But the surfaces are noisy. Your prospect can scroll past the ad in their feed, filter the email, skip the pre-roll, or fast-forward through the podcast break. During my time at Meta, we told marketers they had three seconds before the scroll. That pressure hasn’t gotten easier. It’s compounded.

This is the gap TV fills. Not instead of the stack you’ve already built, but alongside it.

The Buyer Is Already Watching

The people approving vendor budgets and signing software contracts are the same people watching TV after work. Reaching them there isn’t a departure from B2B marketing logic. It’s an extension of it.

Streaming or CTV ads have made this practical in ways that weren’t possible five years ago. Audience-based buying, geo tests, frequency controls, survey attribution — the measurement mechanics B2B performance marketers already know map directly onto CTV. Relay, the fintech platform for small businesses, started on CTV because it offered tighter targeting and faster feedback loops than TV, with measurement they could actually explain to a CFO. Early results showed a direct lift in branded search and site traffic. That’s not a brand metric. That’s demand generation.

IAB data put CTV ad spend at $23.6 billion in 2024, up 16% year over year. The channel is no longer experimental. Most B2B marketers just haven’t caught up to that yet.

What TV Does That Digital Can’t

Search ads get six words. LinkedIn posts compete with every other hot take and humblebrag in the feed. A 30-second TV spot is unskippable. It gets the full screen and the viewer’s attention in a way that no digital format can guarantee. Your prospect can keep scrolling past your social ad. They can skip your pre-roll. They cannot skip the TV spot.

For B2B brands with complex products, that guaranteed attention is valuable. Gusto, the payroll and HR platform, builds its TV strategy around live tentpole moments, major sporting events and cultural moments, because that’s when their customers are most engaged and most likely to be thinking about the problems Gusto solves. It’s awareness-building timed to purchase intent.

One tactic that connects TV directly to the performance stack: CTV retargeting. Someone visits your pricing page on Tuesday. By Thursday, they’re seeing your ad in their living room, on a full screen, in an environment that carries more weight than another banner in a crowded feed. It closes the loop between your ABM motion and a channel your competitors almost certainly aren’t using against the same accounts.

Marketing Technology News: MarTech Interview With Jay H. Lee, Chief Marketing and Growth Officer @ Five9

TV Raises the Bar (and Maybe Your Next Round)

There’s a credibility effect to TV that doesn’t get discussed honestly enough in B2B circles. It isn’t just about awareness scores. It’s about what showing up on TV signals to the people evaluating you.

Consider the AI SDR space. Dozens of companies competing for the same accounts with near-identical pitches. If your brand has been on TV and your competitors haven’t, your prospect takes the call. You’re no longer one of many vendors in an inbox. You’re a company that operates at a different scale. That perception change happens before your sales team says a word, and it makes everything downstream more efficient.

The CMO is certainly focused on building brand awareness among their ICP, but corporate marketing is another area they own, one focused on raising the company’s profile within its industry and ultimately increasing its perceived value.

When a founder or board member sees their company’s ad during a live sporting event, their phone lights up. Fellow founders text. Investors notice. Raising a round is a different conversation when your brand has been on TV. Acquisition discussions go differently when the other side’s partners recognize your name. This rarely gets framed as marketing’s job. It is.

The Stack Is Good. It’s Just Missing a Layer.

The B2B marketers seeing the biggest results from TV aren’t treating it as a replacement for their performance programs. They’re using it as the layer those programs can’t provide: broad, credible, high-attention reach that introduces the brand to future buyers before they’re searching, and reinforces it with buyers already in your funnel.

Otter, the AI-powered meeting intelligence platform, found that well-crafted TV spots drive immediate engagement even for a complex multi-platform product. Viewers, particularly on mobile, check out the product right away. Top-of-funnel reach converting to bottom-of-funnel action. That’s the full motion.

Your ABM programs, your intent tools, your LinkedIn campaigns are all more effective when the buyer has already seen your brand somewhere that commanded their full attention. TV is that somewhere. The stack you’ve built is good. This is the layer it’s missing.

About Tatari

Tatari is building the infrastructure to modernize TV advertising for Brands, Agencies, and Publishers.

They are tuning it out.

Not because AI lacks value, but because the way it is being marketed often fails to align with what buyers actually prioritize. For organizations making high-stakes, long-term technology decisions, the fundamentals still matter most: engineering quality, technical expertise, reliable delivery, and reduced risk.

The growing disconnect between what vendors emphasize and what buyers need is creating friction in the buying process and, in some cases, eroding trust.

The AI Messaging Overload

Right now, nearly every platform, service, and solution touts some form of AI integration. While this proliferation reflects genuine advancements, it has also created a crowded and often confusing landscape.

For buyers, the challenge isn’t access to innovation—it is clarity.

When every vendor claims to be “AI-driven,” true differentiation actually becomes more difficult. Messaging starts to sound the same across the board, leaving buyers unsure what genuinely matters versus what’s just baseline capability. In this crowded landscape, bold AI claims without clear context or proof points don’t signal innovation, they blend into the noise.

To break through, marketers can’t rely on generic AI language alone. They need to be more technically fluent and deeply informed about emerging technologies, so they can engage increasingly sophisticated buyers and craft messaging that rises above vague, cookie-cutter AI narratives.

What Buyers Actually Prioritize

Despite the emphasis on AI, B2B buyers consistently return to a core set of priorities when evaluating solutions:

1. Engineering Quality

Buyers want to know that a product is well-built, scalable, and designed to perform under real-world conditions. According to recent BIXA research of 480+ business buyer decision-makers, quality of engineering and technical expertise are statistically tied for the most important attributes they look for in a vendor.

