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 Projection pursuit random forest using discriminant feature analysis model for churners prediction in telecom industry

2020 ◽  
Vol 10 (2) ◽  
pp. 1406
Author(s):  
Asia Mahdi Naser alzubaidi ◽  
Eman Salih Al-Shamery
Keyword(s):  
Discriminant Analysis ◽  
Random Forest ◽  
Prediction Model ◽  
Lift Coefficient ◽  
Telecommunications Industry ◽  
Feature Analysis ◽  
Support Vector ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model.

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 PREDIKSI TINGKAT PELANGGAN CHURN PADA PERUSAHAAN TELEKOMUNIKASI DENGAN ALGORITMA ADABOOST

2021 ◽  
Vol 21 (1) ◽  
pp. 34-43
Author(s):  
Iqbal Muhammad Latief ◽  
Agus Subekti ◽  
Windu Gata
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Secondary Data ◽  
Telecommunications Industry ◽  
Customer Relationships ◽  
Training Data ◽  
Churn Prediction ◽  
Customer Data ◽  
Adaboost Algorithm ◽  
Customer Churn

With the rapid advancement of the telecommunications industry, and competition between telecommunications companies is increasing, companies need to predict their customers to determine the level of customer loyalty. One of them is by analyzing customer data by doing a Customer Churn Prediction. Predicting Customer Churn is an important business strategy for the company. To acquire new customers is much higher cost than retaining existing customers. The ease of operator switching is one of the serious challenges that the telecommunications industry must face. By predicting customer churn, companies can take immediate action to retain customers. To retain existing customers, the company must improve customer service, improve product quality, and must know in advance which customers have the possibility to leave the company. Prediction can be done by analyzing customer data using data mining techniques. In line with this, gathering information from the telecommunications business can help predict whether customer relationships will leave the company. The data used in this study are secondary data and amount to 7.403 data customers. The data has 21 variables. This study proposes to use the ensemble method namely adaboost, xgboost and random forest and compare them. Algorithm is validated through training data and testing data with a ratio of 80:20. From the results we got using python tools, it was found that the adaboost algorithm has an accuracy of 80%.Keywords—accuracy, adaboost, churn prediction, compare model, data mining.

scholarly journals The Impact of a Time Gap on the Process of Building a Sustainable Relationship between Employee and Customer Satisfaction

2020 ◽  
Vol 12 (18) ◽  
pp. 7446
Author(s):  
Lukasz Skowron ◽  
Marcin Gąsior ◽  
Monika Sak-Skowron
Keyword(s):  
Customer Satisfaction ◽  
Customer Service ◽  
Strong Relationship ◽  
Management Policy ◽  
Time Shift ◽  
Time Gap ◽  
One Year ◽  
The Impact ◽  
The Relationship

The aim of this paper is to describe the relationships between changes in employee indices (motivation and satisfaction) and customer indices (satisfaction and loyalty) in a single- and multi-term perspective. The article presents the results of primary research conducted in two industries (banking services and shopping centers) during three annual reference periods. The authors used the PLS-SEM method in the analytical process. The results of the research suggest that there is a strong relationship between changes in the areas of employee and customer satisfaction in the studied sectors, with a one-year time shift, which the authors called the “time gap”. In addition, it turned out that the strength of influence of the employee’s motivation level on customers is clearly lower than the strength of influence of the employee satisfaction. The occurrence of a “time gap” between employee and customer processes suggests that any changes introduced in the area of customer service as well as broadly understood human resource management policy need some time to become sustainable—to be noticed by the market and coded in the minds of the recipients of the offer as the new and currently applicable standard. The article makes a successful attempt at a long-term analysis of the relationship between employees and customers, assuming a time delay between both phenomena. As a result of the conducted research, it was possible to operationalize the discussed relationship in terms of strength and direction as well as the time shift.

scholarly journals Optimal customer relationship management in telecalling industry by using data mining and business intelligence

