Echo State Network with SVM-readout for customer churn prediction

In all customer based industries, customer churn is considered as one of the most important and challenging concerns since it can lead to a serious profit loss. Therefore, developing accurate churn prediction models can significantly help Customer Relationship Management in planning effective retention campaigns and consequently helps in maximizing the profit of the service provider. In this paper, we propose the use of an Echo State Network (ESN) with a Support Vector Machine (SVM) training algorithm for predicting customer churn in telecommunication companies. The proposed approach is trained and tested based on two datasets: the first is a popular online available dataset while the second is obtained from a local service provider. Experiment results show that ESN with SVM readout outperform other popular machine learning models used in the literature for the same customer churn prediction problems.

[1]  C. Anderson,et al.  Modeling reward functions for incomplete state representations via echo state networks , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[2]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[3]  Herbert Jaeger,et al.  A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar , 2005 .

[4]  Alex Berson,et al.  Building Data Mining Applications for CRM , 1999 .

[5]  Kristof Coussement,et al.  Integrating the voice of customers through call center emails into a decision support system for churn prediction , 2008, Inf. Manag..

[6]  Z. Ibrahim,et al.  Mobile phone customers churn prediction using elman and Jordan Recurrent Neural Network , 2012, 2012 7th International Conference on Computing and Convergence Technology (ICCCT).

[7]  Garrison W. Cottrell,et al.  2007 Special Issue: Learning grammatical structure with Echo State Networks , 2007 .

[8]  Hossam Faris,et al.  Negative Correlation Learning for Customer Churn Prediction: A Comparison Study , 2015, TheScientificWorldJournal.

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Zhu Guo.  Support Vector Machine and Its Applications to Function Approximation , 2002 .

[11]  Guo-en Xia,et al.  Model of Customer Churn Prediction on Support Vector Machine , 2008 .

[12]  Vladimir Vapnik,et al.  A new learning paradigm: Learning using privileged information , 2009, Neural Networks.

[13]  Chris J. Harris,et al.  On the modelling of nonlinear dynamic systems using support vector neural networks , 2001 .

[14]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[15]  Herbert Jaeger,et al.  The''echo state''approach to analysing and training recurrent neural networks , 2001 .

[16]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[17]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[18]  John G. Harris,et al.  Minimum mean squared error time series classification using an echo state network prediction model , 2006, 2006 IEEE International Symposium on Circuits and Systems.

[19]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

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

[21]  Peter Tiño,et al.  Simple Deterministically Constructed Recurrent Neural Networks , 2010, IDEAL.

[22]  Jun Guo,et al.  An extended support vector machine forecasting framework for customer churn in e-commerce , 2011, Expert Syst. Appl..