Customer churn prediction model: a case of the telecommunication market

Abarca Sánchez, Y., Barreto Rivera, U., Barreto Jara, O., Díaz Ugarte, J.L. (2022). Customer loyalty and retention at a leading telecommunications company in Perú. Revista Venezolana De Gerencia, 27(98), 729-743. https://doi.org/10.52080/rvgluz.27.98.2210.52080/rvgluz.27.98.22 Search in Google Scholar

Abd-Elrahman, A.H., Ahmed Kamal, J.M. (2022). Relational capital, service quality and organizational performance in the Egyptian telecommunication sector. International Journal of Emerging Markets, 17(1), 299-324. https://doi.org/10.1108/IJOEM-11-2019-098310.1108/IJOEM-11-2019-0983 Search in Google Scholar

Agafonova, A.N., Novikova, E.N., Shakirov, R.A. (2022). New marketing aspects in the digital economy. In: Ashmarina, S.I., Mantulenko, V.V. (eds) Digital Technologies in the New Socio-Economic Reality. ISCDTE 2021. Lecture Notes in Networks and Systems, 304, Springer, Cham. https://doi.org/10.1007/978-3-030-83175-2_6510.1007/978-3-030-83175-2_65 Search in Google Scholar

Aljanabi, A.R.A. (2022). The role of innovation capability in the relationship between marketing capability and new product development: Evidence from the telecommunication sector. European Journal of Innovation Management, 25(1), 73-94. https://doi.org/10.1108/EJIM-04-2020-014610.1108/EJIM-04-2020-0146 Search in Google Scholar

Al-Shatnwai, A. M., Faris, M. (2020). Predicting customer retention using XGBoost and balancing methods. International Journal of Advanced Computer Science and Applications, 11(7), 704-712. https://doi.org/10.14569/IJACSA.2020.011078510.14569/IJACSA.2020.0110785 Search in Google Scholar

Bandam, A., Busari, E., Syranidou, C., Linssen, J., Stolten, D. (2022). Classification of building types in Germany: a data-driven modeling approach. Data, 7(4), 45. https://doi.org/10.3390/data704004510.3390/data7040045 Search in Google Scholar

Belbahri, M., Murua, A., Gandouet, O., Nia, V.P. (2021). Qini-based uplift regression. Annals of Applied Statistics, 15(3), 1247-1272. https://doi.org/10.1214/21-AOAS146510.1214/21-AOAS1465 Search in Google Scholar

Cacciarelli, D., Boresta, M. (2022). What drives a donor? A machine learning-based approach for predicting responses of nonprofit direct marketing campaigns. Journal of Philanthropy and Marketing, 27(2), 1724. https://doi.org/10.1002/nvsm.172410.1002/nvsm.1724 Search in Google Scholar

Cambier, A., Chardy, M., Figueiredo, R., Ouorou, A., Poss, M. (2022). Optimizing subscriber migrations for a telecommunication operator in uncertain context. European Journal of Operational Research, 298(1), 308-321. https://doi.org/10.1016/j.ejor.2021.06.03210.1016/j.ejor.2021.06.032 Search in Google Scholar

Chernyak, O., Fareniuk, Y. (2020). Modeling of effectiveness of media investment based on Data Science technologies for Ukrainian Bank. CEUR Workshop Proceedings, 2732, 282-289. http://ceur-ws.org/Vol-2732/20200282.pdf Search in Google Scholar

Dadfarnia, M., Matinpour, A.A., Abdoos, M. (2020). Churn prediction in payment terminals using RFM model and deep neural network. 11^th International Conference on Information and Knowledge Technology, 98-101. https://doi.org/10.1109/IKT51791.2020.934562610.1109/IKT51791.2020.9345626 Search in Google Scholar

De Caigny, A., Coussement, K., Verbeke, W., Idbenjra, K., Phan, M. (2021). Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach. Industrial Marketing Management, 99, 28-39. https://doi.org/10.1016/j.indmarman.2021.10.00110.1016/j.indmarman.2021.10.001 Search in Google Scholar

De, S., Prabu, P., Paulose, J. (2021). Effective ML techniques to predict customer churn. Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA, 895-902. https://doi.org/10.1109/ICIRCA51532.2021.954478510.1109/ICIRCA51532.2021.9544785 Search in Google Scholar

