A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn

2017 ◽  
Vol 09 (03) ◽  
pp. 1750007 ◽  
Author(s):  
Mehreen Ahmed ◽  
Hammad Afzal ◽  
Awais Majeed ◽  
Behram Khan
Keyword(s):  
Customer Service ◽  
Probabilistic Models ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Churn Prediction ◽  
Customer Churn ◽  
Service Response Time ◽  
Telecom Industry ◽  
Simple Features

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.

scholarly journals A Critical Examination of Different Models for Customer Churn Prediction using Data Mining

2019 ◽  
Vol 8 (6S3) ◽  
pp. 850-854
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Critical Examination ◽  
Churn Prediction ◽  
Customer Churn ◽  
Learning Techniques ◽  
Customer Churn Prediction ◽  
Using Data

Due to competition between online retailers, the need for providing improved customer service has grown rapidly. In addition to reduction in sales due to loss of customers, more investments are needed to be done to attract new customers. Companies now are working continuously to improve their perceived quality by way of giving timely and quality service to their customers. Customer churn has become one of the primary challenges that many firms are facing nowadays. Several churn prediction models and techniques are proposed previously in literature to predict customer churn in areas such as finance, telecom, banking etc. Researchers are also working on customer churn prediction in e-commerce using data mining and machine learning techniques. In this paper, a comprehensive review of various models to predict customer churn in e-commerce data mining and machine learning techniques has been presented. A critical review of recent research papers in the field of customer churn prediction in e-commerce using data mining has been done. Thereafter, important inferences and research gaps after studying the literature are presented. Finally, the research significance and concluding remarks are described in the end.

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.  

Retention Futility: Targeting High-Risk Customers Might be Ineffective

2018 ◽  
Vol 55 (1) ◽  
pp. 80-98 ◽  
Author(s):  
Eva Ascarza
Keyword(s):  
Randomized Trials ◽  
Field Experiments ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Churn Prediction ◽  
Average Effect ◽  
Customer Churn ◽  
Learning Techniques ◽  
Vast Literature ◽  
The Right

Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models that identify customers at the highest risk of churning, no research has investigated whether it is indeed optimal to target those individuals. Combining two field experiments with machine learning techniques, the author demonstrates that customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs. This finding is not only contrary to common wisdom but also suggests that retention programs are sometimes futile not because firms offer the wrong incentives but because they do not apply the right targeting rules. Accordingly, firms should focus their modeling efforts on identifying the observed heterogeneity in response to the intervention and to target customers on the basis of their sensitivity to the intervention, regardless of their risk of churning. This approach is empirically demonstrated to be significantly more effective than the standard practice of targeting customers with the highest risk of churning. More broadly, the author encourages firms and researchers using randomized trials (or A/B tests) to look beyond the average effect of interventions and leverage the observed heterogeneity in customers' response to select customer targets.

scholarly journals Customer churn prediction for telecommunication industry: A Malaysian Case Study

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1274
Author(s):  
Nurulhuda Mustafa ◽  
Lew Sook Ling ◽  
Siti Fatimah Abdul Razak
Keyword(s):  
Customer Satisfaction ◽  
Customer Service ◽  
Telecommunications Industry ◽  
Support Vector ◽  
Churn Prediction ◽  
Linear Discriminant ◽  
Customer Churn ◽  
Customer Information ◽  
Nearest Neighbours

