Customer Segmentation Using K-Means Clustering Algorithm for Digital Marketing Strategy

The rapid advancement of information technology and increasing business competition have encouraged organizations to utilize customer transaction data as a basis for decision-making. However, many businesses have not yet optimized the use of such data to understand customer behavior and develop more effective marketing strategies. This study aims to implement the K-Means Clustering algorithm for customer segmentation at RM Serdang using the Recency, Frequency, and Monetary (RFM) approach to support data-driven digital marketing strategies. The study began with processing customer transaction data by calculating Recency, Frequency, and Monetary values, which were subsequently normalized using the Min-Max Normalization method. The optimal number of clusters was determined using the Elbow Method, resulting in four clusters. The K-Means algorithm was then implemented through Euclidean Distance calculations and iterative centroid updates until convergence was achieved at the fifth iteration. The results indicate that 20 customer records used as manual calculation samples were successfully grouped into four clusters: Cluster 1 (Active Customers) consisting of 7 customers, Cluster 2 (High-Value Customers) consisting of 4 customers, Cluster 3 (Potential Customers) consisting of 5 customers, and Cluster 4 (Less Active Customers) consisting of 4 customers. The segmentation results demonstrate that each cluster exhibits distinct transactional behavior characteristics based on the RFM attributes. These findings indicate that the combination of the RFM method and the K-Means Clustering algorithm is capable of producing representative customer segmentation and can serve as a foundation for developing more targeted digital marketing strategies, such as loyalty programs, personalized promotions, and customer retention initiatives. In conclusion, this study demonstrates that the implementation of RFM-based K-Means Clustering contributes to supporting data-driven decision-making and enhancing the effectiveness of digital marketing strategies at RM Serdang.