CTV’s Frequency Problem Isn’t Fixed … But It Can Be
CTV was supposed to fix this. With granular targeting, real-time delivery, and household-level data, CTV infrastructure promised a level of precision that linear TV could never match. However one of advertising’s oldest problems—frequency—has proven surprisingly resistant to all that innovation.
Despite having more tools, data, and control, overexposure remains a defining flaw of the CTV experience. Viewers still see the same ads too often. Advertisers still struggle to manage delivery across different platforms. The inefficiencies this creates are not a result of outdated technology, but a consequence of endemic fragmentation.
A Fragmented Ecosystem with No Unified View
Frequency mismanagement is a structural problem rooted in fragmentation. CTV campaigns typically run across a combination of direct publisher buys, programmatic platforms, and often linear TV extensions. Each of these channels applies its own frequency caps and generates its own reports, but none provide visibility into delivery beyond their own walls.
This siloed execution leads to undetected duplication. A household capped at five impressions in one DSP might receive five more through another, plus additional exposures via a direct buy or an app’s owned-and-operated inventory. Buyers may apply reasonable limits within each environment, but without a unified view, total exposure frequently exceeds intended thresholds.
Aggregate data from recent campaigns confirms how pervasive this imbalance can be. In several large-scale analyses, only 8% to 15% of households were found to be over their intended frequency limit—yet those households accounted for between 42% and 60% of total impressions. That’s nearly half of all delivery concentrated on a small, already saturated audience.
Standard reporting metrics often rely on averages, with many campaigns showing a frequency of three or four impressions per household. But averages can be misleading—they obscure how impressions are actually distributed. In reality, a small group of households may be served far more ads than intended, while large portions of the audience are underexposed or unreached. With no unified view, marketers are flying blind. These gaps are rarely visible in aggregate metrics and often go unaddressed until results fall short.
Timing further constrains remediation. Frequency insights are typically surfaced in post-campaign reporting, well after spend has cleared and audiences have been saturated. This retroactive model limits marketers to reactive adjustments, forcing each campaign to start from the same limited vantage point as the one before it.
Why It Matters
These operational blind spots have tangible performance costs. Overserved households exhibit diminishing returns and are more likely to disengage. Ad fatigue sets in, brand recall erodes, and dollars are wasted. Meanwhile, underexposed segments—those still within optimal exposure ranges—remain untapped.
Marketers with real-time insight into frequency distribution can shift delivery dynamically. By suppressing overexposed audiences and reallocating impressions toward underexposed or net-new households they can improve performance without expanding budget. In recent campaigns that employed this approach, advertisers saw CPM reductions of up to 35% and incremental reach gains of 28% to 41%.
Enabling that kind of precision requires overcoming fragmentation with system-level interoperability.
What Solving Frequency Actually Takes
Solving frequency starts with cross-platform identity resolution. Impression delivery must be mapped to a persistent household ID across all inventory sources—programmatic, direct-sold, O&O, and linear. Without a consistent identifier, frequency exposure remains fragmented and untrackable.
That unified identity graph must then be integrated into the bidding layer. To be effective, frequency management must happen before the bid, not after the campaign. If a household has already reached its cap across other sources, the bid must be suppressed. If the household remains within efficient exposure limits, the bid proceeds. This is what turns frequency from a measurement artifact into an optimization input.
Collaboration & Interoperability
Solving for frequency demands tight coordination across the media supply chain. DSPs need to ingest impression-level data from ad servers and supply-side partners to inform smarter bidding decisions. Publishers must share exposure signals that are identity-resolved and actionable. Ad servers play a central role in connecting the dots, and the broader ecosystem needs to support common data structures that enable cross-platform suppression logic to function in flight.
These aren’t speculative ideas. Recent deployments of frequency optimization at scale have shown clear impact: campaigns achieved up to 57% reduction in ineffective impressions, minimized overexposure across publisher overlaps, and improved audience quality by prioritizing net-new households. As adoption expands and integrations deepen, frequency management will shift from a reactive pain point to a proactive driver of media value.
This is one of advertising’s oldest problems, and thanks to fragmentation, it has become one of its most modern ones. Solving it today depends on operational alignment: identity resolution that spans platforms, exposure data that updates in real time, and workflows that make frequency actionable before a bid is placed.
[Editor's note: This is a contributed article from Innovid. Streaming Media accepts vendor bylines based solely on their value to our readers.]
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