2. Technical Expertise

Beyond the product itself, buyers assess the depth of knowledge behind it. Research shows that technical expertise and guidance are tied as the most important attributes buyers look for in a vendor. They prioritize teams that understand their industry, technical challenges, and implementation complexities. For instance, 97% of buyers say it is important that a vendor both understands and uses AI technologies in their own processes.

Expertise ultimately signals credibility: 41% of tech leaders who augment their existing teams with external engineers say certified AI experts make a vendor stand out—and that credibility directly reduces perceived risk.

3. Reliable Delivery

Execution matters as much as vision. Buyers need confidence that timelines will be met, deployments will go smoothly, and ongoing support will be dependable. Efficient delivery is a top-five priority for buyers, and it is the single most important factor for 17% of UK-based decision-makers. Overpromising, particularly in emerging technologies, can quickly erode confidence when delivery does not keep pace. Buyers are also pragmatic about how to build that confidence quickly — 47% value paid workshops specifically because they accelerate project momentum. Buyers also increasingly expect AI to reinforce that reliability through faster code generation and automated code reviews.

4. Risk Reduction

At its core, every B2B purchase is a risk management decision. Whether it’s financial risk, operational disruption, or reputational impact, buyers are evaluating how a solution minimizes uncertainty. A bold guarantee is the top determining factor for buyers, carrying 40% of the relative importance in their decision-making process. In fact, 88% of buyers would choose a vendor offering a 100% bug-free guarantee even if their price was 30% higher than competitors. Clear documentation, proven use cases, and transparent communication, such as through de-risking workshops favored by 34% of buyers, all contribute to lowering the #1 hesitation in the market: concerns over code quality and security.

These priorities are not new. What has changed is how easily they can be overshadowed by trend-driven messaging.

The Cost of Misalignment

When marketing narratives focus on AI but ignore these foundational concerns, a gap forms between expectation and reality. This misalignment leads to clear consequences:

• Longer sales cycles, as buyers seek additional validation and clarity
• Increased skepticism, particularly toward bold or vague claims
• Missed opportunities, when solutions fail to resonate despite strong underlying value

In some cases, the emphasis on AI can even distract from a company’s true strengths. A well-engineered product with a track record of reliable delivery may be far more compelling than a newer, AI-heavy offering that lacks maturity. But if the messaging doesn’t reflect that strength, buyers may never fully recognize it.

Reframing the Narrative

The solution isn’t to move away from AI, it’s to stop pretending the tools are the transformation.

The teams winning with AI aren’t the ones with the most tools. They’re the ones that changed how their engineers work. This is a people and process problem, not a procurement decision—and most vendors avoid saying it because it’s harder to sell.

The shift in messaging is simple but demanding: stop leading with what the technology is and start with what it takes to make it work. That means structured workflows, validated output at every stage, and a clear acknowledgment that AI without governance doesn’t reduce costs—it increases them. AI usage is not free; it is metered in tokens and accumulates quickly.

There’s also a risk that almost no transformation partner raises: internal resistance. AI champions inside a client organization pull ahead. Resistors create drag. If you don’t address adoption at the engineer level from day one, the transformation fails at the people layer, not the technology layer. Buyers should be asking their vendors how they handle this. Most can’t answer.

Marketing Technology News: MarTech Interview With Jay H. Lee, Chief Marketing and Growth Officer @ Five9

Questions to challenge yourself and your teams:

• Is your AI architected well enough that the economics actually work?
• Do you have a structured methodology or just a capability?
• What happens when your engineers resist?

Building Trust Through Substance

Trust is the currency of B2B relationships. It is built through consistency, transparency, and proof. In a market saturated with AI claims, substance is the differentiator.

• That substance shows up in four ways:
• Clear, specific use cases that demonstrate real-world impact
• Technical depth that proves how solutions are built and maintained
• Evidence of reliability, backed by performance metrics and long-term customer outcomes
• Honest communication about capabilities and limitations

When buyers see that a company is willing to go beyond surface-level messaging, it signals confidence—and that confidence is often more persuasive than any single feature or capability.

The Opportunity Ahead

The current wave of AI enthusiasm is real, and so is the backlash forming underneath it. Buyers aren’t rejecting AI. They’re rejecting the version of AI that showed up late, overpromised, and left their teams holding the complexity.

The companies that will win this window aren’t the ones with the boldest AI narrative. They’re the ones who can answer the questions a sophisticated buyer will eventually ask: Is your AI architected well enough that the economics actually work? Do you have a structured methodology or just a capability? And what happens when our engineers resist?

Bad AI is expensive AI. Buyers are starting to do the math, and the vendors who can’t show their work are going to lose deals they don’t even know they’re losing.

The fundamentals haven’t changed. Proof, expertise, reliable delivery, and reduced risk. What’s changed is that AI has raised the stakes on all of them. The companies that understand that distinction – and can demonstrate it – are the ones that will eventually define this market.

About Vention

Vention is the premier global leader in software engineering, synonymous with technology designed for scale and the common denominator behind the world’s most successful tech-empowered enterprises, industry innovators, and startups.

Retailers are sitting on more customer data than ever. Most of it is contradicting itself

Most organisations can agree on how many customers they have. Sometimes, the agreement ends there. Ask which channel best reaches a specific customer, what their lifetime value actually is, or whether they already own the product you are about to recommend, and you will get different answers from every team.

Retailers have invested heavily in omnichannel strategies, AI-powered customer experiences, and personalisation tools, yet many are building those capabilities on a foundation of data that does not align across systems. The investment is real. The unified view of the customer it depends on often is not.

