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Journal articles on the topic 'Customer churning'

Author: Grafiati

Published: 30 June 2021

Last updated: 29 July 2025

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1

Shafiei, Gol Elham, Abbas Ahmadi, and Azadeh Mohebi. "Intelligent approach for attracting churning customers in banking industry based on collaborative filtering." Journal of Industrial and Systems Engineering 9, no. 4 (2016): 9–25. https://doi.org/10.5281/zenodo.13999871.

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During recent years, increased competition among banks has caused many developments in banking experiences and technology, while leading to even more churning customers due to their desire of having the best services.&lrm; Therefore, it is an extremely significant issue for the banks to identify churning customers and attract them to the banking system again.&lrm; In order to tackle this issue, this paper proposes a novel personalized collaborating filtering recommendation approach joint with the user clustering technology.&lrm; In the proposed approach, first a hybrid algorithm based on Parti

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S.P. Valli, Sharmila Sankar, C. Hema, and Mohammad Munzir. "Enhancement of XG-Boost Using Custom Hyper Parameter Tuning for Bank Churning." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 07 (2024): 1909–14. http://dx.doi.org/10.47392/irjaeh.2024.0261.

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Bank is an important component of our society which deals with money transaction i.e., lending and deposit of money. Customer churn is termination of business of a customer with the company. Bank customer churn creates an impact on revenue and operational efficiencies of banks, where a customer switches or leaves availing the services of bank. Bank is an important part of our society since it makes money by lending money to others. To understand customer churning behavior it is necessary to retain customers and increase the number of customers. In order to predict the bank customer churning be

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Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might be Ineffective." Journal of Marketing Research 55, no. 1 (2018): 80–98. http://dx.doi.org/10.1509/jmr.16.0163.

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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 ta

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Kim, Myung-Joong, Juil Kim, and Sun-Young Park. "Understanding IPTV churning behaviors: focus on users in South Korea." Asia Pacific Journal of Innovation and Entrepreneurship 11, no. 2 (2017): 190–213. http://dx.doi.org/10.1108/apjie-08-2017-026.

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PurposeThis study aims to investigate customers’ churning out of Internet Protocol Television (IPTV) service, one of the most prevalent forms of IT convergence. Design/methodology/approachBased on the review of current literature, a research model is introduced to depict the effects of select independent variables on customer churning behavior. First of all, the two groups are compared in terms of predictor variables, including switching barriers, voice of customer (VOC), membership period and degree of contents usage. Then, a curvilinear regression was applied to understand the association re

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Elena, Felice, Robyn Irawan, and Benny Yong. "APPLICATION OF THE SUPPORT VECTOR MACHINE, LIGHT GRADIENT BOOSTING MACHINE, ADAPTIVE BOOSTING, AND HYBRID ADABOOST-SVM MODEL ON CUSTOMERS CHURN DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 3 (2025): 1957–72. https://doi.org/10.30598/barekengvol19iss3pp1957-1972.

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A service provider is a business that provides services or the expertise of an individual in a certain sector. A service provider’s customer flow could be very dynamic, with both new and churning customers. For the purpose of minimizing the number of churning customers, the company should perform a customer churn analysis. Customer churn analysis is the process of identifying a pattern or trend in churning customers. In order to classify and predict churning customers, machine learning techniques are required to build the classifier model. This paper will use the Support Vector Machine (SVM),

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Abdullah Kafabih. "The Influence of Network Coverage Image and Digital Marketing Promotion on Churning Intention Mediated by Digital Satisfaction and Moderated by Private Identity: Approach on Telecommunication Customers in Indonesia." Journal of Information Systems Engineering and Management 10, no. 6s (2025): 127–37. https://doi.org/10.52783/jisem.v10i6s.706.

