Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example

The customer churn is a crucial activity in the competitive and rapidly growing telecommunication industry. Due to the high cost of acquiring a new customer, customer churn prediction is one of the greatest importance for project managers. It is important to forecast customer churn behavior in order to retain those customers that will churn or possibly may churn. This study is another attempt which makes use of rough set theory as one-class classifier and multi-class classifier to reveal the trade-off in the selection of an effective classification model for customer churn prediction. Experiments were performed to explore the performance of four different rule generation algorithms (i.e. Exhaustive, genetic, covering and LEM2). It is observed that rough set as one-class classifier and multi-class classifier based on genetic algorithm yields more suitable performance out of four rule generation algorithms. Furthermore, by applying the proposed techniques (i.e. Rough sets as one-class and multi-class classifiers) on publicly available dataset, the results show that rough set as a multi-class classifier provides more accurate results for binary/multi-class classification problems.

[1]  Mona Nasr,et al.  A Proposed Churn Prediction Model , 2012 .

[2]  Jakub Wróblewski,et al.  Genetic Algorithms in Decomposition and Classification Problems , 1998 .

[3]  Adnan Amin,et al.  Churn Prediction in Telecommunication Industry Using Rough Set Approach , 2015, New Trends in Computational Collective Intelligence.

[4]  Lian Yan,et al.  Predicting customer behavior in telecommunications , 2004, IEEE Intelligent Systems.

[5]  Blaz Zupan,et al.  Predictive data mining in clinical medicine: Current issues and guidelines , 2008, Int. J. Medical Informatics.

[6]  Ali Mustafa Qamar,et al.  Telecommunication subscribers' churn prediction model using machine learning , 2013, Eighth International Conference on Digital Information Management (ICDIM 2013).

[7]  Anuj Sharma,et al.  A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services , 2011 .

[8]  Maja Matijasevic,et al.  MMORPG player behavior model based on player action categories , 2011, 2011 10th Annual Workshop on Network and Systems Support for Games.

[9]  Bart Baesens,et al.  Building comprehensible customer churn prediction models with advanced rule induction techniques , 2011, Expert Syst. Appl..

[10]  Vadlamani Ravi,et al.  Churn prediction using comprehensible support vector machine: An analytical CRM application , 2014, Appl. Soft Comput..

[11]  Dirk Van den Poel,et al.  Customer attrition analysis for financial services using proportional hazard models , 2004, Eur. J. Oper. Res..

[12]  Dirk Van den Poel,et al.  Handling class imbalance in customer churn prediction , 2009, Expert Syst. Appl..

[13]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[14]  Eric Johnson,et al.  Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry , 2000, IEEE Trans. Neural Networks Learn. Syst..

[15]  Hung Son Nguyen,et al.  Analysis of STULONG Data by Rough Set Exploration System (RSES) , 2003 .

[16]  Girish Keshav Palshikar,et al.  Employee churn prediction , 2011, Expert Syst. Appl..

[17]  Reza Allahyari Soeini,et al.  Applying Data Mining to Insurance Customer Churn Management , 2012 .

[18]  Baoxu Liu,et al.  Attack Detection by Rough Set Theory in Recommendation System , 2010, 2010 IEEE International Conference on Granular Computing.

[19]  Li Hong,et al.  Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining , 2013 .

[20]  Bart Baesens,et al.  Social network analysis for customer churn prediction , 2014, Appl. Soft Comput..

[21]  Neil J. Hurley,et al.  ChurnVis: Visualizing mobile telecommunications churn on a social network with attributes , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[22]  David M. J. Tax,et al.  One-class classification , 2001 .

[23]  Setak Mostafa,et al.  A Neuro-Fuzzy Classifier for Customer Churn Prediction , 2011 .

[24]  Marcin S. Szczuka,et al.  The Rough Set Exploration System , 2005, Trans. Rough Sets.

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

[26]  Bart Baesens,et al.  Mining software repositories for comprehensible software fault prediction models , 2008, J. Syst. Softw..

[27]  Jae-Hyeon Ahn,et al.  Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry , 2006 .

[28]  Zahoor Jan,et al.  Selection of Accurate and Robust Classification Model for Binary Classification Problems , 2009, FGIT-SIP.

[29]  Robert P. W. Duin,et al.  Uniform Object Generation for Optimizing One-class Classifiers , 2002, J. Mach. Learn. Res..

[30]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

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

[32]  Shehroz S. Khan,et al.  One-class classification: taxonomy of study and review of techniques , 2013, The Knowledge Engineering Review.

[33]  이건창,et al.  신용카드 시장에서 데이터 마이닝을 이용한 이탈고객 분석 ( An Artificial Intelligence-based Data Mining Approach to Extracting Strategies for reducing the Churning Rate in Credit Card Industry ) , 2002 .

[34]  Ruth N. Bolton,et al.  A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction , 1994 .

[35]  Gwo-Hshiung Tzeng,et al.  Combined rough set theory and flow network graph to predict customer churn in credit card accounts , 2011, Expert Syst. Appl..

[36]  Zdzislaw Pawlak,et al.  Rough Sets, Rough Relations and Rough Functions , 1996, Fundam. Informaticae.

[37]  Jan G. Bazan,et al.  Rough set algorithms in classification problem , 2000 .

[38]  Ateeq Ur Rehman,et al.  Intelligent Churn prediction for Telecommunication Industry , 2013 .

[39]  David C. Yen,et al.  Applying data mining to telecom churn management , 2006, Expert Syst. Appl..

[40]  Susan M. Keaveney,et al.  Customer Switching Behavior in Service Industries: An Exploratory Study , 1995 .

[41]  Moon-Koo Kim,et al.  The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunication services , 2004 .

[42]  Jasmina Novakovic,et al.  Using Information Gain Attribute Evaluation to Classify Sonar Targets , 2009 .

[43]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.