"Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model
"Counting Your Customers" One by One: A Hierarchical Bayes Extension to the Pareto/NBD Model
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DOI：
10.1287/mksc.1090.0502
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发表时间：
2009-05-01
影响因子：
5
通讯作者：
Abe, Makoto
中科院分区：
文献类型：
--
作者：
Abe, Makoto
本研究扩展了帕累托/NBD模型的客户群分析使用层次贝叶斯（HB）框架，以适应今天的定制营销。所提出的HB模型假定了Pareto/NBD模型的三个经过验证的假设：（1）泊松购买过程，（2）无记忆辍学过程（即，常数风险率），和（3）异质性的客户，而放松的独立性假设的购买和辍学率，并纳入客户特征作为协变量。该模型还提供了有用的输出CRM，如客户特定的生命周期和生存率，作为MCMC estimation.Using三种不同类型的数据库音乐CD的电子商务，FSP数据的百货公司和音乐CD连锁店，HB模型与基准Pareto/NBD模型进行了比较。该研究表明，最近的频率数据，结合客户行为和特征，可以为直接营销问题提供重要的见解，例如最佳客户的人口统计资料以及长寿客户是否花费更多。
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.