Decision Support System for CRM based on an Individual Level Model

在客户关系管理（CRM）中，经常使用特别规则来判断客户是否在“非合同”环境中活跃。例如，如果客户超过三个月没有购买，则视为退出。但是，对于间隔购买时间较长的客户，这三个月的时间框架将不适用。因此，在评估客户流失时，考虑客户异质性是很重要的。尽管Schmittlein等人（1987）早在20年前就发现了这个问题，并提出了帕累托/NBD“计算你的客户”框架，但今天的营销需要更多的个人层面的分析。本研究提出了一个建议模型，该模型通过估计个人特定参数来捕获客户异质性，同时在帕累托/NBD模型（泊松购买过程和无记忆退出过程）中保持理论上合理的个人行为假设。该模型不仅放松了对两个行为过程独立性的假设，而且还为CRM提供了有用的输出，例如客户特定的生命周期和保留率，这些是其他方法无法获得的。将其预测性能与基准Pareto/NBD模型进行比较。该模型扩展应用于扫描仪面板数据，表明最近频率（RF）数据与客户行为和人口统计数据相结合，可以为直接营销问题提供重要见解，例如长期客户是否花费更多，是否更有利可图。

In customer relationship management (CRM), ad hoc rules are often employed to judge whether customers are active in a "non-contractual" setting. For example, a customer is considered to have dropped out if he or she has not made purchase for over three months. However, for customers with a long interpurchase time, this three-month time frame would not apply. Hence, when assessing customer attrition, it is important to account for customer heterogeneity. Although this issue was recognized by Schmittlein et al. (1987), who proposed the Pareto/NBD "counting your customers" framework almost 20 years ago, today's marketing demands a more individual level analysis.This research presents a proposed model that captures customer heterogeneity through estimation of individual-specific parameters, while maintaining theoretically sound assumptions of individual behavior in a Pareto/NBD model (a Poisson purchase process and a memoryless dropout process). The model not only relaxes the assumption of independence of the two behavioral processes, it also provides useful outputs for CRM, such as a customer-specific lifetime and retention rate, which could not have been obtained otherwise. Its predictive performance is compared against the benchmark Pareto/NBD model. The model extension, as applied to scanner panel data, demonstrates that recency-frequency (RF) data, in conjunction with customer behavior and demographics, can provide important insights into direct marketing issues, such as whether long-life customers spend more and are more profitable.