Behavior Analysis of Customer Churn for a Customer Relationship System

Abstract

This article describes how the bank industry in Taiwan must function in today's tough and fiercely competitive domestic credit card market and subdued global market. Banks are increasingly emphasizing the importance of retaining customers in order to sustain market share and remain profitable. This study proposes a new model which local banks can use to detect potential customer churn and provide an early warning indicator of problems that could lead to loss of customers. The model incorporates a customer relationship management database with a built-in time factor and applied temporal abstraction to represent data for a specific time period as defined by experts. Association rule mining is applied to analyze and detect abnormal customer behavior. The results of this article indicate that the system is relatively effective in detecting customer churn early on and thus helpful at assisting banks to address issues before they escalate. Furthermore, the tested rules are further scrutinized by experts to establish the relationship between the defined rules and management. This study provides an expert system for banks to assess the quality of their marketing campaigns and reestablish faltering customer relationships.