and selling of CTV media more akin to traditional TV, via support for key tenets like competitive separation and slot-based pricing.

What’s more, ad pods help to deliver pristine ad experiences to CTV viewers. The challenges and shortcomings of CTV advertising have been well-documented in recent years – from blank slates to timeouts to frequency issues – and the implementation of ad pods with powerful ad serving technology will help to solve for these issues.

If you’re increasingly operating in CTV, or looking to gain an edge, adoption of and support for ad pods, including via the oRTB 2.6 spec, is a must. To learn more about ad pods and how they enable a more efficient, viewer-friendly, and profitable way to buy and sell CTV media, reach out to us here:

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The UK automotive market has never been more complex. Stricter privacy regulations, economic headwinds and the surge of electric-vehicle challengers have rewritten the rules for automotive brands. Today’s car buyers move seamlessly between devices and researching family SUVs during morning coffee, comparing EV range at lunch, then exploring finance deals after work. To win their business, brands and agencies must understand not just who is in the market but why, when and how their intentions evolve.

Seedtag’s contextual AI platform Liz has analysed over 10,000 automotive-related articles and webpages to decode the audiences driving new cars home. In this post, we share the strategic insights that will help UK automotive marketers reach each segment precisely capturing fleeting purchase moments in a privacy-first environment.

Who’s Buying: A Deeper Dive into UK Car-Buyer Segments?

Moving beyond broad demographics, our research identifies five high-value audience cohorts, each defined by motivations, content habits and media touchpoints. Understanding these profiles enables brands to tailor creative, media and messaging for maximum relevance and efficiency.

1. First-Time Buyers: Cautious Digital Natives

Profile

• Age: 24–35, early career or young families.
• Priorities: Affordability, reliability, ease of ownership.
• Values: Financial prudence, wellness and work-life balance.

Media Habits

• Start with “best first car” articles, obsess over budget calculators
• Compare user forums and video reviews of entry-level models
• Search “PCP vs. hire purchase” and “insurance cost by age”

2. Young Urbans: Trendsetters on the Move

Profile

• Age: 25–40, professionals in metropolitan areas.
• Priorities: Sustainability, connectivity, compact-city practicality.
• Values: Social currency, experience-driven purchases.

Media Habits

• Read urban-mobility think-pieces; watch “EV for city living” video explainers.
• Scroll social feeds for trending EV scooter or compact car content.
• Engage with lifestyle influencers showcasing tech-integrated dashboards.

3. Family Upgraders: Space, Safety and Stability

Profile

• Age: 35–50, growing families upgrading from compact cars.
• Priorities: Interior room, safety tech, running costs.
• Values: Reliability, long-term value, lifestyle consistency.

Media Habits

• Binge-read “top seven-seater SUVs” and “best electric family car” comparisons.
• Click through to “family car maintenance” and “insurance discounts for minivans”.
• Watch sponsored test drives by family vloggers.

4. Luxury Seekers: Prestige and Performance

Profile

• Age: 40+, affluent professionals and executives.
• Priorities: Brand heritage, driving experience, exclusivity.
• Values: Craftsmanship, bespoke options, peer recognition.

Media Habits

• Digest in-depth features on Nürburgring lap times, bespoke customisation options.
• Follow automotive journalists and fine-living platforms.
• Engage with virtual reality showrooms and high-res imagery.

5. Technophiles: Innovators Embracing Tomorrow

Profile

• Age: 25–45, tech industry professionals and early adopters.
• Priorities: Autonomous features, AI-driven safety, seamless connectivity.
• Values: Cutting-edge innovation, data-driven performance.

Media Habits

• Read “Tesla software updates” and “AI co-pilot reviews”.
• Watch footage of self-parking demos and LIDAR tests.
• Participate in online forums about OTA updates and app integrations.

Aligning Media to Moment: Seizing Intent-Driven Windows

Effective automotive advertising hinges on matching each audience’s mindset with the right content at the right stage of their journey. Whether you’re speaking to cautious First-Time Buyers or tech-obsessed Technophiles, recognising the moments that trigger purchase intent (and embedding your message in those contexts) is critical.

At the Awareness & Discovery stage, consumers encounter broad research queries and inspirational editorial. First-Time Buyers often begin by searching “best starter cars under £10 000,” while Young Urbans look for “EVs for city commuting.” Family Upgraders binge on “top seven-seater SUVs” lists, Luxury Seekers digest “luxury performance SUV design trends,” and Technophiles stream “latest autonomous driving demos.” To capture attention here, brands should place high-impact, value-driven creative such as eco-credentials or design innovations, in think-pieces and first-look videos that these segments naturally explore.

