A Personalized Defection Detection and Prevention Procedure based on the Self-Organizing Map and Association Rule Mining: Applied to Online Game Site

Customer retention is an increasinglypressing issue in today's competitiveenvironment. This paper proposes a personalizeddefection detection and prevention procedurebased on the observation that potentialdefectors have a tendency to take a couple ofmonths or weeks to gradually change theirbehaviour (i.e., trim-out their usage volume)before their eventual withdrawal. For thispurpose, we suggest a SOM (Self-Organizing Map)based procedure to determine the possiblestates of customer behaviour from pastbehaviour data. Based on this staterepresentation, potential defectors aredetected by comparing their monitoredtrajectories of behaviour states with frequentand confident trajectories of past defectors.Also, the proposed procedure is extended toprevent the defection of potential defectors byrecommending the desirable behaviour state forthe next period so as to lower the likelihoodof defection. For the evaluation of theproposed procedure, a case study has beenconducted for a Korean online game site. Theresult demonstrates that the proposed procedureis effective for defection prevention andefficiently detects potential defectors withoutdeterioration of prediction accuracy whencompared to that of the MLP (Multi-LayerPerceptron) neural networks.