Data Mining Techniques To Uncovering Customer Segments: K-Means Clustering Using The Elbow Method Approach In Medium-Scale Grocery

This study aims to use data mining techniques to segment customers from a grocery store based on their age, spending, city, gender, and product purchases. We use K-Means clustering algorithms to identify trends and segment clients from the grocery store. The data set comes from a grocery store in a Greater Bandung area. Using sales data for three months from April 2022 to October 2022. Consumer data are consumers who are members of the loyalty program with more than 6000 consumers. Seven groups were formed, which were determined based on the elbow method. Additionally, the study was able to provide information on the characteristics of the various consumer categories and how they could be used to inform sales and marketing strategies using these tactics. The study has contributed to the field of machine learning by demonstrating the successful use of clustering methods to particular datasets, providing significant insights into how these aspects influence consumer decision making.