IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v32y1983i3p249-259.html
   My bibliography  Save this article

An Investigation of the Assumptions of the Nbd Model as Applied to Purchasing at Individual Stores

Author

Listed:
  • R. Dunn
  • S. Reader
  • N. Wrigley

Abstract

This paper presents an empirical investigation of the assumptions of the NBD model in the context of purchasing at individual stores. A formal testing procedure against the assumption that individuals' inter‐purchase times follow an exponential distribution shows that the Poisson assumption of the NBD model holds for the majority of consumers. The theoretical negative binomial also fits closely for centrally located stores. For suburban stores the relevant population hypothesis is of some importance, and refitting the model to a local subset of the sample removes certain small but consistent discrepancies. The NBD model may thus play an important role in studies of urban consumer behaviour.

Suggested Citation

  • R. Dunn & S. Reader & N. Wrigley, 1983. "An Investigation of the Assumptions of the Nbd Model as Applied to Purchasing at Individual Stores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(3), pages 249-259, November.
    • Handle: RePEc:bla:jorssc:v:32:y:1983:i:3:p:249-259
    DOI: 10.2307/2347947
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2347947
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2347947?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
    ---><---

    Citations

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


    Cited by:

    1. Zhiqiang (Eric) Zheng & Peter Fader & Balaji Padmanabhan, 2012. "From Business Intelligence to Competitive Intelligence: Inferring Competitive Measures Using Augmented Site-Centric Data," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 698-720, September.
    2. Brian W. Gould, 1996. "Consumer Promotion and Purchase Timing: The Case of Cheese," Wisconsin-Madison Agricultural and Applied Economics Staff Papers 396, Wisconsin-Madison Agricultural and Applied Economics Department.
    3. Gould, Brian W., 1996. "Consumer Promotion And Purchase Timing: The Case Of Cheese," Staff Papers 12664, University of Wisconsin-Madison, Department of Agricultural and Applied Economics.
    4. Torsten Bornemann & Cornelia Hattula & Stefan Hattula, 2020. "Successive product generations: financial implications of industry release rhythm alignment," Journal of the Academy of Marketing Science, Springer, vol. 48(6), pages 1174-1191, November.
    5. Ehrenberg, Andrew S. C. & Uncles, Mark D. & Goodhardt, Gerald J., 2004. "Understanding brand performance measures: using Dirichlet benchmarks," Journal of Business Research, Elsevier, vol. 57(12), pages 1307-1325, December.
    6. N Wrigley & R Dunn, 1984. "Stochastic Panel-Data Models of Urban Shopping Behaviour: 1. Purchasing at Individual Stores in a Single City," Environment and Planning A, , vol. 16(5), pages 629-650, May.
    7. Jeongwen Chiang & Ching-Fan Chung & Emily Cremers, 2001. "Promotions and the pattern of grocery shopping time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(7), pages 801-819.
    8. Dawes, John, 2014. "Cigarette brand loyalty and purchase patterns: An examination using US consumer panel data," Journal of Business Research, Elsevier, vol. 67(9), pages 1933-1943.
    9. Brian GOULD, 1996. "Consumer Promotion And Purchase Timing: The Case Of Cheese," Staff Papers 396, University of Wisconsin Madison, AAE.
    10. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    11. Antonio Ladrón de Guevara, 2001. "A dynamic choice model of hybrid behavior in the attribute-space," Economics Working Papers 589, Department of Economics and Business, Universitat Pompeu Fabra.
    12. John D. Rice & Robert L. Strawderman & Brent A. Johnson, 2018. "Regularity of a renewal process estimated from binary data," Biometrics, The International Biometric Society, vol. 74(2), pages 566-574, June.
    13. Habel, Cullen & Lockshin, Larry, 2013. "Realizing the value of extensive replication: A theoretically robust portrayal of double jeopardy," Journal of Business Research, Elsevier, vol. 66(9), pages 1448-1456.
    14. N Wrigley & R Dunn, 1985. "Stochastic Panel-Data Models of Urban Shopping Behaviour: 4. Incorporating Independent Variables into the NBD and Dirichlet Models," Environment and Planning A, , vol. 17(3), pages 319-331, March.
    15. Govert Bijwaard, 2010. "Regularity in individual shopping trips: implications for duration models in marketing," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1931-1945.
    16. N Wrigley & R Dunn, 1984. "Stochastic Panel-Data Models of Urban Shopping Behaviour: 2. Multistore Purchasing Patterns and the Dirichlet Model," Environment and Planning A, , vol. 16(6), pages 759-778, June.
    17. Anesbury, Zachary William & Talbot, Danielle & Day, Chanel Andrea & Bogomolov, Tim & Bogomolova, Svetlana, 2020. "The fallacy of the heavy buyer: Exploring purchasing frequencies of fresh fruit and vegetable categories," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    18. Shi, Ruixia & Chen, Hongyu & Sethi, Suresh P., 2019. "A generalized count model on customers' purchases in O2O market," International Journal of Production Economics, Elsevier, vol. 215(C), pages 121-130.

    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:bla:jorssc:v:32:y:1983:i:3:p:249-259. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.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.