Topic Modeling of Market Responses for Large-Scale Transaction Data

Large-scale databases in marketing track multiple consumers across multiple product categories. A challenge in modeling these data is the resulting size of the data cube, which often has thousands of consumers and thousands of choice alternatives with prices and merchandising variables changing over time. We develop a heterogeneous topic model for these data, and employ variational Bayes techniques for estimation that are shown to be accurate in a simulation study. We find the model to be highly scalable and useful for identifying effective marketing variables for consumers, including infrequent purchasers.

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