Customer Behavior Analysis by Using Multiple Databases: A Case of University Students' Use of Online Bookstore Services

The rapid development of network and distributed computing technology has made multiple-database applications increasingly popular. Meanwhile, the cost-saving cloud computing technologies are being used by more and more enterprises for developing their information systems. Thus, it becomes very important for an enterprise to have the capability to integrate data efficiently and effectively from multiple databases while performing customer relationship management (CRM) analysis. In this study, we applied the collaborative fuzzy clustering algorithm proposed by Pedrycz to multiple databases to analyze customer motivations and the major factors influencing customers as they use electronic commerce (eCommerce) services. We used questionnaires to build three databases for student customers of online bookstore services: an internal situation database, a transaction motivation database, and an information cognition database. The analysis results were also used to clearly define the market segmentations. There are two major contributions of this study. The first is to define customer behavior and preference criteria in today's eCommerce era. The second is to provide the results to related industries for strengthening their CRM practices and developing different quality products and services for their various customers.