The Implementation of hybrid methods in data mining for Predicting customer churn in the telecommunications sector
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Published:
May 30, 2023
Keywords:
Artificial Neural Network, Decision Tree, Hybrid Algorithm
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Computer Science
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Abstract
In recent years, the telecommunication industry is growth and become very competitive where has reached the point maintaining customer is very essential than acquiring new customer. And the two key factor for maintaining customer, the first is defining the segment of customer want to churn and the second is accuracy of predictive model. In this article we propose the hybrid model based on decision tree and artificial neural network (ANN) with the two stages of process to answer the problem of maintaining customer, the first is a segmentation phase with decision rules and the second is a prediction phase with artificial neural network (ANN). Our finding in benchmarked against the previous algorithms (decision tree and ANN) with the AUC metrics show the proposed model or hybrid achieves better accuracy and with the comprehensive information of what a drive customer churn
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Herdian, R. H. and Abba Suganda Girsang (2023) “The Implementation of hybrid methods in data mining for Predicting customer churn in the telecommunications sector”, Jurnal Mantik, 7(1), pp. 216-228. doi: 10.35335/mantik.v7i1.3706.
References
Abdulsalam, S. O., Arowolo, M. O., Saheed, Y. K., & Afolayan, J. O. (2022). Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms. Indonesian Journal of Electrical Engineering and Informatics, 10(2), 431–440. https://doi.org/10.52549/ijeei.v10i2.2985
Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., & Anwar, S. (2019). Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research, 94(October 2017), 290–301. https://doi.org/10.1016/j.jbusres.2018.03.003
Amin, A., Shah, B., Khattak, A. M., Lopes Moreira, F. J., Ali, G., Rocha, A., & Anwar, S. (2019). Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods. International Journal of Information Management, 46(June), 304–319. https://doi.org/10.1016/j.ijinfomgt.2018.08.015
Cenggoro, T. W., Wirastari, R. A., Rudianto, E., Mohadi, M. I., Ratj, D., & Pardamean, B. (2021). Deep Learning as a Vector Embedding Model for Customer Churn. Procedia Computer Science, 179(2019), 624–631. https://doi.org/10.1016/j.procs.2021.01.048
Chakraborty, T., Chattopadhyay, S., & Chakraborty, A. K. (2018). A novel hybridization of classification trees and artificial neural networks for selection of students in a business school. Opsearch, 55(2), 434–446. https://doi.org/10.1007/s12597-017-0329-2
Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020). Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model. Decision Support Systems, 135(May), 113325. https://doi.org/10.1016/j.dss.2020.113325
De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), 760–772. https://doi.org/10.1016/j.ejor.2018.02.009
Halibas, A. S., Cherian Matthew, A., Pillai, I. G., Harold Reazol, J., Delvo, E. G., & Bonachita Reazol, L. (2019). Determining the intervening effects of exploratory data analysis and feature engineering in telecoms customer churn modelling. 2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019, 1–7. https://doi.org/10.1109/ICBDSC.2019.8645578
Höppner, S., Stripling, E., Baesens, B., Broucke, S. vanden, & Verdonck, T. (2020). Profit driven decision trees for churn prediction. European Journal of Operational Research, 284(3), 920–933. https://doi.org/10.1016/j.ejor.2018.11.072
Ismail, M. R., Awang, M. K., Rahman, M. N. A., & Makhtar, M. (2015). A multi-layer perceptron approach for customer churn prediction. International Journal of Multimedia and Ubiquitous Engineering, 10(7), 213–222. https://doi.org/10.14257/ijmue.2015.10.7.22
Kumar, S., & Kumar, M. (2019). Predicting customer churn using artificial neural network. In Communications in Computer and Information Science (Vol. 1000). Springer International Publishing. https://doi.org/10.1007/978-3-030-20257-6_25
Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022). Customer churn prediction system: a machine learning approach. Computing, 104(2), 271–294. https://doi.org/10.1007/s00607-021-00908-y
Latha, K. J., Baburao, M., & Kavitha, C. (2019). A Comparative study on Logit leaf model (LLM) and Support leaf model (SLM) for predicting the customer churn. International Journal of Computer Sciences and Engineering, 7(5), 1628–1632. https://doi.org/10.26438/ijcse/v7i5.16281632
Li, P., Wang, X., Qin, Z., & Metzler, D. (2019). Combining decision trees and neural networks for learning-to-rank in personal search. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2032–2040. https://doi.org/10.1145/3292500.3330676
Liu, R., Ali, S., Bilal, S. F., Sakhawat, Z., Imran, A., Almuhaimeed, A., Alzahrani, A., & Sun, G. (2022). An Intelligent Hybrid Scheme for Customer Churn Prediction Integrating Clustering and Classification Algorithms. Applied Sciences (Switzerland), 12(18), 1–17. https://doi.org/10.3390/app12189355
Nguyen, T. D., Kasmarik, K. E., & Abbass, H. A. (2020). Towards Interpretable Deep Neural Networks: An Exact Transformation to Multi-Class Multivariate Decision Trees. ArXiv.
