A Model for Customer Churn Management of an Internet Service Provider

Customer churning is one of the most important issues facing Internet Service Providers in a competitive and rapidly saturating market. Due to the high costs associated with attracting new customers, ISPs have turned to a customer retention approach that explicitly seeks to reduce churn. This study has been surveyed the churning of internet service customers in one of the largest telecommunications companies in Iran. In order to predict the churn, customer data has been collected during six months, and their churning behavior has been investigated over a period of one year after. In addition to churn prediction, the most important factors affecting churn have been identified. In the preprocessing step, the "Random Under-Sampling" method is used to balance the data set and the "minimum-Redundancy, Maximum-Relevance" method is used to feature selection. Then, the "Random Forest", "Support Vector Machine" and "K-Nearest Neighbors" algorithms were applied to classify churning and non-churning customers, and the evaluation criteria show the superiority of the random forest algorithm. The final model, which was obtained from a combination of balancing, feature selection and classification methods, called the RUS-mRMR-RF model, is considered as an efficient model in customer churn prediction and identifying the most important factors affecting churn. The results of this study provide valuable insights to the company to develop customer retention strategies

PREDICCIÓN DE FUGA DE CLIENTES EN UNA EMPRESA DE DISTRIBUCIÓN DE GAS NATURAL MEDIANTE EL USO DE MINERÍA DE DATOS

Customer churn is a relevant problem faced by service companies and that can generate significant economic losses. Identifying the elements that lead a customer to stop consuming a service is a complex task. However, through their behavior, it is possible to estimate a churn probability associated with each one of them. This research applies data mining to predict customer churn in a natural gas distribution company, using two machine learning techniques: neural networks and support vector machine. The results show that by applying these techniques it is possible to identify customers with the highest probability of churn to take retention actions timely and focused, minimizing the costs associated with the error in the identification of these customers. Keywords: Customer churn, Data mining, Machine learning, Natural gas distribution. References [1]J. Miranda, P. Rey and R. Weber, «Predicción de Fugas de Clientes para una Institución Financiera Mediante Support Vector Machines,» Revista Ingeniería de Sistemas Volumen XIX, pp. 49-68, 2005. [2]P. A. Pérez V., «Modelo de predicción de fuga de clientes de telefonía movil post pago,» Universidad de Chile, Santiago, Chile, 2014. [3]Gas Sur S.A., «https://www.gassur.cl/Quienes-Somos/,» [En línea]. [4]J. Xiao, X. Jiang, C. He and G. Teng, «Churn prediction in customer relationship management via GMDH-based multiple classifiers ensemble,» IEEE IntelligentSystems, vol. 31, nº 2, pp. 37-44, 2016. [5]A. M. Almana, M. S. Aksoy and R. Alzahrani, «A survey on data mining techniques in customer churn analysis for telecom industry,» International Journal of Engineering Research and Applications, vol. 4, nº 5, pp. 165-171, 2014. [6]A. Jelvez, M. Moreno, V. Ovalle, C. Torres and F. Troncoso, «Modelo predictivo de fuga de clientes utilizando mineríaa de datos para una empresa de telecomunicaciones en chile,» Universidad, Ciencia y Tecnología, vol. 18, nº 72, pp. 100-109, 2014. [7]D. Anil Kumar and V. Ravi, «Predicting credit card customer churn in banks using data mining,» International Journal of Data Analysis Techniques and Strategies, vol. 1, nº 1, pp. 4-28, 2008. [8]E. Aydoğan, C. Gencer and S. Akbulut, «Churn analysis and customer segmentation of a cosmetics brand using data mining techniques,» Journal of Engineeringand Natural Sciences, vol. 26, nº 1, 2008. [9]G. Dror, D. Pelleg, O. Rokhlenko and I. Szpektor, «Churn prediction in new users of Yahoo! answers,» de Proceedings of the 21st International Conference onWorld Wide Web, 2012. [10]T. Vafeiadis, K. Diamantaras, G. Sarigiannidis and K. Chatzisavvas, «A comparison of machine learning techniques for customer churn prediction,» SimulationModelling Practice and Theory, vol. 55, pp. 1-9, 2015. [11]Y. Xie, X. Li, E. Ngai and W. Ying, «Customer churn prediction using improved balanced random forests,» Expert