摘要
针对传统RFM模型存在用户特征不能充分提取的问题,提出改进的RFM模型。通过IV值进行指标选择,在传统RFM模型三个指标基础上加入新指标,采用熵权法对各指标赋权,同时采用数据分箱减少模型离散特征。通过KMeans聚类对分箱后的数据聚类分析,进行客户特征提取,制定相应的挽留措施,以实现基于客户细分的精准营销。结果表明,改进模型较原模型有显著提升。
Aiming at the problem that user characteristics cannot be fully extracted in the traditional RFM model,this paper proposes an improved RFM model.Selecting the index by IV value,adds new indexes for traditional indexes of RFM model.Entropy weight method is used to weight each index,and data binning is used to reduce the discrete characteristics of the model.Use K-Means clustering to cluster the data after binning,extract customer characteristics,and formulate corresponding retention measures to achieve precise marketing based on customer segmentation.The results show that the improved model has a significant improvement over the original model.
作者
王玉凤
孙文秀
杜梦娇
Wang Yufeng;Sun Wenxiu;Du Mengjiao(Northeastern University,Shenyang,Liaoning 110004,China)
出处
《计算机时代》
2021年第6期44-48,共5页
Computer Era