2017 ◽  
Vol 7 (1.1) ◽  
pp. 12
Author(s):  
T. Kamalakannan ◽  
P. Mayilvaghnan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Business Intelligence ◽  
Research Method ◽  
Support Vector ◽  
Telecommunication Industry ◽  
Churn Prediction ◽  
Customer Churn ◽  
Positive Rate ◽  
Customer Churn Prediction

Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. It can be handled effectively by introducing the data mining techniques which select the most useful information from the gathered data set from which decision making can be done efficiently. In this research method, telecommunication industry is considered in which customer churn prediction application is focused. The main goal of this research method is to introduce the data mining technique which can select the most useful information from the telecommunication industry dataset. This is done by introducing the Hybrid Genetic Algorithm with Particle Swarm Optimization (HGAPSO) method which can select the most useful information. In this research, the hybrid HGAPSO combines the advantages of PSO and GA optimally. From the selected information, decision making about the customer churn prediction can be done accurately. Finally decision making is done by predicting the customer behaviour using Support Vector Machine classification approach. The performance metrics are considered such as precision, recall, f-measure, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), time complexity and ROC. Experimental results demonstrated that HGAPSO provides highly scalable which is used for prediction examination in the business intelligence.

scholarly journals Customer Loyalty In Indonesian’s Telecommunication Industry

2019 ◽  
Vol 9 (1) ◽  
pp. 4571-4577
Keyword(s):  
Customer Satisfaction ◽  
Customer Service ◽  
Customer Loyalty ◽  
Service Providers ◽  
Telecommunications Industry ◽  
Technological Factors ◽  
Financial Factors ◽  
Service Factors ◽  
Major Data ◽  
Structured Questionnaire

The study seekss to determine factors that affect customer satisfaction towards their loyalty in Indonesian telecommunications industry. The research seeks to find out how the influence of financial factors, Technology Factors and Customer Service Factors are on Customer Satisfaction and the influence of financial factors, Technology Factors and Customer Service Factors on Customer Loyalty directly or indirectly. The sources of data used in the article are major data and minor data. The major data were attained from a structured questionnaire. The questionnaire was opened online and the total respondents obtained were 160 respondents and they were valid. The results of this questionnaire indicate that the factors that influence Customer Satisfaction, namely, financial factors, technological factors and customer service factors also affect Customer Loyalty to telecommunications service providers in Indonesia. This study found that Financial Factors, Technology Factors and Customer Service Factors had a prospective influence on Customer Satisfaction, Customer Satisfaction had a determining relationship with Customer Loyalty and it was found that indirectly Financial Factors and Technology Factors through Customer Customer Satisfaction had a determining influence on Customer Loyalty whereas Customer Service Factor did not.

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 Indonesian online travel agent sentiment analysis using machine learning methods

2019 ◽  
Vol 14 (1) ◽  
pp. 113 ◽  
Author(s):  
Abimanyu Dharma Poernomo ◽  
Suharjito Suharjito
Keyword(s):  
Machine Learning ◽  
Customer Satisfaction ◽  
Customer Service ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Travel Agent ◽  
Sentiment Score ◽  
Online Travel

Many companies use social media to support their business activities. Three leading online travel agent such as Traveloka, Tiket.com, and Agoda use Facebook for supporting their business as customer service tool. This study is to measure customer satisfaction of Traveloka, Tiket.com, and Agoda by analyzing Facebook posts and comments data from their fan pages. That data will be analyzed with three machine learning algorithms such as K-Nearest Neighbors (KNN), Naïve Bayes, and Support Vector Machine (SVM) to determine the sentiment.  From the classification results, data will be selected with the highest f-score to be used to calculate the Net Sentiment Score used to measure customer satisfaction. The result shows that KNN result better than Naive Bayes and SVM based on f-score. Based on Net Sentiment Score shows companies that get the highest satisfaction value of Traveloka followed by Tiket.com and Agoda

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