A Personalized Defection Detection and Prevention Procedure based on the Self-Organizing Map and Association Rule Mining: Applie

Abstract. Customer retention is an increasingly pressing issue in today's competitive environment. This paper proposes a personalized defection detection and prevention procedure based on the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behaviour (i.e., trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behaviour from past behaviour data. Based on this state representation, potential defectors are detected by comparing their monitored trajectories of behaviour states with frequent and confident trajectories of past defectors. Also, the proposed procedure is extended to prevent the defection of potential defectors by recommending the desirable behaviour state for the next period so as to lower the likelihood of defection. For the evaluation of the proposed procedure, a case study has b...

• Type: Journal
• Year: 2004
• Where: AIR
• Authors: Hee Seok Song, Jae Kyeong Kim, Yeong Bin Cho, Soung Hie Kim
• Keywords: AIR 2004, Defection, Potential Defectors, Procedure