Amperity’s recent expansion into the AWS Asia-Pacific (Sydney) and Asia-Pacific (Melbourne) regions brings that challenge into sharper focus for local enterprises, for whom data residency and governance are now operational requirements, not just strategic considerations.

Every team has their own version of customer data governance. Marketing deduplicates aggressively to maximise campaign reach. Analytics applies strict matching rules to avoid inflating customer counts. Operations relies on whatever the CRM says. Loyalty uses its own member ID. Each team’s logic is defensible in isolation.

However, when those conflicting views feed the same personalisation engine, the same AI models, or the same board report, the brand cannot deliver the experiences leadership is asking for.

The customer count might line up. But the loyalty programme cannot reconcile purchase history across channels because each channel defines “same customer” differently. And that is before you account for the customers who forget to scan their loyalty card, share an account with someone in their household, or never enrol in the programme at all despite being high-value repeat buyers.

Marketing sends reactivation campaigns to customers who are active in the loyalty programme but dormant in the email platform. The data is not wrong in any one system. It is wrong in aggregate.

One Amperity customer discovered that a single shopper appeared as four separate profiles in their system because they used email as their golden record. Each profile had a different lifetime value and different shopping preferences. None was a complete or accurate representation of the actual person. When that happens at scale, personalisation is not just imprecise. It is fiction.

Why customer data governance breaks down without identity resolution

Most companies govern data at the system level, and some agree on an overarching standard like email or loyalty ID. But no single identifier captures every customer interaction.

Each platform, be it email, point of sale, loyalty, support, or the data warehouse, still applies its own matching rules, its own thresholds, its own definition of what makes two records the same person. Over time, the gaps between those definitions add up.

This is the core challenge of customer data unification: not collecting more data, but connecting the data you already have into a unified customer profile that every team trusts.

Customer identity resolution connects fragmented records across systems, linking identifiers like email addresses, phone numbers, device IDs, loyalty accounts, and transactions into a single, accurate customer profile.

Identity resolution approaches fall on a spectrum. Deterministic matching links records through exact identifiers, such as a shared email address or login credential. Probabilistic and AI-based methods go further, evaluating patterns across data points to surface connections that exact matching misses, like when the same person uses different email addresses across channels or checks out as a guest in-store.

The most effective systems combine both, using deterministic rules as a foundation and machine learning to find the connections that rules alone cannot.

That gap compounds with every new tool and data source, each introducing its own governance logic. And when leadership asks the brand to personalise at scale, to recommend the right product on the right channel at the right time, the teams cannot deliver. Not because they lack the tools or the talent, but because no one has a complete picture of the customer to work from.

Try this thought experiment: pick a customer at random. How long would it take to gather enough detail to confidently send the right message, on the right channel, to drive their next purchase? Now imagine doing that for every customer.

How contextual identity graphs produce a unified customer profile

Before you can contextualise a customer, you need a complete picture. You cannot recommend the right product if you do not know what they have already purchased or returned.

You cannot choose between a discount code via SMS and an exclusive preview via email if you do not know which channel drives their purchases. You cannot calculate real lifetime value if the same person exists as four separate records.

That complete profile is the foundation. Contextual identity is what makes it useful.

Preferences change. A customer who never buys from a particular category might be shopping for a gift next week, or for someone else in their household. A full-price buyer exploring a new category for the first time might or might not respond to a promotional code.

Marketing Technology News: MarTech Interview With Jay H. Lee, Chief Marketing and Growth Officer @ Five9

A single, static customer identity graph cannot handle that complexity. It forces every team into the same rigid view, and someone is always compromising.

Amperity’s Customer Data Cloud takes a contextual identity approach: purpose-built identity graphs optimised for each use case, all constructed from the same resolved foundation using first-party identity resolution.

Marketing: maximise reach. Identity graphs tuned for broad audience coverage so campaigns connect with as many real customers as possible, without duplicates inflating the numbers.

Analytics: consistency. Identity graphs built for accurate customer counts, reliable lifetime value calculations, and reporting that holds up across teams and time periods.

Operations: precision. Identity graphs optimised for transactional accuracy, where matching the right record to the right person at the right moment matters most.

Every graph is built from your first-party data. IDs stay consistent day to day. When data changes, the system learns and adapts. Connections are transparent, rules are tuneable, and every decision is auditable. No black box. No third-party data spine. No vendor lock-in.

One resolved foundation. Multiple purpose-built views. Every team works from the same truth, expressed for their specific need.

Identity infrastructure is now a compliance requirement

Transparency in data handling carries legal weight. Organisations cannot make accurate disclosures about automated decision-making unless they have clear visibility into how personal data moves through their live systems.

Consent signals, data lineage, and access controls need to be built into the foundation of customer data infrastructure from the outset.

As mentioned, Amperity’s platform is available in the AWS Asia-Pacific (Sydney) and Asia-Pacific (Melbourne) regions, allowing organisations to keep customer data resident locally while supporting performance and scalability requirements for real-time customer intelligence.

Brands that treat identity resolution as a compliance exercise end up reacting to problems. Those that build it into their data infrastructure from the start solve them before they surface, with a governed, trusted customer view that serves marketing, analytics, operations, and regulators alike.

About Amperity

Amperity’s Customer Data Cloud empowers brands to transform raw customer data into strategic business assets with unprecedented speed and accuracy. Through AI-powered identity resolution, customisable data models, and intelligent automation, Amperity helps technologists eliminate data bottlenecks and accelerate business impact. More than 400 leading brands worldwide, including Accent Group, Alaska Airlines, DICK’S Sporting Goods, BECU, and Wyndham Hotels & Resorts, rely on Amperity to drive customer insights and revenue growth. Founded in 2016, Amperity operates globally with offices in Seattle, New York City, London, and Melbourne. For more information, visit amperity.com or follow us on LinkedIn, X, Facebook and Instagram.

