Big data and consumer behavior: A macroeconomic perspective through supermarket analytics

This study explores how big data analytics can be used on supermarket transaction data to reveal patterns in consumer behavior with broader macroeconomic implications. Using a comprehensive dataset from a multinational retail chain, we employed advanced analytical methods—including machine learning algorithms, time series forecasting (ARIMA), clustering, and recommendation systems—to model purchasing behavior and segment customers. The analysis revealed distinct consumer profiles, habitual spending patterns, and time-sensitive trends that inform both retail decision-making and economic interpretation. The results demonstrated that consumer transaction data can serve not only to improve operational efficiency and personalized marketing in the retail sector but also to provide real-time indicators of economic sentiment and household financial health. This dual contribution highlights the potential of retail big data as a tool for both business strategy and macroeconomic policy analysis. The study also outlines implementation considerations and ethical challenges, offering a foundation for future research in data-driven retail and economic analytics.