Targeting Customers with Statistical and Data-Mining Techniques
提出结合统计与数据挖掘方法估计客户的风险函数和剩余服务期，从而优化客户保留策略并推广生命周期价值模型，对营销人员制定客户定位策略有参考价值。
Operationalizing a relationship management program requires a retention strategy that is sensitive to an individual customer’s position in the service life cycle, while being financially sound for the provider. To this end, estimating a customer’s hazard function and remaining tenure with the company can lead to important insights into marketing tactics and constitute fundamental building blocks for methods of targeting important customers. The authors describe a way of estimating these quantities using a combination of statistical and data-mining techniques. The resulting customer hazard information leads to a generalization of lifetime value (GLTV) that explicitly accounts for company actions and their success in relationship management.