基于数据挖掘的客户细分模型研究及应用

为企业更深入了解消费者的行为和偏好,帮助企业制定决策和发展客户关系,结合现有的客户细分方法,提出一种多指标客户细分模型。从宏观和微观角度,对传统指标进行优化,构建RFMPA多指标客户体系;采用熵值法客观赋权;采用因子分析降维;采用改进的K-means算法完成客户细分。利用大型连锁超市客户消费数据进行实证研究,对比数据实验结果表明,该模型能够更好解决客户细分问题,提高企业客户关系管理和决策质量。

Research and application of customer segmentation model based on data mining

To understand consumers’behaviors and preferences more deeply and help enterprises make decisions and develop customer relationships,a multi index customer segmentation model was proposed based on the existing customer segmentation methods.Through data analysis technology,from the macro and micro perspectives,the traditional indicators were updated and refined to build an RFMPA customer indicator system.Objective weighting was implemented using entropy method.Data dimension reduction was carried out using factor analysis.The improved K-means algorithm was used for customer segmentation.Using the customer consumption data of a large supermarket chain for empirical research,and comparing the data experimental results,the model can better solve the problem of customer segmentation,improve the quality of enterprise customer relationship management and decision-making.