INTEGRATION OF MULTIPLE PRIORS INTO MEDIA MIX MODELING

Abstract

In integrating multiple priors into media mix modeling, a processing device receives multiple priors that each includes contribution share for one or more marketing channels, a time period, and a geographical region. A machine-learning model generates a transferred model for each prior by performing hyperparameter tuning of a base model based on the corresponding contribution share. The processing device uses the transferred models to generate a combined prior that includes a proportional contribution of the multiple priors. The machine-learning model then generates a combined model by performing hyperparameter tuning of the base model using the combined prior. Marketers can utilize the combined model to assess the contribution of different marketing efforts and perform budget planning.

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