IDEAS home Printed from
   My bibliography  Save this article

Models for purchase frequency


  • Nadarajah, Saralees
  • Kotz, Samuel


Purchase frequency modeling began with the pioneering work of Ehrenberg [Ehrenberg, A.S.C., 1959. The pattern of consumer purchases. Applied Statistics 8, 26-41]. This note provides an extension of this work. A collection of some seventeen flexible distributions is discussed for purchase frequency modeling. The corresponding estimation procedures are derived by the method of moments and the method of maximum likelihood. An application is illustrated to a consumer purchasing data used by Ehrenberg.

Suggested Citation

  • Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
  • Handle: RePEc:eee:ejores:v:192:y:2009:i:3:p:1014-1026

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    1. Neeraj Arora & Greg M. Allenby & James L. Ginter, 1998. "A Hierarchical Bayes Model of Primary and Secondary Demand," Marketing Science, INFORMS, pages 29-44.
    2. Brannas, Kurt & Rosenqvist, Gunnar, 1994. "Semiparametric estimation of heterogeneous count data models," European Journal of Operational Research, Elsevier, vol. 76(2), pages 247-258, July.
    3. Wu, Couchen & Chen, Hsiu-Li, 2000. "Counting your customers: Compounding customer's in-store decisions, interpurchase time and repurchasing behavior," European Journal of Operational Research, Elsevier, vol. 127(1), pages 109-119, November.
    4. Rinus Haaijer & Michel Wedel & Marco Vriens & Tom Wansbeek, 1998. "Utility Covariances and Context Effects in Conjoint MNP Models," Marketing Science, INFORMS, vol. 17(3), pages 236-252.
    5. Pedrick, James H. & Zufryden, Fred S., 1994. "An examination of consumer heterogeneity in a stochastic model of consumer purchase dynamics with explanatory variables," European Journal of Operational Research, Elsevier, vol. 76(2), pages 259-272, July.
    6. Asim Ansari & Nicholas Economides & Avijit Ghosh, 1994. "Competitive Positioning in Markets with Nonuniform Preferences," Marketing Science, INFORMS, vol. 13(3), pages 248-273.
    7. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    8. Aradhna Krishna, 1994. "The Impact of Dealing Patterns on Purchase Behavior," Marketing Science, INFORMS, vol. 13(4), pages 351-373.
    9. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
    10. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
    11. Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, pages 229-250.
    12. Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, pages 113-131.
    13. Whitmore, G. A., 1976. "Management applications of the inverse gaussian distribution," Omega, Elsevier, vol. 4(2), pages 215-223.
    14. Ajay Kalra & Mengze Shi, 2001. "Designing Optimal Sales Contests: A Theoretical Perspective," Marketing Science, INFORMS, pages 170-193.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
    2. Chen, Zhiyuan & Liang, Xiaoying & Xie, Lei, 2016. "Inter-temporal price discrimination and satiety-driven repeat purchases," European Journal of Operational Research, Elsevier, vol. 251(1), pages 225-236.


    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:eee:ejores:v:192:y:2009:i:3:p:1014-1026. 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: (Dana Niculescu). General contact details of provider: .

    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.