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Gupta, G., Aggarwal, H., & Rani, R. (2016) · International Journal of Business Information Systems · doi:10.1504/ijbis.2016.078907 · Cited by 7 · see citing works

Profitable Retail Customer Identification Based on a Combined Prediction Strategy of Customer Lifetime Value

Sun, Y., Cheng, D., Bandyopadhyay, S. K., & Xue, W. (2021) · Midwest Social Sciences Journal · doi:10.22543/0796.241.1053 · Cited by 6 · see citing works

Implementation of Machine Learning Model for Employee Retention Prediction

Thakre, G., Wankhede, P., Patle, S., Joshi, S., & Chauhan, P. A. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21557981 · Cited by 6 · see citing works

Assessing Efficiency of Service Quality on Consumers Retention in Nigerian Mobile Service Industry

Sarkindaji, B. D., Hashim, N. A., & Abdullateef, A. O. (2015) · Mediterranean Journal of Social Sciences · doi:10.5901/mjss.2015.v6n1p195 · Cited by 6 · see citing works

A BI‐LEVEL DECISION MODEL FOR CUSTOMER CHURN ANALYSIS

Gao, Y., Zhang, G., Jie Lu, B., & Ma, J. (2013) · Computational Intelligence · doi:10.1111/coin.12008 · Cited by 5 · see citing works

Employee Churn walkthrough using KNN

Patil, S. S., Patil, S. H., Pawar, A. M., Pandey, P. K., Sharma, S., & Bewoor, M. S. (2022) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon55314.2022.9909382 · Cited by 5 · see citing works

Post Mining of Multiple Criteria Linear Programming Classification Model for Actionable Knowledge in Credit Card Churning Management

Chen, Y., Zhang, L., & Shi, Y. (2011) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2011.138 · Cited by 5 · see citing works

The Worst Case GARCH-Copula CVaR Approach for Portfolio Optimisation: Evidence from Financial Markets

Alotaibi, T. S., Valle, L. D., & Craven, M. J. (2022) · Journal of risk and financial management · doi:10.3390/jrfm15100482 · Cited by 4 · see citing works

Churn Prediction of Customers in a Retail Business using Exploratory Data Analysis

Abbas, W., Usman, M., & Qamar, U. (2022) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit57066.2022.00033 · Cited by 4 · see citing works

An Investigation on Consumer Switching Behavior in an Asian Telecommunication Market

Bhattacharyya, J., Kundu, S., Dash, M. K., & Dolhey, S. (2021) · International Journal of Service Science Management Engineering and Technology · doi:10.4018/ijssmet.2021110107 · Cited by 4 · see citing works

Clustering Marketing Datasets with Data Mining Techniques

Örnek, Ö., & Subaşı, A. (2010) · IBU Repository · Cited by 4 · see citing works

The foundations of cost-sensitive causal classificationPreprint

Verbeke, W., Olaya, D., Berrevoets, J., Verboven, S., & Maldonado, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.12582 · Cited by 4 · see citing works

Volume 24, Full Contents

Staff, M. (2021) · Midwest Social Sciences Journal · doi:10.22543/2766-0796.1072 · Cited by 3 · see citing works

Volume 23, Full Contents

Staff, M. (2020) · Midwest Social Sciences Journal · doi:10.22543/2766-0796.1036 · Cited by 3 · see citing works

Estimating Risk Relativity of Driving Records using Generalized Additive Models: A Statistical Approach for Auto Insurance Rate Regulation

Xie, S. (2023) · Asia-Pacific Journal of Risk and Insurance · doi:10.1515/apjri-2023-0032 · Cited by 3 · see citing works

The Impact of Customer Churn Factors (CCF) on Customer's Loyalty

Ahmed, H. M. S. (2018) · International Journal of Customer Relationship Marketing and Management · doi:10.4018/ijcrmm.2019010104 · Cited by 4 · see citing works

Volume 22, Full Contents

Staff, M. (2020) · Midwest Social Sciences Journal · doi:10.22543/2766-0796.1019 · Cited by 3 · see citing works

