<b>Why frequency caps leak across the open programmatic graph</b>

<b>Why frequency caps leak across the open programmatic graph</b>

A frequency cap (limit how many times one user sees your ad) sounds like a counter. In open programmatic it's closer to an estimate, and the leak is structural, not a bug.

1. Capping requires stable identity. When match rate is partial and identifiers fragment across exchanges, the same person can appear as several distinct users to your DSP.
2. Each apparent identity gets its own cap allowance, so a user who resolves to three IDs can see your ad three times the intended limit.
3. Cross-device and in-app environments amplify this; the deterministic link between a phone and a desktop is often missing.

The consequence is overexposure on your heaviest users and wasted impressions that also depress performance through ad fatigue — the opposite of what the cap was meant to prevent.

Mitigation leans on a unified identity layer and, where deterministic IDs are absent, probabilistic stitching with known error rates. But you should treat your cap as a soft target with a leakage percentage, not a hard ceiling.

<b>Why it matters:</b> if you report a 3x cap but your true distribution has a tail of users seeing your ad 8–10 times, your reach and effectiveness numbers are both wrong. Measure realized frequency from log-level impression data against your set cap; the gap is your identity fragmentation, quantified.