A Review on Classification Algorithm for Customer Churn Classification
Main Article Content
Abstract
Any sector faces a huge obstacle when it comes to retaining existing customers. The percentage of consumers who have quit using a product or service is referred to as customer churn, and it is a vital indication that offers reliable information about this percentage. When it comes to achieving long-term success in a market or industry, one of the most significant challenges that any company must face is the ability to keep their precious clients and to fulfill their needs. A review of the most significant studies on Customer Churn Prediction is presented in this paper so as to furnish the reader with an overview of frequently employed data mining methodologies and their respective performances. We provide the available statistics in addition to customer information in order to approximate customer attrition. The time period encompassing the survey extends from 2003 to 2023. During the process of Customer Churn Prediction, we identified the issues and difficulties that were linked with it and offered guidance and potential remedies.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
A. Idris, “Customer Churn Prediction for Telecommunication : Employing various various features selection techniques and tree based ensemble classifiers,” no. September, 2015, doi: 10.1109/INMIC.2012.6511498. https://doi.org/10.1109/INMIC.2012.6511498
X. I. A. Guo-en and J. I. N. Wei-dong, “Model of Customer Churn Prediction on Support Vector Machine,” Syst. Eng. - Theory Pract., vol. 28, no. 1, pp. 71–77, 2008, doi: 10.1016/S1874-8651(09)60003-X. https://doi.org/10.1016/S1874-8651(09)60003-X
A. Hudaib, R. Dannoun, O. Harfoushi, R. Obiedat, and H. Faris, “Hybrid Data Mining Models for Predicting Customer Churn,” no. May, pp. 91–96, 2015. https://doi.org/10.4236/ijcns.2015.85012
M. C. Mozer, R. Wolniewicz, and D. B. Grimes, “Churn Reduction in the Wireless Industry,” no. January 1999, 2015.
J. Pamina, T. D. Rajkumar, S. Kiruthika, T. Suganya, F. Femila, and I. Introduction, “Exploring Hybrid and Ensemble Models for Customer Churn Prediction in Telecom Sector,” vol. 3878, no. 2, pp. 299–308, 2019, doi: 10.35940/ijrte.A9170.078219. https://doi.org/10.35940/ijrte.A9170.078219
S. O. Abdulsalam, J. F. Ajao, B. F. Balogun, and M. Olaolu, “EAI Endorsed Transactions A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms,” vol. 7, no. 21, pp. 1–8, 2022. https://doi.org/10.4108/eetmca.v6i21.2181
G. Thakre, P. Wankhede, S. Patle, S. Joshi, and P. A. Chauhan, “Implementation of Machine Learning Model for Employee Retention Prediction,” pp. 503–508.
I. Ullah, B. Raza, A. K. Malik, S. U. L. Islam, S. W. O. N. Kim, and M. Imran, “A Churn Prediction Model Using Random Forest : Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector,” IEEE Access, vol. 7, pp. 60134–60149, 2019, doi: 10.1109/ACCESS.2019.2914999. https://doi.org/10.1109/ACCESS.2019.2914999
I. O. P. C. Series and M. Science, “Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes,” 2020, doi: 10.1088/1757-899X/879/1/012090. https://doi.org/10.1088/1757-899X/879/1/012090
I. Pathan, N. A. Kanasro, F. B. Shaikh, M. U. R. Maree, and A. A. Chandio, “An Evolutionary Approach of Machine Learning for Monitoring Churn Prediction of Broadband Customer,” vol. 10, no. 3, pp. 2623–2629, 2021. https://doi.org/10.30534/ijatcse/2021/1561032021
J. Qi et al., “ADTreesLogit model for customer churn prediction,” pp. 247–265, 2009, doi: 10.1007/s10479-008-0400-8. https://doi.org/10.1007/s10479-008-0400-8
N. Hashmi, N. A. Butt, and M. Iqbal, “Customer Churn Prediction in Telecommunication A Decade Review and Classification,” no. May 2014, 2013.
