Unleashing Customer Insights through K-Means Clustering for Enhanced Retail Decision-Making

Abstract

The modern retail industry now has access to vast volumes of data thanks to rising standards, automation, and technology, but the commercial decision-making process has become complicated. The utilization of Data Mining technologies for retail businesses has become indispensable for making decisions concerning sales, profit, customer satisfaction, and reduced cost. This study's foundation is segmentation principles using K-Means Algorithm in RapidMiner. This research adds to the development of useful insights into the future of Data Mining and its applications in the retail business. The results obtained from the survey indicate how retail businesses can make more informed decisions on how to keep their already customers satisfied and happy as well as how to alter factors to be more attractive to other customers.