Segmentasi Pelanggan Berdasarkan Kebutuhan Primer Skunder dan Tersier Menggunakan K-Means Clustering - Indah, Caesaliana Indah Mu’assyaroh; Arkan, M. Rizieq Sultan; Galuh, Galuh Sitoresmi; Sabrina, Sabrina Rizkiya; Zida, Zida Nadhifah Aulia Kencana (Journal of Data Science Methods and Applications, 2025)
In the digital marketing era, companies are required to deeply understand customer behavior in order to develop targeted strategies. Customer segmentation is a common technique used to group customers based on similarities in their characteristics and consumption behaviors. This study aims to identify customer segments using unsupervised learning techniques with the K-Means clustering algorithm. The dataset, obtained from Kaggle, contains 2,240 customer records with demographic and purchase behavior attributes. The six primary features analyzed include Income, Age, TotalChildren, MntMeatProducts, NumCatalogPurchases, and Recency. The clustering results reveal distinct customer groups with different characteristics and purchasing tendencies, which can be used to develop more personalized and efficient marketing strategies.