E. Radmehr and M. Bazmara, “A Survey on Business Intelligence Solutions in Banking Industry and Big Data Applications,” vol. 7, no. 23, pp. 3280–3298, 2017.
H. Cho, Y. Lee, H. Lee, H. Lee, and Y. Lee, “Toward Optimal Churn Management : A Partial Least Square ( PLS ) Model Toward Optimal Churn Management : A Partial Least Square ( PLS ) Model,” 2010.
Z. Chen, Z. Chen, Z. Fan, and M. Sun, “A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data,” Eur. J. Oper. Res., vol. 223, no. 2, pp. 461–472, 2012, doi: 10.1016/j.ejor.2012.06.040. https://doi.org/10.1016/j.ejor.2012.06.040
V. Jinde and P. A. Savyanavar, “Customer Churn Prediction System using Machine Learning,” vol. 29, no. 5, pp. 7957–7964, 2020.
A. Keramati, R. Jafari-marandi, M. Aliannejadi, I. Ahmadian, and M. Mozaffari, “Improved churn prediction in telecommunication industry using data mining techniques,” Appl. Soft Comput. J., vol. 24, pp. 994–1012, 2014, doi: 10.1016/j.asoc.2014.08.041. https://doi.org/10.1016/j.asoc.2014.08.041
M. Ewieda, E. M. Shaaban, and M. Roushdy, “Review of Data Mining Techniques for Detecting Churners in the Telecommunication Industry”.
M. Azeem, M. Usman, and A. C. M. Fong, “A churn prediction model for prepaid customers in telecom using fuzzy classifiers,” Telecommun. Syst., vol. 66, no. 4, pp. 603–614, 2017, doi: 10.1007/s11235-017-0310-7. https://doi.org/10.1007/s11235-017-0310-7
U. G. Inyang, O. O. Obot, M. E. Ekpenyong, and A. M. Bolanle, “Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification,” vol. 11, no. 9, pp. 151–164, 2017, doi: 10.5539/mas.v11n9p151. https://doi.org/10.5539/mas.v11n9p151
A. Amin, B. Shah, A. Masood, F. Joaquim, and L. Moreira, “Just-in-time Customer Churn Prediction : With and Without Data Transformation International Journal of Information Management Cross-company customer churn prediction in telecommunication : A comparison of data transformation methods,” Int. J. Inf. Manage., no. August 2020, pp. 0–1, 2018, doi: 10.1016/j.ijinfomgt.2018.08.015. https://doi.org/10.1016/j.ijinfomgt.2018.08.015
E. S. J. Vijaya, “Hybrid PPFCM-ANN model : an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network,” Neural Comput. Appl., vol. 31, no. 11, pp. 7181–7200, 2019, doi: 10.1007/s00521-018-3548-4. https://doi.org/10.1007/s00521-018-3548-4
C. G. Mena, A. De Caigny, K. Coussement, K. W. De Bock, and S. Lessmann, “Churn Prediction with Sequential Data and Deep Neural Networks A Comparative Analysis ∗,” pp. 1–12, 2019.
E. Stripling and B. Baesens, “Profit Driven Decision Trees for Churn Prediction,” no. December 2017, 2018, doi: 10.1016/j.ejor.2018.11.072. https://doi.org/10.1016/j.ejor.2018.11.072
S. Babu and N. R. Ananthanarayanan, “Enhanced Prediction Model for Customer Churn in Telecommunication Using Enhanced Prediction Model for Customer Churn in Telecommunication Using EMOTE,” no. February, 2018, doi: 10.1007/978-981-10-5520-1. https://doi.org/10.1007/978-981-10-5520-1
