Data Mining Techniques on the Evaluation of Wireless Churn

Jorge Ferreira · Marley M. B. R. Vellasco · Marco Aurélio C. Pacheco · Carlos Roberto Hall Barbosa

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Abstract. This work focuses on one of the most critical issues to plague the wireless telecommunications industry today: the loss of a valuable subscriber to a competitor, also defined as churn. Analytical methods and models intrinsic to decision technology and machine learning are here evaluated, in an effort to provide the necessary intelligence to identify and understand troublesome customers in order to act upon them before they churn. Making use of a large real-world database, a thorough analysis is performed. First, due attention is given to data representation, with input selection methods being employed in the search of the most relevant attributes. Then, the predictive and explanatory power of four families of models is compared: neural networks, decision trees, genetic algorithms and neuro-fuzzy systems. To conclude, light is shed upon the possible savings and profits resulting from the application of the developed methodology in the retention strategies of wireless carriers. 1.

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References

2000Mastering Data Mining: The Art and Science of Customer Relationship Management 2000Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry

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2012User Behavior Modelling Approach for Churn Prediction in Online Games 2010Churn in Social Networks 2018Predict employee attrition by using predictive analytics 2010The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does the Customer Experience Matter?2010Customer churn prediction – a case study in retail banking 2006Customer churn analysis - a case study 2010User Modeling for Telecommunication Applications: Experiences and Practical Implications 2017A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn

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