Have a personal or library account? Click to login
Customer churn prediction model: a case of the telecommunication market Cover

Customer churn prediction model: a case of the telecommunication market

Open Access
|Dec 2022

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. Dadfarnia, M., Matinpour, A.A., Abdoos, M. (2020). Churn prediction in payment terminals using RFM model and deep neural network. 11th International Conference on Information and Knowledge Technology, 98-101. https://doi.org/10.1109/IKT51791.2020.934562610.1109/IKT51791.2020.9345626
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. Gartner Research (2021). The Annual Tech Marketing Report: Insights from Gartner’s Benchmarks Survey. https://www.gartner.com/en/documents/4006589
  21. 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
  22. 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
  23. Goy, G., Kolukisa, B., Bahcevan, C., Gungor, V.C. (2020). Ensemble churn prediction for internet service provider with machine learning techniques. 5th International Conference on Computer Science and Engineering, UBMK, 248-253. https://doi.org/10.1109/UBMK50275.2020.921936910.1109/UBMK50275.2020.9219369
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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 6th International Conference on Science and Technology, ICST. https://doi.org/10.1109/ICST50505.2020.973279710.1109/ICST50505.2020.9732797
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. Qu, Y. (2022). Using data mining techniques to discover customer behavioral patterns for direct marketing. 7th International Conference on Big Data Analytics, ICBDA, 361-365. https://doi.org/10.1109/ICBDA55095.2022.976030910.1109/ICBDA55095.2022.9760309
  50. Rabiul Alam, M.G., Hussain, S., Mim, M.M.I., Islam, M.T. (2021). Telecom customer behavior analysis using naïve bayes classifier. IEEE 4th International Conference on Computer and Communication Engineering Technology, CCET, 308-312. https://doi.org/10.1109/CCET52649.2021.954416910.1109/CCET52649.2021.9544169
  51. 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
  52. 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
  53. 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
  54. Rezaeian, O., Haghighi, S.S., Shahrabi, J. (2021). Customer churn prediction using data mining techniques for an Iranian payment application. 12th International Conference on Information and Knowledge Technology, IKT, 134-138. https://doi.org/10.1109/IKT54664.2021.968550210.1109/IKT54664.2021.9685502
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
DOI: https://doi.org/10.2478/eoik-2022-0021 | Journal eISSN: 2303-5013 | Journal ISSN: 2303-5005
Language: English
Page range: 109 - 130
Submitted on: Aug 22, 2022
|
Accepted on: Nov 4, 2022
|
Published on: Dec 12, 2022
Published by: Oikos Institut d.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 3 issues per year

© 2022 Yana Fareniuk, Tetiana Zatonatska, Oleksandr Dluhopolskyi, Oksana Kovalenko, published by Oikos Institut d.o.o.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.