Social network analysis for customer churn prediction
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Filling missing data improved performance instead of dropping them.
“Nimmagadda, Sravya, Akshay Subramaniam, and Man Long Wong.(2017) argued that filling the missing data improved performance instead of dropping them [58].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [58]
Nimmagadda et al. used log-normalization or quantile normalization to build a prediction model other than 0-1 normalization.
“Nimmagadda, Sravya, Akshay Subramaniam, and Man Long Wong.(2017) used techniques such as log-normalization or quantile normalization to build a prediction model other than 0-1 normalization [58].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [58]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567 · see citing works
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010 · see citing works
Proceedings of the 25th international conference on Machine learning - ICML '08
(2008) · doi:10.1145/1390156 · Cited by 6,865 · see citing works
Newman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595 · see citing works
Proceedings of the 25th international conference on Machine learning
Cohen, W. W., McCallum, A., & Roweis, S. T. (2008) · Cited by 5,691 · see citing works
The Spread of Obesity in a Large Social Network over 32 Years
Christakis, N. A., & Fowler, J. H. (2007) · New England Journal of Medicine · doi:10.1056/nejmsa066082 · Cited by 5,007 · see citing works
Esmark, K., Orning, H. J., & Hermanson, L. (2011) · Choice Reviews Online · doi:10.5860/choice.48-3328 · Cited by 2,950 · see citing works
Inequality and Heterogeneity: A Primitive Theory of Social Structure
Blau, P. M. (1977) · Medical Entomology and Zoology · Cited by 2,792 · see citing works
Relevance feedback in information retrieval
Rocchio, J. J. (1971) · Medical Entomology and Zoology · Cited by 2,680 · see citing works
The SMART Retrieval System—Experiments in Automatic Document Processing
Salton, G. (1971) · CERN Document Server (European Organization for Nuclear Research) · Cited by 2,261 · see citing works
Storing and querying ordered XML using a relational database system
Tatarinov, I., Viglas, S. D., Beyer, K. S., Shanmugasundaram, J., Shekita, E. J., & Zhang, C. (2002) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/564691.564715 · Cited by 2,236 · see citing works
Enhanced hypertext categorization using hyperlinks
Chakrabarti, S., Dom, B., & Indyk, P. (1998) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/276304.276332 · Cited by 768 · see citing works
The Alternating Decision Tree Learning Algorithm
Freund, Y., & Mason, L. (1999) · Cited by 711 · see citing works
Getoor, L. C. (2006) · Springer eBooks · doi:10.1007/1-84628-284-5_7 · Cited by 688 · see citing works
Defection detection: measuring and understanding the predictive accuracy of customer churn models
Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.2.204 · Cited by 574 · see citing works
Handling class imbalance in customer churn prediction
Burez, J., & den Poel, D. V. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.05.027 · Cited by 563 · see citing works
Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation
Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003) · Management Science · doi:10.1287/mnsc.49.3.312.12739 · Cited by 536 · see citing works
Self-citationExplanations From Intelligent Systems: Theoretical Foundations and Implications for Practice1
Gregor, S., & Benbasat, I. (1999) · MIS Quarterly · doi:10.2307/249487 · Cited by 535 · see citing works
Džeroski, S. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_41 · Cited by 523 · see citing works
Multivariate Dependencies: Models, Analysis and Interpretation.
