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Customer Base Analysis: An Industrial Purchase Process Application


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  • David C. Schmittlein

    (University of Pennsylvania)

  • Robert A. Peterson

    (University of Texas at Austin)

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    Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases to infer • the number of customers currently active, • how that number has changed over time, • which individual customers are most likely still active, • how much longer each is likely to remain an active customer, and • how many purchases can be expected from each during any future time period of interest. In this paper we empirically validate the model proposed by Schmittlein et al. In doing so, we provide one of the few applications of stochastic models to industrial purchase processes and industrial marketing decisions. Besides showing that the model does capture key aspects of the purchase process, we also present a more effective parameter estimation method and some results regarding sampling properties of the parameter estimates. Finally, we extend the model to explicitly incorporate dollar volume of past purchases. Our results indicate that this kind of customer base analysis can be both effective in predicting purchase patterns and in generating insights into how key customer groups differ. The link of both these benefits to industrial marketing decision making is also discussed.

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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 13 (1994)
    Issue (Month): 1 ()
    Pages: 41-67

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    Handle: RePEc:inm:ormksc:v:13:y:1994:i:1:p:41-67

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    Keywords: estimation and other statistical techniques; industrial marketing; measurement; promotion;


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    Cited by:
    1. Siddharth Singh & Sharad Borle & Dipak Jain, 2009. "A generalized framework for estimating customer lifetime value when customer lifetimes are not observed," Quantitative Marketing and Economics, Springer, vol. 7(2), pages 181-205, June.
    2. Eymann, Torsten (Ed.), 2009. "Tagungsband zum Doctoral Consortium der WI 2009," Bayreuth Reports on Information Systems Management 40, University of Bayreuth, Chair of Information Systems Management.
    3. Verhoef, P.C. & Donkers, B., 2001. "Predicting Customer Potential Value: an application in the insurance industry," ERIM Report Series Research in Management ERS-2001-01-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.
    4. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    5. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    6. Gary Lilien & Rajdeep Grewal & Douglas Bowman & Min Ding & Abbie Griffin & V. Kumar & Das Narayandas & Renana Peres & Raji Srinivasan & Qiong Wang, 2010. "Calculating, creating, and claiming value in business markets: Status and research agenda," Marketing Letters, Springer, vol. 21(3), pages 287-299, September.
    7. John Robst & Kimmarie McGOLDRICK, 1999. "The Measurement of Firm Information About Product Demand," Review of Industrial Organization, Springer, vol. 15(2), pages 149-163, September.
    8. 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.
    9. Mercedes Esteban Bravo & José M. Vidal-Sanz & Gökhan Yildirim, 2012. "Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a stochastic dynamic programming decomposition," Business Economics Working Papers wb121304, Universidad Carlos III, Departamento de Economía de la Empresa.
    10. W. Buckinx & D. Van Den Poel, 2003. "Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/178, Ghent University, Faculty of Economics and Business Administration.
    11. Netzer, Oded & Lattin, James M. & Srinivasan, V. "Seenu", 2007. "A Hidden Markov Model of Customer Relationship Dynamics," Research Papers 1904r, Stanford University, Graduate School of Business.
    12. 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.
    13. Reimer, Kerstin & Albers, Sönke, 2011. "Modeling Repeat Purchases in the Internet when RFM Captures Past Influence of Marketing," EconStor Preprints 50730, ZBW - German National Library of Economics.
    14. 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.
    15. 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.


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