Why Enabling Equal Attribution Window for CTV

2 min read·Jun 13, 2025

I’d like to offer some context on why enabling an equal attribution window for CTV is strongly recommended—particularly when you're working with a mobile measurement partner (MMP).

1. CTV drives incrementality without clicks
CTV is fundamentally a view-based channel. Unlike performance networks that are click-heavy, CTV’s strength lies in its ability to reach users in a lean-back, high-attention environment. These impressions often precede installs that later get incorrectly credited to last-click sources—even if the CTV impression did the heavy lifting.By not enabling the equal attribution window, you’re essentially giving full credit to click networks that may have shown a low-effort retargeting ad after the user already made a decision influenced by the CTV experience. This leads to underreporting CTV's actual contribution and overvaluing lower-funnel noise.

2. Probabilistic measurement ≠ unreliable measurement
While probabilistic attribution (used for CTV) is not deterministic by nature, that doesn’t mean it’s less accurate or less valuable. Probabilistic models are often more reflective of actual influence, particularly in environments like CTV where IPs are stable and intent signals are clear.In contrast, deterministic click-based attribution often misleads by attributing credit to ads shown after the user made up their mind—a false sense of precision.Industry experts increasingly agree that we are evolving towards probabilistic measurement as a necessity, not a compromise. Privacy changes, cross-device behavior, and signal loss have made deterministic models less reflective of true influence.

Enabling equal attribution priority gives you:

  • A truer sense of CTV’s role in driving installs and upper-funnel awareness.
  • The ability to optimize spend based on full-funnel contribution, not just what gets last-click credit.
  • A more accurate picture of where to reinvest—especially useful when performance-based budgets are under scrutiny

Let us know if you'd like help adjusting your attribution settings to reflect this approach.

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