Deng, Y., Li, D., Yang, L., Tang, J., Zhao, J. (2021). Analysis and prediction of bank user churn based on ensemble learning algorithm. Proceedings of 2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA, 288-291. https://doi.org/10.1109/ICPECA51329.2021.936252010.1109/ICPECA51329.2021.9362520 Search in Google Scholar

Ding, Y. (2022). Retention strategy for existing users of mobile communications. In: J. Jansen, B., Liang, H., Ye, J. (eds) International Conference on Cognitive based Information Processing and Applications (CIPA 2021). Lecture Notes on Data Engineering and Communications Technologies, 84. Springer, Singapore. https://doi.org/10.1007/978-981-16-5857-0_3910.1007/978-981-16-5857-0_39 Search in Google Scholar

Du, L., Chen, H., Fang, Y., Liang, X., Zhang, Y., Qiao, Y., Guo, Z. (2022). Research on the method of acquiring customer individual demand based on the Quantitative Kano Model. Comput Intell Neurosci, 5052711. https://doi.org/10.1155/2022/505271110.1155/2022/5052711 Search in Google Scholar

Fang, X. (2021). Research on digital marketing strategy of telecommunication service based on computer complex network model. Journal of Physics: Conference Series, 1992(4), 042002. https://doi.org/10.1088/1742-6596/1992/4/04200210.1088/1742-6596/1992/4/042002 Search in Google Scholar

Fedirko, O., Zatonatska, T., Wolowiec, T., Skowron, S. (2021). Data Science and marketing in e-commerce amid COVID-19 pandemic. European Research Studies Journal, 2, 3-16. https://doi.org/10.35808/ersj/218710.35808/ersj/2187 Search in Google Scholar

Fridrich, M. (2020). Understanding customer churn prediction research with structural topic models. Economic Computation and Economic Cybernetics Studies and Research, 54(4), 301-317. https://doi.org/10.24818/18423264/54.4.20.1910.24818/18423264/54.4.20.19 Search in Google Scholar

Gartner Research (2021). The Annual Tech Marketing Report: Insights from Gartner’s Benchmarks Survey. https://www.gartner.com/en/documents/4006589 Search in Google Scholar

Gattermann-Itschert, T., Thonemann, U.W. (2021). How training on multiple time slices improves performance in churn prediction? European Journal of Operational Research, 295(2), 664-674. https://doi.org/10.1016/j.ejor.2021.05.03510.1016/j.ejor.2021.05.035 Search in Google Scholar

Gopal, P., MohdNawi, N.B. (2021). A survey on customer churn prediction using machine learning and data mining techniques in E-commerce. 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE. https://doi.org/10.1109/CSDE53843.2021.971846010.1109/CSDE53843.2021.9718460 Search in Google Scholar

Goy, G., Kolukisa, B., Bahcevan, C., Gungor, V.C. (2020). Ensemble churn prediction for internet service provider with machine learning techniques. 5^th International Conference on Computer Science and Engineering, UBMK, 248-253. https://doi.org/10.1109/UBMK50275.2020.921936910.1109/UBMK50275.2020.9219369 Search in Google Scholar

Grandhi, B., Patwa, N., Saleem, K. (2021). Data-driven marketing for growth and profitability. EuroMed Journal of Business, 16(4), 381-398. https://doi.org/10.1108/EMJB-09-2018-005410.1108/EMJB-09-2018-0054 Search in Google Scholar

Gu, J. (2022). Research on precision marketing strategy and personalized recommendation method based on big data drive. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2022/675141310.1155/2022/6751413 Search in Google Scholar

Günesen, S.N., Şen, N., Yıldırım, N., Kaya, T. (2021). Customer churn prediction in FMCG sector using machine learning applications, 82-103. https://doi.org/10.1007/978-3-030-80847-1_610.1007/978-3-030-80847-1_6 Search in Google Scholar

Havrylovych, M., Kuznietsova, N. (2019). Survival analysis methods for churn prevention in telecommunications industry. CEUR Workshop Proceedings, 2577, 47-58. http://star.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-2577/paper5.pdf Search in Google Scholar

Hemalatha, M., Mahalakshmi, S. (2020). Customer churns prediction in telecom using adaptive logitboost learning approach. International Journal of Scientific and Technology Research, 9(2), 5703-5713. http://www.ijstr.org/final-print/feb2020/Customer-Churns-Prediction-In-Telecom-Using-Adaptive-Logitboost-Learning-Approach.pdf Search in Google Scholar

