Customer Churn Prediction using Recurrent Neural Network with Reinforcement Learning Algorithm in Mobile Phone Users

The ability to retain customers is an issue in many of the service industries. Industries spend a lot of resources in gaining new customer and trying all their effort to maintain the existing customers and various churn predictor engine has been developed to fulfill this purpose. The objective of this study is to implement Elman Recurrent Neural Network and Jordan Recurrent Neural Network with Reinforcement Learning in predicting probability of mobile phone churning rates. The study had proved that Jordan Recurrent Neural Network gave better accuracy rate than Elman Recurrent Neural Network

[1]  Hong Li,et al.  Application of Feature Extraction Method in Customer Churn Prediction Based on Random Forest and Transduction , 2010, J. Convergence Inf. Technol..

[2]  Eric W. T. Ngai,et al.  Customer churn prediction using improved balanced random forests , 2009, Expert Syst. Appl..

[3]  Zheng Tang,et al.  TRAINING ELMAN NEURAL NETWORK FOR DYNAMIC SYSTEM IDENTIFICATION USING AN ADAPTIVE LOCAL SEARCH ALGORITHM , 2010 .

[4]  É MikaelBoden.  A guide to recurrent neural networks and backpropagation , 2001 .

[5]  Yossi Richter,et al.  Predicting Customer Churn in Mobile Networks through Analysis of Social Groups , 2010, SDM.

[6]  Zhang Weiwei,et al.  A Customer Churn Analysis Model in E-business Environment , 2012 .

[7]  Magdy M. Abdelhameed,et al.  A recurrent neural network-based sequential controller for manufacturing automated systems , 2002 .

[8]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[9]  Kamel Baddari,et al.  Application of feedback connection artificial neural network to seismic data filtering , 2008, 0811.2743.

[10]  Wei-Min Qi,et al.  Dynamic properties of Elman and modified Elman neural network , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[11]  M. Tahar Kechadi,et al.  Customer churn prediction in telecommunications , 2012, Expert Syst. Appl..

[12]  Triin Kadak,et al.  SOFT COMPUTING METHODS FOR CUSTOMER CHURN MANAGEMENT , 2007 .

[13]  Marley M. B. R. Vellasco,et al.  Data Mining Techniques on the Evaluation of Wireless Churn , 2004, ESANN.

[14]  Ashutosh Tiwari,et al.  Computer assisted customer churn management: State-of-the-art and future trends , 2007, Comput. Oper. Res..

[15]  Li Lei,et al.  Customer Churn Prediction Based on Cluster Stratified Sampling Logistic Regression , 2011 .