Segmentasi Nasabah Pengguna Layanan Pembelian Pulsa dan Paket Internet dalam Mobile Banking Menggunakan Teknik Feature Engineering dan Algoritma K-Means

Abstract

The advancement of technology has driven the widespread use of mobile banking as one of main pillars of modern banking services. Understanding the behavioral patterns of mobile banking users has become crucial for designing effective marketing strategies. This research aims to analyze transaction patterns of customer in mobile banking services for purchasing mobile credit and internet packages using feature engineering techniques and the K-Means clustering algorithm. The research methodology includes data collection, data pre-processing, extraction of transaction-based and time-based features, and cluster analysis. The results identified five customer segments with unique characteristics, including regular users, premium users, and heavy users. These findings provide valuable insights for banks to develop more targeted and personalized marketing strategies according to the characteristics of each customer segment.