A Dynamic Model of Purchase Timing with Application to Direct Marketing

TLDR

Predicting changes in individual customer behavior is key to success in direct marketing. The study develops a hierarchical Bayes model of customer interpurchase times using the generalized gamma distribution. The model incorporates cross‑sectional and temporal heterogeneity, with temporal effects modeled via a component mixture dependent on lagged covariates, and is applied to personal investment data to forecast when and if a customer will increase the time between purchases. The model’s predictions can serve as a managerial signal for firms to intervene and retain customers. Keywords: generalized gamma distribution, hierarchical Bayes, panel data.

Abstract

Abstract 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. Key Words: Generalized gamma distributionHierarchical BayesPanel data