Idris, A., Rizwan, M. and Khan, A. (2012) Churn Prediction in Telecom Using Random Forest and PSO Based Data Balancing in Combination with Various Feature Selection Strategies. Computers & Electrical Engineering, 38, 1808-1819. http://dx.doi.org/10.1016/j.compeleceng.2012.09.001

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.