For nearly two decades, SEO was the digital marketer’s most reliable tool. Entire strategies and teams were built around a stable premise: rank well on Google, get seen. Visibility followed ranking, and traffic followed visibility. The system relied on Google’s shifting algorithms, but at least it was comprehensible.

The past two years have broken that logic entirely.

Rankings are holding — sometimes improving. But website traffic is declining. Dashboards look healthy. First-page positions remain. The clicks, however, are not there. In 2025, 60 percent of Google searches ended without a single click to any website.

The cause is not a mystery. AI-powered search engines now answer queries directly, absorbing the clicks that websites used to capture. We have entered a zero-click world, where AI synthesizes information and delivers it to users without ever requiring them to visit the source.

For marketers, the implication is significant: the goalposts have not just moved — they have been replaced entirely.

Ranking No Longer Leads to Reach

The old model of search rewarded technical precision. Keywords, backlinks, site architecture, and page speed sent signals that helped search engines determine relevance and authority. In today’s AI-mediated environment, relevance is no longer discovered — it is constructed.

AI chatbots do not surface results. They synthesize them. They draw from multiple sources, weigh credibility signals, and generate responses that may never require a user to visit the referenced content. Ranking highly on a search results page does not mean your brand will appear in an AI-generated answer. And if it does not appear in that answer, visibility effectively disappears — regardless of where the blue link sits.

The new definition of visibility is not where you rank. It is whether you are recognized as a source worth citing.

The End of Silos

Because AI changes how visibility is earned, it dissolves the boundaries that once separated public relations, SEO, and paid content. Historically, these functions operated independently because they influenced different outcomes. SEO drove rankings. PR generated coverage. Paid content drove traffic. Each was measured within its own channel.

Today, all of it is evaluated together — by the AI model deciding whose voice to include in an answer.

AI systems do not draw from a single source. They scan and combine articles, websites, forums, comments, and sponsored content across the entire web to construct a response. The implicit question these systems are asking is: Does this brand appear consistently, clearly, and credibly across multiple sources?

A PR placement is no longer just visibility in one publication — it becomes part of the dataset AI systems are trained on and referenced going forward. SEO content is no longer just about ranking — it is a structured repository of information for AI to extract. Paid placements do not just drive clicks — they shape how frequently and favorably a brand appears across the web.

The problem is consistency. A company might describe itself one way on its website, another way in media coverage, and appear differently still in paid placements. To a human reader, that is messy but navigable. To an AI model, it is noise, and noise weakens the signal. A brand that is difficult for AI to categorize is a brand more likely to be left out of the answer entirely.

Redefining Strategy for the AI Era

If the goal is no longer to rank but to be referenced, the question becomes: how do you build a presence that AI systems will not ignore?

In 2026, an effective search strategy rests on three foundations.

The first is audience-driven visibility. AI systems weigh what real people say and think — customer reviews, forum discussions, and community conversations — more heavily than brand-produced marketing copy. The frequency with which AI Overviews and ChatGPT reference Reddit is not coincidental. If people are discussing your brand in credible, relevant spaces, those discussions become signals AI systems recognize and draw from. Marketers should actively encourage user-generated content and participate in the communities — forums, social channels, industry platforms — where their customers already gather. The goal is contribution, not promotion.

The second is strategic amplification. Search engines have long deprioritized paid content, but AI systems often make no distinction between organic and sponsored material. When placed thoughtfully on high-authority platforms, sponsored content can reinforce a brand’s presence in exactly the sources AI is most likely to reference. Paid and organic content are increasingly parts of the same ecosystem — and should be planned as such.

The third is clarity. AI models prioritize specificity. Users querying AI tools provide far more detail than they would in a traditional search — use cases, specifications, context. For content to be referenced, it must match that level of detail. Brands need to be explicit about what they do, how their products work, and what differentiates them — even when those details feel self-evident. If it is not clearly stated, it is less likely to be surfaced.

The New Definition of Success

The language of SEO has not yet caught up to the shift it is navigating. Rankings, keywords, and traffic remain the default metrics for measuring reach and success. But those metrics are growing increasingly detached from actual visibility in an AI-first environment.

The questions that matter now are different: Are you being cited? Are you being synthesized into answers? Are you present in the conversations and sources that shape those answers?

If the answer is no, it does not matter where you rank.

SEO, as it was understood for two decades, is less useful than it once was. But search is not disappearing — it is being redefined. The brands that understand that distinction and build for it deliberately will not simply adapt to the zero-click era. They will set the terms.

The post SEO Is Not Dead. But It No Longer Works Alone. appeared first on MartechView.

The advertising industry loves a good standard. We’ve seen them come and go: OpenRTB to enable programmatic trading, MRC guidelines to define what’s viewable, ads.txt to clean up the supply chain, and the ARF to bring rigor to audience measurement. Even Google’s Privacy Sandbox, once hailed as the future of privacy-safe advertising, was quietly retired just last week.

Some standards worked brilliantly. Others created more problems than they solved.

Now, we have AdCP (Advertising Context Protocol) promising to standardize how AI agents communicate across the buy and sell sides. The first live agentic media buy happened last week. Industry reaction has ranged from “let’s wait-and-see how it develops” to “we’re automating insertion orders with the most advanced technology humanity has ever created.”

Both perspectives have merit. But the real question isn’t whether AdCP is revolutionary. It’s whether the conditions exist for it to succeed at scale. And who truly benefits if it does?

Lessons From Standards That Worked (and Didn’t)

OpenRTB succeeded for specific reasons. It assembled tech leaders from both supply and demand sides to develop an open protocol that solved a clear market need: enabling automated trading across platforms. Critically, it had broad buy-in from stakeholders of all sizes, not just the giants or start-ups who saw mutual benefit in standardization.

Compare that to ads.txt. Grand Theory: Verify Your Supply Chain Counterparties and Reduce Fraud. However, publishers immediately filled their ads.txt files with every possible partner, as limiting relationships meant limiting revenue. The standard was adopted, true. But it was essentially nullified by the very behavior it was designed to prevent. The standard failed, then, because publishers didn’t trust it.

