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Fusion Modeling
Book chapter

Fusion Modeling

Elea McDonnell Feit and Eric T. Bradlow
Handbook of Market Research, pp 147-180
03 Dec 2021
DOI: https://doi.org/10.1007/978-3-319-57413-4_9

Abstract

Bayesian Data augmentation Data fusion Markov-chain Monte Carlo Missing data
This chapter introduces readers to applications of data fusion in marketing from a Bayesian perspective. We will discuss several applications of data fusion including the classic example of combining data on media viewership for one group of customers with data on category purchases for a different group, a very common problem in marketing. While many missing data approaches focus on creating “fused” data sets that can be analyzed by others, we focus on the overall inferential goal, which, for this classic data fusion problem, is to determine which media outlets attract consumers who purchase in a particular category and are therefore good targets for advertising. The approach we describe is based on a common Bayesian approach to missing data, using data augmentation within MCMC estimation routines. As we will discuss, this approach can also be extended to a variety of other data structures including mismatched groups of customers, data at different levels of aggregation, and more general missing data problems that commonly arise in marketing. This chapter provides readers with a step-by-step guide to developing Bayesian data fusion applications, including an example fully worked out in the Stan modeling language. Readers who are unfamiliar with Bayesian analysis and MCMC estimation may benefit by reading the chapter in this handbook on Bayesian Models first.

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