A personalized defection detection and prevention procedure based on the self-organizing map and association rule mining: Applied to Online game site

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 been conducted for a Korean online game site. The result demonstrates that the proposed procedure is effective for defection prevention and efficiently detects potential defectors without deterioration of prediction accuracy when compared to that of the MLP (Multi-Layer Perceptron) neural networks.