IDEAS home Printed from https://ideas.repec.org/p/toh/tmarga/114.html
   My bibliography  Save this paper

A Large-Scale Marketing Model using Variational Bayes Inference for Sparse Transaction Data

Author

Listed:
  • Tsukasa Ishigaki
  • Nobuhiko Terui
  • Tadahiko Sato
  • Greg M. Allenby

Abstract

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 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.

Suggested Citation

  • Tsukasa Ishigaki & Nobuhiko Terui & Tadahiko Sato & Greg M. Allenby, 2014. "A Large-Scale Marketing Model using Variational Bayes Inference for Sparse Transaction Data," TMARG Discussion Papers 114, Graduate School of Economics and Management, Tohoku University.
  • Handle: RePEc:toh:tmarga:114
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10097/56671
    Download Restriction: no

    References listed on IDEAS

    as
    1. 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.
    2. Grimmer, Justin, 2011. "An Introduction to Bayesian Inference via Variational Approximations," Political Analysis, Cambridge University Press, vol. 19(01), pages 32-47, December.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:toh:tmarga:114. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tohoku University Library). General contact details of provider: http://edirc.repec.org/data/fetohjp.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.