Hu, D., Zhou, K., Li, F., Ma, D. (2022). Electric vehicle user classification and value discovery based on charging big data. Energy, 249, 123698. https://doi.org/10.1016/j.energy.2022.12369810.1016/j.energy.2022.123698 Search in Google Scholar

Huang, J. (2022). Real-time statistical method for marketing profit of Japanese cosmetics online cross-border e-commerce platform. In: Jiang, D., Song, H. (eds) Simulation Tools and Techniques. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 424. Springer, Cham. https://doi.org/10.1007/978-3-030-97124-3_4810.1007/978-3-030-97124-3_48 Search in Google Scholar

Jamjoom, A.A. (2021). The use of knowledge extraction in predicting customer churn in B2B. Journal of Big Data, 8(110). https://doi.org/10.1186/s40537-021-00500-310.1186/s40537-021-00500-3 Search in Google Scholar

Jayadi, R., Kelvin, A., Jery, Rifyansyah, P., Mufarih, M., Firmantyo, H.M. (2020). Predicting customer churn of fire insurance policy: a case study in an Indonesian insurance company. Proceedings of the 6^th International Conference on Science and Technology, ICST. https://doi.org/10.1109/ICST50505.2020.973279710.1109/ICST50505.2020.9732797 Search in Google Scholar

Kelley, K., Todd, M., Hopfer, H., Centinari, M. (2022). Identifying wine consumers interested in environmentally sustainable production practices. International Journal of Wine Business Research, 34(1), 86-111. https://doi.org/10.1108/IJWBR-01-2021-000310.1108/IJWBR-01-2021-0003 Search in Google Scholar

Khrustalоva, V., Kononenko, E. (2019). Market of mobile communication services of Ukraine: trends and prospects of development. Investytsiyi: praktyka ta dosvid, 1, 37-41. https://doi.org/10.32702/2306-6814.2019.1.3710.32702/2306-6814.2019.1.37 Search in Google Scholar

Kiguchi, M., Saeed, W., Medi, I. (2022). Churn prediction in digital game-based learning using data mining techniques: logistic regression, decision tree, and random forest. Applied Soft Computing, 118. https://doi.org/10.1016/j.asoc.2022.10849110.1016/j.asoc.2022.108491 Search in Google Scholar

Kolli, N., Balakrishnan, N. (2020). Hybrid features for churn prediction in mobile telecom networks with data constraints. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM, 734-741. https://doi.org/10.1109/ASONAM49781.2020.938148210.1109/ASONAM49781.2020.9381482 Search in Google Scholar

Kolomiiets, A., Mezentseva, O., Kolesnikova, K. (2021). Customer churn prediction in the software by subscription models its business using machine learning methods. CEUR Workshop Proceedings, 3039, 119-128. http://star.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-3039/paper49.pdf Search in Google Scholar

Kumar, H., Yadav, R.K. (2020). Rule-based customer churn prediction model using artificial neural network based and Rough Set theory. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_910.1007/978-981-15-0751-9_9 Search in Google Scholar

Kuznietsova, N., Bidyuk, P. (2018). Forecasting of financial risk users’ outflow. IEEE 1st International Conference on System Analysis and Intelligent Computing, SAIC – Proceedings. https://doi.org/10.1109/SAIC.2018.851678210.1109/SAIC.2018.8516782 Search in Google Scholar

Kuznietsova, N., Bidyuk, P., Kuznietsova, M. (2022). Data mining methods, models and solutions for Big Data cases in telecommunication industry. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_810.1007/978-3-030-82014-5_8 Search in Google Scholar

Kuznietsova, N.V. (2017). Information technologies for clients’ database analysis and behavior forecasting. CEUR Workshop Proceedings, 2067, 56-62. http://star.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-2067 Search in Google Scholar

Li, W. (2022). Big Data precision marketing approach under IoT cloud platform information mining. Comput Intell Neurosci, 4828108. https://doi.org/10.1155/2022/482810810.1155/2022/4828108 Search in Google Scholar

Lv, S. (2022). Real estate marketing adaptive decision-making algorithm based on big data analysis. Security and Communication Networks, 4(12), 1-11. https://doi.org/10.1155/2022/344318210.1155/2022/3443182 Search in Google Scholar

Mašić, B., Nešić, S., Vladušić, L. (2018). Challenges in creating transformative growth for companies in digital economy. ECONOMICS – Innovative and Economics Research Journal, 6(2), 37-48. https://doi.org/10.2478/eoik-2018-002410.2478/eoik-2018-0024 Search in Google Scholar

