Privacy-Safe Marketing Mix Modeling and Budget Optimization Under Identifier Loss: A Controlled Simulation Study - Bai, Jingwen; Wu, Qiyou (International Journal of Electronics and Communications Systems, 2026)
This study aims to examine whether aggregate marketing mix (MMM) modeling can support the measurement and budget planning of this system when user-level data are unavailable. A controlled simulation study applied specific implementations of ridge regression, adstock, and the Hill transformation to the weekly Robyn simulation dataset, comparing a regulated linear baseline with geometric-adstock–Hill and Weibull-adstock–Hill specifications on a shared chronological holdout set. The linear model provided the strongest holdout forecast, whereas the nonlinear specifications represented carryover and diminishing marginal returns needed for decision analysis. The geometric model shifted spending toward channels with higher fitted marginal returns, although the predicted gain depended materially on the allocation bounds. Across repeated stress-test runs, attribution–MMM disagreement increased as channel measurability was reduced. These findings explain why forecasting accuracy, channel attribution, and allocation utility are interrelated yet distinct components in measuring communication systems, and provide an integrated framework for evaluating all three simultaneously under conditions of partial observability, while limiting conclusions to simulation and observational evidence.