Predictive Analytics in Social Commerce: Enhancing Seller Engagement and Inventory Readiness for Viral Product Demand

The article presents a theoretical analysis of the role of predictive analytics in managing viral demand and shaping seller engagement in social commerce. The study is based on an interdisciplinary approach that integrates developments in information management, e-commerce, machine learning, and behavioral economics. Particular attention is paid to the thematic analysis of publications covering the macroeconomic effects of pandemic-driven online retail growth, algorithmic solutions for demand forecasting, and organizational models of live streaming as drivers of engagement. Key mechanisms are identified, including the reduction of the bullwhip effect, improvement of forecasting accuracy through the use of graph convolutional networks and federated learning, and the strengthening of trust in platforms through the integration of predictive tools. A comparative analysis of methods demonstrates the consistent advantage of graph- and transformer-based algorithms in terms of adaptability and sensitivity to short-term demand surges. The necessity of interpreting predictive analytics not as an auxiliary module but as a fundamental element of digital platform architecture, determining their resilience and competitiveness, is substantiated. Promising directions for development are outlined, including the hybridization of algorithmic approaches, the standardization of data exchange protocols, and the expansion of empirical bases across geographies. The article will be useful to researchers and practitioners in e-commerce, digital platform developers, and supply chain management specialists interested in adaptive strategies for responding to viral demand.