Customer Segmentation Based on Spending Patterns Using K-Means Clustering and PCA

Abstract

Companies face challenges in understanding customer spending patterns, which can lead to ineffective marketing strategies. Traditional customer segmentation approaches often fail to accurately identify groups with different consumption behaviors. Therefore, this study proposes the implementation of the K-Means algorithm combined with Principal Component Analysis (PCA) to segment customers based on their spending patterns. This study uses a dataset containing customer spending information across various product categories, including wine, meat, fish, sweets, fruits, and gold. The Elbow method is applied to determine the optimal number of clusters, followed by K-Means clustering. The results are visualized using PCA to facilitate the interpretation of customer spending patterns. The findings indicate that the optimal number of clusters is six, with the Within-Cluster Sum of Squares (WCSS) decreasing from 50,000 for one cluster to 29,000 for six clusters. Cluster 3 exhibits the highest spending, particularly on meat at 566.91 and fish at 183.58, whereas Cluster 0 has the lowest spending, with its highest value being only 91.60 for wine. Silhouette Score evaluation shows that K-Means achieves a score of 0.4745, outperforming the Gaussian Mixture Model (GMM) with 0.0674