IDEAS home Printed from https://ideas.repec.org/p/toh/dssraa/18.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," DSSR Discussion Papers 18, Graduate School of Economics and Management, Tohoku University.
    • Handle: RePEc:toh:dssraa:18
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10097/64994
    Download Restriction: no
    ---><---

    More about this item

    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:dssraa:18. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

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

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.