Murphy, S. A., Cox, D. R., & Wermouth, N. (1998) · Biometrics · doi:10.2307/2534028 · Cited by 494 · see citing works
Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012) · European Journal of Operational Research · doi:10.1016/j.ejor.2011.09.031 · Cited by 462 · see citing works
Self-citationClassification in Networked Data: A Toolkit and a Univariate Case Study
(2007) · Journal of Machine Learning Research · doi:10.5555/1248659.1248693 · Cited by 452 · see citing works
Buckinx, W., & den Poel, D. V. (2004) · European Journal of Operational Research · doi:10.1016/j.ejor.2003.12.010 · Cited by 430 · see citing works
Bagging and Boosting Classification Trees to Predict Churn
Lemmens, A., & Croux, C. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.2.276 · Cited by 411 · see citing works
Building comprehensible customer churn prediction models with advanced rule induction techniques
Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.08.023 · Cited by 376 · see citing works
Self-citationSocial Network Analysis and Mining for Business Applications
Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961194 · Cited by 361 · see citing works
Social ties and their relevance to churn in mobile telecom networks
Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A. A., & Joshi, A. (2008) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/1353343.1353424 · Cited by 333 · see citing works
(2001) · Springer eBooks · doi:10.1007/978-3-662-04599-2 · Cited by 317 · see citing works
All pairs shortest paths using bridging sets and rectangular matrix multiplication
Zwick, U. (2002) · Journal of the ACM · doi:10.1145/567112.567114 · Cited by 315 · see citing works
Relational Dependency Networks
Neville, J., & Jensen, D. D. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7432.003.0010 · Cited by 285 · see citing works
Explaining Data-Driven Document Classifications1
Martens, D., & Provost, F. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.1.04 · Cited by 264 · see citing works
Self-citationDecompositional Rule Extraction from Support Vector Machines by Active Learning
Martens, D., Baesens, B., & Gestel, T. V. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.131 · Cited by 180 · see citing works
Self-citationPredicting customer churn in mobile networks through analysis of social groups
Richter, Y., Yom‐Tov, E., & Slonim, N. (2010) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972801.64 · Cited by 171 · see citing works
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
Jensen, D. D., & Neville, J. (2002) · Cited by 167 · see citing works
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Subramanian, K. R., & Suresh, S. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2012.06.012 · Cited by 127 · see citing works
Aggregation-based feature invention and relational concept classes
Perlich, C., & Provost, F. J. (2003) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956750.956772 · Cited by 101 · see citing works
Analyzing the Structure and Evolution of Massive Telecom Graphs
Nanavati, A. A., Singh, R. K., Chakraborty, D., Dasgupta, K., Mukherjea, S., Das, G., Gurumurthy, S., & Joshi, A. (2008) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190733 · Cited by 96 · see citing works
Distribution-based aggregation for relational learning with identifier attributes
Perlich, C., & Provost, F. J. (2006) · Machine Learning · doi:10.1007/s10994-006-6064-1 · Cited by 94 · see citing works
Random Graphs for Statistical Pattern Recognition
Marchette, D. J. (2004) · Wiley series in probability and statistics · doi:10.1002/047172209x · Cited by 80 · see citing works
Statistical Relational Learning
Getoor, L. C. (2017) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/3034786.3056450 · Cited by 24 · see citing works
Betti numbers of graphs with an application to anomaly detection
Johannsen, D. A., & Marchette, D. J. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11141 · Cited by 7 · see citing works
Flagged first, then by citations
1 of the 49 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., Hawalah, A., & Hussain, A. (2016) · IEEE Access · doi:10.1109/access.2016.2619719 · Cited by 310 · see citing works
