Bayesian Hierarchical Media Mix Model Incorporating Reach and Frequency Data

Mike Wurm
Ying Liu


Reach and frequency (R&F) is a core lever in the execution of ad campaigns, but it is not widely captured in the marketing mix models (MMMs) being fitted today due to the unavailability of accurate R&F metrics for some traditional media channels. Current practice usually uses impressions aggregated at regional level as inputs for MMMs, which does not take into account the fact that individuals can be exposed to an advertisement multiple times, and that the impact of an advertisement on an individual can change based on the number of times they are exposed. To address this limitation, we propose a R&F MMM which is an extension to Geo-level Bayesian Hierarchical Media Mix Modeling (GBHMMM) and is applicable when R&F data is available for at least one media channel. By incorporating R&F into MMM models, the new methodology is shown to produce more accurate estimates of the impact of marketing on business outcomes, and helps users optimize their campaign execution based on optimal frequency recommendations.