Fusing Aggregate and Disaggregate Data with an Application to Multi-Platform Media Consumption

Abstract

As firms collect greater amounts of data about their customers, from an ever broader set of "touchpoints," a new set of methodological challenges arise. Data from the various platforms are often collected at differing levels of aggregation, and it is not clear how to merge these data sources in order to draw meaningful inferences about customer-level behavior patterns. In this paper, we provide a method that firms can use, based on readily available data, to gauge and monitor multi-platform media usage. The key innovation in the method is a Bayesian data-fusion approach that allows us to combine individual-level usage data (readily available for most digital platforms) with aggregated data on usage over time (typically available for traditional platforms). This method allows us to disentangle the intra-day correlations among platforms (i.e. usage of one platform versus another on a given day) from longer-term correlations across users (i.e. heavy/light usage of multiple platforms over time). We conclude with a discussion of how this method can be used in a variety of marketing contexts for which data is now becoming readily available, such as gauging the interplay between online and brick-and-mortar purchasing behavior.

Keywords: data fusion, Bayesian multivariate model, multi-platform behavior, media usage