IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v29y2010i6p1086-1108.html
   My bibliography  Save this article

Customer-Base Analysis in a Discrete-Time Noncontractual Setting

Author

Listed:
  • Peter S. Fader

    (The Wharton School of the University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Bruce G. S. Hardie

    (London Business School, London NW1 4SA, United Kingdom)

  • Jen Shang

    (School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana 47405)

Abstract

Many businesses track repeat transactions on a discrete-time basis. These include (1) companies for whom transactions can only occur at fixed regular intervals, (2) firms that frequently associate transactions with specific events (e.g., a charity that records whether supporters respond to a particular appeal), and (3) organizations that choose to utilize discrete reporting periods even though the transactions can occur at any time. Furthermore, many of these businesses operate in a noncontractual setting, so they have a difficult time differentiating between those customers who have ended their relationship with the firm versus those who are in the midst of a long hiatus between transactions. We develop a model to predict future purchasing patterns for a customer base that can be described by these structural characteristics. Our beta-geometric/beta-Bernoulli (BG/BB) model captures both of the underlying behavioral processes (i.e., customers' purchasing while "alive" and time until each customer permanently "dies"). The model is easy to implement in a standard spreadsheet environment and yields relatively simple closed-form expressions for the expected number of future transactions conditional on past observed behavior (and other quantities of managerial interest). We apply this discrete-time analog of the well-known Pareto/NBD model to a data set on donations made by the supporters of a nonprofit organization located in the midwestern United States. Our analysis demonstrates the excellent ability of the BG/BB model to describe and predict the future behavior of a customer base.

Suggested Citation

  • Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, vol. 29(6), pages 1086-1108, 11-12.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:6:p:1086-1108
    DOI: 10.1287/mksc.1100.0580
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1100.0580
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.1100.0580?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
    ---><---

    References listed on IDEAS

    as
    1. V. Kumar & Rajkumar Venkatesan & Tim Bohling & Denise Beckmann, 2008. "—The Power of CLV: Managing Customer Lifetime Value at IBM," Marketing Science, INFORMS, vol. 27(4), pages 585-599, 07-08.
    2. Easton, G, 1980. "Stochastic models of industrial buying behaviour," Omega, Elsevier, vol. 8(1), pages 63-69.
    3. Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
    4. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    5. Danaher, Peter J. & Hardie, Bruce G.S., 2005. "Bacon With Your Eggs? Applications of a New Bivariate Beta-Binomial Distribution," The American Statistician, American Statistical Association, vol. 59, pages 282-286, November.
    6. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.
    7. Morrison, Donald G & Schmittlein, David C, 1988. "Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort? Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 165-166, April.
    8. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
    9. Piersma, Nanda & Jonker, Jedid-Jah, 2004. "Determining the optimal direct mailing frequency," European Journal of Operational Research, Elsevier, vol. 158(1), pages 173-182, October.
    10. Blattberg, Robert C. & Malthouse, Edward C. & Neslin, Scott A., 2009. "Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 157-168.
    11. Makoto Abe, 2009. "“Counting Your Customers” One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," Marketing Science, INFORMS, vol. 28(3), pages 541-553, 05-06.
    12. Donald G. Morrison & Richard D. H. Chen & Sandra L. Karpis & Kathryn E. A. Britney, 1982. "Modelling Retail Customer Behavior at Merrill Lynch," Marketing Science, INFORMS, vol. 1(2), pages 123-141.
    13. Morrison, Donald G & Schmittlein, David C, 1988. "Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort?," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 145-159, April.
    14. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
    3. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
    4. Glady, Nicolas & Lemmens, Aurélie & Croux, Christophe, 2015. "Unveiling the relationship between the transaction timing, spending and dropout behavior of customers," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 78-93.
    5. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.
    6. Peter J. Danaher & Michael S. Smith, 2011. "Modeling Multivariate Distributions Using Copulas: Applications in Marketing," Marketing Science, INFORMS, vol. 30(1), pages 4-21, 01-02.
    7. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
    8. Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
    9. Kinshuk Jerath & Peter S. Fader & Bruce G. S. Hardie, 2011. "New Perspectives on Customer "Death" Using a Generalization of the Pareto/NBD Model," Marketing Science, INFORMS, vol. 30(5), pages 866-880, September.
    10. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    11. Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
    12. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
    13. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    14. Peter S. Fader & Bruce G. S. Hardie, 2001. "Forecasting Repeat Sales at CDNOW: A Case Study," Interfaces, INFORMS, vol. 31(3_supplem), pages 94-107, June.
    15. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    16. Makoto Abe, 2015. "Deriving Customer Lifetime Value from RFM Measures:Insights into Customer Retention and Acquisition," CIRJE F-Series CIRJE-F-962, CIRJE, Faculty of Economics, University of Tokyo.
    17. Giang Trinh & Cam Rungie & Malcolm Wright & Carl Driesener & John Dawes, 2014. "Predicting future purchases with the Poisson log-normal model," Marketing Letters, Springer, vol. 25(2), pages 219-234, June.
    18. Huang, Chun-Yao, 2012. "To model, or not to model: Forecasting for customer prioritization," International Journal of Forecasting, Elsevier, vol. 28(2), pages 497-506.
    19. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
    20. Leslie Hannah & Makoto Kasuya, 2015. "Twentieth Century Enterprise Forms: Japan in Comparative Perspective," CIRJE F-Series CIRJE-F-966, CIRJE, Faculty of Economics, University of Tokyo.
    21. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.

    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:inm:ormksc:v:29:y:2010:i:6:p:1086-1108. 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.

    If CitEc recognized a bibliographic 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.

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