Nisha Saini, Monika, & Dr. Kanwal Garg. (2017). Churn Prediction in Telecommunication Industry using Decision Tree. International Journal of Engineering Research And, V6(04). https://doi.org/10.17577/ijertv6is040379
Peji? Bach, M., Pivar, J., & Jakovi?, B. (2021). Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees. Journal of Risk and Financial Management, 14(11), 544. https://doi.org/10.3390/jrfm14110544
Rehman, A., & Ali, A. R. (2014). Customer Churn Prediction , Segmentation and Fraud Detection in Telecommunication Industry. ASE BigData/SocialInformatics/PASSAT/BioMedCom 2014 Conference, Harvard University, December, 1–9. https://www.researchgate.net/publication/304822719
Routh, P., Roy, A., & Meyer, J. (2020). Estimating customer churn under competing risks. Journal of the Operational Research Society, 0(0), 1–18. https://doi.org/10.1080/01605682.2020.1776166
Seymen, O. F., Dogan, O., & Hiziroglu, A. (2021). Customer Churn Prediction Using Deep Learning. Advances in Intelligent Systems and Computing, 1383 AISC(April), 520–529. https://doi.org/10.1007/978-3-030-73689-7_50
Shumaly, S., Neysaryan, P., & Guo, Y. (2020). Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees. 2020 10h International Conference on Computer and Knowledge Engineering, ICCKE 2020, 82–87. https://doi.org/10.1109/ICCKE50421.2020.9303698
Sivasankar, E., & Vijaya, J. (2019). A study of feature selection techniques for predicting customer retention in telecommunication sector. International Journal of Business Information Systems, 31(1), 1–26. https://doi.org/10.1504/IJBIS.2019.099524
Ulas Vural, M. Ergun Okay, E. M. Y. (2020). Churn Prediction for Telecommunication Industry Using Artificial Neural Networks. International Journal of Computer and Information Engineering, 14(11).
Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., & Anwar, S. (2019). Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research, 94(October 2017), 290–301. https://doi.org/10.1016/j.jbusres.2018.03.003
Amin, A., Shah, B., Khattak, A. M., Lopes Moreira, F. J., Ali, G., Rocha, A., & Anwar, S. (2019). Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods. International Journal of Information Management, 46(June), 304–319. https://doi.org/10.1016/j.ijinfomgt.2018.08.015
Cenggoro, T. W., Wirastari, R. A., Rudianto, E., Mohadi, M. I., Ratj, D., & Pardamean, B. (2021). Deep Learning as a Vector Embedding Model for Customer Churn. Procedia Computer Science, 179(2019), 624–631. https://doi.org/10.1016/j.procs.2021.01.048
Chakraborty, T., Chattopadhyay, S., & Chakraborty, A. K. (2018). A novel hybridization of classification trees and artificial neural networks for selection of students in a business school. Opsearch, 55(2), 434–446. https://doi.org/10.1007/s12597-017-0329-2
Coussement, K., Phan, M., De Caigny, A., Benoit, D. F., & Raes, A. (2020). Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model. Decision Support Systems, 135(May), 113325. https://doi.org/10.1016/j.dss.2020.113325
De Caigny, A., Coussement, K., & De Bock, K. W. (2018). A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees. European Journal of Operational Research, 269(2), 760–772. https://doi.org/10.1016/j.ejor.2018.02.009
Halibas, A. S., Cherian Matthew, A., Pillai, I. G., Harold Reazol, J., Delvo, E. G., & Bonachita Reazol, L. (2019). Determining the intervening effects of exploratory data analysis and feature engineering in telecoms customer churn modelling. 2019 4th MEC International Conference on Big Data and Smart City, ICBDSC 2019, 1–7. https://doi.org/10.1109/ICBDSC.2019.8645578