Special issue on feature engineering editorial
Verdonck, T., Baesens, B., Óskarsdóttir, M., & Broucke, S. V. (2021) · Machine Learning · doi:10.1007/s10994-021-06042-2 · Cited by 242 · see citing works
Self-citationÓskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J., & Baesens, B. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.10.004 · Cited by 229 · see citing works
Self-citationCustomer churn prediction in the telecommunication sector using a rough set approach
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Al-awfi, K. S., Hussain, A., & Huang, K. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.12.009 · Cited by 227 · see citing works
In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions
Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., Hardie, B. G. S., Lemmens, A., Libai, B., Neal, D., et al. (2018) · Customer Needs and Solutions · doi:10.1007/s40547-017-0080-0 · Cited by 190 · see citing works
Improved churn prediction in telecommunication industry using data mining techniques
Keramati, A., Jafari-Marandi, R., Aliannejadi, M., Ahmadian, I., Mozaffari, M., & Abbasi, U. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.08.041 · Cited by 182 · see citing works
An empirical comparison of techniques for the class imbalance problem in churn prediction
Zhu, B., Baesens, B., & Broucke, S. V. (2017) · Information Sciences · doi:10.1016/j.ins.2017.04.015 · Cited by 163 · see citing works
Self-citationMining Massive Fine-Grained Behavior Data to Improve Predictive Analytics1
Martens, D., Provost, F., Clark, J., & de Fortuny, E. J. (2016) · MIS Quarterly · doi:10.25300/misq/2016/40.4.04 · Cited by 140 · see citing works
Self-citationA big data analytics model for customer churn prediction in the retiree segment
Shirazi, F., & Mohammadi, M. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.10.005 · Cited by 128 · see citing works
A Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108 · see citing works
Óskarsdóttir, M., Bravo, C., Verbeke, W., Sarraute, C., Baesens, B., & Vanthienen, J. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.028 · Cited by 108 · see citing works
Self-citationAmin, A., Shah, B., Khattak, A. M., Moreira, F., Ali, G., Rocha, Á., & Anwar, S. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.08.015 · Cited by 105 · see citing works
Why you should stop predicting customer churn and start using uplift models
Devriendt, F., Berrevoets, J., & Verbeke, W. (2021) · Information Sciences · doi:10.1016/j.ins.2019.12.075 · Cited by 90 · see citing works
Self-citationEarly churn prediction with personalized targeting in mobile social games
Milošević, M., Živić, N., & Andjelković, I. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.056 · Cited by 83 · see citing works
Liu, Z., Jiang, P., Bock, K. W. D., Wang, J., Zhang, L., & Niu, X. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122945 · Cited by 81 · see citing works
User segmentation for retention management in online social games
Fu, X., Chen, X., Shi, Y., Bose, I., & Cai, S. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.05.015 · Cited by 80 · see citing works
Maldonado, S., Flores, Á., Verbraken, T., Baesens, B., & Weber, R. W. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.05.058 · Cited by 79 · see citing works
Self-citationIntelligent data analysis approaches to churn as a business problem: a survey
García, D. L., Àngela Nebot, B., & Vellido, A. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-016-0995-z · Cited by 77 · see citing works
Moeyersoms, J., & Martens, D. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.02.007 · Cited by 76 · see citing works
Self-citationLooy, A. V. (2015) · Springer texts in business and economics · doi:10.1007/978-3-319-21990-5 · Cited by 76 · see citing works
Cnudde, S. D., Moeyersoms, J., Stankova, M., Tobback, E., Javaly, V., & Martens, D. (2018) · Journal of the Operational Research Society · doi:10.1080/01605682.2018.1434402 · Cited by 69 · see citing works
Self-citationIdentifying influencers in a social network: The value of real referral data
Roelens, I., Baecke, P., & Benoit, D. F. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.07.005 · Cited by 68 · see citing works