Marketing professionals should also be thinking in narratives. In the digital age, connection and recognition are important to both consumers and brand performance.

Outcome-based marketing is exposing a structural problem within agencies: channel-based teams are still organized around how media is bought, not how growth actually occurs or how consumers purchase. Search, social, programmatic, CTV, analytics, and measurement often operate in parallel sectors, each optimizing its own piece of performance. But consumers do not move in siloed channels. They move through connected, nonlinear journeys.

As brands demand a clearer indicator of what is driving business outcomes, agencies built around channel isolation are finding it harder to deliver a coherent answer. With a view across the full digital ecosystem, the inefficiency of siloed teams becomes harder to ignore.

Measurement, historically a performance tool, is becoming the connective tissue that drives organizational change and shapes campaigns. Though each channel is a different component of campaign metrics, measurement is the thread stitching the chapters together, resulting in a cohesive, complete story.

Structural Shifts Across Agencies

Some agencies are already consolidating digital functions and building more unified performance groups. While unifying solutions streamlines the tech stack, the shift goes beyond aesthetics. It reflects a broader truth; agencies best positioned for the next phase of growth design themselves around outcomes rather than media types.

In that sense, measurement is reshaping both reporting and internal org charts. As AI proliferates, brands now scrutinize every dollar of spend, and agencies feel the pressure too.

Publicis stands out as one of only a few holding companies with a strong mobile practice. It hosts an annual mobile summit and continues to win significant new business. The company has not only taken more of a consultative approach to measurement, but has invested in educating its teams.

Marketing Technology News: MarTech Interview with Max Groth, CEO at Decentriq

Educating Teams

Restructuring alone does not solve the problem. Without the right foundation, consolidation creates new inefficiencies instead of eliminating old ones.

Several agencies have already tried to unify programmatic, search, and social under a single performance structure. These early efforts exposed a consistent challenge: teams had to manage new disciplines without the training or operational support they needed. As a result, agencies ended up with a reorganization that failed to deliver on its promise, rather than achieving true integration.

That lesson is resurfacing as agencies revisit outcome-based models. Agencies are restructuring again, this time under greater pressure and with less room for trial and error. Bringing channels under one umbrella requires more than alignment on paper. It demands that agencies invest in upskilling, build shared methodologies, and establish a common understanding of how teams measure performance across the full journey.

Always Advancing in AI

The context has also shifted significantly. Advances in AI and the continued compression of the purchase funnel have raised expectations across the board. Marketing budgets are now scrutinized more closely, and every dollar is expected to contribute to measurable business outcomes. This pressure is no longer limited to digitally native brands. It applies equally to traditional enterprises navigating their own transformation.

As a result, agencies are being pushed to operate with greater accountability. Success is increasingly tied to their ability to connect data, strategy, and execution into a unified, outcome-driven approach. Organizations that can bridge these elements and act as strategic measurement partners are gaining an advantage in new business and client retention. With so many AI tools available, there is an opportunity for agencies to get clear on how AI can aid in connecting the dots across those teams. Yes, change takes time; but advances in AI can speed up the data orchestration component.

Breaking down silos is not simply an organizational exercise. It requires building teams that can operate across channels, interpret performance holistically, and translate fragmented signals into clear business impact.

In many ways, this mirrors how people naturally process information. We do not understand the world through isolated data points, but through narratives that connect them. The same is now true for marketing. Agencies that can move beyond channel-level optimization and construct a cohesive story of how growth actually happens will be the ones that deliver real outcomes.

Those who cannot will remain stuck reading disconnected chapters, without ever understanding the full plot.

About AppsFlyer

AppsFlyer is the Modern Marketing Cloud that helps businesses transform complex data into clarity and growth. A foundation for unified, measurable, autonomous marketing, AppsFlyer breaks down silos across measurement, deep linking, data collaboration, and autonomous AI workflows. For more than a decade, AppsFlyer has been the leader in mobile attribution, trusted by over 15,000 businesses worldwide.

Meta projected that 10% of its 2024 revenue came from scams and banned goods, Reuters reported, with the social media giant internally estimating that its platforms show 15 billion scam ads a day. Meanwhile, our analysis of more than 100 million clicks found invalid click rates running about 50% higher than what Google reports. Both cases demonstrate the conflict of interest in fighting fraud.

The current model – where the platform giants act as both ad salesman and fraud policeman – is broken. In this new landscape, supercharged by increasingly autonomous bots, advertisers should no longer outsource fraud detection to the platforms that profit from it.

The ad fraud conflict of interest

At this point, fraud is more of a feature than a bug in digital advertising. Remember that most ad networks operate on a volume-driven revenue model in which every click, regardless of authenticity, contributes to the platform’s bottom line. Aggressively eliminating fraud would mean admitting their reach is smaller than marketed. These publicly traded companies face pressure to maintain traffic metrics and billable inventory. As a result, we’re more often seeing platforms catch the most obvious bots while sophisticated invalid traffic persists.

Meta’s leaked documents offer valuable insight into the inner workings. Internal memos revealed that enforcement teams track fraud but operate under strict guardrails on how to act on it. One review found the company ignored or rejected 96% of valid user reports flagging scam ads and the threshold for actually banning an advertiser required 95% certainty of fraud. Otherwise, anyone below that just got charged higher ad rates and kept running.

And actions against fraudulent advertisers were capped at 0.15% of revenue. This tells us that fraud is flagged but the bottom line matters in deciding what to do with it.

