• by Shirley Marschall , February 14, 2025

The digital advertising industry has spent years fine-tuning audience targeting, identity solutions, and AI-driven optimization. We've built complex algorithms to deliver hyper-relevant ads, navigate privacy regulations, and maximize efficiency. But there's one glaring issue we still haven't solved: ad frequency.

We've all seen it: the same ad follows you for weeks across websites, social platforms, and streaming services. Whether it's a perfectly targeted offer or not, after the tenth (or twentieth) time, it's just plain annoying. And it's not just a consumer frustration. It's a waste of ad spend that directly impacts performance and brand perception.

The Fragmentation Problem: Too Many Walled Gardens, Too Little Coordination

In theory, frequency capping should be simple: Just set a limit, and once a user reaches it, stop serving the ad. In reality, fragmentation makes this nearly impossible. Brands don't operate in a single ecosystem. They buy ads across multiple demand-side platforms, social platforms, retail media networks, and CTV providers, each with its own isolated frequency controls.

CTV & streaming: As ad-supported streaming services expand, the lack of unified frequency controls means the same ad can run back-to-back within a single streaming session.

Retail media networks: These platforms are growing exponentially, but most operate independently, meaning there's little oversight of how often an ad appears to the same user across different channels.

Cross-platform buying: A campaign might run across Google, The Trade Desk, Meta, Amazon, and direct publisher deals -- each managing frequency separately, leading to overexposure and wasted budget.

Why Hasn't Frequency Capping Been a Priority?

The industry has been laser-focused on identity solutions, targeting precision, and AI-driven ad delivery, but frequency management remains an afterthought.

But it’s one that needs immediate attention. Why? Because controlling ad frequency across fragmented platforms is a far bigger technical challenge than most realize.

No universal identity framework: The demise of third-party cookies and rise of alternative ID solutions (UID 2.0, Google's Privacy Sandbox, etc.) make it harder to track unique users consistently.

Competing business interests: Publishers and platforms benefit from more impressions, not fewer. Limiting frequency might optimize spend for brands, but it doesn't maximize revenue for platforms.

Lack of standardization: While the Interactive Advertising Bureau Tech Lab has proposed frequency-capping standards, widespread adoption is slow, and interoperability between platforms remains a challenge.

Can AI Fix This? The Path Forward

If AI can optimize targeting, why not apply it to frequency capping? Some companies are already exploring predictive AI models to balance reach, frequency, and performance across platforms. But for AI-driven frequency management to work at scale, we need:

Cross-platform collaboration: Ad tech vendors, agencies, and brands must push for interoperable frequency-capping standards.

Greater transparency: Advertisers need clearer reporting on frequency across the entire media mix.

Industry-wide prioritization: We don't need another isolated tool. We do need a fundamental shift in how we approach campaign planning.

The industry talks about attention metrics, incrementality, and AI-driven optimization --  but what good is any of it if consumers are bombarded with the same ad 50 times?

It's time to make frequency capping a strategic priority, not an afterthought.