Or consider seller-defined audiences (SDA). This was a fairly recent attempt (March 2021) to pass contextual audience data through the bid stream. It never gained traction because buyers didn’t trust sellers to define those audiences accurately. Also, there was no verification mechanism. The standard failed because incentives weren’t aligned.

Standards succeed when all parties trust the process and see tangible benefits; and standards fail when they’re gamed to preserve (perceived or real) individual advantage over collective progress.

What AdCP Needs to Flourish

For AdCP to move beyond demonstrations and pilot programs, five conditions must be met:

True tripartite adoption

To establish trust and promote uniformity, advertisers, agencies, and sellers must be equally represented in the development and evolution of standards. Currently, AdCP’s early coalition, initiated by Scope3, skews heavily toward supply-side platforms and ad tech companies. That’s not inherently problematic — someone has to start somewhere, right

However, sustainable success will require that demand-side platforms, holding companies, and major advertisers all actively shape the direction of the protocol. Every participant should ask: “Do I have a competitive advantage if everyone operates on the same standard?” If the answer is “I’ll need to customize it to fit my needs,” then the standard will likely fail in the face of yet another fragmented implementation.

Expansion beyond guaranteed buys

Starting with insertion orders and guaranteed inventory makes sense. It’s straightforward and high-value. However, the bulk of digital advertising occurs in performance-driven, programmatically traded environments. AdCP needs a clear roadmap for incorporating audience targeting, identity resolution, conversion tracking, real-time optimization, and measurement.

The protocol must quickly evolve to support the full spectrum of advertising workflows, not just the simplest ones. Otherwise, it remains a niche solution for premium direct buys, while the rest of the industry continues to operate through fragmented systems.

Integration with existing infrastructure

Here’s what many discussions about agentic advertising miss: AI agents don’t replace the platforms and systems that currently run advertising; they augment them. Every DSP, SSP, ad server, and measurement platform operates through APIs,  proprietary interfaces that actually execute campaigns, serve ads, and report results.

AdCP provides a standardized language for agents to communicate, but those agents still need to connect to existing platform APIs to take action. The protocol only works at scale if there’s a clear path for integrating agent communication with the operational reality of today’s fragmented tech stack.

Privacy and data governance frameworks

First-party data no longer leaves enterprise firewalls. Companies won’t allow AI agents to freely access and activate proprietary customer data without robust controls, transparency, and a deployed architecture that keeps processing within their own environments.AdCP must address how agentic systems handle sensitive data and establish guardrails to prevent agents from making unauthorized decisions. It needs to determine how human oversight operates within automated workflows. Without addressing privacy and governance concerns, enterprise adoption stalls, regardless of how elegant the protocol may be.

Clear value proposition for each stakeholder

This is where most standards are either adopted or rejected. Publishers need one standard, not five competing implementations that force them to support multiple systems. Agencies need workflow automation that genuinely reduces operational overhead without eliminating their strategic value. Advertisers need better outcomes at lower cost with greater transparency.

If AdCP simply recreates the same margin pressures, hidden fees, and information asymmetry that plagues programmatic, it won’t matter how technically sophisticated it is. Nobody will adopt it at scale.

Who Wins, Who Loses, and Who Breaks Even

If AdCP attracts broad adoption, the winners are clear.

• Publishers need only one standardized protocol, rather than a myriad of customized integrations for every demand partner. Smaller publishers gain access to automated buying workflows that were previously available only to premium inventory, with dedicated sales teams.
• Agencies and trading desks that embrace agentic systems early gain efficiency advantages. 
• Advertisers increase efficiency by accessing diverse inventory without incurring the costs of intermediary overhead.

The potential losers? 

• Platforms whose competitive advantage relies on proprietary workflows and closed systems. If standardized agent communication reduces switching costs, market power shifts toward those who deliver the best outcomes, rather than those with the stickiest platform.
• Like any automation wave, some roles that primarily execute manual tasks will be displaced. The media buyers who survive and thrive will be the ones who understand the industry deeply enough to ask the right questions and interpret results; the ones who know math before picking up the calculator.

The Five-Year Outlook

Suppose AdCP achieves actual multi-stakeholder adoption and evolves beyond insertion orders. In that case, we’ll soon see parallel infrastructure and legacy programmatic systems running alongside agentic workflows, gradually shifting toward the latter as trust and capability expand.

If it fragments or gets gamed by individual platforms, creating incompatible implementations, we’ll have another acronym that promised transformation but delivered marginal improvement at best.

The deciding factor won’t be the technology. It will be whether the industry can align incentives long enough to build something that genuinely benefits everyone.

That’s a harder problem than building AI agents. But it’s the only one that actually matters.

The post Will AdCP Be Advertising’s Next Great Standard? appeared first on MartechView.

Retail media has quickly become one of the fastest-growing areas of advertising, reshaping how brands connect with consumers. While much of the conversation still centers on online channels, physical stores are joining the landscape through in-store digital signage, smart displays, and interactive kiosks – technologies that turn every wall, aisle, and endcap into a measurable media environment, delivering both engagement and insight. 

The market is expanding fast. Criteo projects that global retail media spend will reach $179.5 billion this year, with in-store networks expected to surpass $500 million in 2025. These figures signal a clear evolution: the physical store is no longer just a point of sale, it’s a media channel in its own right. 

But beyond the revenue opportunity, the advantage is practical. Screens and other in-store technologies generate data on how shoppers notice, pause, and respond to products and promotions in real time. These behavioral insights provide retailers with a clearer understanding of what captures attention, what inspires action, and what ultimately drives conversions. 

Understanding in-store behavior 

Retailers have long measured sales, but the behavior behind those numbers remained out of view. Today, in-store media such as video walls and smart kiosks are changing that picture by producing analytics that reveal how shoppers actually engage with merchandise throughout their visit. 

