Download PDFOpen PDF in browserOptimizing Business Success Through Data-Driven Customer Segmentation: an Analysis of Clustering TechniquesEasyChair Preprint 1444412 pages•Date: August 14, 2024AbstractIn today's competitive business environment, understanding customer behavior and tailoring strategies to meet their needs is crucial for optimizing success. This study explores the role of data-driven customer segmentation in enhancing business performance through advanced clustering techniques. By analyzing various clustering methods, including K-means, hierarchical clustering, and DBSCAN, this research aims to identify the most effective approach for segmenting customers based on behavioral and demographic data. The analysis leverages a comprehensive dataset comprising customer interactions, purchase history, and socio-economic factors. Key metrics such as cluster cohesion, separation, and stability are evaluated to assess the performance of each technique. Keyphrases: Cluster Cohesion, Clustering Techniques, optimizing success, separation, stability
|