Customer Segmentation in a Campus-Area Convenience Store Using K-Means Clustering - Alfani, Musaddad; Qalbi, Putri Aysha (Tibuana, 2026)
Customer segmentation is an important strategy for understanding differences in customer characteristics and supporting more targeted marketing decisions. In campus areas, convenience store customers generally consist of students with diverse demographic, economic, and accessibility characteristics, resulting in different purchasing behaviors. This study aims to identify customer segments of a convenience store located in the Campus X area of Yogyakarta using the K-Means Clustering algorithm. The study employed a quantitative approach with data mining techniques using customer data from students. The variables analyzed included age, income, shopping intensity, and residential distance from the store. The optimal number of clusters was determined using the Elbow Method before applying the K-Means algorithm. The results identified three customer segments with distinct characteristics. The largest segment consisted of customers living relatively close to the store and exhibiting high shopping intensity, while another segment was characterized by lower purchasing activity and greater residential distance. The smallest segment showed the highest income level and more diverse product preferences. Furthermore, differences in customer characteristics were reflected in product purchasing patterns across the identified segments. These findings indicate that accessibility and economic factors play important roles in shaping customer purchasing behavior in campus-area convenience stores. The results can serve as a reference for developing more targeted promotional and product management strategies.