Recency, Frequency, and Monetary-Based Customer Segmentation Using K-Means for Analysing Transactional Behaviour in a Service-Based Micro, Small, and Medium Enterprises

Abstrak

Micro, Small, and Medium Enterprises (MSMEs) often faced challenges in designing effective promotional initiatives due to the limited use of systematic customer behavior analysis. This study examined the application of (Recency, Frequency, Monetary) RFM analysis combined with K-Means clustering to explore customer segmentation in a service-based MSME context. Transaction data from a local laundry service operating in Palu, Indonesia, consisting of 2,220 digital transaction records collected between 2022 and 2025, were processed and transformed into RFM variables using min–max normalization. The optimal number of clusters was determined using the Elbow method, resulting in four customer segments. Cluster quality was evaluated using internal validation metrics, yielding a Davies–Bouldin Index (DBI) of 0.61 and a Sum of Squared Errors (SSE) value of 1.73, indicating reasonably compact and well-separated clusters. The resulting segments exhibited distinct transactional profiles across recency, transaction frequency, and monetary contribution, reflecting heterogeneity in customer engagement within the studied MSME. Rather than prescribing specific marketing actions, the findings provided an interpretable analytical basis for considering differentiated promotional strategies aligned with observed customer behavior patterns. Overall, this study demonstrated that RFM-based segmentation offered a feasible and data-driven approach to supporting evidence-informed promotional planning in service-oriented MSMEs operating under data and resource constraints.