Assessing Machine Learning Algorithms for Customer Segmentation: A Comparative Study

In today’s highly competitive business landscape, entrepreneurs face challenges when it comes to expanding and retaining their customer base. One effective approach to address this is through behavioral-based customer segmentation. By employing this strategy, entrepreneurs can gain valuable insight into prospective customers, and their purchasing routines and shared interests. This, in turn, enables them to devise efficient strategies for increasing their customer base and boosting product trades. Our research focuses on comparing the effectiveness of intelligent machine learning algorithms: K-Means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), agglomerative Clustering, and Principal Components Analysis (PCA) with K-Means, in conducting customer segmentation based on their buying behavioral.