• Tetiana Zatonatska Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
  • Oleksandr Dluhopolskyi Higher School of Economics and Innovation (WSEI), Lublin, Poland; West Ukrainian National University, Ternopil, Ukraine
  • Tatiana Artyukh Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
  • Kateryna Tymchenko Taras Shevchenko National University of Kyiv, Kyiv, Ukraine



market segment, telecommunication sector, target, advertising, scoring models, big data


Today, the use of machine learning technology in combination with the use of big data are topics that are actively discussed in business around the world. This topic has long gone beyond the information sphere, as it now applies to almost every sphere of life: economic, telecommunications, education, medicine, administration, and especially defense. Predicting customer behavior based on scoring models is in its infancy in Ukrainian companies, the main ones being the introduction of artificial intelligence technologies and machine learning, which will be the leading catalyst that will facilitate decision-making in business in the nearest future. The aim of the study is to develop a scoring model that predicts the behavior of target segments, namely, updating their activity to activate advertising tools. To achieve the goal of the work a set of research methods was used: dialectical – to reveal the theoretical foundations of models and types of forecasting models; analytical – in the study of the functioning of the environment SAS, Anaconda; optimization methods – to choose the best model and generate features. Scientific novelty and theoretical significance lie in the development of a scoring model for predicting the activity of subscribers of the telecommunications company “VF Ukraine”, on the basis of which marketing campaigns are conducted. With the help of the built-in scoring model, the company “VF Ukraine” can target its campaigns to retain subscribers. The marketing directorate of the enterprise can choose the TOP-20 or TOP-30 of the most prone subscribers to non-resumption of activity, i.e., tend to switch to other mobile operators, and hold promotions for them – providing additional gifs and bonuses, money to mobile account.


Airola, A., Pahikkala, T., Waegeman, W., De Baets, B., & Salakoski, T. (2011). An experimental comparison of cross-validation techniques for estimating the area under the ROCcurve. Computational Statistics & Data Analysis, 55(4), 1828-1844.

Celisse, A. (2014). Optimal cross-validation in density estimation with the L2-loss. The Annals of Statistics, 42(5), 1879-1910.

Dai, Х. (2017). Identifying dissatisfied 4G customers from network indicators: a comparison between complaint and survey data. Big Data Applications in the Telecommunications Industry, 41-53.

Dang, Ch. (2017). Network-based targeting: Big Data application in mobile industry. Big Data Applications in the Telecommunications Industry, 78-107.

Deroos, D., Zikopoulos, P.C., Melnyk, R.B., Brown, B., & Coss, R. (2014). Hadoop for dummies. John Wiley & Sons, Inc., Hoboken, New Jersey. ISBN-13:978-1118607558

Dluhopolskyi, O., Simakhova, A., Zatonatska, T., Oleksiv, I., & Kozlovskyi, S. (2021). Potential of virtual reality in the current digital society: economic perspectives. 11th International Conference on Advanced Computer Information Technologies (September 15-17, 2021). Deggendorf, Germany, 360-363.

Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.

Goworek, К. (2021). The big impact of Big Data on the telecom industry.

Hastie, T., Tibshirani, R., & Friedman, J. (2017). Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.

Hssina, B., Merbouha, A., Ezzikouri, H., Erritali, M. (2014). A comparative study of decision tree ID3 and C4.5. International Journal of Advanced Computer Science and Applications (IJACSA), Special Issue on Advances in Vehicular Ad Hoc Networking and Applications, 13-19.

Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. New York: Springer.

Lurie, A. (2014). 39 Data Visualization Tools for Big Data. Profit Bricks, The Laas Company.

Machine Learning Mastery (2015). Discover Feature Engineering, How to Engineer Features and How to Get Good at It.

McLachlan, G.J., Do, K.-A., & Ambroise, C. (2004). Analyzing microarray gene expression data. Wiley. ISBN: 978-0-471-72842-9

Molinaro, A.M., Simon, R., & Pfeiffer, R.M. (2005). Prediction error estimation: a comparison of resampling methods. Bioinformatics, 21(15), 3301-3307.

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. Te 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). Vol.1 (September 22-25, 2021). Cracow, Poland, 527-535.

Perkhofer, L., Walchshofer, C., & Hofer, P. (2020). Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use. Journal of Management Control, 31, 55-95.

Poel, M., Meyer, E.T., & Schroeder, R. (2018). Big data for policymaking: great expectations, but with limited progress? Policy & Internet, 10(3), 347-367.

