User Segmentation of Digital Banking in The Jabodetabek Region Using The Python Based K-Means Clustering Method
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Abstract
The rapid rise of digital banking adoption in Indonesia, particularly in the Jabodetabek metropolitan region, has created both opportunities and challenges for financial institutions. Although usage levels are high, many digital banks still struggle to align their product features with the behavioral patterns and expectations of their users. This study applies Python based K-Means Clustering to segment 139 active digital banking users using demographic and behavioral variables, including age, occupation, income, usage duration, transaction types, and communication channels. The clustering process generated four user segments: (1) The Practical Young Worker, (2) The Entertainment Oriented Digital Native, (3) The Civil Service Functionalist, and (4) The Mature Entertainer. Among these, Cluster 2 emerged as the dominant segment (45%), consisting primarily of young female digital natives aged 18–25 who heavily consume entertainment services and are highly responsive to social media-driven promotions. Based on this finding, the study identifies Gen Z urban females as the target persona and proposes an Online Value Proposition (OVP), “A fun, stylish, and reward driven digital banking experience tailored for digital natives.” This research emphasises the importance of data driven segmentation, persona development, and STP alignment in enhancing user engagement and loyalty within Indonesia’s digital banking ecosystem.
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