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Using Data Science Tools in E-Commerce: Client’s Advertising Campaigns vs. Sales of Enterprise Products

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Information Technology for Education, Science, and Technics (ITEST 2022)

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.

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Correspondence to Oleksandr Dluhopolskyi .

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Appendix A

Appendix A

Table A1. Descriptive statistics of the studied data set. Source: own evaluation

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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

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  • DOI: https://doi.org/10.1007/978-3-031-35467-0_22

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