基于改进RFM模型和K-means算法的淘宝用户行为分析
Analysis of Taobao User Behavior Based on Improved RFM Model and K-means Algorithm
陈海燕 1张经纬2
作者信息
- 1. 滁州学院经济与管理学院
- 2. 滁州学院科研处(安徽滁州239000)
- 折叠
摘要
大数据时代下,我国电子商务发展迅速,用户行为数据日益增多,利用海量数据对用户行为进行剖析,为精准营销提供决策依据,进而提高用户忠诚度、满意度和活跃度,成为电商平台关注的焦点.基于淘宝用户真实数据集,提出基于改进RFM模型和K-means算法的用户行为分析方法,为了更好地描述用户行为特征,创建"活跃度转化率"指标进行分析,实验结果表明,该方法能够有效地进行用户类别划分,划分结果符合"二八定律",能够协助电商平台完成精确化的客户关系管理.
Abstract
In the era of big data,China's e-commerce is developing rapidly,with an increasing amount of user behavior data.Utilizing massive amounts of data to analyze user behavior and provide decision-making basis for precision marketing,thereby improving user loyalty,satisfaction,and activity,has become the focus of attention for e-commerce platforms.Based on the real data set of Taobao users,a user behavior analysis method based on the improved RFM model and K-means algorithm is proposed.In order to better describe the characteristics of user behavior,the"activity conversion rate"indicator is created for analysis.The experimental results show that this method can effectively divide user catego-ries,and the division results comply with the"Pareto principle",which can help e-commerce platforms complete accurate customer relationship management.
关键词
改进的RFM模型/K-means算法/用户行为分析Key words
improved RFM model/K-means algorithm/user behavior analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据