Paper Key : IRJ************295
Author: Sharath Chandra Kumar Reddy,Shivam Kuamr Taliyan,Tushar Pradhan
Date Published: 03 Apr 2025
Abstract
Market Basket Analysis (MBA), is one of the most important data analysis techniques used in the retail sector which helps to reveal the hidden relationship among the products. This allows retailers to optimize our sales strategies, improve product placement, and enhance the overall customer experience. Hence, this study uses MBA on actual retail transaction data and explores how we can derive information about shopping patterns and derive actionable insights out of it. Association rule mining is a well-established method in data mining and the research uses this method, specifically the Apriori algorithm, to extract frequent item sets in the data and derive strong association rules. The results show which product categories are correlated, and why bundling, cross-selling with cross-palettes, and targeted discounting work with creating more sales and higher customer lifetime value. Retailers, the results show, can use MBA insights to improve store layouts, target marketing campaigns to specific customers in a differentiated manner, and better manage inventory. This study also emphasizes the need for data-driven decisionmaking processes in contemporary retail, indicating that predictive modeling can have a considerable impact on key performance indicators such as profitability and operational efficiency. Sumarizing, through Market Basket Analysis if retailers want to develop revenue and customer satisfaction. Leveraging the Mba with advanced analytics driven by Ai can enable companies to get beyond the outdated tactics of selling and adopt more personalized, effective and profitable strategy. Furthermore, future studies could apply machine learning techniques to improve predictive accuracy and support business decision-making.
DOI Requested