K. U. Leuven, “B AGGING AND B OOSTING C LASSIFICATION T REES TO Aurélie Lemmens and Christophe Croux”.
K. W. De Bock and D. Van Den Poel, “Expert Systems with Applications Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models,” vol. 39, pp. 6816–6826, 2012, doi: 10.1016/j.eswa.2012.01.014. https://doi.org/10.1016/j.eswa.2012.01.014
A. Idris and A. Khan, “Genetic Programming and Adaboosting based churn prediction for Telecom,” no. October, 2012, doi: 10.1109/ICSMC.2012.6377917. https://doi.org/10.1109/ICSMC.2012.6377917
A. Idris and A. Iftikhar, “Intelligent churn prediction for telecom using GP-AdaBoost learning and PSO undersampling,” Cluster Comput., vol. 22, no. s3, pp. 7241–7255, 2019, doi: 10.1007/s10586-017-1154-3. https://doi.org/10.1007/s10586-017-1154-3
C. Science and Z. Zhang, “Using Combined Model Approach for Churn Prediction in Telecommunication Fa-Gui LIU, Zhi-Jie ZHANG*, Xin YANG,” vol. 131, no. Eeeis, pp. 269–276, 2017.
J. V. E. Sivasankar, “An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing,” Cluster Comput., vol. 22, no. s5, pp. 10757–10768, 2019, doi: 10.1007/s10586-017-1172-1. https://doi.org/10.1007/s10586-017-1172-1
M. K. Awang, M. Makhtar, N. Udin, and N. F. Mansor, “Improving Customer Churn Classification with Ensemble Stacking Method,” vol. 12, no. 11, 2021. https://doi.org/10.14569/IJACSA.2021.0121132
H. Tran, N. Le, and V. Nguyen, “C USTOMER C HURN P REDICTION IN THE B ANKING S ECTOR U SING M ACHINE L EARNING -B ASED,” vol. 18, pp. 87–105, 2023.
J. Vijaya and E. Sivasankar, “Computing efficient features using rough set theory combined with ensemble classification techniques to,” Computing, vol. 100, no. 8, pp. 839–860, 2018, doi: 10.1007/s00607-018-0633-6. https://doi.org/10.1007/s00607-018-0633-6
T. Y. Lin et al., “Journal of Engineering Technology and Applied Physics Stacking Ensemble Approach for Churn Prediction : Integrating CNN and Machine Learning Models with CatBoost Meta-Learner,” vol. 5, no. 2, pp. 99–107, 2023. https://doi.org/10.33093/jetap.2023.5.2.12
S. F. Sabbeh, “Machine-Learning Techniques for Customer Retention : A Comparative Study,” vol. 9, no. 2, pp. 273–281, 2018. https://doi.org/10.14569/IJACSA.2018.090238
B. Zhu, B. Baesens, and K. L. M. Seppe, “An empirical comparison of techniques for the class imbalance problem in churn prediction,” vol. 32, no. 0.
C. K. N, “RESEARCH ON CHURN PREDICTION IN MOBILE COMMERCE USING SUPERVISED MODEL .,” no. 05, pp. 29–43, 2022, doi: 10.17605/OSF.IO/ZRX7H.
Sharma, N., Raj, A., Kesireddy, V., & Akunuri, P. (2021). Machine Learning Implementation in Electronic Commerce for Churn Prediction of End User. In International Journal of Soft Computing and Engineering (Vol. 10, Issue 5, pp. 20–25). https://doi.org/10.35940/ijsce.f3502.0510521
Thakur, T. B., & Mittal, A. K. (2020). Real Time IoT Application for Classification of Crop Diseases using Machine Learning in Cloud Environment. In International Journal of Innovative Science and Modern Engineering (Vol. 6, Issue 4, pp. 1–4). https://doi.org/10.35940/ijisme.d1186.016420
Tamilarasi, Dr. A., Karthick, T. J., R. Dharani, & S. Jeevitha. (2023). Eye Disease Prediction Among Corporate Employees using Machine Learning Techniques. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 10, pp. 1–5). https://doi.org/10.35940/ijese.c7895.09111023