摘要
研究客户关系管理系统实施中的若干关键技术,设计基于OLAM技术的客户分析挖掘原型系统.结合业务分析构建客户分析的各主题数据仓库,对各模块功能设计,进一步分析客户销售数据、挖掘客户产品销售规律.提出基于客户终生价值、客户忠诚度、客户资信综合因素的CLV/CL/CC客户细分模型,采用相应数据挖掘算法对各指标计算预测,有效实现客户细分,提高了客户分类判别的精度.对产品销售数据进行关联分析,剖析了销售产品间的潜在规律,为企业促销提供决策支持.对产品交易数据通过多维度多层次的上卷、下钻、横切、纵切等在线分析,剖析了深层的客户属性因素.该研究对于AM技术及其可视化的理论和实践进行了深入的探索.
Some key technologies of actualizing customer relationship management(CRM)systems are researched.The customer analysis mining prototype systems on the basis of on-line analytical mining(OLAM)is designed.After transaction analysis,the data warehouse of CRM is constructed.The CLV/CL/CC customer division model based on customer lifetime value,customer loyalty and customer credit is emphatically researched.Three parameters of customer division—customer lifetime value,customer loyalty and customer credit—are calculated by corresponding algorithms,which can realize customer divisions effectively and improve the accuracy of distinguishing among customers.The data of product sales are analyzed by the sequence association rules algorithm,the potential rules of the products relevance are discovered,which can provide evidence for supporting decisions such as promotion strategies.The transaction data such as product sales volumes and order lists are analyzed on-line through multi-dimensional and multi-level up-drills,down-drills,and horizontal/longitudinal sections.The customer property factors are analyzed as well.The theory and practice of OLAM and its visualization are further explored.
基金
The National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
关键词
OLAM
分析型CRM
数据仓库
on-line analytical mining(OLAM)
analytical customer relation management(CRM)
data warehouse