Profit-based churn prediction based on Minimax Probability Machines
Maldonado, S., López, J., & Vairetti, C. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.12.007 · Cited by 66 · see citing works
Improved credit card churn prediction based on rough clustering and supervised learning techniques
Rajamohamed, R., & Manokaran, J. (2017) · Cluster Computing · doi:10.1007/s10586-017-0933-1 · Cited by 66 · see citing works
Tekouabou, S. C. K., Gherghina, Ş. C., Toulni, H., Mata, P. N., & Martins, J. M. (2022) · Mathematics · doi:10.3390/math10142379 · Cited by 60 · see citing works
Social Network Analytics for Supervised Fraud Detection in Insurance
Óskarsdóttir, M., Ahmed, W., Antonio, K., Baesens, B., Dendievel, R., Donas, T., & Reynkens, T. (2021) · Risk Analysis · doi:10.1111/risa.13693 · Cited by 58 · see citing works
Self-citationPredicting tax avoidance by means of social network analytics
Lismont, J., Cardinaels, E., Bruynseels, L., De Groote, S., Baesens, B., Lemahieu, W., & Vanthienen, J. (2018) · Decision Support Systems · doi:10.1016/j.dss.2018.02.001 · Cited by 51 · see citing works
Self-citationTime series for early churn detection: Using similarity based classification for dynamic networks
Óskarsdóttir, M., Calster, T. V., Baesens, B., Lemahieu, W., & Vanthienen, J. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.003 · Cited by 51 · see citing works
Self-citationA Customer Churn Prediction Model Based on XGBoost and MLP
Tang, Q., Xia, G., Zhang, X., & Feng, L. (2020) · International Conference on Computer Engineering and Application (ICCEA) · doi:10.1109/iccea50009.2020.00133 · Cited by 48 · see citing works
Data-driven agent-based exploration of customer behavior
Bell, D., & Mgbemena, C. (2017) · SIMULATION · doi:10.1177/0037549717743106 · Cited by 47 · see citing works
Vijaya, J., & Sivasankar, E. (2018) · Computing · doi:10.1007/s00607-018-0633-6 · Cited by 47 · see citing works
Predicting time-to-churn of prepaid mobile telephone customers using social network analysis
Backiel, A., Baesens, B., & Claeskens, G. (2016) · Journal of the Operational Research Society · doi:10.1057/jors.2016.8 · Cited by 46 · see citing works
Self-citationJust-in-time customer churn prediction in the telecommunication sector
Amin, A., Al-Obeidat, F. N., Shah, B., Tae, M. A., Khan, C., Durrani, H. U. R., & Anwar, S. (2017) · The Journal of Supercomputing · doi:10.1007/s11227-017-2149-9 · Cited by 46 · see citing works
Liu, J., Wu, C., & Li, Y. (2017) · Computational Economics · doi:10.1007/s10614-017-9768-3 · Cited by 45 · see citing works
Swarm intelligence goal-oriented approach to data-driven innovation in customer churn management
Kozák, J. T., Kania, K., Juszczuk, P., & Mitręga, M. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102357 · Cited by 43 · see citing works
Management of social networks in the educational process
Mora, H., Pont, M. T. S., Casado, G. D. M., & Gilart-Iglesias, V. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.11.010 · Cited by 42 · see citing works
Ming, Z., Zeng, Q., Chang, M., Qian, T., & Su, J. (2021) · Discrete Dynamics in Nature and Society · doi:10.1155/2021/7160527 · Cited by 41 · see citing works
RetractedProfit-based credit scoring based on robust optimization and feature selection
López, J., & Maldonado, S. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.093 · Cited by 39 · see citing works
Mobile Phone Data: A Survey of Techniques, Features, and Applications
Okmi, M., Por, L. Y., Ang, T. F., & Ku, C. S. (2023) · Sensors · doi:10.3390/s23020908 · Cited by 38 · see citing works
Ranjan, S., Sood, S., & Verma, V. (2018) · International Conference on Computing Sciences (ICCS) · doi:10.1109/iccs.2018.00035 · Cited by 38 · see citing works
Sivasankar, E., & Vijaya, J. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3548-4 · Cited by 35 · see citing works
Churn Prediction System for Telecom using Filter–Wrapper and Ensemble Classification
Idris, A., & Khan, A. (2016) · The Computer Journal · doi:10.1093/comjnl/bxv123 · Cited by 34 · see citing works
Profit-driven churn prediction for the mutual fund industry: A multisegment approach
Maldonado, S., Domínguez, G., Olaya, D., & Verbeke, W. (2020) · Omega · doi:10.1016/j.omega.2020.102380 · Cited by 33 · see citing works
