A machine learning approach to consumer behavior in supermarket analytics

The rapid advancement of Big Data technologies has significantly influenced multiple sectors, with the retail industry being a key impact area. This study explores the relationship between Big Data analytics and consumer behavior, focusing on supermarket transaction data to extract macroeconomic insights. Using a dataset of over two million records from a multinational supermarket chain, the research employs a suite of advanced analytical techniques, including the Apriori algorithm for association rule mining, K-Means clustering for customer segmentation, collaborative filtering for recommendation systems, and AutoRegressive Integrated Moving Average (ARIMA) for time series forecasting. The study identifies purchasing patterns, segment-specific preferences, and temporal shopping behaviors. The results reveal distinct associations among frequently co-purchased products, clear segmentations based on shopping habits, and predictive trends in consumer demand, offering valuable input for marketing, inventory management, and policy-making. Beyond the operational insights, this study highlights the potential of transactional data to reflect broader economic shifts, such as changes in consumption patterns during periods of economic uncertainty, thus linking consumer micro-behaviors to macroeconomic indicators. The novelty of this work lies in its integration of multiple machine learning techniques within a unified framework that connects retail analytics to economic policymaking, thereby extending the application of Big Data from commercial strategy to public economics. • Identify consumer spending trends using advanced analytics techniques. • Apply machine learning to predict consumer behavior in retail. • Unveil insights from supermarket data to enhance business strategies. • Highlight the role of data-driven decision-making in economic policies. • Demonstrate how Big Data improves retail efficiency and customer experience.