Using SOM-Ward Clustering and Predictive Analytics for Conducting Customer Segmentation

Abstract

Continuously increasing amounts of data in data warehouses are providing companies with ample opportunity to conduct analytical customer relationship management (CRM). However, how to utilize the information retrieved from the analysis of these data to retain the most valuable customers, identify customers with additional revenue potential, and achieve cost-effective customer relationship management, continue to pose challenges for companies. This study proposes a two-level approach combining SOM-Ward clustering and predictive analytics to segment the customer base of a case company with 1.5 million customers. First, according to the spending amount, demographic and behavioral characteristics of the customers, we adopt SOM-Ward clustering to segment the customer base into seven segments: exclusive customers, high-spending customers, and five segments of mass customers. Then, three classification models - the support vector machine (SVM), the neural network, and the decision tree, are employed to classify high-spending and low-spending customers. The performance of the three classification models is evaluated and compared. The three models are then combined to predict potential high-spending customers from the mass customers. It is found that this hybrid approach could provide more thorough and detailed information about the customer base, especially the untapped mass market with potential high revenue contribution, for tailoring actionable marketing strategies.