One of the most fundamental metrics is impressions – the number of shoppers who see a given message or display. Another measure is dwell time, which measures how long someone pauses in front of a message. When retailers pair these two together, they start to measure attention, not just exposure. Research published in the Journal of Advertising Research has even found that dwell time can be a stronger predictor of ad effectiveness than traditional reach and frequency. 

A third key metric, lift analysis, connects exposure to results such as product sales or promotional uptake. This is where the value becomes concrete. Studies show that featuring products on digital signage can raise purchase likelihood by more than eight percent, demonstrating a clear link between on-screen messaging and shopper decisions. 

Together, these measures mirror the benchmarks marketers rely on in digital campaigns – click-through rates, time on page, conversion lift. The difference is that now, retailers can apply that same performance logic to physical environments, testing whether a display at the end of an aisle drives category sales or whether new creative content keeps customers engaged

longer. In short, analytics are enabling retailers to measure what was once invisible to them. 

Putting data to work 

If impressions, dwell time, and lift reveal what’s happening in the store, the next step is turning those insights into actions. The real purpose of analytics isn’t just to validate ad spend or celebrate performance after the fact; it’s to guide smarter decisions and continuously refine how advertisers deliver content in real time. 

Those adjustments can be both practical and straightforward. Impressions and dwell time pinpoint which creative choices resonate most with shoppers. Location testing – whether at checkout, an aisle end, or an entrance – highlights where customer attention naturally concentrates. Scheduling adds another layer. If dwell time consistently spikes in the produce department on weekends, programming can shift to meet that pattern, and ad inventory for those hours can be priced accordingly. This process makes retail media less like a static fixture and more like a living channel, adapting to shopper behavior. 

But optimization is as much about restraint as it is about opportunity. More screens doesn’t automatically mean more revenue. There’s a balance to be struck, and beyond a certain point, returns begin to taper off. Analytics help define that balance, showing where additional investment adds value and where it simply becomes noise. 

Seen this way, analytics turn in-store media from a one-time install into a living system that grows smarter with every campaign. Each insight builds on the last, refining creative, placement, and timing until content performs at its highest potential. It’s a more disciplined, evidence-based approach that connects physical retail to the same performance mindset already shaping the rest of the media mix. 

Proving value 

ROI ultimately determines whether retail media should be included in a brand’s budget. Online channels have long thrived because they deliver performance data, and now physical retail is expected to do the same. With in-store analytics, retailers can finally connect exposure to outcomes in ways that were once impossible: tracking how a message influences dwell time, how that attention translates into category sales, and how a promotion lifts purchase rates against baseline performance. These connections close the loop between marketing spend and shopper behavior. 

That ability to demonstrate performance opens the door to new business models. Retailers can package screen time as sellable inventory, treating each display or time slot as a measurable unit of media value. High-traffic zones—like produce sections on weekends or checkout lanes during peak hours—can command premium pricing based on engagement data. Seasonal and

Campaign-specific buys can be offered with guaranteed reporting, giving brands tangible proof that their dollars are driving impact. 

The result is twofold: retailers generate incremental revenue through in-store media sales, and brands gain a verifiable, high-performing channel inside the store. Together, those capabilities position physical retail as a true performance channel—one that competes not just on foot traffic, but on data, accountability, and results. 

The store as a media network 

Analytics have pushed retail media beyond guesswork, transforming in-store displays from passive fixtures into measurable, high-performing channels.. For retailers, this shift means the physical store is no longer just a place to sell products; it’s part of the broader media network, capable of generating new revenue while delivering the transparency and accountability brands expect. 

The opportunity now is to treat these spaces with the same discipline used in modern media planning: testing creative, refining placement, and proving what works through real data. As analytics continue to evolve, physical stores will coexist alongside digital channels, not as an imitation, but as a complement, bridging the gap between the on-screen experience and the point of purchase.

The post Is the Store the Next Big Media Channel? appeared first on MartechView.

August 2025 served as a stark reminder of the volatility inherent in ad tech. Despite tightening supply paths and pushing platform adoption, The Trade Desk’s stock slid. Then investors expressed dissatisfaction with the industry bellwether’s response to questions about competing with Amazon. The latter company, meanwhile, continued to flex its media muscle, cutting out intermediaries and reshaping how budgets will be allocated. 

For buyers, publishers, and platforms focused on the open web, the signal is clear: Scale is no longer enough. The next wave of performance needs to come from cleaner, more intelligent real-time pathways between supply and demand. 

It’s no wonder, then, that curation has become one of the industry’s defining trends, as players from across the programmatic landscape realize the benefits of using data and technology to filter, organize, and package inventory for performance.

Curation’s great, but, like scale, it is not enough on its own. That’s where the cloud comes in. 

When curation runs on cloud-native infrastructure, it becomes a real-time system that can filter noise, handle signals, and optimize impressions in milliseconds. And in a space increasingly dominated by walled gardens, that combination could be the open internet’s best shot at keeping pace.

Why can’t packaging afford to stay static?

Traditional programmatic operated on scale above all, sending millions of impressions through the pipes. DSPs were required to handle millions of bid requests per second, many of which were duplicative or irrelevant. Deal IDs offered some relief, packaging inventory into manageable bundles. Yet, these deals were often static, fixed lists of sites or categories that could only be re-optimized after performance data had been collected.

The “burn and learn” approach is no longer sustainable. Amazon is proving the power of real-time optimization with its closed-loop transaction data, raising expectations for what performance should look like. Buyers who opt for walled gardens over the open internet can seemingly avoid waiting weeks just to see what worked, instead making decisions in milliseconds with impressions scored on quality, context, and the likelihood of outcomes.

How does the cloud make curation more dynamic?

In this environment, cloud-native curation is the open internet’s ticket to viability.

Cloud infrastructure enables the evaluation of billions of impressions in real-time, applying contextual and performance signals before they reach the DSP. The result is fewer but higher-quality bid requests, reduced QPS strain, and cleaner inputs for algorithms. 

