A Large-Scale Marketing Model using Variational Bayes Inference for Sparse Transaction Data
AbstractLarge-scale databases in marketing track multiple consumers across multiple product categories. A challenge in modeling these data is the resulting size of the data matrix, 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 Monte Carlo simulation study. We find the model to be highly scalable and useful for identifying effective marketing variables for different consumers, and for predicting the choices of infrequent purchasers.
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Bibliographic InfoPaper provided by Graduate School of Economics and Management, Tohoku University in its series TMARG Discussion Papers with number 114.
Length: 31 pages
Date of creation: Jan 2014
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-04-18 (All new papers)
- NEP-ECM-2014-04-18 (Econometrics)
- NEP-MKT-2014-04-18 (Marketing)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Braun, Michael & McAuliffe, Jon, 2010. "Variational Inference for Large-Scale Models of Discrete Choice," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 324-335.
- Prasad Naik & Michel Wedel & Lynd Bacon & Anand Bodapati & Eric Bradlow & Wagner Kamakura & Jeffrey Kreulen & Peter Lenk & David Madigan & Alan Montgomery, 2008. "Challenges and opportunities in high-dimensional choice data analyses," Marketing Letters, Springer, vol. 19(3), pages 201-213, December.
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