A Bivariate Timing Model of Customer Acquisition and Retention

Loading...
Thumbnail Image
Penn collection
Statistics Papers
Degree type
Discipline
Subject
customer acquisition
customer retention
customer retention management
stochastic models
Applied Statistics
Business
Business Administration, Management, and Operations
Business Analytics
Business and Corporate Communications
Management Information Systems
Management Sciences and Quantitative Methods
Statistical Models
Statistics and Probability
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Schweidel, David A
Fader, Peter S
Bradlow, Eric T
Contributor
Abstract

Two widely recognized components, central to the calculation of customer value, are acquisition and retention propensities. However, while extant research has incorporated such components into different types of models, limited work has investigated the kinds of associations that may exist between them. In this research, we focus on the relationship between a prospective customer's time until acquisition of a particular service and the subsequent duration for which he retains it, and examine the implications of this relationship on the value of prospects and customers. To accomplish these tasks, we use a bivariate timing model to capture the relationship between acquisition and retention. Using a split-hazard model, we link the acquisition and retention processes in two distinct yet complementary ways. First, we use the Sarmonov family of bivariate distributions to allow for correlations in the observed acquisition and retention times within a customer; next, we allow for differences across customers using latent classes for the parameters that govern the two processes. We then demonstrate how the proposed methodology can be used to calculate the discounted expected value of a subscription based on the time of acquisition, and discuss possible applications of the modeling framework to problems such as customer targeting and resource allocation.

Advisor
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Publication date
2008-01-01
Journal title
Marekting Science
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Recommended citation
Collection