THE USE OF ARTIFICIAL INTELLIGENCE IN OMNICHANNEL COMMUNICATION STRATEGIES OF RETAIL

Abstract

The article investigates the transformative role of artificial intelligence in shaping omnichannel communication strategies within the retail sector. The omnichannel approach is examined as a fundamental direction in the evolution of modern trade, emphasizing its capacity to integrate multiple customer touchpoints into a seamless and consistent experience. It is substantiated that the implementation of intelligent technologies is no longer optional but rather a prerequisite for retailers striving to maintain competitiveness in highly dynamic markets. The research demonstrates that the application of machine learning algorithms enables retailers to forecast demand with higher precision, identify customers at risk of churn, assess individual purchase propensities, and design personalized offers in real time, thereby increasing both operational efficiency and customer engagement.

The article systematizes the main artificial intelligence tools applied in omnichannel strategies, ranging from natural language processing for chatbots and virtual assistants to recommendation engines, predictive analytics, and computer vision systems. Based on their functionality, three conceptual groups of AI use in the omnichannel environment are distinguished: tools that analyze and interpret customer data, technologies that enable personalization and adaptive communication, and systems that optimize decision-making processes in marketing and pricing management.

Attention is devoted to practical experiences of both foreign and Ukrainian retailers, with illustrative examples of AI implementation for service personalization, dynamic pricing, and the optimization of omnichannel marketing campaigns. The findings demonstrate that the integration of AI into communication strategies significantly enhances the efficiency of retail business models, strengthens consumer trust and loyalty, and creates sustainable competitive advantages in a market characterized by rapid digitalization.

The study also outlines the main vectors for the future development of AI in retail. These include advancing big data analytics and real-time data processing systems, embedding predictive models into CRM and ERP platforms, and expanding the capabilities of marketing process automation to cover the full customer journey. In addition, the importance of ethical considerations, transparency in algorithmic decision-making, and data security is highlighted as essential conditions for the responsible use of AI. The article concludes that the synergy between the omnichannel approach and artificial intelligence represents a decisive factor in shaping the next stage of retail transformation, enabling businesses to not only respond to current market demands but also proactively anticipate consumer expectations.