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DOI: 10.15862/16FAOR425 (https://doi.org/10.15862/16FAOR425)

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Berlin M.S. Artificial intelligence in personalizing the shopping experience in e-commerce. Russian journal of resources, conservation and recycling. 2025; 12(s4). Available at: https://resources.today/PDF/16FAOR425.pdf (in Russian). DOI: 10.15862/16FAOR425

Artificial intelligence in personalizing the shopping experience in e-commerce

Berlin Matvey Sergeevich
Russian Presidential Academy of National Economy and Public Administration, Moscow, Russia
E-mail: matveyberlin2003@gmail.com

Abstract. The rapid penetration of artificial intelligence technologies into the marketing strategies of e-commerce entities, whose volume in Russia reached 11,5 trillion rubles by 2025, and the global AI personalization market is estimated at $263 million and is projected to grow to $2,4 billion by 2033 at a CAGR of 24,8 %. This study highlights the need for a scientific understanding of the transformation of marketing tools and consumer behavior in the context of hyper-personalization. This study examines the mechanisms of AI personalization of the shopping experience as a marketing strategy tool in e-commerce and their impact on consumer behavior, conversion, and customer lifetime value. The study utilizes methods of systemic analysis of domestic and international scientific literature, comparative analysis of marketing personalization models, and content analysis of analytical reports from leading research organizations, including McKinsey, BCG, Gartner, Salesforce, and the Big Data Association. An analysis of the evolution of marketing approaches to personalization revealed a consistent shift from segmentation-based mass marketing models to real-time hyper-personalization based on predictive analytics, generative AI, and agent-based systems, fundamentally rethinking the classical concept of the marketing mix. Key findings indicate that personalization companies achieve revenue growth of 5 to 15 %, return on marketing spend increases by 10–30 %, and sessions involving engagement with recommendations demonstrate an average check increase of up to 369 %. However, the gap between consumer expectations and the actual level of personalization remains significant: 66 % of customers expect a personalized approach, while only 34 % believe that companies are delivering on this. A proprietary model for integrating AI personalization into the e-commerce marketing funnel has been developed, differentiating tools based on the stages of the consumer journey. The practical significance of the results lies in the potential for using them to shape retailers’ marketing strategies and develop industry standards for personalization in Russian e-commerce.

Keywords: artificial intelligence; personalization; e-commerce; marketing strategy; customer experience; hyper-personalization; recommendation systems; CRM marketing; consumer behavior; conversion; customer lifetime value

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