Marketing Technology News: MarTech Interview with Max Groth, CEO at Decentriq

The problem with self-reporting

A similar dynamic plays out with invalid clicks. For example, Google reports an average invalid click rate of 11.4%. Our independent analysis across 43,000 accounts, however, finds a rate of 17.8%. Adding to the issue, invalid click rates have doubled since 2010 thanks to the increased sophistication of AI-driven bots and ad fraud malware.

Since then, it’s become much harder to differentiate between human and bot engagement. This is because bad actors are using artificial intelligence and machine learning to create bots that pause on content and simulate scrolling, thereby mimicking human viewing behavior and making detection far more difficult. Additionally, they’re using malware to infect user devices, secretly drive traffic to scammer-controlled domains, and make it unclear whether ad clicks are coming from users or “ghost click farms”. In turn, ad fraud is only growing and marketers are losing about one in five dollars.

And downstream, bad data means autobidding starts chasing bot patterns and retargeting non-existent users. Google’s Smart Bidding, Performance Max, and automated campaigns start learning from signals that increasingly include traffic that isn’t real. The result is inflated cost per action (CPA), distorted return on advertising spend (ROAS), and budget being allocated to interactions that never convert. Data poisoning like this makes up look like down and down look like up.

The fact of the matter is that ad platforms have skin in the game. Right now, these corporations are essentially grading their own ad fraud homework with little incentive to accurately report the true scale and scope. It’s time for marketers to stand up for themselves and start treating every click as a security event worthy of independent verification.

How we restore accountability

We need to stop taking platform-reported analytics as gospel. Instead, the status quo demands due diligence with closer monitoring of user verification, behavioral analytics, and fraud scoring.

For example, teams can and should keep a closer eye on inflated CTRs without corresponding conversions, as well as on traffic spikes from unusual countries, to better understand actual performance. This foundation is then strengthened with independent fraud detection tools that analyze traffic patterns, device fingerprints, IP behavior, and engagement signals. These complementary solutions go a long way to creating an independent source of marketing truth.

Armed with this information, teams can better push back on the sophisticated invalid traffic they miss. And, by partnering with platforms that enable real-time metric monitoring, it’s also possible to block bad traffic before it enters the funnel and corrupts bidding, targeting, and forecasting data.

Practitioners need to go the extra mile to keep the tech giants honest. This way, we can refund more questionable clicks, show the platforms we’re watching, and better protect the integrity of campaign data.

About Fraud Blocker

Fraud Blocker, is a leading click fraud prevention software.

Those distinctions will become increasingly important. Done well, regulation can protect users, foster competition and sustain innovation. Done poorly, it risks entrenching the dominance of the largest technology platforms. In many ways, those same platforms are already best positioned to shape and absorb regulatory change, potentially leaving everyone else at a disadvantage.

The sheer momentum of AI to date makes it easy to feel helpless in the face of such a technological revolution. How can any of us hope to help shape and guide the ways in which AI is to unfold?

Fortunately, the digital media industry has faced a similar inflection point before in its recent history. The journey towards cookie deprecation offers a valuable lesson, and perhaps a blueprint, for what comes next.

The Privacy Sandbox experience

When browsers began phasing out third-party cookies, it triggered a wave of uncertainty and, in some cases, outright panic across the digital ecosystem. Advertisers, publishers and ad tech vendors all faced the challenge of maintaining addressability and monetisation while ensuring user privacy. Google’s Privacy Sandbox initiative was the most notable attempt to strike that balance.

The Privacy Sandbox was not perfect, but its intent is instructive. Rather than abruptly removing a foundational technology and leaving the ecosystem to adapt overnight, it introduced a standards-based framework designed to evolve over time. It sought input from across the industry (including publishers, advertisers, developers and regulators) and aimed to create privacy-preserving alternatives that could support the economic model of the open web.

One could argue that Google could deprecate cookies, as Apple did, and introduce its own unique way of targeting users in Chrome. Instead, it opened up the discussion with the ecosystem around collaboration and iteration. This created a space, however imperfect, for broader participation and conversation, demonstrating that large-scale ecosystem change can be coordinated by consensus rather than imposed.

The cookie deprecation process made it clear that simply “switching off” a core capability at scale is not viable. Sudden changes risk destabilising publishers who rely on advertising revenue, limiting the ability of smaller tech providers to compete, and forcing advertisers into narrower, less transparent buying environments. Meaningful progress required frameworks that could be refined in real time, informed by data and shaped by those operating across the ecosystem, not only those at the top of it.

Marketing Technology News: MarTech Interview with Max Groth, CEO at Decentriq

Regulating agentic AI

Today, the digital media industry faces a parallel moment with the rise of agentic AI. These systems are increasingly acting as intermediaries between users and the digital world. It’s shaping what content is discovered, which products are surfaced, and how decisions are made. In effect, they are becoming gatekeepers to information, commerce, and attention.

As control over these systems concentrates in the hands of a few large players, questions around transparency, fairness and access become more urgent. Regulation is clearly necessary, but it must be approached with care.

A “sandbox approach” to AI regulation, at its core, means developing standards collaboratively across the industry, rather than imposing rigid rules from the top down. It also necessitates creating environments where new approaches can be tested, evaluated and iterated before being scaled. Finally, it requires that any regulation evolves alongside the technology it seeks to govern.

Large technology platforms have a critical role to play in this process. As with the Privacy Sandbox, companies like Google have the scale, data and infrastructure to help develop and test new approaches. But with that role comes responsibility. Their contribution should be to support industry-wide solutions, not to define the rules in isolation.

Collaboration, transparency and iteration

There are already signs that the stakes are rising. As AI systems become more embedded in advertising, commerce, and content discovery, brands need to collaborate effectively with chat interfaces, which act as intermediaries, and with end users. Without clear and collaborative frameworks, the risk is that regulation, however well-intentioned, ends up reinforcing the very dynamics it seeks to address.