Self-citationCustomer Behavior Mining Framework (CBMF) using clustering and classification techniques
Abdi, F., & Abolmakarem, S. (2018) · Journal of industrial engineering international · doi:10.1007/s40092-018-0285-3 · Cited by 31 · see citing works
Marketing Models for the Customer-Centric Firm
Ascarza, E., Fader, P. S., & Hardie, B. G. S. (2017) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-56941-3_10 · Cited by 31 · see citing works
Leveraging Predictive Analytics for Customer Churn: A Cross-Industry Approach in the US Market
Fowowe, O. O., & Agboluaje, R. (2025) · International Journal of Applied Sciences and Radiation Research · doi:10.22399/ijasrar.20 · Cited by 30 · see citing works
Short Text Representation for Detecting Churn in Microblogs
Amiri, H., & Daumé, H. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10333 · Cited by 28 · see citing works
Óskarsdóttir, M., Bravo, C., Verbeke, W., Sarraute, C., Baesens, B., & Vanthienen, J. (2016) · International Conference on Advances in Social Networks Analysis and Mining · doi:10.1109/asonam.2016.7752384 · Cited by 30 · see citing works
Self-citationChurn prediction methods based on mutual customer interdependence
Ljubičić, K., Merćep, A., & Kostanjčar, Z. (2023) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101940 · Cited by 26 · see citing works
An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach
Almuqren, L., Alrayes, F. S., & Cristea, A. I. (2021) · Future Internet · doi:10.3390/fi13070175 · Cited by 25 · see citing works
Retail Credit Scoring using Fine-Grained Payment Data
Tobback, E., & Martens, D. (2019) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12469 · Cited by 25 · see citing works
Self-citationA fuzzy based churn prediction and retention model for prepaid customers in telecom industry
Azeem, M., & Usman, M. (2017) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.11.1.6 · Cited by 24 · see citing works
Boost customer churn prediction in the insurance industry using meta-heuristic models
Jajam, N., & Vijaya, J. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01017-5 · Cited by 23 · see citing works
Social networks for enhanced player churn prediction in mobile free-to-play games
Óskarsdóttir, M., Gísladóttir, K. E., Stefánsson, R., Aleman, D., & Sarraute, C. (2022) · Applied Network Science · doi:10.1007/s41109-022-00524-5 · Cited by 23 · see citing works
Welsch, A., Eitle, V., & Buxmann, P. (2018) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-018-0404-z · Cited by 23 · see citing works
A multi-period analysis of the integrated item-sharing and crowdshipping problem
Behrend, M., Meisel, F., Fagerholt, K., & Andersson, H. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.10.034 · Cited by 23 · see citing works
Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example
Amin, A., Khan, C., Ali, I., & Anwar, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13650-9_19 · Cited by 21 · see citing works
Greene, T., Martens, D., & Galit Shmueli, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00475-7 · Cited by 21 · see citing works
Self-citationA comprehensive survey on customer churn analysis studies
Shahabikargar, M., Beheshti, A., Zhang, X., Foo, J., & Jolfaei, A. (2025) · Journal of Information and Telecommunication · doi:10.1080/24751839.2025.2528440 · Cited by 20 · see citing works
Churn modeling with probabilistic meta paths-based representation learning
Mitrović, S., & Weerdt, J. D. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.06.001 · Cited by 19 · see citing works
ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
Shahabikargar, M., Beheshti, A., Mansoor, W., Zhang, X., Foo, J., Jolfaei, A., Hanif, A., & Shabani, N. (2025) · Algorithms · doi:10.3390/a18040238 · Cited by 18 · see citing works
Combining local and social network classifiers to improve churn prediction
Backiel, A., Verbinnen, Y., Baesens, B., & Claeskens, G. (2015) · International Conference on Advances in Social Networks Analysis and Mining · doi:10.1145/2808797.2808850 · Cited by 22 · see citing works
Self-citationAmiri, B., & Hosseini, S. H. (2024) · IEEE Access · doi:10.1109/access.2024.3402684 · Cited by 17 · see citing works