Other industry headlines further emphasize the need for this shift. Clean Tap, for example, now filters low-quality CTV inventory before buyers ever see it. At the same time, Magnite has opened direct-sold CTV deals to programmatic access, flooding new supply into the pipes. However, moves like these will deliver real performance only if they leverage cloud curation to filter out noise upstream and package only the impressions that matter.

Who benefits from real-time curation?

The impact of this shift isn’t limited to technology. Cloud-powered curation alters the flow of value across the ecosystem, yielding benefits for all participants. 

• For buyers, it means campaigns that adapt continuously instead of retroactively. Impressions are filtered upstream, reducing wasted spend and accelerating ROI.
• For publishers, it means better monetization of high-quality placements (rather than broad bundles), giving them a better seat at the table.
• For DSPs, it means less strain. Lower QPS loads, reduced compute waste, and cleaner signals improve both efficiency and algorithmic accuracy.
• For programmatic as a whole, less wasted processing reduces energy consumption, while more performant supply paths help the open internet keep pace with the walled gardens. 

Taken together, these benefits demonstrate why curation has evolved from a tactical tool into a strategic lever, enabling every participant in the supply chain to achieve more with less, while raising the industry’s overall quality bar.

Why cloud-powered curation is the path forward

This summer’s headlines make one thing clear: Ad tech is moving toward speed, precision, and tighter control of the supply chain. To remain competitive, the open internet requires approaches that deliver these qualities without locking buyers and publishers into closed ecosystems.

Cloud-powered curation offers that approach. It evolves the familiar practice of packaging into a real-time, performance-driven discipline. In a market where every millisecond counts, this gives the open web a way to compete on both quality and scale. For everyone who has realized that curation is the next stage of packaging, it’s time to maximize its potential on the cloud.  

The post Can Cloud Curation Save the Open Internet? appeared first on MartechView.

The European digital regulatory landscape, long defined by the rigid frameworks of the General Data Protection Regulation (GDPR), is bracing for a seismic shift. The European Commission is poised to unveil its “Digital Omnibus” package, a comprehensive set of reforms officially presented as a bureaucratic cleanup but viewed by industry insiders as a fundamental rewriting of the rules governing digital advertising, data privacy, and artificial intelligence.

For years, the adtech ecosystem has operated under the heavy blanket of GDPR, navigating complex consent strings, transparency frameworks, and the constant threat of regulatory fines. The Digital Omnibus promises to cut through this red tape. Still, it may fundamentally alter the power dynamics between consumers, privacy advocates, and the tech giants that fuel the digital economy.

The End of “Cookie Fatigue”?

One of the most consumer-facing aspects of the Digital Omnibus is its direct attack on “cookie fatigue.” For nearly a decade, European internet users—and indeed, users globally visiting EU sites—have been bombarded with consent banners. This “click to accept” model, intended to empower users, has resulted mainly in apathy, with frustrated consumers blindly clicking “accept all” just to access content.

The Omnibus aims to reform the ePrivacy Directive, the specific legislation that mandates these banners. Leaked drafts suggest a move toward “streamlined” consent mechanisms. This could mean allowing browsers or device settings to signal consent globally, reducing the need for site-by-site interruptions. For the adtech industry, this is a double-edged sword. While it promises a smoother user experience and potentially higher conversion rates for publishers who no longer annoy their traffic, it also threatens to centralize consent power in the hands of browser gatekeepers, such as Google (Chrome) and Apple (Safari), who could unilaterally block third-party tracking signals.

AI, Legitimate Interest, and the Future of Targeting

Perhaps the most significant development for the adtech sector lies in the Omnibus’s treatment of Artificial Intelligence. As AI becomes the engine of modern programmatic advertising—powering everything from dynamic creative optimization to predictive audience modeling—the legal basis for processing data to train these models has been murky. Currently, GDPR heavily favors “explicit consent” (opt-in) for data processing. However, the Digital Omnibus appears ready to codify “legitimate interest” as a valid legal basis for training AI models on personal data.

This is a potential game-changer. Suppose adtech vendors and platforms can claim a legitimate interest in processing user data to refine their AI algorithms without needing explicit, granular consent for every single data point. In that case, the friction in the data supply chain drops dramatically. It would enable more robust modeling and targeting capabilities, breathing new life into an industry that has been stifled by signal loss (due to the demise of third-party cookies).

Critics, however, warn that this opens a “loophole” large enough to drive a server farm through. Privacy advocates argue that “legitimate interest” is a vague standard that companies will inevitably stretch to justify invasive profiling under the guise of “AI optimization.”

Centralizing Enforcement: A New Sheriff in Town?

Beyond the technicalities of cookies and AI, the Digital Omnibus aims to address the “enforcement bottleneck” in the current GDPR regime. Historically, enforcement has been left to national Data Protection Authorities (DPAs), resulting in the “forum shopping” phenomenon, where tech giants establish headquarters in countries perceived as more lenient, such as Ireland or Luxembourg.

The Omnibus proposes a more centralized enforcement mechanism, potentially giving the European Commission itself greater oversight powers or streamlining cross-border cooperation. For adtech companies, this signals the end of the era where they could hide behind an overwhelmed local regulator. A centralized, harmonized enforcement body could operate more efficiently and effectively, creating a more predictable yet potentially more punitive regulatory environment.

What This Means for 2026 and Beyond

As the Digital Omnibus moves from draft to law, the adtech industry must prepare for a period of significant transition. The definitions of “personal data” may be reinterpreted, and reliance on consent banners will likely fade in favor of browser-based signals or AI-driven inference, leading to a shift in the justification for data use from “user permission” to “business necessity” under the legitimate interest clause.

For marketers, the Omnibus represents a pivot point. The era of blunt-force data collection is coming to an end, replaced by a more sophisticated, AI-mediated relationship with consumer data. Those who can build robust, privacy-safe AI models that leverage “legitimate interest” responsibly will thrive. Those who continue to rely on the crumbling infrastructure of third-party cookies and confusing consent pop-ups may find themselves regulated out of existence.

