Using support vector machine to predict consumers’ repurchase behavior

Encouraging customers’ repurchase behavior is becoming one of the most important goals for many firms. However, existing methods have their limitations in accurately predicting repurchase behavior. They either require independence and normality assumptions of predicting variables, or have the danger of over-fitting, or result in poor generalization performance. A support vector machines (SVM) based method is proposed to predict customers’ repurchase behavior. After using sequential pattern to discover repurchase behavior, SVM was used to classify and predict repurchase behavior. The empirical study using customers’ data from a commercial bank shows that, SVM doesn’t require specific assumption of variables; the prediction error of the proposed method decreases by 37% and 54% respectively compared with those of logistic regression and artificial neural network; moreover, both the prediction error and its standard deviation decrease with the increase of sample size. Those evidences demonstrate the effectiveness and superiority of the proposed method.