Target-Dependent Churn Classification in Microblogs
Amiri, H., & Daumé, H. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9532 · Cited by 17 · see citing works
Twitter opinion mining predicts broadband internet's customer churn rate
Napitu, F., Bijaksana, M. A., Trisetyarso, A., & Heryadi, Y. (2017) · IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) · doi:10.1109/cyberneticscom.2017.8311699 · Cited by 17 · see citing works
Lismont, J., Ram, S., Vanthienen, J., Lemahieu, W., & Baesens, B. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.016 · Cited by 16 · see citing works
Self-citationCustomer Churn Prediction in Telecommunication Industry: With and without Counter-Example
Amin, A., Khan, C., Ali, I., & Anwar, S. (2014) · European Network Intelligence Conference (ENIC) · doi:10.1109/enic.2014.29 · Cited by 16 · see citing works
A Prudent Based Approach for Customer Churn Prediction
Amin, A., Rahim, F., Ramzan, M., & Anwar, S. (2015) · Communications in computer and information science · doi:10.1007/978-3-319-18422-7_29 · Cited by 14 · see citing works
Koufi, N. E., & Belangour, A. (2024) · Journal of Open Innovation Technology Market and Complexity · doi:10.1016/j.joitmc.2024.100425 · Cited by 13 · see citing works
A benchmarking study of classification techniques for behavioral data
Cnudde, S. D., Martens, D., Evgeniou, T., & Provost, F. J. (2019) · International Journal of Data Science and Analytics · doi:10.1007/s41060-019-00185-1 · Cited by 13 · see citing works
Self-citationTelecom Churn Prediction based on Deep Learning Approach
Mahmood, I., & Abdullah, H. S. (2022) · Iraqi Journal of Science · doi:10.24996/ijs.2022.63.6.32 · Cited by 13 · see citing works
Mirshahi, S., Vahedi, B., Aryana, K., & Sazgarnia, A. (2023) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2023.100280 · Cited by 13 · see citing works
Chowdhury, F. A., Liu, Y., Saha, K., Vincent, N., Neves, L., Shah, N., & Bos, M. W. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18046 · Cited by 12 · see citing works
Socioscope: I know who you are, a robo, human caller or service number
Azad, M. A., Alazab, M., Riaz, F., Arshad, J., & Abullah, T. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.11.007 · Cited by 12 · see citing works
Understanding IPTV churning behaviors: focus on users in South Korea
Kim, M.-J., Kim, J., & Park, S. (2017) · Asia Pacific Journal of Innovation and Entrepreneurship · doi:10.1108/apjie-08-2017-026 · Cited by 11 · see citing works
Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking
Chen, Y., Gel, Y. R., Lyubchich, V., & Winship, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93034-3_30 · Cited by 11 · see citing works
Deep Learning on Big, Sparse, Behavioral Data
Cnudde, S. D., Ramon, Y., Martens, D., & Provost, F. J. (2019) · Big Data · doi:10.1089/big.2019.0095 · Cited by 11 · see citing works
Self-citationScalable RFM-enriched Representation Learning for Churn Prediction
Mitrović, S., Singh, G., Baesens, B., Lemahieu, W., & Weerdt, J. D. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.42 · Cited by 10 · see citing works
Self-citationA hybrid classification model for churn prediction based on customer clustering
Tang, Q., Xia, G., & Zhang, X. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190677 · Cited by 10 · see citing works
Machado, M., Asadi, A., Souza, R., & Ugulino, W. C. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112343 · Cited by 9 · see citing works
One-Class Text Document Classification with OCSVM and LSI
Kumar, B. S., & Ravi, V. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-3174-8_50 · Cited by 9 · see citing works
Manivannan, R., Saminathan, R., & Saravanan, S. (2019) · Evolutionary Intelligence · doi:10.1007/s12065-019-00282-x · Cited by 9 · see citing works
The Effect of Overspending on Tariff Choices and Customer Churn: Evidence From Mobile Plan Choices
Jin, H. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.102914 · Cited by 9 · see citing works
Customer Churn Prediction in the Online New Media Platform: A Case Study on Juzi Entertainment
Yuan, S., Bai, S., Song, M., & Zhou, Z. (2017) · International Conference on Platform Technology and Service · doi:10.1109/platcon.2017.7883698 · Cited by 9 · see citing works
