A Study on the Impact of Data-Driven Sales Forecasting on Revenue at Big Bazaar, Nagpur

Abstract

Efficient sales forecasting plays a pivotal role in the retail industry, enabling businesses to predict consumer demand and optimize inventory, marketing strategies, and resource allocation. Leveraging data-driven approaches, this study examines the impact of advanced forecasting methodologies on revenue generation at Big Bazaar, Nagpur. The research investigates how historical sales data, customer preferences, and market trends can be synthesized using analytics tools to create accurate predictions. It further explores the role of technology and artificial intelligence in refining forecasting accuracy, reducing operational costs, and enhancing customer satisfaction. The findings reveal that adopting data-driven sales forecasting not only improves revenue streams but also fosters strategic decision-making processes, enabling agility in a competitive market. The study underscores the significance of integrating robust data analytics platforms in retail operations and provides actionable insights for practitioners aiming to harness predictive analytics for sustained growth. By demonstrating the practical implications of these techniques, the research contributes to the broader discourse on the intersection of data science and revenue optimization in retail.