Leveraging K-Means Clustering and Hierarchical Agglomerative Algorithms for Scalable AI-Driven Customer Segmentation

Abstract

This research paper explores the integration of K-Means Clustering and Hierarchical Agglomerative Algorithms to develop a scalable AI-driven framework for customer segmentation. In the rapidly growing landscape of digital marketing, effective customer segmentation is crucial for personalizing experiences and optimizing resource allocation. The study begins with a comprehensive review of existing segmentation techniques, highlighting the limitations of traditional methods in handling large, complex datasets. By leveraging K-Means for its computational efficiency and Hierarchical Agglomerative Clustering for its detailed analysis of intrinsic data structures, the proposed hybrid approach seeks to capitalize on the strengths of each algorithm. The methodology involves the application of this dual-algorithm strategy on a diverse set of real-world customer data, assessing performance metrics such as silhouette scores, inter-cluster distance, and within-cluster variance. Results demonstrate a marked improvement in segmentation quality and scalability, with enhanced cluster cohesiveness and separation compared to standalone techniques. Furthermore, the paper discusses the computational trade-offs and implementation challenges of integrating these algorithms, providing insights into parameter optimization and algorithmic tuning. The findings suggest that this hybrid approach not only scales efficiently with large datasets but also delivers granular insights into customer behaviors and preferences, thus offering a powerful tool for businesses seeking competitive advantage through targeted marketing strategies.