Paper Title
Uncovering Consumer Purchase Patterns: A Market Basket Analysis for Cross-Selling in Retail

Abstract
Retailers are increasingly using data-driven strategies to boost sales and enhance customer satisfaction. This study employs Market Basket Analysis (MBA) techniques, such as Apriori and FP-Growth algorithms, on point-of-sale transactional data to find correlations between frequently co-purchased products. Customer segmentation via clustering is studied in order to identify distinct purchasing patterns and enable tailored cross-selling strategies. Associations translate data into useful retail strategies like product bundling, targeted promotions, and optimal shop layout using statistical measures like Lift, Confidence, and Support. The findings demonstrate that an MBA can improve customer satisfaction, increase average basket value, and assist with inventory decisions. The study offers suggestions for future advancements like real-time recommendation systems and personalized loyalty-based bundling in addition to addressing problems like algorithm scalability, a lack of temporal insights, and data quality. Keywords - Market Basket Analysis, Association Rule Mining, Apriori Algorithm, Cross-Selling Strategies, Customer Segmentation, Retail Analytics, Data-Driven Marketing