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Neslin, S.A., Gupta, S., Kamakura, W., Lu, J.X. and Mason, C.H. (2006) Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models. Journal of Marketing Research, 43, 204-211. http://dx.doi.org/10.1509/jmkr.43.2.204

has been cited by the following article:

• TITLE: Research Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data

  AUTHORS: Yong Liu, Yongrui Zhuang

  KEYWORDS: Big Data, Churn Prediction, Customer Segmentation, Misclassification Cost

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.6, June 4, 2015

  ABSTRACT: Enterprises have vast amounts of customer behavior data in the era of big data. How to take advantage of these data to evaluate custom forfeit risks effectively is a common issue faced by enterprises. Most of traditional customer churn predicting models ignore customer segmentation and misclassification cost, which reduces the rationality of model. Dealing with these deficiencies, we established a research model of customer churn based on customer segmentation and misclassification cost. We utilized this model to analyze customer behavior data of a telecom company. The results show that this model is better than those models without customer segmentation and misclassification cost in terms of the performance, accuracy and coverage of model.