Contextual advertising is not a new coinage in search of a use case. It is a response to a structural shift in how digital advertising works — and, increasingly, how it is permitted to work.

At its core, contextual advertising is the practice of placing ads based on the content of the page a user is currently viewing, rather than on a profile built from their browsing history. It does not require cookies, does not rely on third-party data, and does not track a user across the internet to build a behavioral profile. Instead, it attempts to reach the right person at the right moment by understanding the context in which they are already engaged — and placing a relevant message there.

Contextual vs. Behavioral Advertising

The distinction between contextual and behavioral advertising is worth understanding precisely, because the two are frequently conflated.

Behavioral advertising serves ads based on what a user has already done — their search history, the pages they have visited, and the purchases they have made. It is retrospective, drawing on accumulated data to infer likely future interest. Contextual advertising works differently. It does not wait for a user to display identifiable behavior. It attempts to anticipate relevance before that behavior occurs, matching the message to the moment rather than to the person’s recorded past.

A user reading a review of running shoes is, in that moment, a more receptive audience for athletic gear than any browsing history alone could confirm. Contextual advertising acts on that signal directly — without a cookie, without a data broker, and without the user having searched for anything at all.

Why It Is Gaining Ground

Today’s consumers are more sophisticated about advertising than previous generations. Continuous exposure to marketing across multiple platforms has produced a kind of selective attention — most people have developed an instinct for filtering out messages that do not feel immediately relevant. Contextual advertising is, in part, a response to that dynamic. By placing messages where they are genuinely pertinent to what a user is already thinking about, it improves the odds of cutting through.

It is also gaining ground for structural reasons. The deprecation of third-party cookies — a process that has been uneven but directionally consistent — has eroded the data infrastructure on which behavioral advertising depends. Privacy regulations in Europe, the United States, and a growing number of other markets have raised the compliance costs of tracking-based approaches. Contextual advertising sidesteps most of those constraints by design, making it an increasingly attractive option for publishers and advertisers navigating a more restrictive data environment.

Also Read: AI Is Supercharging Returns Fraud. Retailers Are Behind.

How It Works

Contextual advertising uses machine learning to analyze the content of a webpage — keywords, page type, topic category, and media format — and identifies the most relevant advertising placement without referencing user data. The system reads the page, not the person.

For publishers using platforms such as Google AdSense, contextual targeting is built in. Google’s system analyzes the content of each page in its display network and attempts to match the ad to the most relevant available content. For those using Google Ad Manager, ensuring that foundational targeting values are correctly configured is the essential first step.

Getting Started: A Practical Checklist

For advertisers and campaign managers approaching contextual targeting, preparation is less technical than strategic—and begins with a thorough understanding of the product being advertised and the content environments where it is most likely to resonate.

The following steps provide a working framework.

Build a robust keyword repository. Select target keywords, key topics, and commonly used phrases with care. These will determine which pages your ads appear on and, by extension, which moments of user attention you are buying.

Configure reach settings deliberately. Display network settings can be set to a broad or specific reach. Broad reach places ads based on topic targeting; specific reach restricts placement to pages that match both specified keywords and at least one targeted topic. The right choice depends on campaign objectives and the degree of contextual precision required.

Verify the ad order. Before a campaign goes live, confirm that placements have been identified that contextually match the content of the intended web pages. This step closes the loop between targeting intent and actual placement.

Monitor and refine continuously. Contextual advertising is not a set-and-forget discipline. The culture, language, and content landscape in which ads appear shift over time, and targeting parameters should be reviewed and updated accordingly.

Also Read: Peak Martech? The Landscape Has Plateaued, but the Real Story Lies Beneath

The Broader Opportunity

For advertisers, contextual advertising represents a path toward relevance that does not depend on surveillance. It is a model that aligns with where privacy regulation is heading, with what consumers say they prefer, and with what the data suggests actually works — messages placed in context perform better than messages placed by default.

Personalization has long been the stated goal of digital advertising. Contextual advertising offers a version of it that does not require knowing who someone is — only what they are paying attention to right now.

In an attention economy, that distinction turns out to matter quite a lot. 

The post Contextual Advertising: What It Is and Why It Matters appeared first on MartechView.

For years, advertising has been built around precision. The goal was simple: identify the right person, on the right device, at the right time, and deliver the right message.

And that worked…up to a point.

Today’s ecosystem is more fragmented, more regulated, and more complex than ever. Consumers move fluidly between smartphones, laptops, connected TVs, and platforms like YouTube, TikTok, and streaming services. At the same time, privacy expectations are rising, and traditional identifiers are becoming less reliable.

