Title: Using improved RFM model to classify consumer in big data environment

Authors: Guang Sun; XiaoFeng Xie; Jiayibei Zeng; Wangdong Jiang; Meisi Lin; YuXuan Huang; Yanfei Xiao

Addresses: Hunan University of Finance and Economics, No. 139, Fenglin 2nd Road, Changsha, Hunan, China ' Hunan University of Finance and Economics, No. 139, Fenglin 2nd Road, Changsha, Hunan, China ' Hunan University of Finance and Economics, No. 139, Fenglin 2nd Road, Changsha, Hunan, China ' Hunan University of Finance and Economics, No. 139, Fenglin 2nd Road, Changsha, Hunan, China ' Hunan University of Finance and Economics, No. 139, Fenglin 2nd Road, Changsha, Hunan, China ' Hunan University of Finance and Economics, No. 139, Fenglin 2nd Road, Changsha, Hunan, China ' Hunan University of Finance and Economics, No. 139, Fenglin 2nd Road, Changsha, Hunan, China

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.

Keywords: RFM model; customer segmentation; big data; cluster analysis.

DOI: 10.1504/IJES.2021.10033846

International Journal of Embedded Systems, 2021 Vol.14 No.1, pp.54 - 64

Received: 27 Jul 2019
Accepted: 06 Sep 2019

Published online: 22 Dec 2020 *

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