The MMM-Incrementality Bridge: Reconciling Top-Down and Bottom-Up Attribution

Media Mix Modeling and multi-touch attribution have coexisted as separate disciplines with contradictory outputs — MMM says paid search drives 15% of revenue, MTA says 40%. This paper presents the MMM-Incrementality Bridge, a Bayesian reconciliation framework that jointly estimates channel contributions using both aggregate MMM signals and individual-level MTA data. The bridge produces unified spend recommendations with calibrated uncertainty, resolving the top-down vs. bottom-up attribution paradox. Synthesizes Robinson (2026a) through (2026h) into a unified measurement framework.