Systems with Applications, vol. 36, nº 3, pp.5445-5449, 2009. [12]U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, «Knowledge Discovery and Data Mining: Towards a Unifying Framework,» de KDD-96 Proceedings, 1996. [13]R. Brachman and T. Anand, «The process of knowledge discovery in databases,» de Advances in knowledge discovery and data mining, 1996. [14]K. Lakshminarayan, S. Harp, R. Goldman and T. Samad, «Imputation of Missing Data Using Machine Learning Techniques,» de KDD, 1996. [15]B. Nguyen , J. L. Rivero and C. Morell, «Aprendizaje supervisado de funciones de distancia: estado del arte,» Revista Cubana de Ciencias Informáticas, vol. 9, nº 2, pp. 14-28, 2015. [16]I. Monedero, F. Biscarri, J. Guerrero, M. Peña, M. Roldán and C. León, «Detection of water meter under-registration using statistical algorithms,» Journal of Water Resources Planning and Management, vol. 142, nº 1, p. 04015036, 2016. [17]I. Guyon and A. Elisseeff, «An introduction to variable and feature selection,» Journal of machine learning research, vol. 3, nº Mar, pp. 1157-1182, 2003. [18]K. Polat and S. Güneş, «A new feature selection method on classification of medical datasets: Kernel F-score feature selection,» Expert Systems with Applications, vol. 36, nº 7, pp. 10367-10373, 2009. [19]D. J. Matich, «Redes Neuronales. Conceptos Básicos y Aplicaciones,» de Cátedra: Informática Aplicada ala Ingeniería de Procesos- Orientación I, 2001. [20]E. Acevedo M., A. Serna A. and E. Serna M., «Principios y Características de las Redes Neuronales Artificiales, » de Desarrollo e Innovación en Ingeniería, Medellín, Editorial Instituto Antioqueño de Investigación, 2017, pp. Capítulo 10, 173-182. [21]M. Hofmann and R. Klinkenberg, RapidMiner: Data mining use cases and business analytics applications, CRC Press, 2016. [22]R. Pupale, «Towards Data Science,» 2018. [En línea]. Available: https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989. [23]F. H. Troncoso Espinosa, «Prediction of recidivismin thefts and burglaries using machine learning,» Indian Journal of Science and Technology, vol. 13, nº 6, pp. 696-711, 2020. [24]L. Tashman, «Out-of-sample tests of forecasting accuracy: an analysis and review,» International journal of forecasting, vol. 16, nº 4, pp. 437-450, 2000. [25]S. Varma and R. Simon, «Bias in error estimation when using cross-validation for model selection,» BMC bioinformatics, vol. 7, nº 1, p. 91, 2006. [26]N. V. Chawla, K. W. Bowyer, L. O. Hall and W. Kegelmeyer, «SMOTE: Synthetic Minority Over-sampling Technique,» Journal of Artificial Inteligence Research16, pp. 321-357, 2002. [27]M. Sokolova and G. Lapalme, «A systematic analysis of performance measures for classification tasks,» Information processing & management, vol. 45, nº 4, pp. 427-437, 2009. [28]S. Narkhede, «Understanding AUC-ROC Curve,» Towards Data Science, vol. 26, 2018. [29]R. Westermann and W. Hager, «Error Probabilities in Educational and Psychological Research,» Journal of Educational Statistics, Vol 11, No 2, pp. 117-146, 1986.La fuga de clientes es un problema relevante al que enfrentan las empresas de servicios y que les puede generar pérdidas económicas significativas. Identificar los elementos que llevan a un cliente a dejar de consumir un servicio es una tarea compleja, sin embargo, mediante su comportamiento es posible estimar una probabilidad de fuga asociada a cada uno de ellos. Esta investigación aplica minería de datos para la predicción de la fuga de clientes en una empresa de distribución de gas natural, mediante dos técnicas de machine learning: redes neuronales y support vector machine. Los resultados muestran que mediante la aplicación de estas técnicas es posible identificar los clientes con mayor probabilidad de fuga para tomar sobre estas acciones de retenciónoportunas y focalizadas, minimizando los costos asociados al error en la identificación de estos clientes. Palabras Clave: fuga de clientes, minería de datos, machine learning, distribución de gas natural. Referencias [1]J. Miranda, P. Rey y R. Weber, «Predicción de Fugas de Clientes para una Institución Financiera Mediante Support Vector Machines,» Revista Ingeniería de Sistemas Volumen XIX, pp. 49-68, 2005. [2]P. A. Pérez V., «Modelo de predicción de fuga de clientes de telefonía movil post pago,» Universidad de Chile, Santiago, Chile, 2014. [3]Gas Sur S.A., «https://www.gassur.cl/Quienes-Somos/,» [En línea]. [4]J. Xiao, X. Jiang, C. He y G. Teng, «Churn prediction in customer