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The telecommunications industry is a key sector contributing significantly to the national economy. Amidst intense competition among telecommunications operators, many telecommunications customers tend to switch to other operators (Churning). The objective of this research is to investigate how Network Coverage Image and Digital Marketing Promotion impact Churning Intention, with Digital Satisfaction acting as a mediator and Private Identity as a moderating factor. The research targeted telecommunication customers from all operators in Jakarta who had experienced switching SIM cards to another

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Aldhafferi, Nahier, Abdullah Alqahtani, Fatema Sabeen Shaikh, et al. "Learning trends in customer churn with rule-based and kernel methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5364. http://dx.doi.org/10.11591/ijece.v12i5.pp5364-5374.

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<span>In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature o

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Nahier, Aldhafferi, Alqahtani Abdullah, Sabeen Shaikh Fatema, et al. "Learning trends in customer churn with rule-based and kernel methods." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5364–74. https://doi.org/10.11591/ijece.v12i5.pp5364-5374.

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In the present article an attempt has been made to predict the occurrences of customers leaving or ‘churning’ a business enterprise and explain the possible causes for the customer churning. Three different algorithms are used to predict churn, viz. decision tree, support vector machine and rough set theory. While two are rule-based learning methods which lead to more interpretable results that might help the marketing division to retain or hasten cross-sell of customers, one of them is a kernel-based classification that separates the customers on a feature hyperplane. The nature o

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P A, Jufin, and Amrutha N. "Bank Customer Churn Prediction." Indian Journal of Data Mining 2, no. 2 (2023): 1–5. http://dx.doi.org/10.54105/ijdm.b1628.112222.

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In the current challenging era, there is a stiff competition happening between the banking industries. To strengthen the grade and level of services they provide, banks focus on customer retention as well as the customer churning. Customer churning becomes one of the duties of corporate intelligences to speculate the number of customers leaving from the bank or presumed to be churned. It also helps in predicting the number of customers retained. The primary objective of this paper is "Bank customer churn prediction" is to build a model that can distinguish and visualize which factors or attrib

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Jufin, P. A. "Bank Customer Churn Prediction." Indian Journal of Data Mining (IJDM) 2, no. 2 (2023): 1–5. https://doi.org/10.54105/ijdm.B1628.112222.

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<strong>Abstract: </strong>In the current challenging era, there is a stiff competition happening between the banking industries. To strengthen the grade and level of services they provide, banks focus on customer retention as well as the customer churning. Customer churning becomes one of the duties of corporate intelligences to speculate the number of customers leaving from the bank or presumed to be churned. It also helps in predicting the number of customers retained. The primary objective of this paper is "Bank customer churn prediction" is to build a model that can distinguish and visual

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Muneer, Amgad, Rao Faizan Ali, Amal Alghamdi, Shakirah Mohd Taib, Ahmed Almaghthawi, and Ebrahim Abdulwasea Abdullah Ghaleb. "Predicting customers churning in banking industry: A machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 539–49. https://doi.org/10.11591/ijeecs.v26.i1.pp539-549.

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In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking industry. For this purpo

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Kabue, Hellen W. "Creating Customer Value for Enhanced Customer Satisfaction and Retention." Research in Economics and Management 5, no. 3 (2020): p7. http://dx.doi.org/10.22158/rem.v5n3p7.

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Customers are increasingly becoming sophisticated due to forces such as advancement in technology, changing social roles and globalization. As a result, customer churning is today a common reality that most companies have to deal with in order to satisfy and retain their customers. Creating customer value has emerged as one of the winning strategic tools that firms could use to gain competitive advantage in the contemporary marketing environment. This paper is an empirical study that presents a comprehensive analysis of the relationship between customer value, customer satisfaction and custome

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Muneer, Amgad, Rao Faizan Ali, Amal Alghamdi, Shakirah Mohd Taib, Ahmed Almaghthawi, and Ebrahim Abdulwasea Abdullah Ghaleb. "Predicting customers churning in banking industry: A machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 539. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp539-549.

