Integration of artificial intelligence models for building consumer behavioral profiles and developing personalized marketing strategies

Abstract

The article explores the problem of using integrated artificial intelligence models to build consumer behavioral profiles and develop personalized marketing strategies. The topic's relevance is due to the growing need for businesses to accurately analyze consumer data, including digital activity, purchase history, social media interactions, and other sources of Big Data. The aim of the study is to substantiate the theoretical and methodological foundations for integrating artificial intelligence models (including machine learning, deep learning, neural networks, and natural language processing) to enhance segmentation accuracy and predict consumer behavior. Methods. The research employs content analysis, modeling, comparative analysis, and data mining techniques, with a focus on classification, clustering, and predictive analytics. A comparative evaluation was conducted to assess the performance of models such as Random Forest, Decision Trees, Convolutional Neural Networks, Transformers, and Hybrid artificial intelligence architectures. Results. The results of the study show that the integration of artificial intelligence models enables a 25–35% improvement in consumer segmentation accuracy, effective real-time prediction of individual consumer behavior, and automation of personalized marketing message delivery. By analyzing large volumes of data and applying advanced machine learning algorithms, it becomes possible to build accurate behavioral profiles, which support the development of more relevant and effective marketing strategies. This, in turn, leads to increased conversion rates, higher customer engagement, and an overall enhancement of the customer experience. Conclusions. The study confirms the effectiveness of implementing comprehensive artificial intelligence solutions in marketing practice to design relevant, targeted communication strategies. Future research should focus on developing hybrid models that combine artificial intelligence  algorithms with socio-psychological analysis to achieve a higher level of personalization.