The Digital Omnibus is not just a cleanup; it is a new constitution for the digital market. And for adtech, it is the signal that the next phase of the privacy wars has just begun.

The post Digital Omnibus: The EU’s Bold Move to Rewrite the AdTech Rulebook appeared first on MartechView.

As a first-timer at this year’s Advertising Week, I can say it was a truly unique experience. For four days, the space that once served as a shopping mall in the Old Penn District—I did my research before attending!—transformed into a buzzing hub for our industry. People rode escalators up and down, chasing friends, rushing between sessions, and hunting for content. The energy was intense, with more than ten stages packed with talks from top industry leaders. It’s the kind of place where you meet people who share your passion for building brands and driving business forward.

With that much content and that many people, chaos was inevitable. (PS: if you ever plan to go, get a fast-track ticket—otherwise, you might spend more time waiting in line than learning. But it’s worth it.) The density of ideas, conversations, and spontaneous encounters more than makes up for the confusion. With that in mind, here are some of the big ad takeaways I had as a first-timer.

AI Matures from Hype to Utility

AI, unsurprisingly, was the dominant theme—but the conversation finally moved beyond the hype. This year, the question wasn’t whether AI would change advertising, but how far it could actually go, what its limits were, and what it truly meant for consumers and marketers. Depending on the stage, you could hear every possible take on AI applications in advertising—from creative generation to optimization and analytics.

What I appreciated most was the practical tone. AI is no longer seen as a magical solution to every problem. It has its limits—sometimes replacing human labor or focus—but more often acts as a multiplier of what people are already capable of achieving. AI is here to stay, and while some of the buzz still needs trimming, the industry is slowly learning how to use it with purpose and perspective.

Every Dollar Is Being Judged

Measurement was another recurring theme. Beyond the usual complaints about fragmentation, what really stood out was a clear focus on growth. Marketing is no longer viewed as a cost center or a team that “spends money well,” but increasingly as a growth engine at the core of every company. The era of brand building is back in full force. I didn’t meet a single marketer who still believes viewability or last-click attribution are meaningful proxies for impact. Even accounting for the gap between stage talk and daily execution, it’s clear the funnel has collapsed. Every dollar spent must now be judged by its contribution to growth and ROI.

Here’s where things get interesting: there’s a growing recognition that not everything needs to be tracked to the last click or impression. Some of the most potent insights emerge from connecting simple signals—such as geolocation or point-of-sale data—and interpreting them in the proper context. You don’t need to follow consumers across every device and touchpoint (at the cost of their privacy) to understand what drives them. When overlaid with the correct data and perspective, these simpler signals can reveal patterns that are far more actionable—and far more sustainable—than those derived from compulsive tracking.

The mindset is shifting from trying to fix fragmentation to learning how to thrive within it. Whether a brand interaction happens in the real world, the digital ecosystem, or even the metaverse, marketers are learning to respect those differences and adapt measurement strategies accordingly.

This new perspective on outcomes is also reshaping how marketing positions itself within organizations. With a better understanding of customer behavior and clearer links between activity and impact, marketers are finally being recognized not as cost centers but as investments. Strong brands drive stronger business results—they make performance campaigns more effective, protect companies in tough moments, and build loyalty that lasts. Those are the kinds of outcomes that directly connect to what matters most for any C-suite: enterprise value.

Advertising Is Too Complicated

AWNY also highlighted the increasing complexity of marketing. The agenda encompassed a range of topics, from technology and data to creativity and thought leadership. Each session touched on a different part of the marketer’s job, and while it was impossible to attend everything, everything somehow felt relevant. That’s the reality of our industry today—endless opportunities, infinite tools, and constant pressure to prioritize. Complexity itself isn’t bad, but if it’s not managed, it turns into noise: chaotic actions, half-baked insights, and unclear outcomes.

If I could make one wish for next year’s event, it would be to hear more about how to make marketers’ jobs less complex—not by reducing ambition, but by improving focus. Our industry thrives on change and innovation, but we also need clarity to keep moving forward. Perhaps AI can actually help with that—not by doing the work for us, but by enabling us to focus on what truly matters: connecting, creating, and driving growth.

After four days of nonstop sessions, escalator chats, and caffeine, I left Advertising Week both exhausted and energized. The industry is certainly in flux. If this year proved anything, it’s that the future won’t be built on hype or buzzwords, but on clarity, creativity, and purpose.

The post Is Advertising Finally Trading Hype for Clarity? appeared first on MartechView.

Adtech is undergoing a shift in how it defines signal quality, traffic transparency, and regulatory readiness. Even as third-party cookies linger in Chrome, their role as a reliable verification signal has already eroded. 

What once helped confirm whether an impression came from a real user has now been restricted, opening a visibility gap that invalid traffic (IVT) exploits. IVT refers to ad impressions or clicks that don’t come from real users, including bots, hijacked devices, and anonymized or misrepresented connections. 

Regulatory enforcement is also picking up speed. Campaign infrastructure built on aging assumptions is less reliable in a market that demands proof of audience integrity. 

With cookie-based verification fading and fraud tactics advancing, adtech needs a new foundation for trust built on verifiable network context. Advertisers and platforms are paying closer attention to a signal that has often been underutilized: the IP address. 

Invalid Traffic and the New Signal Baseline 

As cookies disappear, platforms lose one of their most persistent signals for linking impressions to real user devices. IVT thrives in this vacuum. It’s a direct challenge to performance, attribution, and credibility. 

An estimated $84 billion in ad spend was lost to fraud in 2023. Much of it came from traffic that passed initial filters but originated from data centers, residential proxy networks, or other anonymized sources. IP intelligence helps surface IP anonymization techniques by identifying: 

• Non-human activity such as bots and crawlers 
• Data center and cloud traffic associated with mass scraping or automation ● Masked IPs using VPNs, proxies, or Tors 

Many platforms now treat these signals as core inputs to traffic evaluation. They use them to inform routing, shape bidding logic, and build confidence in the quality of audience delivery. 

