ASSESSMENT OF THE EFFICIENCY OF UKRAINIAN E-COMMERCE ENTERPRISES USING A MULTI-CRITERIA ANALYSIS METHOD

The topic of evaluating the performance of Ukrainian e-commerce enterprises using the method of multi-criteria analysis of alternatives is extremely relevant in the conditions of the rapid development of the digital economy and the growing competition in the online market. Within the framework of the study, the key criteria for evaluating the efficiency of enterprises are determined, in particular: financial stability, the level of customer service, innovativeness, market position, and adaptability to changing market conditions. The application of multi-criteria analysis methods makes it possible to comprehensively take into account various factors that influence the results of enterprises’ activities and contributes to the adoption of well-founded management decisions. The methodology involves several stages, including data collection and normalization, determining the weights of the criteria, and conducting an analysis with the subsequent interpretation of the results. The study highlights both the advantages and disadvantages of methods of multi-criteria analysis of alternatives, emphasizing the importance of high-quality data and the objectivity of expert evaluations for achieving accurate and reliable results. In particular, it is important to note that qualitative criteria and their adequate weighting can significantly affect the effectiveness of the analysis. The obtained results can become the basis for strategic planning, helping enterprises improve business processes, increase competitiveness, and adapt to dynamic changes in the e-commerce market in Ukraine. Determining the strengths and weaknesses of enterprises will allow not only the optimization of their internal processes, but also the improvement of interaction with customers and partners, which, in turn, will contribute to the development of the entire industry. In further research, it is proposed to focus on the analysis and methods of integrating multi- criteria analysis methods with machine learning algorithms.