Abstract

Competition between marketing strategies of enterprises shifts to the use of artificial intelligence and begins to be considered in the context of competition between Data Science projects. Therefore, the issue of developing methodology and building a model in a particular area is relevant, which will make the project quite effective and ensure the achievement of goals for the company. The banking services market has a certain specificity of consumer behaviour, so forming marketing strategies is a somewhat complex process. Thus, banks face the task of maintaining the loyalty of their existing customers and attracting new ones. This article aims to build a marketing strategy to attract new customers in the banking sector using Data Science tools. The result of the study is the construction of two econometric models of the different bank’s credit products: cash loans and credit cards, which determine the influence of various factors on this process and helps to distribute the advertising budget between different types of advertising. Using the built model, it was determined that advertising campaigns directly affect the increase in the number of new customers in the bank and the overall growth of brand knowledge about the banking institution in society. In addition, the determined weights of each influencing factor helped form an advertising budget, which increased customer inflows by 12%, with an average ROI of 3.18. Taking all into account, the model had shown its effectiveness in organising the bank’s advertising campaign when decisions were made using Data Science technologies. The results obtained based on the models give a fairly clear understanding of the factors influencing the inflow of new customers in the bank, which will model the distribution of the budget for advertising campaigns in future periods and predict their effectiveness. Competition in the country’s financial sector is forcing banking institutions to use data science in their marketing activities.

Keywords: bank, marketing activity, advertising, regression, involvement of new clients, Data Science

JEL Classification: С35, G21, М30.

Cite as: Zatonatska, T., Hubska, M., & Shpyrko, V. (2022). Marketing Strategies in the Banking Services Sector With the Help of Data Science. Marketing and Management of Innovations, 2, 121-127. https://doi.org/10.21272/mmi.2022.2-11

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Alalwan, A. A. (2018). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65-77. [Google Scholar] [CrossRef]
2. Chernyak, O., & Fareniuk, Y. (2020). Modeling of Effectiveness of Media Investment Based on Data Science Technologies for Ukrainian Bank. In ICTERI Workshops (pp. 282-289). [Google Scholar]
3. DePino F. (2021). Modern Bank Marketing – A Comprehensive Guide. 2021. Retrieved from [Link]
4. Dolega, L., Rowe, F., & Branagan, E. (2021). Going digital? The impact of social media marketing on retail website traffic, orders and sales. Journal of Retailing and Consumer Services, 60, 102501. [Google Scholar]
5. Fedirko, O., Zatonatska, T., Wołowiec, T., & Skowron, S. (2021). Data Science and Marketing in E-Commerce Amid COVID-19 Pandemic. European Research Studies, 24, 3-16. [Google Scholar]
6. Financial Conduct Authority. Strategic Review of Retail Banking Business Models – Final Report (2022). Retrieved from [Link]
7. Kuznyetsova, A., Kozmuk, N., Zherebylo, I., Sydorova, O., & Zvarych, M. (2018). The Essence of Retail and Development of Retail Banking Innovative Mechanisms in Ukraine. Marketing and Management of Innovations, 4, 316-331. [Google Scholar] [CrossRef]
8. Lin, Y., Ahmad, Z., Shafik, W., Khosa, S. K., Almaspoor, Z., Alsuhabi, H., & Abbas, F. (2021). Impact of Facebook and Newspaper Advertising on Sales: A Comparative Study of Online and Print Media. Computational intelligence and neuroscience, 2021. [Google Scholar] [CrossRef]
9. Mykhalchuk, T., Zatonatska, T., Dluhopolskyi, O., Zhukovska, A., Dluhopolska, T., & Liakhovych, L. (2021, September). Development of Recommendation System in e-Commerce using Emotional Analysis and Machine Learning Methods. In 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 1, pp. 527-535). IEEE. [Google Scholar]
10. Rodriguez, M., Peterson, R. M., & Ajjan, H. (2015). CRM/social media technology: impact on customer orientation process and organisational sales performance. In Ideas in marketing: Finding the new and polishing the old (pp. 636-638). Springer, Cham. [Google Scholar] [CrossRef]
11. Shapiro, B., Hitsch, G., & Tuchman, A. (2020). Generalisable and Robust TV Advertising Effects (No. 2020-111). Becker Friedman Institute for Research In Economics. [Google Scholar] [CrossRef]
12. Zhou, Y., Ahmad, Z., Alsuhabi, H., Yusuf, M., Alkhairy, I., & Sharawy, A. M. (2021). Impact of YouTube Advertising on Sales with Regression Analysis and Statistical Modeling: Usefulness of Online Media in Business. Computational Intelligence and Neuroscience, 2021. [Google Scholar] [CrossRef]