Compliance Pressure Has Grown Independently of Cookies 

While cookies remain on Chrome for now, regulatory pressure continues to build. In the past year, major enforcement actions in the EU and new requirements in U.S. states like California have raised the bar for compliance.

Modern privacy laws expect platforms to enforce region-specific consent, prove how data is collected, and document how campaigns are targeted. These expectations exist regardless of browser-level tracking capabilities. 

Static IP blocklists aren’t good enough. They might show how an IP once behaved, but they don’t capture how that connection is acting on the network right now.. Bots evolve their signatures, and residential proxy networks rotate IPs dynamically turning yesterday’s clean IP into today’s source of invalid traffic. VPN providers often route traffic through “virtual” exit nodes that don’t match their advertised geographies infrastructure location, creating blind spots for anyone relying on inferred or static data for geo compliance. 

To stay ahead of these discrepancies, IP data must be grounded in active measurement, continuously testing connection paths and verifying how traffic actually exits VPN and proxy networks. Only then can you accurately identify invalid traffic in real time. 

Verified IP data plays a direct role in supporting these functions. When IP data is accurate and used correctly, it can help: 

• Detect and suppress invalid traffic before bidding occurs 
• Differentiate between legitimate users and anonymized automation 
• Protect performance metrics and attribution integrity 
• Build durable audience trust through verifiable signal quality 

Relying on verified infrastructure-level data is becoming a standard part of responsible media delivery. 

Network Signals Now Drive Campaign Decisions 

Third-party cookies are no longer the default signal for campaign optimization. Firefox and Safari have blocked them for years. Chrome’s delays have not reversed the broader industry shift toward connection-based targeting. 

Many teams are refocusing on the connection context of an IP address, rather than who they are. This approach prioritizes IP data that can identify: 

• VPNs, proxies, or anonymizers in real time 
• Cloud providers 
• Shared Wi-Fi networks 
• Venue environments such as airports or hotel chains 
• Signals of frequent reassignment or mobility, which can indicate manipulation 

These signals work across browsers and devices. They support improved ROI, fraud prevention, and campaign personalization regardless of cookie state. 

Action Items for Adtech Teams

As the ecosystem shifts, teams have an opportunity to operationalize IP intelligence more effectively. The following actions help strengthen both campaign quality and regulatory alignment while reducing invalid traffic: 

Expand traffic filter logic to include behavioral and network-based signals

Basic bot filtering is no longer enough. Teams should evaluate whether traffic filters account for high-risk ASNs, residential proxies, and other IP anonymization tools. Adding IP-based intelligence directly into pre-bid or pre-delivery workflows improves accuracy and reduces post-campaign cleanup. 

Use IP data to validate compliance boundaries

No matter the regulation or policy, verified geolocation data can validate that traffic stays within the required legal framework. This is especially useful for enforcing consent, managing cross-border campaign delivery, and showing auditors how user segmentation is handled in real time. 

Implement real-time fraud scoring using persistent network traits

Teams that use dynamic IP data signals, such as proxy detection, mobility scores, or ISP classification, can assign risk scores to traffic as it enters the pipeline. These persistent network traits can be weighted into real-time bid suppression logic, reducing wasted spend upstream. This enables inline suppression of suspicious requests and helps maintain signal quality throughout the delivery chain. 

Leverage persistent context for better attribution and suppression

IP-level attributes like hosting provider, ASN, and venue type offer a consistent way to enrich each request without cookies. That persistent context helps link behaviors despite fragmentation challenges, while also exposing invalid traffic that might otherwise be missed. 

Prioritize providers that measure and refresh data continuously

Static feeds are prone to drift. IP ownership and usage change rapidly, especially with cloud hosting or anonymization networks. Teams should evaluate whether their IP data sources are generated through active measurement and updated daily, ensuring alignment with real-world infrastructure shifts. 

IP Data as Strategic Infrastructure 

The IP address has remerged as a central signal for improved campaign delivery, fraud prevention, and data integrity. Its value depends on how it’s interpreted, enriched, and applied.

Even the Media Rating Council’s IVT standards make IP-level analysis a baseline requirement, mandating that vendors evaluate how IP and user-agent data underpin fraud detection. It’s a reminder that true traffic validation starts with verifiable network context. 

IP intelligence delivers proof of how connections behave on the network, not just where they are registered. Continuous active measurement provides the context advertisers need to validate audiences, reduce invalid traffic, and build a better system from pre-bid to performance analysis. 

This network-level visibility allows platforms to tag and classify connections with meaningful context: whether traffic comes from a mobile gateway, a hotel, an in-flight network, or a data center. It’s context adtech can act on right now. 

In an environment where every impression must be defensible, better campaigns start with better signals. And better signals start with the IP address.

The post Adtech’s New Secret Weapon: The IP Signal appeared first on MartechView.

Targeted advertising based on behavioral signals is facing criticism. Recent reports indicate that data flaws are causing advertisers to miss their intended targets, while others suggest that prioritizing the audience above all else is leading to the monetization of horrific content.

There has been some reflection on why audience targeting often misses the mark, with some advocating for contextual targeting to replace audience-based targeting. The problem with these arguments is that they often pit advertisers’ choice as an either/or, when the future is more of a both/and. 

A fatalistic approach that prioritizes audience data over context misses the larger issue, which is that audience is still a tactic that relies heavily on data quality. The future of targeting is not a matter of audience or contextual. Nor is it simply about audience plus contextual data. The future is a quality audience and contextual. 

Much of the contextual versus audience argument relies on outdated thinking that places far too much weight on the notion that all third-party data is inherently bad. This reputation is tied to the fact that, in the late 2010s, the market indeed had bad actors putting ineffective data on the shelf. Yet today, much of the third-party audience data available is better than ever. The industry has made significant progress in validation and accreditation practices. The top vendors enhanced their products to ensure survival following Chrome’s previously planned cookie deprecation. Meanwhile, many have developed products that comply with the ever-increasing state privacy statutes. 

