Incrementality Proof Gap

The Incrementality Proof Gap is the distance between what an attribution model claims a channel produced and what a controlled test proves it produced. Multi-touch platforms reconstruct credit from observed touchpoints, so they inherit the same tracking gaps, cookie loss, and correlated-spend bias that make a channel look productive when it is merely present. Closing the gap requires running holdout or geo-lift experiments that calibrate the model rather than replace it. For agencies, the gap is a retention instrument: a client who sees a modeled ROAS number will eventually ask whether it is real, and the agency that already has a test result answers in one meeting. SegmentStream pairs cross-channel attribution with incrementality testing and marginal analytics for exactly this reason, while Measured builds causal media mix models calibrated by real-world experiments. Prescient AI goes further by quantifying halo effects that touchpoint models cannot see, which is where the largest unverified claims usually sit.