SEGMENTATION OF SUPERBANK APPLICATION USER CHARACTERISTICS USING K-MEANS CLUSTERING - Romi Pandu Wynalda; Rudi Setiawan (PT. GWEX NET PUBLISHER, 2026)
This study aims to identify the segmentation of Superbank user behavior based on application usage patterns and their relationship with onboarding channels. The research was conducted using a data analytics approach using the K-Means clustering method on user behavior data, which includes logins month, transactions month, feature usage and onboarding source. The results showed that the grouping of users resulted in three main clusters that were aligned with the onboarding channel, namely OVO, Grab, and Superbank App. The user clusters of the Superbank App showed the highest level of activity, characterized by a greater frequency of logins, higher transaction intensity, and wider exploration of features. Grab's user cluster is at moderate usage and shows potential to be improved through a phased engagement strategy. Meanwhile, the user cluster of OVO tends to have low login frequency, more transactional usage patterns, and limited feature exploration. These findings suggest that onboarding channels not only serve as user acquisition pathways, but also relate to post-acquisition behavior in app usage. Thus, the results of clustering can be used as the basis for developing a more targeted business strategy, especially in efforts to retain users, activate features, and increase engagement according to the characteristics of each channel.