Segmenting Online Customers Based on their Lifetime Value and RFM Model by Data Mining Techniques

Abstract

Nowadays, marketing managers are more concerned with identifying and understanding customer behavior in the online space. Since the customers in online space are not visible, it is much essential to have more information about them to provide better services. Customer segmentation is one way to improve the customer problems in an online space. Identifying characteristics of customers and optimal resource allocation to them according to their value to the company is one of the major concerns in the field of ​​customer relationship management and determining factors in E-business success. The purpose of this study is clustering customers online of a mobile sales website based on their lifetime value and RFM model. At the proposed framework in this study after determining the values ​​of RFM model include recently, frequency and monetary of purchase and weighting them using Shannon entropy, a self-organizing map is applied to the segmentation of customers. The customers are categorized into four main segments and characteristics of customers online in each of the segments are identified. Mobile sales website customers are identified by segmenting customers in terms of the pyramid of customer lifetime value. Finally, suggestions are proposed to improve customer relationship management system.