Enhancing Retail Decision-Making through Market Basket Analysis: An Apriori Algorithm Approach
Year: 2025
Type: article
Abstract: Retail businesses constantly seek data-driven insights to optimize sales strategies and enhance customer experience. Market Basket Analysis (MBA), a key data mining technique, uncovers hidden patterns in consumer purchasing behavior by identifying associations between products. This study employs the Apriori algorithm, a widely used approach for frequent itemset mining, to analyze transactional data and extract meaningful correlations. By leveraging real-world retail datasets, this research highlights how retailers can optimize product placement, crossselling, and promotional strategies. The findings demonstrate the effectiveness of Apriori in improving decision-making by providing actionable insights into consumer buying patterns. Additionally, the study discusses computational efficiency challenges and potential enhancements to the algorithm for large-scale data processing. The results offer valuable implications for retailers, enabling data-driven inventory management, pricing strategies, and personalized recommendations to enhance customer satisfaction and profitability.
DOI: 10.55041/ijsrem42142
Author: Dr Soumitra Das
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408175808