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<span lang="EN-US">In this era, machines can understand human activities and their meanings. We can utilize this ability of machines in various fields or applications. One specific field of interest is a prediction of churning customers in any industry. Prediction of churning customers is the state of art approach which predicts which customer is near to leave the services of the specific bank. We can use this approach in any big organization that is very conscious about their customers. However, this study aims to develop a model that offers a meaningful churn prediction for the banking

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Ait, Daqud Rachid, Abdellah Amine, Belaid Bouikhalene, and Rachid Lbibb. "Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2367–83. https://doi.org/10.11591/ijece.v8i4.pp2367-2383.

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With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors" products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the i

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Zdziebko, Tomasz, Piotr Sulikowski, Wojciech Sałabun, Małgorzata Przybyła-Kasperek, and Iwona Bąk. "Optimizing Customer Retention in the Telecom Industry: A Fuzzy-Based Churn Modeling with Usage Data." Electronics 13, no. 3 (2024): 469. http://dx.doi.org/10.3390/electronics13030469.

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Churn is a serious challenge for the telecommunications industry because of the much higher costs of gaining new customers than maintaining existing ones. Therefore, efforts to increase loyalty and decrease customer churn are the focus of telecom’s retention departments. In order to direct antichurn activities, profitable clients who have the highest probability of churning need to be identified. The data used to identify churners are often inaccurate and vague. In this paper, a fuzzy approach to modeling churn intent based on usage data in mobile telecommunications is presented. It appreciate

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KOKO, Joe BALANGA, Guylit KIALA LUTUMBA, Francis KANGA SALU, et al. "Machine Learning-based Customer Churn Analysis in Telecommunications Using Support Vector Machines." Asian Journal of Research in Computer Science 18, no. 5 (2025): 187–203. https://doi.org/10.9734/ajrcos/2025/v18i5648.

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Faced with globalization and increasing competition, the information available via the Internet and the many connected objects continues to increase. This explosion of data, often heterogeneous and from diverse sources, poses major challenges in terms of storage, analysis and exploitation. This paper is the result of the present research on the analysis and classification of churning customers in a telecommunications company. These data, often heterogeneous and coming from various sources, require in-depth analysis as well as new storage and exploration paradigms to extract value from them. Th

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Sam, Glory, Philip Asuquo, and Bliss Stephen. "Customer Churn Prediction using Machine Learning Models." Journal of Engineering Research and Reports 26, no. 2 (2024): 181–93. http://dx.doi.org/10.9734/jerr/2024/v26i21081.

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Customer churn is a critical concern for the telecommunication industry. Understanding and predicting customer churn can lead to more effective retention strategies and an increase in profitability. Predicting customer churn allows telecommunication companies to identify potentially dissatisfied customers early on and take proactive measures to retain them. Due to a large client base, the telecom industry generates a large volume of data on a daily basis. Decision makers and business analysts stressed that acquiring new customers is more expensive than retaining existing ones. Business analyst

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Makinde, Ayodeji Samuel, Abayomi O. Agbeyangi, and Wilson Nwankwo. "Predicting Mobile Portability Across Telecommunication Networks Using the Integrated-KLR." International Journal of Intelligent Information Technologies 17, no. 3 (2021): 50–62. http://dx.doi.org/10.4018/ijiit.2021070104.

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Mobile number portability (MNP) across telecommunication networks entails the movement of a customer from one mobile service provider to another. This, often, is as a result of seeking better service delivery or personal choice. Churning prediction techniques seek to predict customers tending to churn and allow for improved customer sustenance campaigns and the cost therein through an improved service efficiency to customer. In this paper, MNP predicting model using integrated kernel logistic regression (integrated-KLR) is proposed. The Integrated-KLR is a combination of kernel logistic regres

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Rachid, Ait Daoud, Amine Abdellah, Bouikhalene Belaid, and Lbibb Rachid. "Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (2018): 2367. http://dx.doi.org/10.11591/ijece.v8i4.pp2367-2383.

