An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm-Reference-Cited by-同舟云学术

An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm

Published:2020-11-19 Issue: Volume:2020 Page:1-7
ISSN:1563-5147
Container-title:Mathematical Problems in Engineering
language:en
Short-container-title:Mathematical Problems in Engineering

Author:

Wu Jun¹²,Shi Li¹,Lin Wen-Pin³^ORCID,Tsai Sang-Bing⁴^ORCID,Li Yuanyuan²,Yang Liping²,Xu Guangshu⁵

Affiliation:

1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

2. School of Economic and Management, Beijing University of Chemical Technology, Beijing 100029, China

3. Department of Marketing, College of Business Administration, Baise University, Baise, Guangxi, China

4. Regional Green Economy Development Research Center, School of Business, Wuyi University, Wuyishan 354300, China

5. School of Logistics, Beijing Wuzi University, Beijing 101149, China

Abstract

In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Link

http://downloads.hindawi.com/journals/mpe/2020/8884227.pdf

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