A SLR on Customer Dropout Prediction
Sobreiro, P., Martinho, D. D. S., Alonso, J. G., & Berrocal, J. (2022) · IEEE Access · doi:10.1109/access.2022.3146397 · Cited by 8 · see citing works
Comparison of Model in Predicting Customer Churn Based on Users' habits on E-Commerce
Lubis, A. R., Prayudani, S., Julham, J., Nugroho, O., Lase, Y. Y., & Lubis, M. (2022) · International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · doi:10.1109/isriti56927.2022.10052834 · Cited by 8 · see citing works
Optimizing Coverage of Churn Prediction in Telecommunication Industry
Anjum, A., Usman, S., Zeb, A., Uddin, I., Masoom, P., Anwar, Z., Anjum, A., Raza, B., Kamran, A., & Ur, S. (2017) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2017.080523 · Cited by 8 · see citing works
Application of Machine Learning in the Social Network
Belfin, R. V., Kanaga, E. G. M., & Kundu, S. (2020) · Wiley eBooks · doi:10.1002/9781119551621.ch4 · Cited by 8 · see citing works
A Novel Hybrid Forecasting Approach for Customers Churn in Banking Industry
Rouhani, S., & Mohammadi, A. (2022) · Journal of Information & Knowledge Management · doi:10.1142/s0219649222500897 · Cited by 7 · see citing works
Mirshahi, S., Vahedi, B., Oraee‐Yazdani, S., Golab, M., & Sazgarnia, A. (2024) · Journal of Molecular Modeling · doi:10.1007/s00894-024-06012-0 · Cited by 7 · see citing works
Network analytics for insurance fraud detection: a critical case study
Deprez, B., Vandervorst, F., Verbeke, W., Verdonck, T., & Baesens, B. (2024) · European Actuarial Journal · doi:10.1007/s13385-024-00384-6 · Cited by 7 · see citing works
Self-citationTo Predict Customer Churn By Using Different Algorithms
Rahman, M. D. S., Alam, M. D. S., & Hosen, M. D. I. (2022) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa54658.2022.9765155 · Cited by 7 · see citing works
Tensorial graph learning for link prediction in generalized heterogeneous networks
Chen, Z.-Y., Fan, Z., & Sun, M. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.05.062 · Cited by 7 · see citing works
Ehsani, F. (2022) · Research Square · doi:10.21203/rs.3.rs-1630808/v1 · Cited by 7 · see citing works
Assessing the Success of Automotive Sales Transactions Using Selected Machine Learning Algorithms
Mazur, M., Stopka, O., Stopková, M., Hanzl, J., Borucka, A., & Czerniak, R. (2025) · Applied Sciences · doi:10.3390/app152111562 · Cited by 6 · see citing works
Rafi, M. S., & YASSAR, I. K. M. S. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.10.53 · Cited by 6 · see citing works
Berengueres, J. O. L., Duran, G., & Castro, D. (2017) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3110025.3110132 · Cited by 6 · see citing works
Li, M., Wang, Z., Li, K., & Liao, X. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3330100 · Cited by 5 · see citing works
Community-Based Prediction of Activity Change in Skype
Teinemaa, I., Leontjeva, A., Dumas, M., & Kikas, R. (2015) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2808797.2808853 · Cited by 5 · see citing works
Hyun, H., Gunn, F., & Park, J. (2019) · The International Review of Retail Distribution and Consumer Research · doi:10.1080/09593969.2018.1555543 · Cited by 5 · see citing works
- Authors
- Verbeke, W., Martens, D., & Baesens, B.
- Affiliations
- Vrije Universiteit Brussel (BE)
- University of Antwerp (BE)
- Vlerick Business School (BE)
- Corresponding author
- Verbeke, Wouter
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 14
- Pages
- 431-446
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W2001706356
- Cited by
- 164
- References
- 49
- Citations by year
- 2026: 14 · 2025: 12 · 2024: 10 · 2023: 9 · 2022: 14 · 2021: 17 · 2020: 10 · 2019: 15 · 2018: 13 · 2017: 24 · 2016: 12 · 2015: 10 · 2014: 4
- FWCI
- 25.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Soft Computing (publishedVersion)
- Lirias (submittedVersion)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- customer churn prediction, social network analysis, relational learning, skewed class distribution, parallel modeling, customer retention, telecommunications customers, call detail records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review