ECONOMETRIC MODELING OF CONSUMER DECISION MAKING IN ONLINE RETAIL

This article is devoted to econometric modeling of the consumer decision-making process in online retail. The relevance of econometric modeling based on a multifactor model of online retail development is the increase in the volume of information data on consumer decisions, intentions to make an online purchase by the consumer. This information can be used by online retailers to improve their own marketing strategy, increase conversion and sales margins. The problematic of this article is determined by the fact that the collection and analytical processing of huge amounts of information data is a rather costly and laborintensive process. In addition, the accumulated information data may be heterogeneous. It is also advisable to take into account the determinants affecting consumer decision-making, for example, geographic location, gender status, age, duration of the online session, shopping preferences, making an online purchase on a weekend (holiday) or weekday. These problems are solved by constructing a multifactor model of the consumer decisionmaking process. It can be used to better understand consumer behavior and model trends in consumer decision-making in online retail. The purpose of the study is to create a multifactorial model of the consumer decision-making process in online retail and to develop recommendations for improving the activities of online retailers. Key findings: 1) the methodology for creating and practically testing a multifactorial model of the consumer decision-making process in online retail is outlined; 2) the characteristics of the formation of a multifactorial model of the consumer decision-making process in online retail are given; 3) recommendations for improving the activities of online retailers are presented. Research materials: statistical data from a comprehensive study of the consumer decision-making process in online retail using the example of 12,4 virtual sessions on online platforms of beauty retailers. Research methods: systematization, comparative analysis, contrastive analysis, statistical time series analysis, generalization, abstract logical method, induction, deduction, systems approach, data conceptualization, graphical and tabular data visualization, econometric modeling, decision tree method, logistic regression construction, statistical processing of collected data, study of parameters of virtual sessions on online platforms of beauty product retailers. Main findings of the study: 1) econometric modeling of the consumer decision- making process in online retail is based on the author's algorithm for evaluating a multifactorial model: conducting a basic analysis of information data (creating a standard logistic regression model, basic analysis and further evaluation of the values of marginal effects coefficients), practical use of the decision tree method; 2) econometric modeling of the consumer decision- making process in online retail made it possible to confirm a number of hypotheses set by the authors (in full and in part); 3) recommendations for improving the activities of online retail representatives contribute to increasing regional coverage of the consumer audience, conversion and marginality of sales of beauty products on online platforms, and customer loyalty.