Customers Segmentation for Digital Signature Implementation: RFC Analysis Using KMeans Algorithms

Abstract

To cluster institutions that utilize the digital signature service provided by BSrE, with over 200 million digital signature transactions involving 750 institutions, this study conducted an RFC (Recency, Frequency, Conversion Rate) analysis, which is an adaptation of the RFM (Recency, Frequency, Monetary) framework and the K-Means clustering algorithm. The purpose of this analysis is to find significant clusters that reflect user activity patterns. The study found four clusters by using the Elbow Method to determine the ideal number of clusters. It is expected that these findings will help BSrE optimize resource allocation, increase the adoption of digital signature services, and support Indonesia's digital transformation. The findings contribute to the literature on clustering techniques in the context of public services and provide actionable recommendations to improve government policy strategies.