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
Vol. 1 No. 2 (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.