Höppner, S., Stripling, E., Baesens, B., Broucke, S. vanden, & Verdonck, T. (2020). Profit driven decision trees for churn prediction. European Journal of Operational Research, 284(3), 920–933. https://doi.org/10.1016/j.ejor.2018.11.072
Ismail, M. R., Awang, M. K., Rahman, M. N. A., & Makhtar, M. (2015). A multi-layer perceptron approach for customer churn prediction. International Journal of Multimedia and Ubiquitous Engineering, 10(7), 213–222. https://doi.org/10.14257/ijmue.2015.10.7.22
Kumar, S., & Kumar, M. (2019). Predicting customer churn using artificial neural network. In Communications in Computer and Information Science (Vol. 1000). Springer International Publishing. https://doi.org/10.1007/978-3-030-20257-6_25
Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2022). Customer churn prediction system: a machine learning approach. Computing, 104(2), 271–294. https://doi.org/10.1007/s00607-021-00908-y
Latha, K. J., Baburao, M., & Kavitha, C. (2019). A Comparative study on Logit leaf model (LLM) and Support leaf model (SLM) for predicting the customer churn. International Journal of Computer Sciences and Engineering, 7(5), 1628–1632. https://doi.org/10.26438/ijcse/v7i5.16281632
Li, P., Wang, X., Qin, Z., & Metzler, D. (2019). Combining decision trees and neural networks for learning-to-rank in personal search. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2032–2040. https://doi.org/10.1145/3292500.3330676
Liu, R., Ali, S., Bilal, S. F., Sakhawat, Z., Imran, A., Almuhaimeed, A., Alzahrani, A., & Sun, G. (2022). An Intelligent Hybrid Scheme for Customer Churn Prediction Integrating Clustering and Classification Algorithms. Applied Sciences (Switzerland), 12(18), 1–17. https://doi.org/10.3390/app12189355
Nguyen, T. D., Kasmarik, K. E., & Abbass, H. A. (2020). Towards Interpretable Deep Neural Networks: An Exact Transformation to Multi-Class Multivariate Decision Trees. ArXiv.
Nisha Saini, Monika, & Dr. Kanwal Garg. (2017). Churn Prediction in Telecommunication Industry using Decision Tree. International Journal of Engineering Research And, V6(04). https://doi.org/10.17577/ijertv6is040379
Peji? Bach, M., Pivar, J., & Jakovi?, B. (2021). Churn Management in Telecommunications: Hybrid Approach Using Cluster Analysis and Decision Trees. Journal of Risk and Financial Management, 14(11), 544. https://doi.org/10.3390/jrfm14110544
Rehman, A., & Ali, A. R. (2014). Customer Churn Prediction , Segmentation and Fraud Detection in Telecommunication Industry. ASE BigData/SocialInformatics/PASSAT/BioMedCom 2014 Conference, Harvard University, December, 1–9. https://www.researchgate.net/publication/304822719
Routh, P., Roy, A., & Meyer, J. (2020). Estimating customer churn under competing risks. Journal of the Operational Research Society, 0(0), 1–18. https://doi.org/10.1080/01605682.2020.1776166
Seymen, O. F., Dogan, O., & Hiziroglu, A. (2021). Customer Churn Prediction Using Deep Learning. Advances in Intelligent Systems and Computing, 1383 AISC(April), 520–529. https://doi.org/10.1007/978-3-030-73689-7_50
Shumaly, S., Neysaryan, P., & Guo, Y. (2020). Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees. 2020 10h International Conference on Computer and Knowledge Engineering, ICCKE 2020, 82–87. https://doi.org/10.1109/ICCKE50421.2020.9303698
Sivasankar, E., & Vijaya, J. (2019). A study of feature selection techniques for predicting customer retention in telecommunication sector. International Journal of Business Information Systems, 31(1), 1–26. https://doi.org/10.1504/IJBIS.2019.099524
Ulas Vural, M. Ergun Okay, E. M. Y. (2020). Churn Prediction for Telecommunication Industry Using Artificial Neural Networks. International Journal of Computer and Information Engineering, 14(11).
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