Customer Baskets Insights: A Survey

ABSTRACT The extraction of hidden patterns from customer baskets is crucial for enhancing retail strategies and improving customer satisfaction. This research explores the application of evolutionary learning approaches, specifically genetic algorithms and genetic programming, to identify and analyze purchasing behaviors in transaction data. By these methodologies, the study aims to uncover complex relationships between products and provide actionable insights for retailers. The proposed framework utilizes advanced tools designed for evolutionary analysis, achieving a significant improvement in accuracy and efficiency compared to traditional methods. Study reveals that not only enhances the identification of frequent item sets and association rules but also facilitates more effective marketing strategies and inventory management. This research survey contributes to the growing field of retail analytics, offering a robust methodology for understanding consumer behavior and fostering data-driven decision-making in an increasingly competitive market. Keywords: Customer Basket Analysis, Evolutionary Learning Approaches, Customer Satisfaction, Hidden Patterns, Retail Analytics.