As a result, the industry is beginning to recognize that marketing at scale doesn’t always require pinpointing specific customers with 100 percent accuracy based on their data. Instead, we’re moving toward a new model: probabilistic advertising.

The best way to think about it is this. Instead of aiming for a perfect bullseye, marketers are learning to operate more like meteorologists, using patterns, signals, and probabilities to make informed decisions at scale.

Here are five reasons why the shift toward probabilistic advertising is happening right now.

Deterministic Signals Face Real Limits

Deterministic identity, or knowing exactly who someone is because their data aligns perfectly, still exists, but it’s increasingly limited.

Take a platform like Netflix. When a user logs in on a TV, laptop, and phone with the same email address, Netflix can confidently link those devices to the same person. Hard identifiers (which can also include customer ID and more) are individual-specific; we know it’s the right person because the data matches exactly. 

But most of the internet doesn’t work that way. When someone watches content from a broadcaster like the BBC without logging in, or browses across the open web, there is no single, definitive identifier tying those interactions together. That’s where probabilistic methods come in.

Instead of relying on certainty, advertisers will analyze patterns (device behavior, timing, location, and context) to estimate whether different signals belong to the same user.

At scale, the question shifts from “do we know exactly who this is?” to “do we know enough to act?” 

Identity Fragmentation Is Now the Default

Modern consumers today are, by default, fragmented. 

A single person might stream on a connected TV, browse products on a mobile device, scroll social platforms, and interact with apps, all within a single day. Each of these environments generates its own identifier, often incompatible with the others.

For marketers running campaigns across platforms like Meta, Google, Amazon, and The Trade Desk, this fragmentation creates a fundamental challenge: how do you build a consistent view of your audience?

Probabilistic identity helps unify that picture. It connects disparate signals into a cohesive, privacy-conscious understanding of who is likely behind them.

Just as importantly, it simplifies execution. Instead of stitching together dozens of identifiers across regions and channels, advertisers can operate with a more unified, scalable framework that reflects how consumers actually behave.

Privacy Expectations Are Reshaping Identity

Regulation is tightening and globalizing simultaneously. What began with GDPR in Europe is now influencing frameworks across North America, Latin America, and APAC. The direction sets stricter rules on personal data and higher expectations for transparency and consent, making it increasingly difficult to rely on personally identifiable information (PII) at scale.

Probabilistic approaches offer a path forward. Operating on anonymized signals and statistical inference, they reduce the need to know exactly who someone is while still enabling timely, relevant advertising.

For consumers, this creates a more balanced, less invasive experience. Instead of being tracked individually across dozens of platforms, they can remain effectively anonymous while still receiving useful content.

For marketers, it creates a more durable model that aligns with both regulation and user expectations.

AI Is the Engine Behind Probabilistic Advertising

The math behind probabilistic advertising has always existed. What’s changed is the innovation that can run it. 

Modern AI/ML models can process vast amounts of data, far beyond what traditional systems can handle. They analyze behavioral patterns, device characteristics, and contextual signals, continuously improving their predictions as new data becomes available.

This is what enables probabilistic identity to operate at internet-scale. But AI alone isn’t enough. Without a data infrastructure capable of supporting AI workflows, the models remain only as good as the data they can access.

To unify signals across billions of interactions, run complex models, and activate audiences in near real time, companies need data foundations that can handle massive workloads as they scale. Platforms like Ocient, for example, help companies process and analyze massive datasets efficiently, so probabilistic models can run where the data lives, rather than across fragmented systems.

The combination of AI and scalable infrastructure is what makes probabilistic advertising viable today.

Global Scale Increasingly Favors Probability Over Certainty

Deterministic identity works well in closed ecosystems or specific markets where strong login data exists. But expanding that approach globally requires building and maintaining countless integrations, partnerships, and datasets, often country by country.

Probabilistic models scale differently.

If the underlying infrastructure is in place, expanding into new markets simply means ingesting more data and applying the same modeling approach. There is no need to rebuild identity frameworks from scratch in every region.

For global brands, whether it’s a multinational retailer, an airline, or a company like Microsoft operating across multiple business units, this matters. They need consistent, cross-channel visibility across geographies, not a patchwork of disconnected solutions.

Probabilistic systems provide that consistency.

The Mindset Shift Marketers Need to Make

The biggest change marketers face isn’t technical – it’s conceptual. For years, the industry has been trained to value certainty above all else. But in a fragmented, privacy-first world, certainty is limited and often misleading.

What matters more today is confidence at scale.

That means accepting that you don’t need to know with 100 percent certainty that a specific device belongs to a specific individual. You need to know, with high confidence, that a set of signals represents a real person with likely behaviors, preferences, and intent.