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<p><span>With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several e-commerce sites and compare their competitors’ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering ph

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Zhao, Xue. "Research on E-Commerce Customer Churning Modeling and Prediction." Open Cybernetics & Systemics Journal 8, no. 1 (2014): 800–804. http://dx.doi.org/10.2174/1874110x01408010800.

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Tan, Zhaoyuan. "Comparative Analysis of Several Models for Churning Customer Prediction." SHS Web of Conferences 218 (2025): 02013. https://doi.org/10.1051/shsconf/202521802013.

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Customer churn prediction is critical for financial institutions to retain clients and optimize resource allocation. It is less expensive to keep current clients than to find new ones. There lots of research in this field, but their performance is often limited by data imbalance issues. This study compares three machine learning models: Random Forest, XGBoost Classifier, and Light Gradient Boosting Machine Classifier for predicting credit card customer churn using a dataset from Kaggle. The research addresses data imbalance issues through oversampling techniques (SMOTE, SMOTEENN, Borderline SM

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Zhu, He. "Bank Customer Churn Prediction with Machine Learning Methods." Advances in Economics, Management and Political Sciences 69, no. 1 (2024): 23–29. http://dx.doi.org/10.54254/2754-1169/69/20230773.

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This paper examines and analyses customer churn prediction in the banking sector using the data from ABC Bank. The analysis conducted will document the determinants of bank customer churn and provide insights to the most important factors which influence the customers decision to quit utilizing the services of a bank. The investigation is based on the results of two machine learning algorithms with k-fold-cross-validation and same boosting methods. The result of the analysis reveals that out of logistic regression and random forests algorithms, the random forest methods show a higher accuracy

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Ewieda, Mahmoud, Essam M. Shaaban, and Mohamed Roushdy. "Review of Data Mining Techniques for Detecting Churners in the Telecommunication Industry." Future Computing and Informatics Journal 6, no. 1 (2021): 1–15. http://dx.doi.org/10.54623/fue.fcij.6.1.1.

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The telecommunication sector has been developed rapidly and with large amounts of data obtained as a result of increasing in the number of subscribers, modern techniques, data-based applications, and services. As well as better awareness of customer requirements and excellent quality that meets their satisfaction. This satisfaction raises rivalry between firms to maintain the quality of their services and upgrade them. These data can be helpfully extracted for analysis and used for predicting churners. Researchers around the world have conducted important research to understand the uses of Dat

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Kailash, Alle. "Logistic Regression for Predictive Modeling." Journal of Scientific and Engineering Research 8, no. 9 (2019): 307–14. https://doi.org/10.5281/zenodo.13347981.

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Customer retention, loyalty measurement, and recovery strategies have become crucial for businesses aiming to minimize client loss. Instead of focusing solely on acquiring new customers, companies now prioritize preventing the loss of existing ones. The telecommunications industry, with its rapid technological advancements and growing user base, generates vast amounts of data. However, this rapid and uncontrolled expansion leads to significant losses due to fraud and technical issues, necessitating the development of new analytical methodologies. This paper addresses the urgent need for effect

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Chouiekh, Alae, and El Hassane Ibn El Haj. "Deep Convolutional Neural Networks for Customer Churn Prediction Analysis." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 1 (2020): 1–16. http://dx.doi.org/10.4018/ijcini.2020010101.

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Several machine learning models have been proposed to address customer churn problems. In this work, the authors used a novel method by applying deep convolutional neural networks on a labeled dataset of 18,000 prepaid subscribers to classify/identify customer churn. The learning technique was based on call detail records (CDR) describing customers activity during two-month traffic from a real telecommunication provider. The authors use this method to identify new business use case by considering each subscriber as a single input image describing the churning state. Different experiments were

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Hakim, Muhammad A. A., and Terttiaavini Terttiaavini. "Predictive Buyer Behavior Model as Customer Retention Optimization Strategy in E-commerce." Journal of Intelligent System and Computation 6, no. 1 (2024): 32–38. http://dx.doi.org/10.52985/insyst.v6i1.379.

