摘要
提出了一种基于动态SOM神经网络和RFM指标的客户分类方法。该方法首先利用动态SOM神经网络聚类分析模型产生客户簇,然后以客户的RFM行为作为对客户忠诚度、客户规模以及客户信用的模拟衡量来对客户簇进行标识,产生客户分类结果,最后根据客户分类结果判定各类客户对企业的价值,从而使企业能够有针对性地对不同客户实施差别化服务策略,为企业的客户战略提供了有效的支持和决策。
A customer segmentation method based on dynamic SOM and RFM indicators is presented. Firstly, customer clusters is generated by DSOM clustering analysis. Secondly, customer loyalty, customer scale and customer credit are simulated by RFM behaviors of customer, and then customer clusters are identified. Finally, the value of customers can be decided according to the customer segmentation results, and enterprises are able to provide better different service levels to different customers. It provides customer strategies of enterprises with an effective support and decision-making.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第11期64-67,共4页
Journal of Chongqing University
基金
重庆市科委自然科学基金计划资助项目(2007BB2192)
重庆市高等教育教学改革研究重大项目(0616001)
浦东新区科技发展基金(PKK2005-07)
重庆大学研究生院创新项目(200506Y1A0230130)
关键词
客户知识管理
客户分类
动态SOM
聚类分析
customer knowledge management
customer segmentation
dynamic SOM
clustering analysis
