A Dynamic Model of Purchase Timing with Application to Direct Marketing

Predicting changes in individual customer behavior is an important element for success in any direct marketing activity. In this article we develop a hierarchical Bayes model of customer interpurchase times based on the generalized gamma distribution. The model allows for both cross-sectional and te...

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Bibliographic Details
Published in:Journal of the American Statistical Association 1999-06, Vol.94 (446), p.365-374
Main Authors: Allenby, Greg M., Leone, Robert P., Jen, Lichung
Format: Article
Language:English
Subjects:
Applications and Case Studies
Applied sciences
Bayesian analysis
Bayesian method
Consumer behaviour
Customers
Direct marketing
Dynamic modeling
Dynamic models
Exact sciences and technology
Firm modelling
Generalized gamma distribution
Hierarchical Bayes
Inference from stochastic processes
time series analysis
Investment
Marketing
Marketing strategies
Mathematical models
Mathematics
Modeling
Multilevel models
Operational research and scientific management
Operational research. Management science
Panel data
Parametric models
Portfolio theory
Predictive modeling
Probability and statistics
Sciences and techniques of general use
Statistics
Trade
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Summary:Predicting changes in individual customer behavior is an important element for success in any direct marketing activity. In this article we develop a hierarchical Bayes model of customer interpurchase times based on the generalized gamma distribution. The model allows for both cross-sectional and temporal heterogeneity, with the latter introduced through the component mixture model dependent on lagged covariates. The model is applied to personal investment data to predict when and if a specific customer will likely increase time between purchases. This prediction can be used managerially as a signal for the firm to use some type of intervention to keep that customer.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.1999.10474127