In practice, that shift enables better, measurable outcomes. It allows marketers to reach broader audiences, operate across more channels, and do so in a way that respects privacy while still delivering performance.

In a few years, “probabilistic advertising” won’t feel like a new approach. It will simply be advertising. And for an industry built on understanding people at scale, that’s a long overdue evolution.

The post Why the Future of Advertising Is Built on Probability appeared first on MartechView.

A growing number of television news producers start the day the way most of us do now. Not by opening their inbox, but by opening an AI tool.

Before they read a single pitch, they’re already asking questions. What stories are trending? What experts are credible? What angles are audiences searching for right now? Within seconds, the AI surfaces answers pulled from recent coverage, past interviews, and digital content tied to those topics.

The AI revolution has come to the TV newsroom. It’s the latest shock to the system, already under siege. I’m sure you are familiar with the story after story of job cuts hitting the newsroom. You hear of giant mergers that threaten even more jobs. What you’re probably less familiar with is that local news content has increased dramatically during the recent 10-year period tracked by Pew. They noted a 30-40% increase in English-language local broadcasts and a doubling of Spanish-language local news. 

The key takeaway, more work for fewer journalists. 

It’s an unprecedented opportunity for the PR community. In fact, according to our “AI and the Newsroom” report, 94% of TV producers are now open to being pitched by PR people. That’s the highest it’s ever been. They need PR people and AI.

The data support it. We found 37% of TV producers now use AI to identify stories to cover. Doesn’t sound like much? It’s up from 0% in two years. If a producer knew a story they were pitched was optimized for AI search on Large Language Models (LLMs) like ChatGPT, 68% would be more interested in covering it. They are using AI for research, fact-checking, writing digital stories, and even the graphics they are creating.

The pitches that align with what the AI has surfaced feel relevant, timely, and easy to execute. The rest, even when well written, often get ignored. Not because they are bad stories, but because they were built for a newsroom staff size and workflow that no longer exists.

The good news is that brands are spending hundreds of millions of dollars to figure out how they can be discovered in the AI Search/Generative Engine Optimization (GEO) economy. However, they may be missing out on a huge opportunity by failing to recognize how the newsroom has changed. 

A television broadcast campaign is no longer just about the millions of people who might see it in the moment. Earned media has become the leading contributor to discoverability. When a brand appears in a broadcast segment, producers also create more content and feed it to multiple platforms. More than 90% of stations post their content on both their websites and social media. 86% are posting content to YouTube. According to MuckRack, YouTube content is now the leading driver of discovery for AI search across financial services, travel, entertainment, energy, technology, and healthcare on Google’s Gemini platform. Broadcast hits have become a force multiplier.

For years, we have operated under the assumption that if a story is strong enough, it will find its audience. In an AI-driven environment, the opposite is often true. If a story cannot be found in the way producers and platforms surface information, its quality becomes irrelevant.

Discoverability has become just as important as the story itself. That is a difficult adjustment because it forces us to rethink how we think about earned media. Brands that align their PR strategy with this reality now are effectively building a future-proof growth plan. Every piece of earned media strengthens its position. Every interview, every segment, every piece of content increases the likelihood that they will be discovered again.

Competitors who are slower to adapt are not just missing out on individual placements. They are falling behind in a system that compounds over time. Closing that gap is not a matter of running a better campaign next quarter. It can take years. That is why this moment matters.

The shift is not theoretical. It is already reflected in how newsrooms operate, how producers make decisions, and how audiences find information. Adapting to this reality does not require abandoning the fundamentals of PR; it requires reframing them. It starts with how stories are developed. The most effective campaigns now begin with understanding what people are searching for. That insight shapes the narrative, the spokesperson’s role, and the way the story is positioned.

It continues with a discussion of how content is created. Every interview is an opportunity to produce material that is not only compelling for an audience but also structured in a way that makes it discoverable. The language, the framing, and even the questions themselves all play a role.

And it extends to how success is measured. Reach still matters, but it is no longer the full picture. Visibility over time, frequency of appearance in relevant contexts, and the ability to be surfaced by AI systems are becoming more important.

The newsroom has not disappeared. It has transformed. Producers are still making decisions, stories still need to resonate, and they still need to compete for eyeballs, but the process that determines which stories rise to the top has fundamentally changed.

PR strategies need to catch up. Because in a newsroom where AI has become a first filter, the brands that are easiest to find are the ones that get covered. And the ones that understand that dynamic early are not just keeping pace. They are building an advantage that others will spend years trying to close.

The post Your PR Strategy Was Built for a Newsroom That No Longer Exists appeared first on MartechView.

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.

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