What is Probabilistic Attribution?

Probabilistic attribution uses statistical modeling, based on signals like IP address, device type, OS version, timestamp, and behavioral patterns, to infer which ad a user likely saw before installing an app, when a deterministic identifier (like IDFA) is unavailable.

Prior to ATT, user-level attribution was largely deterministic: a unique device ID matched the click to the install with certainty. After ATT reduced IDFA availability, probabilistic methods became essential for attributing the majority of iOS installs from opted-out users.

Probabilistic attribution is inherently less accurate than deterministic attribution, it operates on probability rather than fact. However, at scale, well-calibrated probabilistic models can provide reasonable accuracy for campaign-level budget allocation decisions even when user-level precision is unavailable.