The relevance of this study is determined by the rapid growth of the e-commerce market, in which the share of online purchases continues to increase. This trend highlights the need to predict consumer behavior to enhance the effectiveness of marketing strategies. The problem lies in the limited applicability of existing approaches, which are mainly based on open datasets that do not reflect the specific features of real user scenarios. Therefore, this research aims to develop an approach for predicting consumer behavior based on event data collected from web analytics systems. The primary research method is experimental modeling using machine learning algorithms. The computational framework integrates with the Yandex.Metrica API and employs gradient boosting. Experiments were conducted on data from six online stores with different profiles and levels of user activity. The results demonstrate that the use of event data and their derived features significantly improves prediction quality: F-measure, Precision, Recall, and AUC-ROC values increase by 10–20 percentage points compared to baseline features. Thus, the proposed approach enables the creation of interpretable and scalable models for predicting consumer behavior, applicable to online stores of different sizes. The findings have practical value for professionals in e-commerce analytics and the development of personalization systems.
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