J-WS: A Hybrid Unsupervised Mining Approach for Customer Segmentation in B2C e-Commerce

Abstract

Nowadays, with the use of technology and the Internet, it is easy to start a business, more specifically an e-commerce business. However, maintaining a consistent sale and having returning customers can prove a challenge as most businesses rely on new customers for profits and does not generate a reliable profit as compared to relying on old customers. One might resort to applying different kinds of marketing strategies but without understanding of their customer base and proper segmentation of customers, these efforts could result in waste of resources and low probability of success. Therefore, an approach named J-WS that can perform customer segmentation based on customer sales data and Recency, Frequency, and Monetary (RFM) model is proposed. Meaningful information from different groups of customers can later be utilized by target marketing strategy to improve customer retention and impactful marketing. The proposed work consists of 5 phases which include data cleaning, identifying the best clustering algorithm between K-Means and Hierarchical clustering in terms of execution time and Sum of Squared Error, applying association rule mining to generate sets of frequent association rules among the clusters. Conclusively, J-WS can be used by e-commerce businesses to segment their customers meaningfully and properly.