If top-flight audience data is widely available, why are we still encountering issues with ads missing their targets? You must begin by examining the quality of the data.

The sad truth of programmatic media buying is that audience data selection remains an afterthought for most traders. Campaigns are lined up, budgets approved, and strategies are put into place, but when it’s time to select the audience provider, the trader will pick whichever segment pops up first in their search box. Or the one with the lowest CPM. 

It goes without saying that none of these is the most effective screening process for gathering audience data. Lining up programmatic budgets and setting campaign run dates and goals is meaningless if the campaign isn’t effectively targeted at the right audience. It’s essentially saying, “We don’t mind if this doesn’t work.” 

It can feel redundant to remind brands and agencies that assessing the available audiences is a critical step in campaign efficacy. If brands find that their demo data is missing the mark, it’s not because all audience data is ineffective. It’s because they are using poor-quality data.

Abandoning audience for contextual data undermines both strategies and renders contextual targeting less effective when used in a silo. Advertisers today have access to a wealth of audience data and contextual data. Even with Chrome abandoning its initial plans to phase out third-party cookies, it’s clear that cookies will not remain the dominant identifier forever. Advertisers seeking to understand better how to target consumers in cookie-free environments must utilize a combination of audience and contextual data to determine what their audience reads and how it responds to ads across various types of content. 

The future is not about isolating different targeting approaches but having them work together. Using top-of-the-line audience data ensures that ads reach the appropriate consumers. All that campaign data can then be matched to contextual signals, creating a map that the brand can follow to target and reach customers, regardless of whether cookies or other identifiers are present. 

Don’t get caught up in the either/or mudslinging that the ad industry is prone to. When it comes to contextual and audience data, the whole is greater than the sum of its parts.

The post The Future of Targeting Isn’t Audience or Contextual. It’s Quality. appeared first on MartechView.

Staying ahead of marketing trends used to mean keeping up with the moves and decisions of major ad holding companies and brand advertisers. No longer. 

Now, if you want to see the future taking shape in real time, pay attention to performance marketers — particularly in the direct-to-consumer space. 

True, performance marketers don’t have all that much in common with agencies and brands. But when it comes to mastering rapid change in tech, these bellwether marketers feel every market shift immediately. 

When customer acquisition costs rise, attribution models break down, or platforms increase take rates without delivering results, DTC advertisers don’t have the luxury of waiting for quarterly strategy reviews or institutional buffers. They spend quickly as soon as performance dips because their survival depends on it.

This urgency has made performance advertisers the canaries in the coal mine for industry shifts. They’re the first to test new channels, the first to adapt measurement strategies, and the first to walk away when tools stop working. And right now, they’re signaling a fundamental transformation in how marketing work gets done—one that larger brands and agencies need to understand before it overtakes them.

The In-Housing Wave

Performance advertisers pioneered the move toward adopting in-house capabilities out of necessity; however, this trend is now extending to traditional enterprise brands. 

At a recent industry conference, I heard from a major global electronics manufacturer that’s methodically bringing programmatic capabilities in-house. They started with media planning and strategy, keeping execution in line with their agency partner—at least for now, I was told. That “for now” is doing heavy lifting.

This pattern is accelerating across categories as brands realize they can control more of their marketing technology stack directly, and as emerging AI tools are making that transition easier than ever before. What once required specialized agency talent and proprietary tools is increasingly accessible to in-house teams willing to invest in the right capabilities.

The biggest impediment, then,  isn’t technology, but talent. 

In speaking with enterprise brands, I’ve been surprised by how many are still struggling to keep pace with advanced programmatic applications. Their definition of digital advertising often stops at search and LinkedIn. Even major multinational companies with substantial resources lack the internal expertise to execute sophisticated campaigns.

The brands that are ahead of the curve share a common trait: they’ve hired talent directly from agencies. That’s where the skillset currently resides. As the agency landscape undergoes seismic shifts, more of that talent will become available for brands seeking to build internal capabilities.

The AI Acceleration

For years, every industry conference featured vague discussions about how AI would transform marketing. People used the term loosely, often without clear applications in mind. That’s changed dramatically lately.

Google’s Performance Max represents a glimpse of what’s coming. Billed as a “goal-based” AI program that allows advertisers to use all of their Google Ads inventory in a single campaign, the platform can identify target audiences, create and test multiple ad variations, develop and optimize landing pages, and essentially automate functions that agencies have traditionally performed. 

Meta is developing similar capabilities. This isn’t theoretical anymore; it’s operational technology that performance advertisers are already testing.

At the industry event I attended, agency representatives insisted their clients would never surrender that much control to automated systems. I suspect those same representatives will change their minds within six months. Once brands test these tools and see results, the progression becomes inevitable. Furthermore, the technology will only improve, so resistance based on current limitations demonstrates a consequential lack of foresight..

Performance advertisers will adopt these tools first, as they always do. They’ll refine the approaches, identify the optimal use cases, and demonstrate ROI. Traditional brands will follow. They’ll initially move cautiously. Soon enough, though, they’ll proceed with increasing confidence as the technology proves itself.

The New Accountability Standard

The shift toward in-house capabilities and AI-powered tools coincides with another trend borrowed from the performance marketing playbook: rigorous, quantifiable accountability between marketing and sales.

In conversations with enterprise marketers, I’ve consistently noticed these themes. They need to prove that marketing budgets deliver tangible value to sales teams. That means saying “no” to vague influence metrics or brand lift studies. Conversely, that means saying “yes” to direct connections between marketing spend and the specific sales outcomes that meet marketers’ exact needs for pipeline size, lead volume, and revenue generation. It’s all about quantifying impact precisely, not approximately.

Performance advertisers have continuously operated under this discipline. They live and breathe by metrics such as return on ad spend, lifetime value, and blended customer acquisition costs, because their business models require it. 

Now that enterprise brands are being held to similar standards, they’re realizing they need the infrastructure and expertise to deliver that level of measurement rigor.