As buyers move to the Consideration & Interest phase, their queries become more focused on direct comparison and evaluation. First-Time Buyers compare PCP versus hire-purchase finance guides, Young Urbans review charging-app comparisons, and Family Upgraders read seven-seat crash-test results. Luxury Seekers investigate bespoke trim options, and Technophiles delve into technical whitepapers on AI safety systems. Serving calculators, detailed spec sheets, and interactive configurators alongside these articles helps nudge evaluators toward enquiry, bridging the gap between page view and test-drive booking.

In the Conversion & Action stage, transactional signals dominate. First-Time Buyers click “book first-time test drive” buttons, Young Urbans reserve subscription services, and Family Upgraders schedule showroom visits. Luxury Seekers RSVP to VIP launch events, while Technophiles sign up for beta tests of self-parking features. By detecting surges in content that indicates these final steps (dealer locators, “click to reserve” features or exclusive invites) brands can deploy geo-targeted offers, rapid-response chat prompts and dynamic call-to-action banners that turn interest into action. Seasonality further sharpens these windows: EV interest peaks in Q1 and Q4 around tax deadlines and New-Year resolutions, while SUV and hybrid demand stays robust year-round. Sports and off-road vehicle searches climb ahead of summer, underscoring the value of real-time bid adjustments whenever intent signals spike.

Three New Rules for Privacy-First Automotive Advertising

Context, Not Cookies

With third-party cookies crumbling, contextual signals are the new currency. Liz analyses page text, imagery and metadata without user data, to infer purchase intent in real time. This respects ICO guidelines while pinpointing engaged audiences.

Intent-Weighted Bidding

Allocate higher CPMs to content with strong transactional cues (e.g., finance breakdowns, dealer locators) and lower bids on general news or lifestyle reads. Intent scoring automates this process, ensuring spend follows real demand.

Agile Campaign Activation

Automotive trends shift with model launches, fiscal events and seasonality. Liz’s continuous intent recalculation enables brands to adapt budgets within hours of content consumption spikes rather than waiting weeks for manual reports.

A Roadmap for Brands and Agencies

1. Deep-Dive into Audience Profiles
   Use Seedtag’s segment definitions to audit current campaigns. Are you reaching Family Upgraders on finance comparison pages? Are Young Urbans seeing your ads during city-mobility reviews?
2. Tailor Creative to Context
   Develop distinct creative sets for each segment: pragmatic messaging for First-Time Buyers, aspirational aesthetics for Luxury Seekers and tech demos for Technophiles.
3. Secure Premium Contextual Inventory
   Partner via private marketplaces to place ads on specialist blogs, enthusiast forums and lifestyle verticals that ensure brand-safe, high-intent environments.
4. Automate Bid Adjustments
   Configure your DSP to ingest Liz’s intent scores, automatically raising bids on high-value pages and suppressing low-relevance inventory.
5. Measure Full-Funnel Impact
   Track downstream engagement metrics such as enquiry submissions, test-drive bookings, dealer visits…to validate that contextual campaigns drive real-world sales.

The Road Ahead: From Reach to Relevance

The UK automotive sector will see increased competition as EV incumbents, startup disruptors and subscription models accelerate. Consumer values, from sustainability to seamless digital experiences, will continue evolving. In this landscape, automotive advertising that focuses on when and why audiences act rather than who they are will outperform legacy approaches centred on broad demographics.

Contextual AI, anchored by real-time intent analysis, empowers brands to:

• Pivot Quickly in response to seasonal spikes and new model launches.
• Speak Directly to each segment’s core motivations, boosting relevance.
• Respect Privacy while maintaining precision in media targeting.

By adopting these strategies, UK automotive brands can shift from mass reach to micro-moments that steer budgets toward the content that truly drives consumer decisions and delivers measurable growth in an increasingly privacy-first world.

Discover the Full Contextual Insights Report

For a comprehensive breakdown of segments, seasonal intent curves and competitive benchmarks, download Seedtag’s Q2 2025 Automotive Contextual Insights: Access the report here.

When you understand who your buyers are, why they choose your brand, and when they’re ready to act, every impression moves them closer to the driver’s seat.

