EVALUATING THE EFFECTIVENESS OF DIGITAL MARKETING TOOLS USING PREDICTIVE ANALYTICS AND MACHINE LEARNING

Abstract

Introduction. The digitalization of enterprises’ marketing activities necessitates the improvement of approaches to evaluating the effectiveness of digital marketing tools through the use of analytical and predictive technologies.

Purpose. The purpose of the article is to generalize methodological approaches to evaluating the effectiveness of digital marketing and to develop an integrated predictive and analytical system for assessing the performance of digital marketing tools.

Materials and Methods. The informational basis of the study included scientific publications, analytical materials from Google Analytics 4, HubSpot, and Adverity. The research employed methods of analysis, generalization, systematization, and comparison. Approaches of web analytics, behavioral analytics, conversion analysis, predictive analytics, and machine learning models were applied to evaluate the effectiveness of digital marketing.

Results. The main groups of indicators for evaluating the effectiveness of digital marketing tools and methodological approaches to their analytical assessment were systematized. The study generalized the directions of applying predictive analytics and machine learning in digital marketing, particularly for conversion forecasting, audience segmentation, behavioral modeling, and customer churn prediction. An integrated system for evaluating the effectiveness of digital marketing was proposed, combining behavioral, conversion, financial, and predictive components, as well as an integral indicator of digital marketing effectiveness with a scale for interpreting the results. The practical significance of the proposed approach lies in the possibility of comprehensive evaluation of digital marketing campaign performance, optimization of advertising expenditures, forecasting user behavior, and improving the efficiency of digital marketing management.

Discussion. Further research should focus on the development of machine learning models in digital marketing, improvement of user behavior forecasting, and integration of artificial intelligence tools into automated marketing campaign management systems.