Enhancing marketing efficiency through data-driven customer segmentation with machine learning approaches

Abstrak

The importance of understanding consumer behavior in transaction data has become a key to improving marketing efficiency. This study aims to explore the application of machine learning (ML) techniques for data-driven consumer segmentation, focusing on improving product marketing strategies. This work addresses the limitations in the existing literature, especially in terms of handling high-dimensional data that can reduce segmentation quality. Previously, various studies have used clustering algorithms such as K-means without considering dimensionality reduction, which often leads to decreased accuracy and long computation time. In this study, we propose a new approach that combines principal component analysis (PCA) for dimensionality reduction and K-means clustering for consumer segmentation based on purchasing behavior. Experimental results show that using PCA to reduce data dimensionality significantly improves segmentation quality with an inertia score of 1,455,650 and a silhouette score of 0.486366. By implementing this method, we can group consumers into three segments based on frequently purchased product categories and the most common payment methods. These findings provide a scalable, data-driven segmentation framework that can be applied to improve marketing effectiveness by providing special discounts on various products based on the payment method used.