A COMPREHENSIVE REVIEW OF THE ROLE OF DATA ANALYTICS IN SHAPING FOOD PRICING STRATEGIES IN THE UNITED STATES: HISTORICAL PERSPECTIVES, CURRENT TRENDS, AND FUTURE PROJECTIONS

This topic is designed as an extensive review that charts the historical evolution, current state, and future potential of data analytics in influencing food pricing strategies within the United States. It will encompass a thorough examination of how data analytics has transformed from basic statistical models to advanced AI and machine learning algorithms in the context of food pricing. The review will include a critical analysis of various case studies and models that have been employed in the food industry, assessing their impact on both market dynamics and consumer behavior. Furthermore, it will explore the challenges and ethical considerations surrounding data usage in pricing strategies, such as privacy concerns and market fairness. The future section will speculate on emerging trends and technologies that could further shape this field. This topic is intended to provide a holistic and in-depth perspective on the intersection of data science and food economics, highlighting its significance in the contemporary economic landscape of the U.S. Keywords: Data Analytics, Food Pricing Strategies, Trends, Future Projections Food Demand, Customer Segmentation, Supply Chain Optimization, Personalized Pricing, Dynamic Pricing, Food Fraud, Food Safety