This creates pressure on traditional agency relationships. When brands demand clear attribution and direct ROI demonstration, agencies that can’t deliver precise measurement lose their value proposition.

The “trust us, brand building takes time” argument holds until CFOs start asking more probing questions about marketing effectiveness.

Meeting the Existential Challenge

The convergence of these trends — in-housing, AI automation, and performance-driven accountability — is directing us all toward a dramatically different marketing landscape that will emerge within the next few years.

For agencies, especially, perhaps, the challenge is existential. 

When AI tools can handle audience identification, creative development, and campaign optimization, what roles remain? 

Strategy is an obvious answer. But even that assumption faces challenges. Some brands are already bringing strategic planning in-house while leaving only execution to agency partners. And execution itself is increasingly automated.

This doesn’t necessarily mean agencies disappear. However, it does mean a massive transformation. Agencies will need to identify new forms of value creation that justify their existence in an AI-augmented, increasingly in-house world. Those that fail to evolve will face intense pressure, including potential consolidation and layoffs, as brands shift more capabilities internally.

For brands, this provides a substantial opportunity,  but those opportunities will require investment in the right talent. 

The marketers who understand advanced programmatic applications, sophisticated measurement frameworks, and AI-powered optimization will become increasingly valuable. Brands that can attract this talent — likely from agencies navigating their own disruptions — will gain competitive advantages in efficiency and effectiveness.

Talent migration has already begun. When we develop outreach strategies for enterprise brands, we specifically target companies that have hired former agency professionals. We know that these individuals understand the possibilities and can navigate the transition effectively. As agency transformation accelerates, this talent pool is expected to expand significantly.

The Imperative to Act

Performance advertisers operate with a “prove it or lose it” mentality because they have no choice. Every dollar must demonstrate a clear return because the alternative is business failure. This discipline has made them early adopters of tools, strategies, and measurement frameworks that the broader market eventually embraces.

Currently, they’re signaling that the traditional marketing services model is undergoing a fundamental shift. AI is eliminating tasks that justified agency relationships. In-house capabilities are becoming more accessible. Accountability standards are tightening.

The broader ecosystem can learn from their discipline by adopting similar rigor around measurement, building feedback loops that catch problems before they become costly, and evaluating platforms and partners with that same prove-it-or-lose-it mentality which drives performance marketers.

Those enterprises that can’t afford to be wrong will make smarter bets, sharpen accountability, and better prepare for what’s coming next. And it is coming fast—faster than six-month planning cycles or annual budget reviews can accommodate. The canaries are already signaling. The question is whether the rest of the industry is paying attention.

The post The Canaries of Marketing’s AI Revolution appeared first on MartechView.

The current conversation around agentic AI in advertising is missing the point.

Yes, it will reshape bidding strategies and improve our targeting. However, the most meaningful change is something few are talking about: Agentic AI won’t just change the delivery system; it will rewrite the advertising experience altogether. 

Agentic AI will fundamentally change consumers’ expectations and tolerance levels for brand interactions and the ad industry’s balance of power regarding data. We’re standing on the precipice of a new standard for our industry—where ads are no longer delivering messages but services to be rendered.

Flying Cars, Not Faster Horses

With agentic AI, the ad industry can’t settle for iterative improvement. We can’t give people faster horses when they’re demanding flying cars. If we focus on agentic AI to enhance targeting, bidding, and optimization, the industry will have completely missed the boat. 

With agentic AI, consumers will become accustomed to getting answers, not just being linked to information. An interruption to this flow simply won’t be tolerated by consumers, which will require the ad industry to rethink what it means to advertise entirely.

What will that look like in practice? First, ads must become capable of interaction—responding to questions, providing recommendations, and adapting messages based on user input. Second, they must show up at the right moment and in the right form; contextual alignment will be essential. Third, ads must know when not to show up; advertisers won’t be able to blast impressions everywhere and take a victory lap when they achieve a tenth of a percent engagement rate. 

This isn’t about inserting ads into AI-generated answers. (That’s called spam.) It’s about creating conditions for users to engage with brand agents willingly and on their terms—like getting a helpful recommendation from a trusted source, not being handed a flyer. The future of ad infrastructure must be built around permission-based entry, context-driven prompts, and clear value in every interaction.

Shift Away from Interruption

There’s another major piece to this: data. As consumers lean into AI-driven assistants, these agents will become the most powerful data and consumer intelligence sources the world has ever seen. Brands’ first-party data sets—even those of leading retailers with significant transactional insights—will pale in comparison. This power shift will have profound implications for the future digital ad economy. But it will also free brands from their obsessive pursuits of first-party data at all costs and allow them to spend more time figuring out how to become truly helpful to people.

AI has already upended search and altered the course of the open web. People have stopped searching and started asking. They don’t want to click, decipher, and distill. They want a clear, clean answer, and they want it now. 

In this new environment, brands can’t just shout for attention. They need to behave like guides. If I’m in-market for a car, I don’t want an offer interrupting my experience. I want help making a decision. What should I know? What should I compare? What’s available near me? A brand experience powered by agentic AI can do that. It stops being an ad and starts being a recommendation engine. It’s not a pop-up. It’s a conversation.

There’s a flip side: If the brand’s agent isn’t helpful, it won’t survive. AI agents are built on trust. Users will shut them down when they break that trust by pushing something irrelevant or aggressive.

The Industry Must Think Bigger

A lot of what’s being said about agentic AI today feels like old ideas in new packaging: better targeting, smarter bidding, and dynamic optimization. That’s fine. But if that’s the ceiling of our imagination, advertising is doomed. AI will change consumer expectations and how they interact with content, and the advertising experience will have to evolve accordingly. 

The future of advertising is not optimization. It’s ads as a service. Agentic AI won’t just change how we reach people. It will change why they bother to listen. And the brands that win will be the ones who show up when needed, speak clearly, and offer something valuable—no tap, no scroll, no ask required.

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