Predicting customer churn through interpersonal influence

Preventing customer churn is an important task for many enterprises and requires customer churn prediction. This paper investigates the effects of interpersonal influence on the accuracy of customer churn predictions and proposes a novel prediction model that is based on interpersonal influence and that combines the propagation process and customers’ personalized characters. Our contributions include the following: (1) the effects of interpersonal influence on prediction accuracy are evaluated while including determinants that other researchers proved effective, and several models are constructed based on machine learning and statistical methods and compared, assuring the validity of the evaluation; and (2) a novel prediction model based on interpersonal influence and information propagation is proposed. The dataset used in the empirical study was obtained from a leading mobile telecommunication service provider and contains the traditional and network attributes of over one million customers. The empirical results show that traditional classification models that incorporate interpersonal influence can greatly improve prediction accuracy, and our proposed prediction model outperforms the traditional models.