scholarly journals Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning

2021 ◽  
Vol 5 (2) ◽  
pp. 62-72
Author(s):  
Asif Yaseen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Service Providers ◽  
Mobile Commerce ◽  
Personal Digital Assistants ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Feature Selection Technique ◽  
Customer Churn ◽  
Feature Selection Approach

With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.

scholarly journals Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector

2021 ◽  
Author(s):  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
Phong Thanh Nguyen ◽  
Mohamed Elhoseny ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Optimization Algorithm ◽  
Service Providers ◽  
Sampling Rate ◽  
Optimal Sampling ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Multi Objective ◽  
Customer Churn ◽  
Customer Churn Prediction

AbstractCustomer retention is a major challenge in several business sectors and diverse companies identify the customer churn prediction (CCP) as an important process for retaining the customers. CCP in the telecommunication sector has become an essential need owing to a rise in the number of the telecommunication service providers. Recently, machine learning (ML) and deep learning (DL) models have begun to develop effective CCP model. This paper presents a new improved synthetic minority over-sampling technique (SMOTE) with optimal weighted extreme machine learning (OWELM) called the ISMOTE-OWELM model for CCP. The presented model comprises preprocessing, balancing the unbalanced dataset, and classification. The multi-objective rain optimization algorithm (MOROA) is used for two purposes: determining the optimal sampling rate of SMOTE and parameter tuning of WELM. Initially, the customer data involve data normalization and class labeling. Then, the ISMOTE is employed to handle the imbalanced dataset where the rain optimization algorithm (ROA) is applied to determine the optimal sampling rate. At last, the WELM model is applied to determine the class labels of the applied data. Extensive experimentation is carried out to ensure the ISMOTE-OWELM model against the CCP Telecommunication dataset. The simulation outcome portrayed that the ISMOTE-OWELM model is superior to other models with the accuracy of 0.94, 0.92, 0.909 on the applied dataset I, II, and III, respectively.

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

2020 ◽  
Vol 24 (106) ◽  
pp. 79-87
Author(s):  
Fredy Humberto Troncoso Espinosa ◽  
Javiera Valentina Ruiz Tapia
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Knowledge Discovery ◽  
Data Science ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Churn Prediction ◽  
Customer Churn ◽  
Using Data

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.  

scholarly journals Customer Churn Prediction for Imbalanced Class Distribution of Data in Business Sector

2019 ◽  
Vol 5 ◽  
pp. 101-110
Author(s):  
Aayush Bhattarai ◽  
Elisha Shrestha ◽  
Ram Prasad Sapkota
Keyword(s):  
Machine Learning ◽  
Imbalanced Data ◽  
Telecommunications Industry ◽  
Business Case ◽  
Machine Learning Algorithms ◽  
Business Sector ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Customer Churn ◽  
Customer Churn Prediction

Churners are those people who are about to transfer their business to a competitor or simply who cancel a subscription to a service. This paper is based on a specific business sector, which is telecommunication sector. With a churn rate of 30%, the telecommunication sector takes the first place on the list. In this paper, we present some advanced data mining methodologies which predicts customer churn in the pre-paid mobile telecommunications industry using a call detail records dataset. To implement the predictive models, we initially propose and then apply four machine learning algorithms: Random Forest, Naïve Bayes, Logistic Regression, and XG Boost. To evaluate the models, we use various evaluation metrics and find the best model which will be suitable for any class imbalanced data and also our business case. This paper can also be viewed as a comparative study on the most popular machine learning methods applied to the challenging problem of customer churn prediction.

A comparison of machine learning techniques for customer churn prediction

2015 ◽  
Vol 55 ◽  
pp. 1-9 ◽  
Author(s):  
T. Vafeiadis ◽  
K.I. Diamantaras ◽  
G. Sarigiannidis ◽  
K.Ch. Chatzisavvas
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Churn Prediction ◽  
Customer Churn ◽  
Learning Techniques ◽  
Customer Churn Prediction
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