Today, the use of machine learning technology in combination with the use of big data are topics that are actively discussed in business around the world. This topic has long gone beyond the information sphere, as it now applies to almost every sphere of life: economic, telecommunications, education, medicine, administration, and especially defense. Predicting customer behavior based on scoring models is in its infancy in Ukrainian companies, the main ones being the introduction of artificial intelligence technologies and machine learning, which will be the leading catalyst that will facilitate decision-making in business in the nearest future. The aim of the study is to develop a scoring model that predicts the behavior of target segments, namely, updating their activity to activate advertising tools. To achieve the goal of the work a set of research methods was used: dialectical – to reveal the theoretical foundations of models and types of forecasting models; analytical – in the study of the functioning of the environment SAS, Anaconda; optimization methods – to choose the best model and generate features. Scientific novelty and theoretical significance lie in the development of a scoring model for predicting the activity of subscribers of the telecommunications company “VF Ukraine”, on the basis of which marketing campaigns are conducted. With the help of the built-in scoring model, the company “VF Ukraine” can target its campaigns to retain subscribers. The marketing directorate of the enterprise can choose the TOP-20 or TOP-30 of the most prone subscribers to non-resumption of activity, i.e., tend to switch to other mobile operators, and hold promotions for them – providing additional gifts and bonuses, money to mobile account.

The article considers the current problem of improving the product range of online store in COVID-19 conditions. There is a steady tendency to increase the share of e-commerce in the global volume of retail trade, the level of visits to online stores. It was found that the system of product range management includes such organizational activities as planning (market analysis, range formation, planning of sales and logistics); organization (organization of order processing, analysis of the effectiveness of Internet commerce); control (monitoring the implementation of key performance indicators (KPI), assessment of the impact of product structure to achieve KPI, adjusting the assortment). In the near future, assortment management in online stores will be affected by such factors as increased demand, the uncertainty of the business environment, increased requirements for the quality of goods and their delivery, the complexity of delivery of goods, especially export goods. To adapt to external conditions, to ensure competitiveness in the long term, it is important to apply strategic and operational management tools. Strategic tools provide for long-term planning based on actual statistical and analytical data of activities and market development. Such tools should be the following: developing a strategy for adapting the nomenclature according to the dynamics of the market, justification of procedures for bringing the interface of the online store, the introduction of a flexible management strategy in terms of structural changes in the product range. In modern conditions the tools of operational management of the online store should be aimed at operational improvement of the range: to reject and/or replace products with low demand, improve customer perception of the goods offered by classifying them, the justification of the system of related services, taking into account economic efficiency.