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The impact of the war in Ukraine and migration has affected the ecommerce markets of the recipient countries, presenting both opportunities, in the form of an increased consumer base, and challenges, such as the lack of a clear development vision. This paper aims to investigate the influence of migration processes on the development of e-commerce in Poland and examine the feasibility of using forecasting methods by e-commerce companies under these conditions for future activity planning. To fulfill the research objective, the following tasks were addressed: investigating the current state of e-commerce development influenced by migration processes; exploring modern migration processes and their impact on global economies; assessing the impact of migration from Ukraine on the Polish market; and analyzing a Polish online store to develop a model for forecasting data and planning activities under the influence of migration processes. To achieve this goal, three models were constructed: a multiple regression model to assess the level of migration processes' influence on e-commerce; a neural network to forecast sales for a Polish e-commerce store; and cluster analysis to identify clusters of goods most affected by migration processes. The study analyzed the nuances of modern migration processes and assessed the reverse effect of migration as a driver of e-commerce development. Migration stimulates e-commerce by altering consumer behavior and logistics routes, increasing exports and imports, and fostering the spread of digital entrepreneurship. Using data from a Polish online store, the study modeled the impact of market changes on the company's operations and identified the most significant factors. Thus, the analysis explored the impact of migration on ebusiness in Poland through constructed models. Regression analysis revealed that migration processes have contributed to the development of the Polish online store's sales, thanks to the increase in migrant consumers and rising price levels. A neural network was developed with machine learning, incorporating macroeconomic and demographic factors into its forecasting typology. Cluster analysis was employed to examine the online store's assortment, identifying clusters by sales volume and migrants' influence. The analysis determined that, following the onset of the migration movement, categories experiencing a surge in demand from refugees, such as baby food products, appliances, telephones, furniture, and communication devices, saw the most significant growth. [ABSTRACT FROM AUTHOR]

The paper describes the marketing mix modeling results for companies in nonfood retailing. The main objectives of the research are to demonstrate the viable way of making effective recommendations for optimizing the media strategy by modeling offline and online traffic to the stores based on econometric modeling and to develop a decision support system, which enhance the effective growth of business KPIs and an effective decision-making process. Econometric modeling, deeper data analysis, decision support were implemented on the data of one of the main retailers in Ukraine in a period before the full-scale Russian invasion. Estimating the impact of different communication channels on business results made basis for ROI calculations and optimization of media investments allocation among media channels by periods, video durations, type of advertising and with optimal weekly media pressure. ROMI calculation was based on the results of regression modeling, which estimate the level of traffic and sales generated by each media channel. The information-analytical decision support system based on an interactive dashboard has been developed for improvement of day-by-day business planning and management. The developed framework of regional strategy selection facilitates to the formation of a strategic vision on a regional scale and improves the quality of a regional media strategy. [ABSTRACT FROM AUTHOR]