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In a rapidly shifting digital landscape, publishers face mounting pressure to boost revenue while preserving audience trust. In Episode 16 of The Pub Way podcast, hosts Tina Iannacchino (Seedtag’s VP Publisher Partnerships, North America) and Mike Villalobos (Seedtag’s SVP Stratey & Success) sit down with Dan Benyamin, founder and CEO of Aeon, to explore AI in publishing - from adtech hurdles to creator collaborations, emerging AI influencers, and practical strategies for AI monetization. Below, we unpack the episode’s key moments, demonstrating how publishers can harness AI responsibly, keep the human touch, and unlock new revenue streams.

From Hardware to Headlines: Dan’s Journey

Dan Benyamin is no stranger to disruption. Over a career spanning chip design, social-media platforms, and data products at Condé Nast, he has seen technologies reshape audiences and ad models. At Aeon, his fourth startup, Dan is charting the next wave: helping media owners use AI to automate video production, personalize experiences, and optimize yield.

His adtech origin story selling the first Facebook beta ads by email underscores one constant truth: publishers succeed when they remain user-centric.

Facing Adtech’s Growing Complexity

The User as North Star

Dan recalls the early days of Facebook advertising with no targeting, no placements, just a postage-stamp ad sold for a day. Fast forward, and the adtech stack has ballooned: multiple platforms, countless measurement standards, opaque programmatic flows. Yet publishers often fall into two traps:

1. Tech First: Investing heavily in bespoke data platforms and complicated pipelines, losing sight of editorial quality and brand loyalty.
2. Platform Dependence: Relying on Google or a handful of ad networks for revenue, ceding control over user relationships.

Dan’s prescription is straightforward: prioritize your readers above all else. Every algorithm tweak, dashboard rollout, or content experiment should answer one question: does this create a better experience for our audience? If not, it risks becoming an expensive dead end.

The Value of Agility

Publishers that treat adtech as an ever-evolving toolkit, rather than a monolithic fortress, will outpace their peers. Dan highlights how rapidly user behaviors can pivot, whether due to a new video format on TikTok or shifting reading habits during major news events. By pairing AI for publishers with nimble workflows, media owners can test new formats, measure engagement in real time, and iterate before budgets are wasted.

Bridging Traditional Publishing and the Creator Economy

A Two-Front Battle

Publishers today are squeezed from both sides. On one hand, the major social platforms (Facebook, Instagram, TikTok) consolidate huge swaths of attention. On the other, a flourishing creator economy has turned individuals into media companies: micro-brands that build dedicated audiences on YouTube, Twitch, or Substack, often with minimal overhead.

Dan warns that consistent collaboration between legacy publishers and independent creators remains uneven. While a handful of partnerships, such as Vogue enlisting YouTube stars for the Met Gala, capture headlines, most publishers have yet to forge sustainable models to co-create content, share revenue, and cross-promote audiences.

Turning Creators into Partners

Yet the potential is immense. Traditional media houses boast seasoned editors, photographers, and brand equity. Creators bring agility, authenticity, and built-in fanbases. Smart alliances can combine those strengths:

• Content Repurposing: Use AI-powered video engines to transform written features or photo essays into engaging short-form clips.
• Co-Branded Sponsorships: Bundle creator channels with publisher sites for advertisers seeking both scale and niche community buy-in.
• Revenue-Share Models: Offer creators a slice of subscription or ad revenue in exchange for exclusive or early-access content.

By leveraging AI for content creation, from automated editing to dynamic personalization, publishers can scale these partnerships without ballooning production costs.

The Rise of AI Influencers and the Case for Transparency

When Bots Become Brand

Dan describes the phenomenon of AI influencers: digital personas designed to engage audiences on social platforms, often without any real human behind the account. Early adopters, such as fashion houses casting AI models, demonstrate AI’s capacity for high-volume, on-brand content. Yet the technology also raises red flags around authenticity, trust, and intellectual property.

Publishers must decide how far to embrace synthetic personalities. Dan suggests two guiding principles:

1. Be Transparent: Clearly label AI-generated content or personas. Readers value honesty; misleading them risks reputational damage.
2. Focus on Value: If an AI persona informs, entertains, or inspires better than a standard post (without pretending to be human) then it has earned its place. The goal is to enhance, not replace, genuine human connection.