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Lazada is one of the rapidly growing E-commerce platforms in this digital era. One of the main challenges faced by Lazada is customer retention, where customers make purchases once or a few times before switching to other platforms. Therefore, it is important to understand buyer behavior in E-commerce through customer prediction to identify factors influencing retention. This study employs the Random Forest (RF) method to analyze Lazada customer data and formulate more effective marketing strategies. The analysis is conducted by loading preprocessed datasets into the KNIME workflow and utilizi

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Karuppaiah, Sivasankar, and N. P. Gopalan. "Enhanced Churn Prediction Using Stacked Heuristic Incorporated Ensemble Model." Journal of Information Technology Research 14, no. 2 (2021): 174–86. http://dx.doi.org/10.4018/jitr.2021040109.

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In a rapidly growing industry like telecommunications, customer churn prediction is a crucial challenge affecting the sustainability of the business as a whole. The fact that retaining a customer is more profitable than acquiring new customers is important to predict potential churners and present them with offers to prevent them from churning. This work presents a stacked CLV-based heuristic incorporated ensemble (SCHIE) to enable identification of potential churners so as to provide them with offers that can eventually aid in retaining them. The proposed model is composed of two levels of pr

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Mengash, Hanan Abdullah, Nuha Alruwais, Fadoua Kouki, Chinu Singla, Elmouez Samir Abd Elhameed, and Ahmed Mahmud. "Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries." Biomimetics 9, no. 1 (2023): 1. http://dx.doi.org/10.3390/biomimetics9010001.

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Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveragin

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Mishachandar, B., and Kakelli Anil Kumar. "Predicting customer churn using targeted proactive retention." International Journal of Engineering & Technology 7, no. 2.27 (2018): 69. http://dx.doi.org/10.14419/ijet.v7i2.27.10180.

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With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this pap

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Zimal, Sudarshan, Chirag Shah, Shivam Borhude, Amit Birajdar, and Prof Shreedhar Patil. "Customer Churn Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (2023): 872–83. http://dx.doi.org/10.22214/ijraset.2023.49142.

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bstract: Rapid technology growth has affected corporate practices. With more items and services to select from, client churning has become a big challenge and threat to all firms. We offer a machine learning-based churn prediction model for a B2B subscription-based service provider. Our research aims to improve churn prediction. We employed machine learning to iteratively create and evaluate the resulting model using accuracy, precision, recall, and F1- score. The data comes from a financial administration subscription service. Since the given dataset is mostly non-churners, we analyzed SMOTE,

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Vaani Gupta and Aman Jatain. "Artificial Intelligence Based Predictive Analysis of Customer Churn." Formosa Journal of Computer and Information Science 2, no. 1 (2023): 95–110. http://dx.doi.org/10.55927/fjcis.v2i1.3926.

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Customer churn, also known as attrition, occurs when subscribers or customers stop doing business with an enterprise or organization by unsubscribing to a service, discontinuing membership or simply stopping payment. Churn is a critical metric because it is more cost-effective to retain existing customers than it is to acquire new ones. Since churning impedes growth, companies usually use a defined method for calculating customer churn in a given period. By monitoring churn rate and the various factors affecting it, organizations determine their customer retention success rates and identify st

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Kalai Selvi, T., S. Sasirekha, N. Deepika, V. Kanagalakshmi, and R. Kavya. "Customer Segmentation in IT Sector using Datamining Techniques." March 2024 6, no. 1 (2024): 15–26. http://dx.doi.org/10.36548/jaicn.2024.1.002.

