A Hierarchical Bayesian Approach to Improve Media Mix Models Using Category Data


One of the major problems in developing media mix models is that the data that is generally available to the modeler lacks sufficient quantity and information content to reliably estimate the parameters in a model of even moderate complexity. Pooling data from different brands within the same product category provides more observations and greater variability in media spend patterns. We either directly use the results from a hierarchical Bayesian model built on the category dataset, or pass the information learned from the category model to a brand-specific media mix model via informative priors within a Bayesian framework, depending on the data sharing restriction across brands. We demonstrate using both simulation and real case studies that our category analysis can improve parameter estimation and reduce uncertainty of model prediction and extrapolation.