IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v29y2011i2p319-326.html
   My bibliography  Save this article

A Comparison of Sales Response Predictions From Demand Models Applied to Store-Level versus Panel Data

Author

Listed:
  • Rick L. Andrews
  • Imran S. Currim
  • Peter S. H. Leeflang

Abstract

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

Suggested Citation

  • Rick L. Andrews & Imran S. Currim & Peter S. H. Leeflang, 2011. "A Comparison of Sales Response Predictions From Demand Models Applied to Store-Level versus Panel Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 319-326, April.
  • Handle: RePEc:taf:jnlbes:v:29:y:2011:i:2:p:319-326
    DOI: 10.1198/jbes.2010.07225
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1198/jbes.2010.07225
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1198/jbes.2010.07225?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
    2. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.
    3. Yu Xia & Ali Arian & Sriram Narayanamoorthy & Joshua Mabry, 2023. "RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation," Papers 2312.14095, arXiv.org.
    4. Choi, Sunhee & Duhan, Dale F. & Dass, Mayukh, 2023. "The influence of corporate social responsibility appeals (CSRAs) on product sales: Which appeal types perform better?," Journal of Retailing, Elsevier, vol. 99(1), pages 115-135.
    5. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    6. Leeflang, Peter, 2011. "Paving the way for “distinguished marketing”," International Journal of Research in Marketing, Elsevier, vol. 28(2), pages 76-88.
    7. Cuellar, Steven S. & Brunamonti, Marco, 2014. "Retail channel price discrimination," Journal of Retailing and Consumer Services, Elsevier, vol. 21(3), pages 339-346.

    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:taf:jnlbes:v:29:y:2011:i:2:p:319-326. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UBES20 .

    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.