Polianovskyi, H., Zatonatska, T., Dluhopolskyi, O., & Liutyi, I. (2021). Digital and technological support of distance learning at universities under COVID-19 (case of Ukraine). Revista Romaneasca pentru Educatie Multidimensionala, 13(4), 595-613.

Radukić, S., Mastilo, Z., & Kostić, Z. (2019). Effects of digital transformation and network externalities in the telecommunication markets. ECONOMICS, 7(2), 31-42.

Riddle, J. (2020). How Will Big Data Transform E-Commerce Marketplaces?

Rosario, A., Moniz, L.B., & Cruz, R. (2021). Data science applied to marketing: a literature review. Journal of Information Science and Engineering, 37(5), 1067-1081.

Ryfak, S. (2020). Big Data is taking eCommerce by storm. Here’s why you can’t wait it out.

Sekli, G.F., & Vega, I. (2021). Adoption of Big Data analytics and its impact on organizational performance in higher education mediated by knowledge management. Journal of Open Innovation: Technology, Market, and Complexity, 7(4), 221.

Simaković, M.N., Cica, Z.G., & Masnikosa, I.B. (2021). Big Data architecture for mobile network operators. 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), 283-286.

Stavytskyy, A., Dluhopolskyi, O., Kharlamova, G., Karpuk, A., Osetskyi, V. (2019). Testing the fruitfulness of the institutional environment for the development of innovativeentrepreneurial universities in Ukraine. Problems and Perspectives in Management, 17(4), 274-288.

Suominen, A., & Hajikhani, A. (2021). Research themes in big data analytics for policymaking: Insights from a mixed-methods systematic literature review. Policy & Internet, 13(4), 464-484.

Suslenko, V., Zatonatska, T., Dluhopolskyi, O., Kuznyetsova, A. (2022). Use of crypto-currencies Bitcoin and Ethereum in the feld of e-commerce: case study of Ukraine. Financial and credit activity: problems of theory and practice, 1(42), 62-72.

Truong, C., Phuong, H., Thi, N., & Trang, H. (2017). Web analytics tools and benefits for entrepreneurs. Bachelor’s Thesis in Business Information Technology, 79 p.

Vanwinckelen, G., Blockeel, H., De Baets, B., Manderick, B., Rademaker, M., & Waegeman, W. (2012). On estimating model accuracy with repeated cross-validation. Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, 39-44. ISBN: 978-94-6197-044-2

Varga, I.E., & Gabor, M.R. (2021). The influence of social networks in travel decisions. ECONOMICS, 9(2), 35-48.

Washington, A.L. (2014). Government information policy in the era of big data. Review of Policy Research, 31(4), 319-325.

White, T. (2015). Hadoop: The Definitive Guide. O’Reilly Media, Inc. 4th Edition. ISBN: 9780596521974

Yusuf-Asaju, A.W., Dahalin, Z.B., & Ta’a, A. (2017). Mobile network quality of experience using big data analytics approach. 8th International Conference on Information Technology (ICIT) (May 17-18, 2017), 658-664.

Zatonatska, T., Dluhopolskyi, O., Chyrak, I., & Kotys, N. (2019). The internet and e-commerce diffusion in European countries (modeling at the example of Austria, Poland, and Ukraine). Innovative Marketing, 15(1), 66-75.

Zatonatska, T., Fedirko, O., Dluhopolskyi, O., & Londar, S. (2021). The impact of e-commerce on he sustainable development: case of Ukraine, Poland, and Austria. IOP Conference Series: Earth and Environmental Science, Volume 915, (ISCES) “International Conference on Environmental Sustainability in Natural Resources Management” (October 15-16, 2021). Odesa, Ukraine.

Zatonatska, T., Suslenko, V., Dluhopolskyi, O., Brych, V., Dluhopolska, T. (2022). Investment models on centralized and decentralized cryptocurrency markets. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 1, 177-182.

Zhou, S., Zhang, X., Liu, J., Zhang, K., Zhao, Y. (2020). Exploring development of smart city research through perspectives of governance and information systems: A scientometric analysis using cite space. Journal of Science and Technology Policy Management, 11(4), 431-454.

Zhou, Z.-H., Chawla, N.V., Jin, Y., & Williams, G.J. (2014). Big data opportunities and challenges: Discussions from data analytics perspectives [discussion forum]. IEEE Computational Intelligence Magazine, 9(4), 62-74.

Zingale, N.C., Cook, D., & Mazanec, M. (2018). Change calls upon public administrators to act, but in what way? Exploring administration as a platform for governance. Administrative Theory & Praxis, 40(3), 180-199.




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