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An SVM based churn detector in prepaid mobile telephony

Cédric Archaux · Arnaud Martin B · Ali Khenchaf

29 citations0/0 files storedInternational Conference on Information and Communication Technologies: From Theory to Applications (ICTTA)

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

Abstract

The context of prepaid mobile telephony is specific in the way that customers are not contractually linked to their operator and thus can cease their activity without notice. In order to estimate the retention efforts which can be engaged towards each individual customer, the operator must distinguish the customers presenting a strong churn risk from the other. This work presents a data mining application leading to a churn detector. We compare artificial neural networks (ANN) which have been historically applied to this problem, to support vectors machines (SVM) which are particularly effective in classification and adapted to noisy data. Thus, the objective of this article is to compare the application of SVM and ANN to churn detection in prepaid cellular telephony. We show that SVM gives better results than ANN on this specific problem.

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2002Customer lifetime value modeling and its use for customer retention planning 1999Statistics and data mining techniques for lifetime value modeling 2000User profiling and classification for fraud detection in mobile communications networks

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2010The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does the Customer Experience Matter?2024Employee turnover in multinational corporations: a supervised machine learning approach 2010User Modeling for Telecommunication Applications: Experiences and Practical Implications 2018The impact of geographical factors on churn prediction: an application to an insurance company in Madrid's urban area 2013The Architecture of a Churn Prediction System Based on Stream Mining 2015Enhancing Telco Service Quality with Big Data Enabled Churn Analysis: Infrastructure, Model, and Deployment

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