(untitled) (Economic journal of Lesya Ukrainka Volyn National University, 2025)
Introduction. In the era of digital transformation, e-commerce is rapidly evolving, offering increasingly convenient opportunities for buying and selling goods and services. According to Statista, the global e-commerce market is expected to reach USD 4.32 trillion by the end of 2025, while in Ukraine the figure may reach USD 3.32 billion. In this context, data analytics has become a critical asset for enterprises to remain competitive and maximize returns on their digital investments. However, despite numerous advances, businesses still face challenges in managing unstructured data and unlocking its strategic value, especially through the use of artificial intelligence. The purpose of the article. The purpose of the article is to study the role of data analytics in the field of ecommerce, analyze its main advantages, methods and application scenarios, as well as assess the impact of generative artificial intelligence on improving business processes. Methods. The research employs a multidisciplinary approach combining theoretical analysis, comparative studies, and empirical observations. The methodology includes content analysis of global analytical reports (from Gartner, Statista, McKinsey, etc.), review of scholarly publications, and evaluation of business case studies involving real-time data systems and generative AI applications in e-commerce. The study pays special attention to the synergy between analytical platforms and AI-driven personalization, chatbots, content automation, and predictive modeling. Results. The study found that data analytics in e-commerce plays a pivotal role in optimizing demand forecasting, inventory management, customer retention, fraud detection, and marketing efficiency. Key analytics types - descriptive, diagnostic, predictive, and prescriptive - are widely used to extract insights from customer behavior, transaction patterns, and social media feedback. It has been established that the combination of modern analytical methods with a powerful accounting and analytical support system creates a reliable foundation for strategic management of ecommerce, reducing risks, increasing efficiency, and contributing to the sustainable development of the enterprise in the digital economy. The integration of generative AI with data analytics enables highly personalized recommendations, real-time content generation, dynamic pricing, and smarter decision-making across customer touchpoints. Moreover, the research outlines critical challenges such as data fragmentation, real-time processing, compliance with data protection regulations, and cost-effectiveness of analytics investments. Conclusions. Data analytics has become a cornerstone of innovation and competitiveness in e-commerce. The combination of analytical methods and generative AI allows businesses to make data-driven decisions, personalize the customer journey, and increase profitability. For companies operating in Ukraine and globally, strategic integration of analytics into business models, supported by scalable and secure data infrastructures, is essential for future growth. Further research should explore advanced AI-analytics convergence scenarios and address the implementation barriers in small and medium-sized enterprises.