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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

    ()
    (Graduate School of Economics, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan)

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    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.

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    File URL: http://dx.doi.org/10.1287/mksc.1090.0502
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 28 (2009)
    Issue (Month): 3 (05-06)
    Pages: 541-553

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    Handle: RePEc:inm:ormksc:v:28:y:2009:i:3:p:541-553

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    Related research

    Keywords: CRM; direct marketing; customer lifetime; Bayesian method; MCMC;

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    Cited by:
    1. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    2. 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.
    3. 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.
    4. 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.

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