Data-Driven Customer Centricity

Boujena, O., Coussement, K., & Bock, K. W. D. (2015) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/978-1-4666-8459-1.ch008 · Cited by 2 · see citing works

Predictive Model on Churn Customers using SMOTE and XG-Boost Additive Model and Machine Learning Techniques in Telecommunication Industries

Lal, B., & Kumar, S. (2021) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst218498 · Cited by 2 · see citing works

Applying Data Mining to Customer Churn Analysis: A Case Study on the Insurance Industry of Iran

Rodpysh, K. V. (2013) · Open journal of artificial intelligence · doi:10.12966/ojai.08.02.2013 · Cited by 2 · see citing works

Analysis of Employee Attrition using for Machine Learning Techniques

Et.al, S. S. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i6.1253 · Cited by 2 · see citing works

Data-Driven Customer Centricity

Boujena, O., Coussement, K., & Bock, K. W. D. (2018) · Intelligent Systems · doi:10.4018/978-1-5225-5643-5.ch084 · Cited by 2 · see citing works

The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?Preprint

Ballings, M., & den Poel, D. V. (2012) · RePEc: Research Papers in Economics · Cited by 2 · see citing works

Telecomm Churn Prediction Using Fundamental Classifiers To Identify Cumulative Probability

Dijendra, A., & Sisodia, J. (2021) · International Conference on Communication Information and Computing Technology (ICCICT) · doi:10.1109/iccict50803.2021.9510111 · Cited by 1 · see citing works

Advancing Telecom Customer Churn using Deep Learning

Seethalakshmi, R., & Varsha, B. S. (2024) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca63461.2024.10801009 · Cited by 1 · see citing works

A Learning Model on Customer Churn Forecasting for Telecom Providers

Poojitha, C. S., Sridhar, A. P., Rao, C. V., Subhash, U. S., Kumar, C. B., & Mohana, T. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7477-0_2 · Cited by 1 · see citing works

Innovative Techniques to Predict Churn in the French Insurance Industry: Integration of Machine Learning With the Grabit Model

Schalck, C., & Yankol‐Schalck, M. (2025) · Journal of Forecasting · doi:10.1002/for.70057 · Cited by 2 · see citing works

Machine Learning–Driven prediction of wettability in brine–co2–mineral systems: A step toward reliable geological carbon storage

Ahmadi, M. (2026) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2026.214545 · Cited by 1 · see citing works

Providing a Method for Determining the Index of Customer Churn in Industry

Rodpysh, K. V. (2013) · International Journal of Information Technology Control and Automation · doi:10.5121/ijitca.2013.3105 · Cited by 1 · see citing works

Churn prediction based on text mining and CRM data analysis

Schatzmann, A., Heitz, C., & Münch, T. (2014) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-1889 · Cited by 1 · see citing works

Essays on data augmentation: the value of additional information

Baecke, P. (2012) · Ghent University Academic Bibliography (Ghent University)

Evaluating the Added Value of Pictorial Data for Customer Churn PredictionPreprint

Ballings, M., den Poel, D. V., & Verhagen, E. (2013) · RePEc: Research Papers in Economics

Preventing Customers from Running Away! Exploring Generalized Additive Models for Customer Churn Prediction

Coussement, K., Benoit, D. F., & den Poel, D. V. (2014) · Developments in marketing science: proceedings of the Academy of Marketing Science · doi:10.1007/978-3-319-10873-5_134 · Cited by 11 · see citing works

Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

Bock, K. W. D., & den Poel, D. V. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.014 · Cited by 73 · see citing works

Reconciling performance and interpretability in customer churn prediction modeling using ensemble learning based on generalized additive models

Bock, K. W. D., & van den Poel, D. (2012) · HAL

Improving customer churn models as one of customer relationship management business solutions for the telecommunication industry

Slavescu, E. O., & Panait, I. (2012) · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)

Maximize What Matters: Predicting Customer Churn With Decision-Centric Ensemble Selection

Baumann, A., Lessmann, S., Coussement, K., & Bock, K. W. D. (2015) · Journal of the Association for Information Systems · doi:10.18151/7217273 · Cited by 17 · see citing works