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Counting Your Customers: Who-Are They and What Will They Do Next?

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Author Info

  • David C. Schmittlein

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

  • Donald G. Morrison

    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Richard Colombo

    (Department of Marketing, Tisch Hall, New York University, New York, New York 10002)

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    Abstract

    This article is concerned with counting and identifying those customers who are still active. The issue is important in at least three settings: monitoring the size and growth rate of a firm's ongoing customer base, evaluating a new product's success based on the pattern of trial and repeat purchases, and targeting a subgroup of customers for advertising and promotions. We develop a model based on the number and timing of the customers' previous transactions. This approach allows computation of the probability that any particular customer is still active. Several numerical examples are used to illustrate applications of the model.

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    File URL: http://dx.doi.org/10.1287/mnsc.33.1.1
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 33 (1987)
    Issue (Month): 1 (January)
    Pages: 1-24

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    Handle: RePEc:inm:ormnsc:v:33:y:1987:i:1:p:1-24

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    Related research

    Keywords: marketing; consumer behavior; poisson process; probability mixture models; new product introductions; market segmentation; brokerage firms;

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    Citations

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    Cited by:
    1. Mihai TICHINDELEAN, 2013. "Models Used for Measuring Customer Engagement," Expert Journal of Marketing, Sprint Investify, vol. 1(1), pages 38-49.
    2. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    3. Makoto Abe, 2006. ""Counting Your Customers" One by One: An Individual Level RF Analysis Based on Consumer Behavior Theory," CIRJE F-Series CIRJE-F-408, CIRJE, Faculty of Economics, University of Tokyo.
    4. Singh, Shweta & Murthi, B.P.S. & Steffes, Erin, 2013. "Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management," European Journal of Operational Research, Elsevier, vol. 224(2), pages 425-434.
    5. Bolton, R.N. & Lemo, K.N. & Verhoef, P.C., 2002. "The Theoretical Underpinnings of Customer Asset Management," ERIM Report Series Research in Management ERS-2002-80-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Audzeyeva, Alena & Summers, Barbara & Schenk-Hoppé, Klaus Reiner, 2012. "Forecasting customer behaviour in a multi-service financial organisation: A profitability perspective," International Journal of Forecasting, Elsevier, vol. 28(2), pages 507-518.
    7. 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.
    8. LEE, Janghyuk & KERBACHE, Laoucine, 2004. "Internet media planning : an optimization model," Les Cahiers de Recherche 806, HEC Paris.
    9. Chao Wang & Ilaria Dalla Pozza, 2014. "The antecedents of customer lifetime duration and discounted expected transactions: Discrete-time based transaction data analysis," Working Papers 2014-203, Department of Research, Ipag Business School.
    10. Bitran, Gabriel R. & Rocha e Oliveira, Paulo & Schilkrut, Ariel, 2008. "Managing customer relationships through price and service quality," IESE Research Papers D/750, IESE Business School.
    11. 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.
    12. Bruhn, Manfred & Georgi, Dominik & Hadwich, Karsten, 2008. "Customer equity management as formative second-order construct," Journal of Business Research, Elsevier, vol. 61(12), pages 1292-1301, December.
    13. Hoppe, Daniel & Wagner, Udo, 2014. "The role of lifetime activity cues in customer base analysis," Journal of Business Research, Elsevier, vol. 67(5), pages 983-989.
    14. Bijmolt, Tammo H.A. & Blömeke, Eva & Clement, Michel, 2010. "Should they stay or should they go? Reactivation and Termination of Low-Tier Customers: Effects on Satisfaction, Word-of-Mouth, and Purchases," Research Report 10008, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    15. Makoto Abe, 2009. "Customer Lifetime Value and RFM Data: Accounting Your Customers: One by One," CIRJE F-Series CIRJE-F-616, CIRJE, Faculty of Economics, University of Tokyo.
    16. Naik, P. & Piersma, N., 2002. "Understanding the role of marketing communications in direct marketing," Econometric Institute Research Papers EI 2002-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    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. D. F. Benoit & D. Van Den Poel, 2009. "Benefits of Quantile Regression for the Analysis of Customer Lifetime Value in a Contractual Setting: An Application in Financial Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/551, Ghent University, Faculty of Economics and Business Administration.

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