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Due to its large client base, the IT industry generates enormous amounts of data every day. Business experts and decision-makers stressed that keeping current clients is less expensive than acquiring new ones. Business analysts and customer relationship management (CRM) analysts must understand the causes of customer attrition as well as the patterns of behavior found in the data of these clients. This research is a comprehensive study about churn prediction in IT industry it also suggests a churn prediction model to identify consumer churn and provide the reasons behind customer churn in the

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Ghantasala, Naga Lakshmi Subha Pavan Kumar. "Enhancing Player Retention in Mobile Gaming through Predictive Customer Lifetime Value Modeling using BG/NBD." AI Matters 10, no. 2 (2024): 9–11. https://doi.org/10.1145/3694712.3695754.

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In the competitive mobile gaming industry, retaining players and maximizing engagement are critical for sustained success. This study employs the Beta-Geometric/Negative Binomial Distribution (BG/NBD) model to predict customer lifetime value (CLTV) and identify players at risk of churning. By targeting these high-risk players with tailored marketing strategies, gaming companies can significantly improve retention rates and overall player engagement.

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Rameshwaraiah, Dr K. "Customer Churn Analysis Using Feature-Based Decision Classifier." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45235.

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To survive in the highly competitive marketplace, customer retention forms the backbone of any business for long-term success. Being able to identify customers that are at risk of churning allows the organization to take measures in time to decrease client attrition and optimize customer lifetime value. In this regard, the work creates a broader system for churn prediction that entails machine learning algorithms-logistic regression, random forest, K-nearest neighbors-and a custom classifier based on decision rules tailored for ad hoc business conditions. It involves interfacing with multiple

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Thapa, Prasanna. "An Analysis of the Implementation of the Factors of Customer Retention by Nepal Telecom." International Journal of Social Sciences and Management 5, no. 3 (2018): 89–97. http://dx.doi.org/10.3126/ijssm.v5i3.20408.

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The world is travelling in information era where information is the essential element for every individual, institution, society and country. Communication of information is done by every individual to perform their daily activities, so the mobile telecommunication service has become a basic need where everybody needs telecommunication services. From the latest MIS Report of Nepal Telecommunication Authority (NTA), Market Penetration Rate (MPR) for mobile service 134.41%, which clearly indicates that the number of mobile service users has surpassed the population, and this actually means, at l

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Edwine, Nabahirwa, Wenjuan Wang, Wei Song, and Denis Ssebuggwawo. "Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study." Mathematical Problems in Engineering 2022 (July 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/8534739.

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Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controll

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Akhmetshin, Elvir, Nurulla Fayzullaev, Elena Klochko, Denis Shakhov, and Valentina Lobanova. "Intelligent Data Analytics using Hybrid Gradient Optimization Algorithm with Machine Learning Model for Customer Churn Prediction." Fusion: Practice and Applications 14, no. 2 (2024): 159–71. http://dx.doi.org/10.54216/fpa.140213.

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Intelligent data analytics for customer churn prediction (CCP) harnesses predictive modelling algorithms, machine learning (ML) techniques, and advanced big data analytics and also uncovers the underlying drivers and patterns of churn and detects customers at risk of churning. This business strategy help organization to implement retention efforts to decrease customer attrition and proactively detect at-risk customers. CCP allows businesses to take proactive measures such as targeted marketing campaigns, personalized offers, or enhanced customer service, to maintain valuable customer and decre

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Rabia, Sarfraz, and Babak Mahmood Dr. "Loyalty Breeds Loyalty: Nailing Customer Churn through Satisfied and Loyal Employees in Telecom Sector of Pakistan." International Journal of Management Sciences and Business Research 6, no. 6 (2017): 52–56. https://doi.org/10.5281/zenodo.3468916.

