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"Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model

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  • Makoto Abe

    (Faculty of Economics, University of Tokyo)

Abstract

This research extends a Pareto/NBD model of customer-base analysis using a hierarchical Bayesian (HB) framework to suit today's customized marketing. The proposed HB model presumes three tried and tested assumptions of Pareto/NBD models: (1) a Poisson purchase process, (2) a memoryless dropout process (i.e., constant hazard rate), and (3) heterogeneity across customers, while relaxing the independence assumption of the purchase and dropout rates and incorporating customer characteristics as covariates. The model also provides useful output for CRM, such as a customer-specific lifetime and survival rate, as by-products of the MCMC estimation. Using three different types of databases --- music CD for e-commerce, FSP data for a department store and a music CD chain, the HB model is compared against the benchmark Pareto/NBD model. The study demonstrates that recency-frequency data, in conjunction with customer behavior and characteristics, can provide important insights into direct marketing issues, such as the demographic profile of best customers and whether long-life customers spend more.

Suggested Citation

  • Makoto Abe, 2008. ""Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model," CIRJE F-Series CIRJE-F-537, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2008cf537
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    References listed on IDEAS

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    1. Baohong Sun, 2006. "—Technology Innovation and Implications for Customer Relationship Management," Marketing Science, INFORMS, vol. 25(6), pages 594-597, 11-12.
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    3. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    4. 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.
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    Cited by:

    1. 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.
    2. 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.

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