Mo, L., Yang, L. (2022). Research on application effective evaluation of artificial intelligence technology in marketing communication. Security and Communication Networks, 3(31), 1-8. https://doi.org/10.1155/2022/350735310.1155/2022/3507353 Search in Google Scholar

Mykhalchuk, T., Zatonatska, T., Dluhopolskyi, O., Zhukovska, A., Dluhopolska, T., Liakhovych, L. (2021). Development of recommendation system in e-commerce using emotional analysis and machine learning methods. The 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). Vol.1. Cracow, Poland, 527-535. https://ieeexplore.ieee.org/document/966085410.1109/IDAACS53288.2021.9660854 Search in Google Scholar

Park, S., Kim, M., Kim, Y., Park, Y. (2022). A deep learning approach to analyze airline customer propensities: the case of South Korea. Applied Sciences (Switzerland), 12(4), 1916. https://doi.org/10.3390/app1204191610.3390/app12041916 Search in Google Scholar

Priyanga, P., Nadira Banu Kamal, A.R. (2022). Mobile app usage pattern prediction using hierarchical flexi-ensemble clustering (HFEC) for mobile service rating. Wireless Personal Communications, 122(4), 3247-3268. https://doi.org/10.1007/s11277-021-09048-010.1007/s11277-021-09048-0 Search in Google Scholar

Qu, Y. (2022). Using data mining techniques to discover customer behavioral patterns for direct marketing. 7^th International Conference on Big Data Analytics, ICBDA, 361-365. https://doi.org/10.1109/ICBDA55095.2022.976030910.1109/ICBDA55095.2022.9760309 Search in Google Scholar

Rabiul Alam, M.G., Hussain, S., Mim, M.M.I., Islam, M.T. (2021). Telecom customer behavior analysis using naïve bayes classifier. IEEE 4^th International Conference on Computer and Communication Engineering Technology, CCET, 308-312. https://doi.org/10.1109/CCET52649.2021.954416910.1109/CCET52649.2021.9544169 Search in Google Scholar

Radukic, S., Mastilo, Z., Kostic, Z., Vladusic, L. (2019). Measuring of the goods and labor markets efficiency: comparative study of Western Balkan countries. Montenegrin Journal of Economics, 15(2), 95-109. https://doi.org/10.14254/1800-5845/2019.15-2.8 Search in Google Scholar

Rakhmatullina, A.R., Shatalova, T.N., Chebykina, M.V. (2022). Conceptual organizational aspects of innovation management processes for industrial enterprises. In: Ashmarina, S.I., Mantulenko, V.V. (eds) Proceedings of the International Conference Engineering Innovations and Sustainable Development. Lecture Notas in Civic Engineering. Springer. https://doi.org/10.1007/978-3-030-90843-0_1710.1007/978-3-030-90843-0_17 Search in Google Scholar

Ram, J., Zhang, Z. (2022). Examining the needs to adopt big data analytics in B2B organizations: Development of propositions and model of needs. Journal of Business and Industrial Marketing, 37(4), 790-809. https://doi.org/10.1108/JBIM-10-2020-046410.1108/JBIM-10-2020-0464 Search in Google Scholar

Rezaeian, O., Haghighi, S.S., Shahrabi, J. (2021). Customer churn prediction using data mining techniques for an Iranian payment application. 12^th International Conference on Information and Knowledge Technology, IKT, 134-138. https://doi.org/10.1109/IKT54664.2021.968550210.1109/IKT54664.2021.9685502 Search in Google Scholar

Saanchay, P.M., Thomas, K.T. (2022). An approach for credit card churn prediction using gradient descent. In: IOT with Smart Systems – Smart Innovation, Systems and Technologies, 689-697. Springer Nature Singapore. https://doi.org/10.1007/978-981-16-3945-6_6810.1007/978-981-16-3945-6_68 Search in Google Scholar

Sánchez, D.M., Moreno, A., López, M.D.J. (2022). Machine learning methods for automatic gender detection. International Journal on Artificial Intelligence Tools, 31(3). https://doi.org/10.1142/S021821302241002010.1142/S0218213022410020 Search in Google Scholar

Sun Y, Tan X. (2022). Customer relationship management based on SPRINT classification algorithm under Data Mining technology. Comput Intell Neurosci, 6170335. https://doi.org/10.1155/2022/617033510.1155/2022/6170335 Search in Google Scholar