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Customer retention is a serious concern for the organizations around the world. They want to retain employees so their profits can elevate but for the fulfillment of this desire, satisfaction and loyalty levels of employees need to be taken care of while making them work for the organization. The customer can’t be convinced not to churn if they are not satisfied with the services being offered to them and this level of satisfactory service can only be provided through the pool of satisfied employees who have loyalty running in their veins, for the organization they are working for. Emplo

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Mohaimin, MD Rashed, Bimol Chandra Das, Rabeya Akter, et al. "Predictive Analytics for Telecom Customer Churn: Enhancing Retention Strategies in the US Market." Journal of Computer Science and Technology Studies 7, no. 1 (2025): 30–45. https://doi.org/10.32996/jcsts.2025.7.1.3.

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The telecommunications industry in America has been characterized by exponential technological advancements and escalated competition, leading to heightened client expectations. Consequently, client retention has emerged as a crucial metric for telecom companies, directly influencing profitability and market share. The chief objective goal of this study was to build strong predictive models that could correctly identify at-risk customers in the US telecom market. This research paper aimed to use machine learning algorithms and advanced data analytics to uncover patterns and trends in customer

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Ako, Rita Erhovwo, Fidelis Obukohwo Aghware, Margaret Dumebi Okpor, et al. "Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost." Journal of Computing Theories and Applications 2, no. 1 (2024): 86–101. http://dx.doi.org/10.62411/jcta.10562.

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Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) sy

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Khodabandehlou, Samira, and Mahmoud Zivari Rahman. "Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior." Journal of Systems and Information Technology 19, no. 1/2 (2017): 65–93. http://dx.doi.org/10.1108/jsit-10-2016-0061.

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Purpose This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business. Design/methodology/approach The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions

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Chajia, Meryem, and El Habib Nfaoui. "Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression." Future Internet 16, no. 12 (2024): 453. https://doi.org/10.3390/fi16120453.

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Nowadays, predicting customer churn is essential for the success of any company. Loyal customers generate continuous revenue streams, resulting in long-term success and growth. Moreover, companies are increasingly prioritizing the retention of existing customers due to the higher costs associated with attracting new ones. Consequently, there has been a growing demand for advanced methods aimed at enhancing customer loyalty and satisfaction, as well as predicting churners. In our work, we focused on building a robust churn prediction model for the telecommunications industry based on large embe

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Taherkhani, Leila, Amir Daneshvar, Hossein Amoozad Khalili, and Mohamad Reza Sanaei. "Analysis of the Customer Churn Prediction Project in the Hotel Industry Based on Text Mining and the Random Forest Algorithm." Advances in Civil Engineering 2023 (August 24, 2023): 1–8. http://dx.doi.org/10.1155/2023/6029121.

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The ability of hotels to differentiate themselves from competitors and continue to operate profitably depends on their ability to retain their customers by building long-term and permanent customer relationships. Technological developments in recent years have made it possible for companies to predict their customers’ behavior by accessing their opinions faster and preventing them from churning. Managing customer churn prediction projects has become an important issue today, especially in the hotel industry. Therefore, this research seeks to analyze projects that predict the churn of hotel cus

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Sreeejesh, S. "Cellular Customer Churns Due to Mobile Number Portability." International Journal of Interdisciplinary Telecommunications and Networking 5, no. 1 (2013): 43–57. http://dx.doi.org/10.4018/jitn.2013010104.

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Retaining existing customer has been considered to be one of the most critical challenges for telecommunication service providers than for attracting new ones. In telecommunication, the service offered is different from that of a general commodity sale as in the former case the service is considered to be a continuous process, wherein the service provider can offer the differentiated services throughout the customer’s tenure. This differentiation in service offered creates a demarcation from the competitors and hence establishes competitive advantage for that service provider for attracting ne

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Khoh, Wee How, Ying Han Pang, Shih Yin Ooi, Lillian-Yee-Kiaw Wang, and Quan Wei Poh. "Predictive Churn Modeling for Sustainable Business in the Telecommunication Industry: Optimized Weighted Ensemble Machine Learning." Sustainability 15, no. 11 (2023): 8631. http://dx.doi.org/10.3390/su15118631.

