The role of user identification in predicting target actions on a website

The relevance of study lies in the need to improve the accuracy of predicting users' target actions on websites, which is a key aspect of optimizing marketing strategies and personalizing user experiences. The complexity of the task is exacerbated by the lack of stable identifiers, leading to data fragmentation and reduced prediction accuracy. This paper aims to analyze the impact of user identification methods and develop approaches to segmentation, which will help to eliminate existing gaps in this area. The primary research method involves applying machine learning algorithms to evaluate the influence of different identifiers, such as client_id and user_id, on prediction accuracy. Segmentation of users was carried out based on the gradient boosting method, as well as an analysis of the effectiveness of retargeting campaigns in the Yandex.Direct system based on conversion rates, customer acquisition costs, and the share of advertising expenses using the example of a client specializing in the sale of e-books. The findings reveal that utilizing the user_id identifier improves purchase prediction accuracy by 8%, recall by 6%, and the F1-score by 7%. Segmenting users into targeted groups demonstrated a 67% reduction in customer acquisition cost, a decrease in advertising expense share to 5.87% compared to Yandex auto-strategies, and an increase in conversion rate to 34%. The article's materials are of significance for specialists in the field of e-commerce and marketing, providing a scientific basis for the implementation of personalized advertising campaigns. The proposed methods also offer potential for further enhancement of analytics and data integration in multichannel environments.

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