Data-driven retail: The interconnected ecosystem of predictive merchandising analytics

This article explores the transformative role of predictive analytics in modern retail merchandising, tracing its evolution from basic inventory management systems to sophisticated AI-driven decision frameworks. The article shows how predictive methodologies have reshaped core retail functions including demand forecasting, inventory optimization, price modeling, product assortment planning, and personalized customer engagement. Through article analysis of implementation approaches and performance outcomes across multiple dimensions, the research reveals how retailers leveraging advanced predictive capabilities achieve significant improvements in forecast accuracy, inventory management, profit margins, and customer lifetime value. The article further examines the technical foundations underpinning these capabilities, including statistical modeling principles, machine learning algorithms, and AI integration, while also addressing critical implementation challenges related to data quality, organizational adoption, human-algorithm collaboration, and ethical considerations. Finally, the article identifies emerging frontiers in retail analytics, including real-time processing, external data integration, automated machine learning, and edge computing, alongside research gaps that present opportunities for future advancement in the field.