IDEAS home Printed from https://ideas.repec.org/a/kap/mktlet/v15y2004i1p37-60.html
   My bibliography  Save this article

Statistical Inference Using Stochastic Switching Models for the Discrimination of Unobserved Display Promotion from POS Data

Author

Listed:
  • Tadahiko Sato
  • Tomoyuki Higuchi
  • Genshiro Kitagawa

Abstract

The execution of price and/or display promotion has a significant effect on the sales of a brand sold in a supermarket. Information on price and/or sales is available from POS data. However, unless an investigator collects information on the execution of display promotions from every retail store, such information is unavailable. This paper presents a method of identifying whether display promotion has been executed without having to visit individual stores. We treat the execution/non-execution of a display promotion as a state variable. An unknown stationary probability matrix is assumed to describe the probability of a transition between states. Each state is characterized by a different stationary time series model with unknown parameters. The objective of the analysis is to identify the model and to assign a probability model for each state at each time instant. Finally, we provide a high precision estimator of a past execution/non-execution of a display promotion based on the proposed model.

Suggested Citation

  • Tadahiko Sato & Tomoyuki Higuchi & Genshiro Kitagawa, 2004. "Statistical Inference Using Stochastic Switching Models for the Discrimination of Unobserved Display Promotion from POS Data," Marketing Letters, Springer, vol. 15(1), pages 37-60, February.
    • Handle: RePEc:kap:mktlet:v:15:y:2004:i:1:p:37-60
    as

    Download full text from publisher

    File URL: http://journals.kluweronline.com/issn/0923-0645/contents
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tomohiro Ando, 2008. "Measuring the baseline sales and the promotion effect for incense products: a Bayesian state-space modeling approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 763-780, December.
    2. Taku Moriyama & Masashi Kuwano, 2022. "Causal inference for contemporaneous effects and its application to tourism product sales data," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 250-260, September.

    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:kap:mktlet:v:15:y:2004:i:1:p:37-60. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.