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The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

This research utilizes intellectual data analysis to deepen insights into educational dynamics by examining university professors' behavioral responses to education management amidst socioeconomic uncertainty. The study applied sophisticated regression and cluster analysis tools to sociological survey data, focusing on professors' attitudes toward distance education. The survey assessed perceptions of its advantages and disadvantages, aiming to uncover factors influencing professors' inclinations toward this mode of teaching. Results revealed six distinct behavioral "profiles" or clusters of professors, each with unique responses to distance education. These insights guide recommendations for educational policy priorities aimed at addressing weaknesses in education management. Key strategies proposed include forming databases of behavioral responses and employing algorithms for deep intellectual analysis. Such measures intend to align educational practices with the values, welfare needs, and communication preferences of the scholarly community, thereby enhancing their propensity for distance education. The study concludes that a higher level of academic engagement in distance education can be achieved by tailoring educational strategies to the specific needs and values of different professor groups. This approach promises to improve the effectiveness of education management and the overall quality of education, benefiting both professors and students by creating a more supportive and effective teaching environment. [ABSTRACT FROM AUTHOR]

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]

This paper examines the impact of migration and changes in the population's age structure on government expenditures on education in Ukraine. The cohort method, considering a group of people attending four main types of education, namely, preschool, general secondary, vocational and technical, and higher education, is used to analyze and forecast government expenditures in the medium and long term. The study results show that migration significantly impacts government budget expenditures on education, with the most significant impact being seen on preschool education, followed by higher education, while the impact on general secondary and vocational education is minimal. The number of people receiving education in one of the four main types of institutions funded by state and local budgets was predicted to gradually decrease during the forecast period, except for secondary and vocational education. The overall volume of state expenditures would gradually decrease, both in absolute terms and as a percentage of GDP. Improving educational processes and the quality of budget spending should become an area for state regulation to ensure quality education in all funding conditions. This research's leading theoretical and practical results show the efficiency of this methodology for analyzing and forecasting budget expenditures on education. They can be helpful in the short and medium terms, considering all potential changes in demographic indicators regarding the population. [ABSTRACT FROM AUTHOR]

The telecommunication company functioned in the market with extremely high competitiveness. Attracting new customers needs 5-10 times more expenses than maintaining an existing one. As a result, effective customer churn management and analysis of the reasons for customer churn are vital tasks for telecommunication operators. As a result, predicting subscriber churn by switching on the competitors becomes very important. Data Science and machine learning create enormous opportunities for solving this task to evaluate customer satisfaction with company services, determine factors that cause disappointment, and forecast which clients are at a greater risk of abandoning and changing services suppliers. A company that implements data analysis and modelling to develop customer churn prediction models has an opportunity to improve customer churn management and increase business results. The purposes of the research are the application of machine learning models for a telecommunications company, in particular, the construction of models for predicting the user churn rate and proving that Data Science models and machine learning are high-quality and effective tools for solving the tasks of forecasting the key marketing metrics of a telecommunications company. Based on the example of Telco, the article contains the results of the implementation of various models for classification, such as logistic regression, Random Forest, SVM, and XGBoost, using Python programming language. All models are characterised by high quality (the general accuracy is over 80%). So, the paper demonstrates the feasibility and possibility of implementing the model to classify customers in the future to anticipate subscriber churn (clients who may abandon the company's services) and minimise consumer outflow based on this. The main factors influencing customer churn are established, which is basic information for further forecasting client outflow. Customer outflow prediction models implementation will help to reduce customer churn and maintain their loyalty. The research results can be useful for optimising marketing activity of managing the outflow of consumers of companies on the telecommunication market by developing effective decisions based on data and improving the mathematical methodology of forecasting the outflow of consumers. Therefore, the study's main theoretical and practical achievements are to develop an efficient forecasting tool for enterprises to control outflow risks and to enrich the research on data analysis and Data Science methodology to identify essential factors that determine the propensity of customers to churn. [ABSTRACT FROM AUTHOR]

The paper contains the results of marketing mix modeling for Ukrainian retail in conditions of the COVID-19 crisis. The main goals of the research are modeling the level of traffic to the store based on regression analysis and forming appropriate recommendations for media strategy. Estimating the influence of media on business KPI makes a basis for ROI calculations and optimization of budget allocation between communication channels by periods, formats, and optimization of media pressure. Models for offline and online traffic were constructed based on weekly data for 2018-2021 and since 2020 there is a strong impact of COVID-19 on traffic and media response. In 2020 there was a significant drop in offline traffic due to the lockdown, but also there was deferred demand, which was compensating for a part of the traffic. The results show that TV is the main driver for offline traffic and digital - for online, but there are also significant impacts of TV and digital on online and offline traffic, respectively. During the lockdown, the mobility of consumers dropped, that is, a decrease in response from Out of Home advertising; therefore, we need to compensate for this by higher activity in other media channels. Scenario forecasting of different media mix helps to select the most efficient strategy taking into account memory decay of advertising, period of activity, and weekly weights. Marketing mix modeling is an effective tool for business management, as it generates opportunities to improve ROI by more than 15% and ensures the achievement of business goals in the most efficient way. [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]

The start of the full-scale Russian-Ukrainian war caused the largest wave of migration in the 21st century. More than five million Ukrainian citizens left for EU countries within a few months of the start of the conflict. The purpose of this paper is to forecast the level of health care expenditure in Ukraine for 2023–2024, considering the scale of migration and the fall in the level of GDP. The authors propose three scenarios for the development of Ukraine's economy in 2023–2024, taking into account changes in the age structure of the population, migration, and the amount of health care expenditure: (1) Pessimistic, in which economic growth will resume only in 2024, with a GDP rise of 5.6%, provided that the war concludes at the end of 2022. Under this scenario, inflation will be about 21% in 2023–2024, a slight decrease compared with the previous year. Some 12% of the population of Ukraine will have emigrated, resulting in a corresponding 12% drop in health care expenditure in 2023–2024. (2) Basic (realistic), in which economic growth will be about 5% in 2023–2024, inflation will be under 10%, and migration will have accounted for 5% of the country's population. Under this scenario, there will be an increase in health care expenditure of more than 40% in 2023–2024. (3) Optimistic, according to which rapid economic growth is expected in 2023–2024, inflation will not exceed 7%, the majority of those who left Ukraine in the early months of the war will return, and health care expenditure will increase by more than 70% in 2023–2024. The methodology of forecasting public expenditure on health care has been based on a six-step cohort method. The results have indicated that the cost of updating the age structure of Ukraine's population every year will decrease due to the aging of the population, and the overall impact of demographic processes will be negative. The impact of mass migration due to the war creates a significant change in health care costs, requiring administrative bodies to monitor the situation promptly and make appropriate changes to the structure of budget expenditure. [ABSTRACT FROM AUTHOR]