Abstract
Quarantine measures to prevent the spread of COVID-19 pandemic has led to a rapid growth of the e-retail market. Online shopping has become commonplace and for some groups of people the only way to provide themselves with the resources. Therefore, due to the excessive accumulation of online data, modern methods of analysis are necessary. Due to a literature overview on the possibilities of using data science tools, such methods and models for processing e-commerce data as data mining (cluster analysis, regression analysis, classification), machine learning, artificial neural networks, visualization and much more have been identified. The purpose of the article is to build a data processing model for improving the efficiency of e-commerce. The authors propose a cluster analysis of online markets that consist of household products of an enterprise. Ward’s methods and cluster visualization have been used during the modeling. The authors determine such methods and models for e-commerce data processing as data mining, machine learning, artificial neural networks. As a result, each cluster is evaluated according to the statistical indicators. Also, options for the development of e-commerce and improvement of marketing strategy are proposed. The use of advertising on social networks is able to significantly increase e-sales, while investing in print media is inefficient. Thus, the using of the built model is effective in improving of sales and planning of marketing costs. The possibilities of using data science tools in e-commerce analysis are a key area for attracting customers, expanding business, and increasing revenue.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akter, S., Wamba, S.F.: Big data analytics in E-commerce: a systematic review and agenda for future research. Electron. Mark. 26(2), 173–194 (2016). https://doi.org/10.1007/s12525-016-0219-0
Alrumiah, S.S., Hadwan, M.: Implementing big data analytics in e-commerce: vendor and customer view. IEEE Access. 9, 37281–37286 (2021). https://doi.org/10.1109/ACCESS.2021.3063615
Amazon. First Quarter Results (2020). https://press.aboutamazon.com/news-releases/news-release-details/amazoncom-announces-first-quarter-results
Cheng, Y., Yang, Y., Jiang, J., Xu, G.C.: Cluster analysis of e-commerce sites with data mining approach. Int. J. Database Theory Appl. 8(3), 343–354 (2015). http://dx.doi.org/10.14257/ijdta.2015.8.3.30
Deloitte. COVID-19 Will Permanently Change E-Commerce (2020). https://www2.deloitte.com/content/dam/Deloitte/dk/Documents/strategy/e-commerce-covid-19-onepage.pdf
Dluhopolskyi, O., Simakhova, A., Zatonatska, T., Oleksiv, I., Kozlovskyi, S.: Potential of virtual reality in the current digital society: economic perspectives. In: 11th International Conference on Advanced Computer Information Technologies, Deggendorf, Germany, pp. 360–363 (2021)
E-commerce. EVO (2021). https://evo.business
Fedirko, O., Zatonatska, T., Dluhopolskyi, O., Londar, S.: The impact of e-commerce on the sustainable development: case of Ukraine, Poland, and Austria. In: IOP Conference Series: Earth and Environmental Science, vol. 915. International Conference on Environmental Sustainability in Natural Resources Management. Odesa, Ukraine (2021). https://iopscience.iop.org/article/10.1088/1755-1315/915/1/012023
Forbes. How COVID-19 Is Transforming E-Commerce (2020). https://www.forbes.com/sites/louiscolumbus/2020/04/28/how-covid-19-is-transforming-e-commerce/?sh=56742aba3544
Kovtoniuk, K., Molchanova, E., Dluhopolskyi, O., Weigang, G., Piankova, O.: The factors’ analysis of influencing the development of digital trade in the leading countries. In: 11th International Conference on Advanced Computer Information Technologies, Deggendorf, Germany, pp. 290–293 (2021)
Malhotra, D., Rishi, O.: An intelligent approach to design of e-commerce metasearch and ranking system using next-generation big data analytics. J. King Saud Univ. – Comput. Inf. Sci. 33(2), 183–194 (2021). https://doi.org/10.1016/j.jksuci.2018.02.015
Mykhalchuk, T., Zatonatska, T., Dluhopolskyi, O., Zhukovska, A., Dluhopolska, T., Liakhovych, L.: Development of recommendation system in e-commerce using emotional analysis and machine learning methods. In: The 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Cracow, Poland, vol. 1, pp. 527–535 (2021)
Naur, P.: Concise Survey of Computer Methods. Studentlitteratur, Lund (1975)
Panchenko, O., Klochko, A., Dluhopolskyi, O., Klochko, O., Shchurova, V., Peker, A.: Impact of the COVID-19 pandemic on the development of artificial intelligence: challenges for the human rights. In: 11th International Conference on Advanced Computer Information Technologies, pp. 744–747. Deggendorf, Germany (2021)
Rymarczyk, T., et al.: Comparison of machine learning methods in electrical tomography for detecting moisture in building walls. Energies 14(2777), 1–22 (2021). https://doi.org/10.3390/en14102777
Statista. Retail E-Commerce Sales Worldwide from 2014 to 2024 (2021). https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales
Wang, Q., Cai, R., Zhao, M.: E-commerce brand marketing based on FPGA and machine learning. Microprocess. Microsyst. 103446 (2020). https://doi.org/10.1016/j.micpro.2020.103446
Yue, Y.S., Li, B.: E-commerce platform and exports performance of Chinese manufacturing enterprises – empirical evidence based on big data from Alibaba. China Industr. Econ. 8, 97–115 (2018)
Zatonatska, T., Dluhopolskyi, O.: Modelling the efficiency of the cloud computing implementation at enterprises. Mark. Manage. Innov. 3, 45–59 (2019)
Zatonatska, T., Dluhopolskyi, O., Bobro, O.: Development of electronic payment systems in the structure of e-commerce in the Visegrad Group and Ukraine. In: Krysovatyy, A., Shengelia, T. (eds.) Visegrad Group: A Form of Establishment and Development of European Integration: Coll. Monograph, pp. 96–112. TSU, Tbilisi (2021)
Zatonatska, T., Dluhopolskyi, O., Chyrak, I., Kotys, N.: The internet and e-commerce diffusion in European countries (modeling at the example of Austria, Poland, and Ukraine). Innov. Mark. 15(1), 66–75 (2019)
Zatonatska, T., Suslenko, V., Dluhopolskyi, O., Brych, V., Dluhopolska, T.: Investment models on centralized and decentralized cryptocurrency markets. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 1, 177–182 (2022). https://doi.org/10.33271/nvngu/2022-1/177
Zhuravka, F., Filatova, H., Šuleř, P., Wołowiec, T.: State debt assessment and forecasting: time series analysis. Invest. Manage. Financ. Innov. 18(1), 65–75 (2021). https://doi.org/10.21511/imfi.18(1).2021.06
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix A
Appendix A
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zatonatska, T., Wołowiec, T., Dluhopolskyi, O., Podskrebko, O., Maksymchuk, O. (2023). Using Data Science Tools in E-Commerce: Client’s Advertising Campaigns vs. Sales of Enterprise Products. In: Faure, E., Danchenko, O., Bondarenko, M., Tryus, Y., Bazilo, C., Zaspa, G. (eds) Information Technology for Education, Science, and Technics. ITEST 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-031-35467-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-35467-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-35466-3
Online ISBN: 978-3-031-35467-0
eBook Packages: EngineeringEngineering (R0)