Content Studio

AI is only as good as the data that fuels it. In advertising, however, that foundation is often flawed.

When Apple launched AppTrackingTransparency (ATT) in 2021, access to deterministic identifiers fell sharply as roughly 80% of users opted out of tracking. User-level feedback loops became sparse and biased, and iOS performance marketing shifted into a different measurement environment under SKAdNetwork (SKAN). Apple’s AdAttributionKit (AAK) later delayed postbacks, compressed conversion values and set privacy thresholds that made signal availability dependent on campaign structure and scale.

No one in programmatic advertising needs another reminder about fragmentation. This complexity stands in sharp contrast to the original promise of the open internet. Programmatic was supposed to give marketers the freedom to reach audiences anywhere online by combining data, automation and inventory from across the web.

It’s widely reported that anywhere from 20% to 30% of programmatic video spend is lost due to supply-side misrepresentation, invalid traffic and low-quality inventory. That’s billions in wasted ad spend – not to mention billions in ad revenue siphoned away from premium publishers – further eroding high-quality video inventory.

Retail media is booming.
Spend is climbing, new networks are launching and every platform seems to deliver strong returns. And yet many marketers are asking the same uncomfortable question: How do we know what’s actually driving growth?

In 2011, three media devices captured 87% of consumer attention. Today, that figure has dropped to 65%, according to McKinsey. The fragmentation isn’t slowing down; it’s accelerating. Streaming services, social media, podcasts and gaming now compete for eyeballs, making traditional media planning feel like navigating with an outdated map.

When Truthset revealed that IP-to-postal matches are accurate only 13% of the time, it exposed an uncomfortable truth about connected TV advertising: The industry has been optimizing for the wrong metric.

If you buy CTV advertising, you’ve likely encountered two approaches to securing premium inventory: programmatic guaranteed (PG) and buying direct, also referred to as direct IO (DIO).

Marketers are projected to have invested nearly $400 billion in US media in 2025, based on estimates from MAGNA and eMarketer. About half of that will go toward finding new customers. Yet, as marketing investment grows, clarity on what actually drives ROI and growth continues to shrink. The loss of third-party identifiers, rising privacy restrictions and closed ecosystems have made it harder to see what’s working.

When marketers get too fixated on measuring short-term performance, they miss opportunities to capitalize on TV’s strength as a brand-building channel.

Consumers today switch between screens and viewing interfaces to seamlessly view content. Now it’s time for advertisers to catch up to this convergence, so they can make ad experiences better – not worse, said Cadent CEO Doug Rozen.

Marketers who want to remain competitive need to reexamine the way they view, plan and measure performance across video and streaming platforms. If they’re still debating programmatic vs. direct or linear vs. streaming, then they’re missing out on the real meaning behind convergent TV, according to Mike Fogarty, head of client development, brand and agency […]

As the video landscape evolves, one thing is clear: CTV is no longer operating as a standalone line item. It is now a foundational component of fully converged, total video strategies. The implications are especially significant for regional and local advertisers.

For most of the last decade, performance marketing was rewarded for momentum. Spend efficiently. Scale quickly. Optimize continuously. If the line went up and to the right, few questions followed. But that era is ending, because performance without accountability is no longer credible.

AI in advertising has gone from a buzzword to a full disruptor in what feels like just a few months. If CES was any indication, there is no sign of this slowing down.

Programmatic’s biggest promise was never automation for its own sake. It was accountability. But today, the industry is moving from reporting performance to steering performance.

When measurement systems are optimized for transactions rather than enterprise value, marketing is forced to justify spend instead of leading growth.

Political advertising has always evolved alongside media consumption. But the shift from linear television to streaming has introduced challenges that campaign managers are still working to navigate. While connected TV (CTV) promises precision and flexibility, the reality facing political advertisers today is far more complex and less standardized.

Viewers aren’t just watching streaming content anymore; they’re living in it. And while they’re streaming, they’re telling us something important about how they want to experience advertising. They want to do more than watch; they want to engage.

Programmatic media buying is expected to account for the majority of growth in the CTV market in 2026. This year alone, over 90% of CTV ad dollars were transacted through programmatic pipes. That data isn’t surprising. Programmatic has solved a real problem by helping brands reach TV viewers with targeted precision at much lower costs.

CTV isn’t an emerging channel anymore. It’s a fully established, essential part of the media mix. Advertisers now understand its reach, flexibility and ability to drive measurable outcomes.

Marketers used to wait for obvious signals, like a product page view, a cart add or a keyword search, before triggering campaigns. But in today’s fractured landscape, those signals can arrive too late.

Mobile apps are now at the center of the consumer relationship. They’re where customers browse, buy, stream, check balances and redeem loyalty rewards. However, app measurement remains one of the weakest links in modern marketing.

There’s a paradox at play in how marketers are adopting artificial intelligence.

Eighty-seven percent of US advertisers say they plan to increase AI usage over the next 12 months. But only 45% feel confident in their understanding of how AI-powered technologies work. That 42-point gap is an indicator of early friction in AI adoption.

As artificial intelligence transforms advertising analytics, many organizations are rushing to adopt AI tools, hoping for a “magic button” that instantly democratizes data access.

Performance marketers are turning to AI to navigate rising costs, data loss and increasingly complex shopper journeys – and it’s working. Creative is more personalized, targeting is sharper and optimization happens in real time.

Take a moment to think about how your day sounds. Maybe you start with the sound of your alarm, followed by a quick news briefing from your smart speaker while you scroll through emails. Your favorite podcast keeps you company during your commute. Your to-go playlist helps you get in the zone at the gym.

AI has raised the bar for what qualifies as “good data.”

The conversation is no longer about how much data marketers can collect but how well that data reflects reality. In this new era, success depends on data that is accurate, fresh, consented and interoperable – principles that ensure AI models learn from accuracy.

Every conversation about addressability eventually lands on the same word: fragmentation. But the real symptom of that fragmentation isn’t just operational complexity; it’s also declining match rates. That single number explains why marketers are struggling to make their data work.

Most of what’s sold as “contextual” today still runs on the same keyword logic we used a decade ago. The industry didn’t reinvent contextual targeting; it simply replaced one keyword with a cluster.