The transition from cookies to privacy-first alternatives showed that the industry is capable of navigating complex change. It also showed that the process matters as much as the outcome. As AI becomes the primary interface for digital decision-making, those same principles must guide the next phase of regulation. Collaboration, transparency, and iteration are not just desirable; they are essential.

A sandbox approach offers a way to balance innovation with accountability, and competition with control. The window to get this right is narrow; fortunately, the blueprint already exists.

About PrimeAudience

PrimeAudience (an RTB House company) is a, AI-driven, privacy-focused adtech platform designed to boost client acquisition and enhance targeting. It uses Generative AI to create custom audiences, reducing ad costs by up to 80% and providing up to 40-60% identity resolution of website visitors without relying solely on third-party cookies

With a single prompt, anyone can now produce polished creative in seconds. Ad creation has been fully democratized. But execution is where the real story is, and where most brands are underestimating the risk.

The issue isn’t the creative. It’s everything that comes after.

As AI-generated ads flood the ecosystem, performance isn’t breaking down at the creative level. It’s breaking down across compliance, targeting precision, and delivery – areas where automation alone isn’t enough.

Creative Has Been Standardized

AI has raised the baseline for creative quality across the board. It’s easier than ever to produce something that looks sharp, reads well, and meets modern expectations.

The tradeoff is subtle but significant. When the same tools power everyone’s output, differentiation starts to erode. Messaging becomes more uniform, tone converges, and campaigns begin to feel interchangeable.

More importantly, AI lacks a true understanding of context. It doesn’t inherently recognize regulatory nuance, platform-specific constraints, or the difference between messaging that resonates and messaging that creates risk. That limitation directly impacts whether campaigns run at all.

An ad can be perfectly written and visually compelling, but still fail in-market if it doesn’t align with how platforms interpret policy or how regulations are applied in practice. In many cases, the difference between a high-performing campaign and one that gets rejected, throttled, or flagged comes down to details that AI isn’t equipped to account for.

It’s about understanding the environment an ad enters, such as how it will be reviewed, where it will appear, and how it will be interpreted by both systems and people. That layer of judgment remains difficult to automate, and increasingly critical as constraints tighten. In categories where precision matters – including healthcare, finance, and politics – that gap becomes impossible to ignore.

Execution Is Where Performance Breaks Down

When campaigns underperform today, the cause is rarely the creative itself – it’s how that creative is executed across a fragmented and increasingly constrained ecosystem.

Compliance is one of the most immediate pressure points. Every platform has its own policies layered on top of broader regulations, from FDA oversight in healthcare to strict financial advertising guidelines, and those standards are constantly evolving. Campaigns can be rejected, limited, or deprioritized without warning if those nuances aren’t accounted for upfront, especially in healthcare.

At the same time, targeting has become more complex. With signal loss and privacy changes reshaping the landscape, reaching the right audience depends less on deterministic identifiers and more on understanding intent. That requires interpreting what people are engaging with in real time and translating it into scalable strategies – something automation alone doesn’t consistently get right.

Even when those pieces align, delivery introduces another layer of complexity. Not every channel supports every category, and not every inventory source is equally accessible. Getting campaigns live – and keeping them performing – requires a level of operational fluency that goes beyond automated workflows.

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Optimization Needs Human Governance

AI is highly effective at optimizing toward measurable outcomes. It can process signals, adjust in real time, and improve efficiency at scale. What it doesn’t do well is judge context.

In practice, that means optimization can push campaigns into environments or audiences that technically drive performance metrics but undermine broader objectives. In more sensitive categories, that can introduce compliance risks or create misalignment with brand standards.

Human oversight plays a structural role here. It ensures that optimization is grounded in strategy, not just performance signals, and that campaigns remain aligned with both regulatory expectations and brand intent as they scale.

That becomes especially critical in healthcare, for example, where the margin for error is significantly smaller. Messaging, targeting, and placement all operate under heightened scrutiny, and even well-intentioned optimization can create risk if it isn’t properly governed. A campaign that shifts toward higher engagement could inadvertently move into sensitive territory – whether that’s how conditions are framed, who is being reached, or where the message appears.

In these environments, performance can’t be separated from compliance. The two have to be managed in tandem, which makes human judgment a necessary part of the optimization process, not a secondary check.

The Next Phase Belongs to Hybrid Execution

The industry is moving toward a model where AI and human expertise operate in tandem. AI will continue to accelerate production and uncover patterns at scale. It will make campaigns faster to build and easier to iterate.

But execution – how campaigns are structured, governed, and adapted across channels – will remain a human-led discipline. It requires judgment, experience, and an understanding of systems that don’t operate uniformly.

This is where the gap is widening. Creative has become widely accessible. Effective execution has not.

Perhaps most important, execution today isn’t about limiting ambition, it’s what enables it. When campaigns account for regulatory nuance, platform dynamics, and data constraints upfront, they move faster, scale more effectively, and avoid the disruptions that stall performance.

The brands that outperform will be the ones that recognize that distinction early. They’ll invest less in producing more ads, and more in ensuring those ads actually run, reach the right audiences, and sustain performance once they’re live.

Because reaching the audience is only part of the equation. Maintaining compliant, effective execution long enough to drive impact is what ultimately determines results.

About Fyllo

Fyllo is a data and advertising partner purpose-built for regulated industries. The company helps brands and agencies in politics & public affairs, healthcare & pharma, financial services, CPG, retail, hospitality, and travel reach high-value audiences that others can’t — compliantly, effectively, and efficiently

Collecting data beyond just name and email, like phone number, birthday, and preferences, enables more personalized messages for consumers. But because the value isn’t clear, consumers are willing to volunteer only a limited subset of low-risk information upfront.