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Customers are prominent resources in every business for its sustainability. Therefore, predicting customer churn is significant for reducing churn, particularly in the high-churn-rate telecommunications business. To identify customers at risk of churning, tactical marketing actions can be strategized to raise the likelihood of the churn-probable customers remaining as customers. This might provide a corporation with significant savings. Hence, in this work, a churn prediction system is developed to assist telecommunication operators in detecting potential churn customers. In the proposed frame

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Gupta, Atharva. "An Unsupervised Learning Based System Employing K-Means Clustering to Perform Customer Segmentation." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1559–64. http://dx.doi.org/10.22214/ijraset.2023.53911.

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Abstract: The job of marketers is to get the right product in front of the right consumer at the moment that they’re most likely to buy that product. The ability for marketers to do just that and to drill down to more and more specific niches of customers has grown exponentially over time. AI technology is now being used to help marketers get even more specific with predictive targeting and personalization. Targeted advertising is a form of online advertising which micro-targets its customers. It is based on the traits and behavioral patterns of different people. Nowadays, people, knowingly or

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Li, Jianfeng, Xue Bai, Qian Xu, and Dexiang Yang. "Identification of Customer Churn Considering Difficult Case Mining." Systems 11, no. 7 (2023): 325. http://dx.doi.org/10.3390/systems11070325.

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In the process of user churn modeling, due to the imbalance between lost users and retained users, the use of traditional classification models often cannot accurately and comprehensively identify users with churn tendency. To address this issue, it is not sufficient to simply increase the misclassification cost of minority class samples in cost-sensitive methods. This paper proposes using the Focal Loss hard example mining technique to add the class weight α and the focus parameter γ to the cross-entropy loss function of LightGBM. In addition, it emphasizes the identification of customers at

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Kuo-Hsiung Wu, Kuo-Hsiung Wu, Po-Yang Chen Kuo-Hsiung Wu, and Chaochang Chiu Po-Yang Chen. "A Social Media Based Profiling Approach for Potential Churning Customers: An Example for Telecom Industry." 網際網路技術學刊 23, no. 7 (2022): 1565–71. http://dx.doi.org/10.53106/160792642022122307011.

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<p>Customer churn prevention has been one of the most pressing concerns for telcos to tackle in the rapidly changing telecom business over the past decade, as the industry has faced increased market saturation and intense competition. Social media electronic word-of-mouth (e-WoM) offers telcos with insights into the consumer experience. Capturing preferences and views about products/services via text messaging might increase the recovery mechanism for clients who may churn. This study collects postings from social media forums concerning Taiwan&rsquo;s top five telecom firms in order

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Ahmadzai, Nazak, Hameedullah Mohammadi, and Naqibullah Mangal. "Data Mining Techniques in Telecommunication Company." Journal for Research in Applied Sciences and Biotechnology 2, no. 1 (2023): 96–98. http://dx.doi.org/10.55544/jrasb.2.1.12.

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Due to emerging of amalgam amount of data from variety sources, the data mining has become a hot trend in field of Computer Science. Data mining extracts useful pattern and information from huge amount of existing data with the help of machine learning algorithms that can be helpful in solving many sophisticated problems. Telecommunication companies also generates big amount of data from providing services to their customers, besides that telecommunication companies suffers from many problems like fraud, Customer churn and …etc. The generated amount of data from these companies can

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P., Ajitha, Sivasangari A., Gomathi R.M., and Indira K. "Prediction of Customer Plan using Churn Analysis for Telecom Industry." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 926–29. http://dx.doi.org/10.2174/2213275912666190410114104.

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Background: In creating nations like India, there are in excess of 10 administrators giving versatile administration in each circle. With the presentation of number convenience portable client are progressively changing starting with one administrator then onto the next. This conduct is called beat. The explanation behind beat might be many like valuing isn't alluring, visit call drops, message drops, more client care calls and so forth. Presently the administrator in INDIA is aware of the need of client. At that point, it is past the point of no return as the client has officially settled on

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