APPLICATION OF MATHEMATICAL METHODS IN WEBANALYSIS OF SOCIO-ECONOMIC SYSTEMS

Abstract

Modern webanalytics is an important tool for studying user behavior, evaluating the effectiveness of digital platforms, and supporting management decision-making in socioeconomic systems. With the growth of data volumes and the complexity of digital interaction, traditional analysis approaches are proving insufficient, which necessitates the use of mathematical methods for modeling, forecasting, and optimizing webanalytics.

The article examines various mathematical methods in webanalytics, including correlation and regression analysis, time series methods, Bayesian networks, Markov chains, clustering, and machine learning algorithms. A comparative analysis of the effectiveness of various methods is carried out, and their advantages and limitations are identified. Particular attention is paid to the use of graph models for analyzing social connections, predicting user behavior, and personalizing content.

The article also highlights the main challenges associated with webanalytics, including the problem of computational complexity, the need for efficient processing of large amounts of data, as well as privacy and ethical aspects of information collection and analysis. The impact of international regulations, such as GDPR and CCPA, on the use of webanalytics, as well as the limitations faced by companies in collecting and processing personal data, are also considered. The findings demonstrate the significant potential of integrating mathematical methods to improve the efficiency of webanalytics and the accuracy of forecasting socio-economic processes. The conclusions emphasize the need for further research on combining classical statistical approaches with modern methods of machine learning and artificial intelligence for the development of intelligent webanalytics systems. The article outlines the prospects for improving webanalysis methods about technological changes taking place in the industry.