Background: Customer churn is a term that refers to the rate at which customers leave the business. Churn could be due to various factors, including switching to a competitor, cancelling their subscription because of poor customer service, or discontinuing all contact with a brand due to insufficient touchpoints. Long-term relationships with customers are more effective than trying to attract new customers. A rise of 5% in customer satisfaction is followed by a 95% increase in sales. By analysing past behaviour, companies can anticipate future revenue. This article will look at which variables in the Net Promoter Score (NPS) dataset influence customer churn in Malaysia's telecommunications industry.  The aim of This study was to identify the factors behind customer churn and propose a churn prediction framework currently lacking in the telecommunications industry.   Methods: This study applied data mining techniques to the NPS dataset from a Malaysian telecommunications company in September 2019 and September 2020, analysing 7776 records with 30 fields to determine which variables were significant for the churn prediction model. We developed a propensity for customer churn using the Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbours Classifier, Classification and Regression Trees (CART), Gaussian Naïve Bayes, and Support Vector Machine using 33 variables.   Results: Customer churn is elevated for customers with a low NPS. However, an immediate helpdesk can act as a neutral party to ensure that the customer needs are met and to determine an employee's ability to obtain customer satisfaction.   Conclusions: It can be concluded that CART has the most accurate churn prediction (98%). However, the research is prohibited from accessing personal customer information under Malaysia's data protection policy. Results are expected for other businesses to measure potential customer churn using NPS scores to gather customer feedback.

scholarly journals Machine Learning Frameworks in Cancer Detection

2021 ◽  
Vol 297 ◽  
pp. 01073
Author(s):  
Sabyasachi Pramanik ◽  
K. Martin Sagayam ◽  
Om Prakash Jena
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Cancer Development ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Fact Finding ◽  
Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

scholarly journals THE EFFICIENCY OF ENSEMBLE CLASSIFIERS IN PREDICTING THE JOHANNESBURG STOCK EXCHANGE ALL-SHARE INDEX DIRECTION

2019 ◽  
Vol 07 (02) ◽  
pp. 1950001
Author(s):  
THABANG MOKOALELI-MOKOTELI ◽  
SHAUN RAMSUMAR ◽  
HIMA VADAPALLI
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Prediction Models ◽  
Stock Exchange ◽  
Machine Learning Techniques ◽  
Ensemble Prediction ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
Ensemble Models ◽  
Johannesburg Stock Exchange

The success of investors in obtaining huge financial rewards from the stock market depends on their ability to predict the direction of the stock market index. The purpose of this study is to evaluate the efficacy of several ensemble prediction models (Boosted, RUS-Boosted, Subspace Disc, Bagged, and Subspace KNN) in predicting the daily direction of the Johannesburg Stock Exchange (JSE) All-Share index compared to other commonly used machine learning techniques including support vector machines (SVM), logistic regression and [Formula: see text]-nearest neighbor (KNN). The findings in this study show that, among all ensemble models, Boosted algorithm is the best performer followed by RUS-Boosted. When compared to the other techniques, ensemble technique (represented by Boosted) outperformed these techniques, followed by KNN, logistic regression and SVM, respectively. These findings suggest that investors should include ensemble models among the index prediction models if they want to make huge profits in the stock markets. However, not all investors can benefit from this as models may suffer from alpha decay as more and more investors use them, implying that the successful algorithms have limited shelf life.

scholarly journals Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction

2020 ◽  
Vol 7 (2) ◽  
pp. 631-647
Author(s):  
Emrana Kabir Hashi ◽  
Md. Shahid Uz Zaman
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Traditional Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Traditional System ◽  
Prediction Approach

Machine learning techniques are widely used in healthcare sectors to predict fatal diseases. The objective of this research was to develop and compare the performance of the traditional system with the proposed system that predicts the heart disease implementing the Logistic regression, K-nearest neighbor, Support vector machine, Decision tree, and Random Forest classification models. The proposed system helped to tune the hyperparameters using the grid search approach to the five mentioned classification algorithms. The performance of the heart disease prediction system is the major research issue. With the hyperparameter tuning model, it can be used to enhance the performance of the prediction models. The achievement of the traditional and proposed system was evaluated and compared in terms of accuracy, precision, recall, and F1 score. As the traditional system achieved accuracies between 81.97% and 90.16%., the proposed hyperparameter tuning model achieved accuracies in the range increased between 85.25% and 91.80%. These evaluations demonstrated that the proposed prediction approach is capable of achieving more accurate results compared with the traditional approach in predicting heart disease with the acquisition of feasible performance.

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