relationship management via GMDH-based multiple classifiers ensemble,» IEEE IntelligentSystems, vol. 31, nº 2, pp. 37-44, 2016. [5]A. M. Almana, M. S. Aksoy y R. Alzahrani, «A survey on data mining techniques in customer churn analysis for telecom industry,» International Journal of Engineering Research and Applications, vol. 4, nº 5, pp. 165-171, 2014. [6]A. Jelvez, M. Moreno, V. Ovalle, C. Torres y F. Troncoso, «Modelo predictivo de fuga de clientes utilizando mineríaa de datos para una empresa de telecomunicaciones en chile,» Universidad, Ciencia y Tecnología, vol. 18, nº 72, pp. 100-109, 2014. [7]D. Anil Kumar y V. Ravi, «Predicting credit card customer churn in banks using data mining,» International Journal of Data Analysis Techniques and Strategies, vol. 1, nº 1, pp. 4-28, 2008. [8]E. Aydoğan, C. Gencer y S. Akbulut, «Churn analysis and customer segmentation of a cosmetics brand using data mining techniques,» Journal of Engineeringand Natural Sciences, vol. 26, nº 1, 2008. [9]G. Dror, D. Pelleg, O. Rokhlenko y I. Szpektor, «Churn prediction in new users of Yahoo! answers,» de Proceedings of the 21st International Conference onWorld Wide Web, 2012. [10]T. Vafeiadis, K. Diamantaras, G. Sarigiannidis y K. Chatzisavvas, «A comparison of machine learning techniques for customer churn prediction,» SimulationModelling Practice and Theory, vol. 55, pp. 1-9, 2015. [11]Y. Xie, X. Li, E. Ngai y W. Ying, «Customer churn prediction using improved balanced random forests,» Expert Systems with Applications, vol. 36, nº 3, pp.5445-5449, 2009. [12]U. Fayyad, G. Piatetsky-Shapiro y P. Smyth, «Knowledge Discovery and Data Mining: Towards a Unifying Framework,» de KDD-96 Proceedings, 1996. [13]R. Brachman y T. Anand, «The process of knowledge discovery in databases,» de Advances in knowledge discovery and data mining, 1996. [14]K. Lakshminarayan, S. Harp, R. Goldman y T. Samad, «Imputation of Missing Data Using Machine Learning Techniques,» de KDD, 1996. [15]B. Nguyen , J. L. Rivero y C. Morell, «Aprendizaje supervisado de funciones de distancia: estado del arte,» Revista Cubana de Ciencias Informáticas, vol. 9, nº 2, pp. 14-28, 2015. [16]I. Monedero, F. Biscarri, J. Guerrero, M. Peña, M. Roldán y C. León, «Detection of water meter under-registration using statistical algorithms,» Journal of Water Resources Planning and Management, vol. 142, nº 1, p. 04015036, 2016. [17]I. Guyon y A. Elisseeff, «An introduction to variable and feature selection,» Journal of machine learning research, vol. 3, nº Mar, pp. 1157-1182, 2003. [18]K. Polat y S. Güneş, «A new feature selection method on classification of medical datasets: Kernel F-score feature selection,» Expert Systems with Applications, vol. 36, nº 7, pp. 10367-10373, 2009. [19]D. J. Matich, «Redes Neuronales. Conceptos Básicos y Aplicaciones,» de Cátedra: Informática Aplicada ala Ingeniería de Procesos- Orientación I, 2001. [20]E. Acevedo M., A. Serna A. y E. Serna M., «Principios y Características de las Redes Neuronales Artificiales, » de Desarrollo e Innovación en Ingeniería, Medellín, Editorial Instituto Antioqueño de Investigación, 2017, pp. Capítulo 10, 173-182. [21]M. Hofmann y R. Klinkenberg, RapidMiner: Data mining use cases and business analytics applications, CRC Press, 2016. [22]R. Pupale, «Towards Data Science,» 2018. [En línea]. Disponible: https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989. [23]F. H. Troncoso Espinosa, «Prediction of recidivismin thefts and burglaries using machine learning,» Indian Journal of Science and Technology, vol. 13, nº 6, pp. 696-711, 2020. [24]L. Tashman, «Out-of-sample tests of forecasting accuracy: an analysis and review,» International journal of forecasting, vol. 16, nº 4, pp. 437-450, 2000. [25]S. Varma y R. Simon, «Bias in error estimation when using cross-validation for model selection,» BMC bioinformatics, vol. 7, nº 1, p. 91, 2006. [26]N. V. Chawla, K. W. Bowyer, L. O. Hall y W. Kegelmeyer, «SMOTE: Synthetic Minority Over-sampling Technique,» Journal of Artificial Inteligence Research16, pp. 321-357, 2002. [27]M. Sokolova y G. Lapalme, «A systematic analysis of performance measures for classification tasks,» Information processing & management, vol. 45, nº 4, pp. 427-437, 2009. [28]S. Narkhede, «Understanding AUC-ROC Curve,» Towards Data Science, vol. 26, 2018. [29]R. Westermann y W. Hager, «Error Probabilities in Educational and Psychological Research,» Journal of Educational Statistics, Vol 11, No 2, pp. 117-146, 1986. &nbsp