Multivariate Data Analysis for Customer Segmentation Using Principal Component Analysis and K-Means Clustering - Sinaga, Bosker (Jurnal Info Sains : Informatika dan Sains, 2025)
This study discusses multivariate data analysis for customer segmentation using Principal Component Analysis (PCA) combined with the K-Means clustering method. The problem faced is the high dimension of customer data which makes it difficult to segment and make targeted marketing decisions. The solution offered is the implementation of PCA to reduce the data dimension without losing important information, then followed by K-Means to segment customers based on demographic attributes and shopping behavior. Using a dataset of 200 customers, three customer clusters with different characteristics in terms of age, annual revenue, and shopping score were found. The results of the PCA show that the first two main components are able to explain more than 78% of the data variation, making it easier to visualize and interpret the cluster. These findings provide the basis for a more targeted marketing strategy according to customer segments. In conclusion, the combination of PCA and K-Means is effective in simplifying complex data and resulting in meaningful customer segmentation.