article

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

Author:

Makoto AbeAuthors Info & Claims

Marketing Science, Volume 28, Issue 3

Pages 541 - 553

Published: 01 May 2009 Publication History

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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https://dl.acm.org/doi/10.1287/mksc.2020.1254
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cover image Marketing Science

Marketing Science Volume 28, Issue 3

May 2009

13 pages

ISSN:1526-548X

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Publisher

INFORMS

Linthicum, MD, United States

Publication History

Published: 01 May 2009

Accepted: 07 December 2007

Received: 29 June 2006

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Cited By

Bachmann PMeierer MNäf J(2021)The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base AnalysisMarketing Science10.1287/mksc.2020.125440:4(783-809)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1287/mksc.2020.1254
Gopalakrishnan ABradlow EFader P(2017)A Cross-Cohort Changepoint Model for Customer-Base AnalysisMarketing Science10.1287/mksc.2016.100736:2(195-213)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1287/mksc.2016.1007
Park CPark Y(2016)Investigating Purchase Conversion by Uncovering Online Visit PatternsMarketing Science10.1287/mksc.2016.099035:6(894-914)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1287/mksc.2016.0990
Platzer MReutterer T(2016)Ticking Away the MomentsMarketing Science10.1287/mksc.2015.096335:5(779-799)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1287/mksc.2015.0963
Chen XGeng RCai S(2015)Predicting microblog users' lifetime activities - A user-based analysisElectronic Commerce Research and Applications10.1016/j.elerap.2014.06.00114:3(150-168)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.elerap.2014.06.001
Tian YYe ZYan YSun M(2015)A practical model to predict the repeat purchasing pattern of consumers in the C2C e-commerceElectronic Commerce Research10.1007/s10660-015-9201-815:4(571-583)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s10660-015-9201-8
Schweidel DPark YJamal Z(2014)A Multiactivity Latent Attrition Model for Customer Base AnalysisMarketing Science10.1287/mksc.2013.083233:2(273-286)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1287/mksc.2013.0832
Schweidel DKnox G(2013)Incorporating Direct Marketing Activity into Latent Attrition ModelsMarketing Science10.1287/mksc.2013.078132:3(471-487)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1287/mksc.2013.0781
Nagano SIchikawa YTakaya NUchiyama TAbe MGrossman RUthurusamy RDhillon IKoren Y(2013)Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival dataProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2487575.2487590(668-676)Online publication date: 11-Aug-2013
https://dl.acm.org/doi/10.1145/2487575.2487590
Bemmaor AGlady N(2012)Modeling Purchasing Behavior with Sudden “Death”Management Science10.1287/mnsc.1110.146158:5(1012-1021)Online publication date: 1-May-2012
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