Systems and methods for machine learning modeling of mixed media marketing

Abstract

A system monitors impression data from a plurality of media channels including attributes of content presented on a respective media channel. The system inputs the impressions data and conversion data into a machine learning predictive model. The machine learning predictive model is trained by determining an impact of historical impression data on historical conversion data for each media channel. A machine learning predictive model incorporates an impressions/conversions predictive model that generates coefficients to measure attribution of different channel/campaign combinations to predicted conversions. The machine learning predictive model also incorporates an optimization model to effectively allocate marketing budget among various channels and campaigns in order to realize incremental conversions. The optimization model utilizes a constrained optimization framework based upon user-inputted budgets and budget constraints. Disclosed mixed media marketing predictive modeling provides a better understanding of budget allocation strategy for mixed media marketing factors.