Customer Churn in Mobile Markets A Comparison of Techniques

The high increase in the number of companies competing in mature markets makes customer retention an important factor for any company to survive. Thus, many methodologies (e.g., data mining and statistics) have been proposed to analyse and study customer retention. The validity of such methods is not yet proved though. This paper tries to fill this gap by empirically comparing two techniques: Customer churn - decision tree and logistic regression models. The paper proves the superiority of decision tree technique and stresses the needs for more advanced methods to churn modelling.

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

[2]  Xiaohui Liu,et al.  The effects of individual differences on e-learning users' behaviour in developing countries: A structural equation model , 2014, Comput. Hum. Behav..

[3]  Ali Tarhini,et al.  SOCIAL, ORGANIZATIONAL, DEMOGRAPHY AND INDIVIDUALS’ TECHNOLOGY ACCEPTANCE BEHAVIOUR: A CONCEPTUAL MODEL , 2015 .

[4]  Mazen El-Masri,et al.  A Design Science Approach To Gamify Education: From Games To Platforms , 2015, ECIS.

[5]  Nur Evin Özdemirel,et al.  Defect Cause Modeling with Decision Tree and Regression Analysis , 2007 .

[6]  Hussain Alenezi,et al.  Investigating the strategic relationship between information quality and e-government benefits , 2013 .

[7]  Ali Tarhini,et al.  Designing Guidelines to Discover Causes of Delays in Construction Projects: The Case of Lebanon , 2015 .

[8]  Hossam Faris,et al.  A Genetic Programming Based Framework for Churn Prediction in Telecommunication Industry , 2014, ICCCI.

[9]  Qian Su,et al.  Research of instrument to measure customer perceived service quality of mobile data services , 2014, Int. J. Netw. Virtual Organisations.

[10]  Stephen C. H. Leung,et al.  Segmentation of telecom customers based on customer value by decision tree model , 2012, Expert Syst. Appl..

[11]  Cynthia Rudin,et al.  Machine learning for science and society , 2013, Machine Learning.

[12]  Mazen El-Masri,et al.  The Impact of IS-Business Alignment Practices on Organizational Choice of IS-Business Alignment Strategies , 2015, PACIS.

[13]  Xiaohui Liu,et al.  A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students , 2015, Br. J. Educ. Technol..

[14]  Yong Shi,et al.  Credit card churn forecasting by logistic regression and decision tree , 2011, Expert Syst. Appl..

[15]  Risk of using logistic regression to illustrate exposure-response relationship of infectious diseases , 2014, BMC Infectious Diseases.

[16]  B. Obeidat,et al.  KNOWLEDGE MANAGEMENT STRATEGIES AS INTERMEDIARY VARIABLES BETWEEN ITBUSINESS STRATEGIC ALIGNMENT AND FIRM PERFORMANCE , 2015 .

[17]  Thomas Reinartz,et al.  CRISP-DM 1.0: Step-by-step data mining guide , 2000 .

[18]  Ra’ed Masa’deh,et al.  STRATEGIC IT-BUSINESS ALIGNMENT AS MANAGERS’ EXPLORATIVE AND EXPLOITATIVE STRATEGIES , 2015 .

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

[20]  Adnan Amin,et al.  Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example , 2014, ENIC.

[21]  Shuai Li,et al.  The Model and Empirical Research of Application Scoring based on Data Mining Methods , 2013, International Conference on Information Technology and Quantitative Management.

[22]  Adnan Amin,et al.  Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example , 2014, 2014 European Network Intelligence Conference.

[23]  Ali Tarhini,et al.  A Framework of IS/Business Alignment Management Practices to Improve the Design of IT Governance Architectures , 2015 .

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

[25]  Paul Twomey,et al.  Agent‐based modelling of customer behaviour in the telecoms and media markets , 2002 .

[26]  Qian Su,et al.  The analysis on the determinants of mobile VIP customer churn: a logistic regression approach , 2012, Int. J. Serv. Technol. Manag..

[27]  S. Weisberg,et al.  Criticism and Influence Analysis in Regression , 1982 .

[28]  John Elder,et al.  Handbook of Statistical Analysis and Data Mining Applications , 2009 .

[29]  Babis Theodoulidis,et al.  Analyzing Customer Experience Feedback Using Text Mining , 2014 .

[30]  Fayaz Ahmad,et al.  Assessment of Land Use/Land Cover Change in Hirpora Wildlife Sanctuary, Kashmir , 2015 .

[31]  Mahir Arzoky,et al.  Agent based modelling and simulation: toward a new model of customer retention in the mobile market , 2011, SCSC 2011.

[32]  Ali Tarhini,et al.  Differences in Intention to Use Educational RSS Feeds between Lebanese and British Students: A Multi-Group Analysis Based on the Technology Acceptance Model. , 2015 .

[33]  G. Özer,et al.  The analysis of antecedents of customer loyalty in the Turkish mobile telecommunication market , 2005 .

[34]  Miha Vuk,et al.  ROC curve, lift chart and calibration plot , 2006, Advances in Methodology and Statistics.

[35]  Luo Bin,et al.  Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service , 2007, 2007 International Conference on Service Systems and Service Management.

[36]  C. Ranganathan,et al.  Two-level model of customer retention in the US mobile telecommunications service market , 2008 .

[37]  João Falcão e Cunha,et al.  Modeling partial customer churn: On the value of first product-category purchase sequences , 2012, Expert systems with applications.

[38]  Tomas Eklund,et al.  A two-level approach to making class predictions , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[39]  Xiaohui Liu,et al.  Measuring the Moderating Effect of Gender and Age on E-Learning Acceptance in England: A Structural Equation Modeling Approach for An Extended Technology Acceptance Model , 2014 .

[40]  Dong-sheng Liu,et al.  A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem , 2014, TheScientificWorldJournal.

[41]  L. Feick,et al.  The impact of switching costs on the customer satisfaction‐loyalty link: mobile phone service in France , 2001 .

[42]  Vera Lucia Matias Oliveira.  Analytical customer relationship management in retailing supported by data mining techniques , 2012 .

[43]  A. Tarhini,et al.  Analysis of the Critical Success Factors for Enterprise Resource Planning Implementation from Stakeholders' Perspective: A Systematic Review , 2015 .

[44]  Bart Baesens,et al.  Modeling churn using customer lifetime value , 2009, Eur. J. Oper. Res..

[45]  Adem Karahoca,et al.  GSM Churn Management Using an Adaptive Neuro-Fuzzy Inference System , 2007, The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007).

[46]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

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

[48]  Ali Tarhini,et al.  Towards the Acceptance of RSS to Support Learning: An Empirical Study to Validate the Technology Acceptance Model in Lebanon. , 2015 .

[49]  Michael J. Shaw,et al.  Knowledge management and data mining for marketing , 2001, Decis. Support Syst..

[50]  Dirk Van den Poel,et al.  CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services , 2007, Expert Syst. Appl..

[51]  Timothy Teo,et al.  A cross-cultural validity of the E-learning Acceptance Measure (ElAM) in Lebanon and England: A confirmatory factor analysis , 2015, Education and Information Technologies.

[52]  A. McIlroy,et al.  Building customer relationships: do discount cards work? , 2000 .

[53]  Chih-Ping Wei,et al.  Turning telecommunications call details to churn prediction: a data mining approach , 2002, Expert Syst. Appl..