New research from Intuit Mailchimp puts hard numbers on what many marketers suspect. While 65% of brands request a phone number via opt-in popups, only 28% of consumers are willing to share it. Just 8% of marketers report opt-in conversion rates above 20%. These low conversion rates point to a broader issue: low confidence.

This is the trust gap, and it’s sitting right at the front door of the customer relationship. The good news is that closing it doesn’t require a complete overhaul. It requires a smarter approach to addressing what brands ask for, when they ask for it, and how they can use what they already have.

List quality is the real growth metric.

For years, ecommerce growth was measured in subscriber counts: The bigger the list, the bigger the revenue. Now we understand that volume isn’t the best indicator for performance. What actually drives results is a list where subscribers open and click, data is current and accurate, and every contact is deliberately acquired rather than passively accumulated over time.

A high-quality list has a direct bottom-line impact; for instance, 50,000 engaged subscribers will naturally convert at a higher rate than 500,000 disengaged subscribers. Intentional audience building reduces costs, protects deliverability, and generates engagement that translates into measurable revenue.

Building that quality starts with trust. Only 31% of consumers assume brands will handle their data responsibly, and more than half say they’re willing to engage but worried about spam. Earning a place in someone’s inbox, let alone influencing their purchasing decisions, requires establishing credibility from the start.

The ask should match the moment.

More than half (51%) of brands place opt-in popups on the homepage, and 62% use page-load popups that fire immediately upon arrival. But asking for contact information before a visitor has browsed a product or read a review can feel premature and erode trust.

The research is clear on when consumers are most receptive: Half of consumers (50%) are most likely to opt in after browsing a brand’s offerings, 33% right before making a purchase, and 24% just before leaving the site. Triggering a popup form offering a discount after visitors have viewed a product aligns with where they actually are in their journey.

In practice, marketers are required to rethink their strategy to acquisition. The brands earning quality signups have stopped asking what data they can collect and started asking what value they’re offering in exchange for it. Incentives like early access or a first-purchase discount are concrete starting points. Optimizing the opt-in moment captures useful data from the start, and surfacing what subscribers do afterward compounds that value.

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Behavioral signals fill in what form fields leave out.

Instead of asking for more than customers are ready to share upfront, marketers should consider another approach: letting customer behavior do some of the work instead.

Every visit generates data. Signals like what someone browses, how long they spend on a product page, whether they return twice in a week, or what they add to a cart and walk away from reveal intent without requiring a single form field. Consumers generate these insights naturally by engaging with your brand, creating opportunities to start building the relationship before an email address is shared.

Behavioral signals fill in the gaps, deepen over time, and make the data brands collect more actionable. The goal is to identify where someone is in their relationship with your brand and meet them there, with the right message at the right moment. Acting on that at scale, however, requires more than good instincts; it requires knowing who to target, when to send, and what to say, and then acting on those decisions when it matters most.

AI turns behavioral data into action.

Collecting behavioral data and actually acting on it are two different problems. Triggering real-time, personalized follow-up messages based on browsing behavior, purchase history, and engagement signals across email and SMS isn’t something a marketing team can do manually at scale. AI can close that operational gap.

Brands with high-quality lists are three times more likely to run fully automated programs (38% vs. 13%), indicating that the brands investing in list quality are also the ones investing in the infrastructure to act on it. Marketers now rank AI-powered optimization among the most sought-after capabilities in their tech stacks.

The practical shift shows up in the customer journey. A high-value customer who has browsed multiple times and completed transactions warrants a different sequence than a new subscriber who went quiet after the first message or someone who abandoned a cart. AI makes it possible to identify those distinctions early and route customers into the right sequence automatically, turning what used to be guesswork into something systematic.

Acting on behavioral data at scale only works within the bounds of consumer trust. Personalization that feels natural and helpful builds the relationship, while personalization that feels intrusive or misaligned breaks it. The brands getting this right are transparent about what they collect and why, which reinforces the same value exchange that drives opt-in quality in the first place.

The opt-in is just the beginning.

The brands positioned for success are treating the opt-in not as a list-building opportunity but as the first moment of a long-term relationship. Strategic timing, relevant incentives, and a clear value exchange up front set the foundation. Behavioral signals and automation build on it, surfacing the right message at the right moment.

The trust gap closes the moment brands start treating the opt-in as an invitation rather than a transaction. The result isn’t just a bigger list; it’s a more valuable one, built on trust that shows up in repeat purchases, higher engagement, and lasting loyalty.

About Intuit Mailchimp

Mailchimp is the all-in-one integrated marketing platform for small businesses.

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However, in 2026, this “less is more” strategy began to show some worrying cracks. Today, a handful of major platforms have come to dominate more than 80% of the $700 billion global ad market. As such, many brands have been encouraged to centralise their retargeting efforts within a single ecosystem in the name of “streamlining” their media spend.

Unfortunately, amid significant economic uncertainty, the decision to rely on a single retargeting partner has become a strategic risk that warrants reconsideration.

The consolidation trap

Avoiding ad fatigue, duplicated bids and wasted ad spend are obviously good ideas. However, it should be noted that the big platforms have been happy to amplify these concerns, in part, because they profit from keeping brands within the boundaries of their own ecosystem. As a result, multi-vendor retargeting strategies have been discouraged.

However, committing to a single retargeting provider comes at a cost to brands, which is often overlooked or downplayed. By placing all retargeting eggs in one supplier’s basket, brands are effectively limiting their visibility into the broader digital landscape. In a very real way, they become dependent on one system’s ability to interpret user intent, to identify an opportunity and to define a valuable target. One set of algorithms, designed and operated by an external party, ends up dictating your brand’s view of the world.

