Mining Patterns of Customer Dynamics in Banking Industry

For decades, enterprises focused on brand and products rather than the customers. But, now, economic enterprises focused on building and maintaining effective customer relationships. In such situations, the recognition of customers and their needs has become vital for organizations. One of the most widely used methods for recognizing customers is to segment them into homogeneous groups and recognize the characteristics of each sector, but traditional and static segmentation of customers is not able to respond to the rapid changes in today's dynamic markets. In the era of modern communication and technology, customers are constantly moving between different segments. Knowing patterns of change and the dynamics of customer segments is a key factor in gaining a deep insight into customers, predicting market changes, and even managing them effectively. Major studies in the literature attempt to develop a general and Cross-industry model for interpreting the dynamics of customers, while the nature of customer segments and the dynamic patterns from industry to industry are completely different. The present study, with the consideration of the characteristics of a particular industry (banking industry), explores the dynamics of customers using big data analytics. The results revealed eight categories of patterns and associations which can be proposed to predict the future dynamics of customers and direct it to improve effectiveness of marketing activities in the related industry.