Counting your customers: who are they and what will they do next?
Management Science
Modeling Browsing Behavior at Multiple Websites
Marketing Science
Marketing Models of Service and Relationships
Marketing Science
How to Compute Optimal Catalog Mailing Decisions
Marketing Science
The MCMC Approach for Solving the Pareto/NBD Model and Possible Extensions
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Customer-Base Analysis in a Discrete-Time Noncontractual Setting
Marketing Science
Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating Direct Marketing Activity into Latent Attrition Models
Marketing Science
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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.