基于RFM模型的商场会员画像的建模与验证

Modeling and Verification of Mall Member Portraits Based on RFM Model

针对某大型百货商场会员画像描绘中的聚类问题进行研究,阐述RFM模型构建用户聚类的建模方法和实现的过程。采用最近消费时间、某段时间间隔内消费次数、消费总金额为模型的三个指标,利用Python软件的K-means算法完成聚类实验。实验结果证明K-means算法很好地实现了聚类性能,同时表明所提出的RFM模型建模方法对会员画像描绘是有效的,从而为商场制定有针对性的会员营销策略提供数据支撑。

This paper studies the clustering problem in the portrayal of a large department store member, and expounds the modeling method and implementation process of RFM model to construct user clustering. The clustering experiment was completed by using the K-Means algorithm of Python software by using the three indicators of the recent consumption time, the number of consumption in a certain interval, and the total amount of consumption. The experimental results show that the K-Means algorithm achieves better clustering performance, and it shows that the proposed RFM model modeling method is effective for the member portrait rendering, which provides data support for the mall to develop targeted member marketing.