Syaglova, Y.V., Bozhenko, E.S., Larkina, N.G., Polyakova, E.Y., Stefanova, I.V. (2022). Value orientation for marketing customer experience management in companies in a digital transformation. In: Trifonov, P.V., Charaeva, M.V. (eds) Strategies and Trends in Organizational and Project Management. Lecture Notes in Networks and Systems, 380. Springer, Cham. https://doi.org/10.1007/978-3-030-94245-8_5710.1007/978-3-030-94245-8_57 Search in Google Scholar

Thakkar, H.K., Desai, A., Ghosh, S., Singh, P., Sharma, G. (2022). Clairvoyant: AdaBoost with cost- enabled cost-sensitive classifier for customer churn prediction. Comput Intell Neurosci, 9028580. https://doi.org/10.1155/2022/902858010.1155/2022/9028580 Search in Google Scholar

Tianyuan, Z., Moro, S. (2021). Research trends in customer churn prediction: a data mining approach. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Correia, A.M.R. World Conference on Information Systems and Technologies, WorldCIST. Springer. https://doi.org/10.1007/978-3-030-72657-7_2210.1007/978-3-030-72657-7_22 Search in Google Scholar

Todevski, D., Georgieva Svrtinov, V. (2021). Machine learning model for customer churn. KNOWLEDGE – International Journal, 47(5), 887-892. https://ikm.mk/ojs/index.php/kij/article/view/4870 Search in Google Scholar

Vezzoli, M., Zogmaister, C., Van den Poel, D. (2020). Will they stay or will they go? predicting customer churn in the energy sector. Applied Marketing Analytics, 6(2), 136-150. https://www.ingentaconnect.com/content/hsp/ama/2020/00000006/00000002/art00006 Search in Google Scholar

Wassouf, W.N., Alkhatib, R., Salloum, K., Balloul, S. (2020). Predictive analytics using big data for increased customer loyalty: Syriatel telecom company case study. Journal of Big Data, 7(29). https://doi.org/10.1186/s40537-020-00290-010.1186/s40537-020-00290-0 Search in Google Scholar

Wu, Z., Li, Z. (2021). Customer churn prediction for commercial banks using customer-value- weighted machine learning models. Journal of Credit Risk, 17(4), 15-42. https://doi.org/10.21314/JCR.2021.01110.21314/JCR.2021.011 Search in Google Scholar

Xiahou, X., Harada, Y. (2022). B2C E-commerce customer churn prediction based on K-means and SVM. Journal of Theoretical and Applied Electronic Commerce Research, 17(2), 458-475. https://doi.org/10.3390/jtaer1702002410.3390/jtaer17020024 Search in Google Scholar

Zatonatska, T., Dluhopolskyi, O., Artyukh, T., Tymchenko, K. (2022). Forecasting the behavior of target segments to activate advertising tools: case of mobile operator Vodafone Ukraine. ECONOMICS – Innovative and Economics Research Journal, 10(1), 87-104. https://doi.org/10.2478/eoik-2022-000510.2478/eoik-2022-0005 Search in Google Scholar

Zatonatska, T., Fedirko, O., Dluhopolskyi, O., Londar, S. (2021). The impact of e-commerce on the sustainable development: case of Ukraine, Poland, and Austria. IOP Conference Series: Earth and Environmental Science, 915 (October 15-16, 2021). Odesa, Ukraine. https://doi.org/10.1088/1755-1315/915/1/01202310.1088/1755-1315/915/1/012023 Search in Google Scholar

Zhang, S., Liao, P., Ye, H., Zhou, Z. (2022). Dynamic marketing resource allocation with two-stage decisions. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 327-344. https://doi.org/10.3390/jtaer1701001710.3390/jtaer17010017 Search in Google Scholar

Zhang, T., Moro, S., Ramos, R.F. (2022). A data-driven approach to improve customer churn prediction based on telecom customer segmentation. Future Internet, 14(3), 94. https://doi.org/10.3390/fi1403009410.3390/fi14030094 Search in Google Scholar

Zhu, B., Qian, C., Pan, X., Chen, H. (2020). A trajectory-based deep sequential method for customer churn prediction. ACM International Conference Proceeding Series, 114-118. https://doi.org/10.1145/3409073.340908310.1145/3409073.3409083 Search in Google Scholar