Using improved RFM model to classify consumer in big data environment. (26th November 2020)

• Record Type: Journal Article
• Authors: Sun, Guang; Xie, XiaoFeng; Zeng, Jiayibei; Jiang, Wangdong; Lin, Meisi; Huang, YuXuan; Xiao, Yanfei
• Is Part Of: International journal of embedded systems. Volume 14:Number 1(2021)
• Page Start: 54
• Page End: 64
• Publication Date: 2020-11-26
• Subjects: RFM model -- customer segmentation -- big data -- cluster analysis; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

Big data makes the marketing focus of enterprises change from products to consumers, so customer relationship management (CRM) becomes a central issue for business operation. Because customer classification is the key question for customer relationship management (CRM), this paper starts with RFM model, combines analysis of K-means clustering, and studies the method for distinguishing between valueless customers and high-value customers. Based on this method, specific management strategies are proposed to help enterprises find core consumers. Also quantitative analysis of the validity of the cluster is done by using the elbow method. Result of the experiment shows that establishing RFM index and using K-means clustering can start from the structure of dataset of consumers of enterprises and finely compare the difference among customer classification by using the clustered scatter plot to provide an effective way of classifying consumers.