As a consequence, campaigns start to stagnate, and incremental growth becomes much harder to achieve. The audiences being targeted are drawn from the same pool, recycled within a closed loop, while potential high-value users just outside that loop remain unseen and undiscovered.

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The myth of internal competition

A persistent myth is that adding a second retargeting provider will inevitably lead to self-competition and inflated costs. This assumption has shaped media strategies for years, yet it seriously misinterprets how modern programmatic ecosystems actually operate.

In reality, a multi-vendor setup does not mean two providers blindly bidding against each other for the same impressions. Today’s advanced bidding technologies are designed to evaluate users differently, using distinct models, signals, and optimisation strategies. Each provider brings its own perspective on what constitutes a valuable impression.

Rather than duplicating effort, this creates a dynamic and competitive environment. Algorithms are forced to work harder to identify unique opportunities, refine targeting, and justify each bid based on performance potential. The result is increased efficiency and improved precision.

Moreover, overlap between providers is often overestimated, meaning that a second provider is more likely to uncover incremental audiences than to compete for the same ones. Even where overlap does occur, controlled competition can help to ensure that only the most valuable impressions are won, at the right price. Rather than inflating budgets, smart use of a multi-vendor retargeting strategy can optimise them.

Future-proofing and resilience

In 2026, market conditions are shifting constantly, consumer behaviours are evolving rapidly, and the limitations of closed ecosystems are becoming increasingly apparent. A diversified approach to retargeting ensures that brands are not overly exposed to fluctuations in one platform’s performance, pricing, or policy changes.

Advances in AI and machine learning have made it easier than ever to identify and engage users across a fragmented digital landscape. Modern retargeting technologies are capable of analysing vast datasets in real time, uncovering patterns and opportunities that would be invisible within a single ecosystem. By leveraging these capabilities across multiple providers, brands can build a more comprehensive, dynamic view of their audience and tap into new sources of incremental value as they emerge.

A multi-vendor strategy transforms retargeting from a passive exercise into a competitive system. It introduces checks and balances, encourages innovation, and drives continuous optimisation. Rather than relying on one algorithm to deliver results, brands create an environment where multiple systems compete to do so.

The dominance of large platforms has made consolidation feel like the default choice in digital advertising. While consolidation may still seem, on face value, to be an attractive option, the increasingly fragmented and unpredictable media landscape means that brand marketers should diversify their retargeting approach using the modern tech tools available to them.

By embracing a multi-vendor retargeting strategy, brands can unlock new audiences, enhance performance, and build resilience against future uncertainty.

About RTB House

RTB House is a next-generation performance demand-side platform (DSP) that uses proprietary Deep Learning AI algorithms to help brands grow. The company is a market leader in driving performance using Deep Learning across the entire purchase funnel.

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Win-price optimization addresses this problem directly. Instead of bidding high to avoid missing impressions, win-price algorithms estimate what an impression is likely to clear for and bid just above that level. The shift is subtle but meaningful – efficiency is no longer just about impacting bidding power, but about improving pricing accuracy.

Why does this distinction matter? Because in a first-price auction, the bid is the price. When a bidding model overestimates an impression’s value, even slightly, the buyer pays the difference. Across thousands of auctions a day, that overpayment compounds –campaigns may appear healthy on the surface while efficiency steadily erodes underneath, with no obvious signal that anything is wrong.

Consider a buyer planning a broad video campaign with a $10 target CPM, for instance. Under conventional bidding logic, the model may routinely bid near that ceiling to secure wins, even when similar impressions frequently clear for far less. In a first-price auction, those inflated bids become the final price. Over time, the campaign wins roughly the same volume of impressions it would have otherwise, but consistently pays more than the market requires.

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With win-price optimization in place, this logic changes. The bidding model looks at how comparable impressions have cleared historically, how competitive the current supply path is and how urgently the campaign needs to spend. If those signals suggest an impression typically clears closer to $4 or $5, the bid reflects that reality and still wins. The audience doesn’t change, nor does the inventory. The alignment between the bid and the true clearing price, however, is transformed for the better, surfacing savings as incremental reach, longer flight time or additional flexibility.

Gaps like these are more common than many buyers realize, though they’re rarely the result of poor planning. More often, they stem from incomplete information. When a demand-side platform, or DSP, operates with a more limited set of signals, it compensates by bidding defensively. Without the appropriate context, the safest assumption is that an impression is valuable and worth paying a premium.

Models with broader visibility behave differently. When supply path dynamics, historical clearing prices, competition intensity and real-time pacing are taken into consideration, the bidding model develops a clearer sense of when aggressive bidding is warranted and when it isn’t. Two DSPs can bid on the same impression and arrive at very different prices, not because one values quality more, but because one has a more complete understanding of price.

There’s also a practical effect. Many buyers still spend time monitoring pacing, reconciling reports and making manual bid adjustments to keep campaigns aligned. As pricing becomes more accurate, much of that reactive work falls away – the bidding model recalibrates continuously, allowing buyers to focus more on strategy and less on maintenance.

Programmatic teams have spent years optimizing who they reach and how outcomes are measured. Pricing – despite shaping the cost and effectiveness of every impression – has received far less scrutiny. Win-price optimization brings that missing dimension back into focus.

For many advertisers, the inputs required to bid more accurately already exist; the opportunity now is to use them deliberately. Because in first-price auctions, overpaying isn’t a rounding error – it’s a strategy flaw. The next phase of programmatic efficiency won’t be defined by who can target more precisely, but by who can price most intelligently.

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