Regulation on the Horizon

As AI content proliferates, regulators and platform owners are racing to define standards. Dan points to spam filters as an early example of automation policing automation: publishers that watermark AI-crafted imagery or sign API agreements with chatbot providers can ensure their content remains discoverable and compliant. Publishers that proactively engage with emerging policies will avoid last-minute compliance headaches.

AI as a Force Multiplier for Monetization

Beyond “Build vs. Buy”

Publishers often wrestle with whether to build in-house AI solutions or license third-party tools. While engineering teams may relish crafting bespoke pipelines, Dan cautions that such projects can distract from core editorial missions. Instead, he recommends:

• Evaluate ROI: Will a custom model truly outperform a commercial offering?
• Partner with Specialists: Use AI for publishers platforms that integrate seamlessly with existing CMS and ad stacks.
• Modular Adoption: Begin with high-impact use cases (automated video summaries, headline generators, or ad yield forecasting) before extending capabilities.

Five Low-Hanging Fruits for AI-Driven Revenue

1. Automated Content Tagging & Personalization
   Natural language processing can instantly categorize articles by topic, sentiment, and reader intent, fueling on-site personalization and boosting subscription conversions.
2. Video Production at Scale
   Leverage AI video editors to turn long-form interviews or articles into short social teasers, generating new ad inventory and ancillary sponsorship opportunities.
3. Dynamic Ad Creative
   Implement responsive banners or video ads that adapt messaging, call-to-action, and imagery based on real-time page context, raising click-through rates and CPMs.
4. Yield Optimization Tools
   AI algorithms can forecast demand, identify under-monetized ad slots, and recommend price floors - putting publishers in control of their programmatic outcomes.
5. Rights Management Automation
   Automated scans can detect copyright flags in submissions or user-generated content, protecting intellectual property and avoiding legal pitfalls.

Data-Driven Decision Making, Powered by AI

From Dashboards to Decisions

Publishers generate vast troves of behavioral data (pageviews, scroll depth, engagement time) but insights often remain locked behind manual reports. AI offers a leap forward:

• Anomaly Detection: Instant alerts when traffic patterns or ad performance deviate, so teams can respond to banner blockers or viral spikes in real time.
• Predictive Churn Models: Early warnings when subscribers show signs of disengagement, triggering targeted retention campaigns.
• Cross-Channel Attribution: A unified view across articles, newsletters, social posts, and podcasts - revealing which touchpoints truly drive ad revenue or subscriptions.

By embedding AI into the publishing process, editorial and commercial teams alike move from gut instinct to evidence-based strategies.

Balancing Automation with the Human Touch

The Perils of Overreliance

While AI can draft news articles or produce quick video summaries, Dan emphasizes that publishers’ most enduring asset is editorial credibility. Over-automation risks diluting brand voice, undermining trust, and scaling errors. Instead, he advocates:

• Human-In-The-Loop: Any AI-generated draft or creative asset should undergo editorial review for accuracy and tone.
• Ethical Guardrails: Establish internal guidelines on which content types (breaking news, investigative features, expert analysis) remain strictly handcrafted.
• Continuous Training: Use feedback loops where editorial teams flag AI missteps, refining models for better performance over time.

In this way, generative AI becomes a collaborator, not a replacement, empowering journalists and marketers to focus on high-value work.

Preparing for AI’s Next Chapter

AI for Publishers: A Mindset Shift

Dan’s closing advice is to treat AI not as a magic bullet but as a performance-enhancing tool. Publishers that:

• Stay Curious about emerging models and platforms
• Invest in Data Literacy across their teams
• Foster Cross-Functional Collaboration between editorial, technology, and sales
  will build the resilience to adapt when the next big disruption arrives, be it voice interfaces, immersive media, or new regulations.

Six Months to Action

1. Audit your current AI and data projects.
2. Pilot one AI-powered tool in content creation or ad yield.
3. Measure its impact on engagement, revenue, or efficiency.
4. Scale successful experiments and sunset low-impact efforts.
5. Document ethical guidelines and compliance checkpoints.
6. Communicate wins and lessons to all stakeholders, building momentum for further AI adoption.

Tune In and Take the Wheel

AI-driven innovation is reshaping the publishing industry at every corner, from automated content creation and video automation to real-time yield optimization and personalized experiences. The Pub Way episode with Dan Benyamin offers a masterclass in how AI in publishing can become a catalyst for growth, not a source of distraction.

Ready for more actionable insights? Subscribe to The Pub Way and explore the full conversation with Aeon’s Dan Benyamin.
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