The rapid growth of digital platforms and online services has significantly reduced the effectiveness of traditional socio-demographic segmentation methods, creating a need for more advanced and data-driven approaches. In response, this study proposes an intelligent support system designed to automate the evaluation of clustering processes within a Clustering-as-a-Service (CaaS) framework. The primary objective is to enhance customer behavior segmentation for improved decision-making, customer retention, and personalized service delivery. The proposed system is developed as part of a broader Customer Loyalty Intelligent Personalization (CLIP) platform, aiming to provide intelligent insights into customer patterns. A hierarchical clustering technique is employed to analyze real-world transactional data comprising 1,659 customers, 146 products, and 5,685 purchase records. Experimental results demonstrate that the proposed system effectively evaluates clustering performance and achieves high segmentation accuracy and efficiency. The findings highlight the potential of integrating intelligent evaluation mechanisms into clustering services to support scalable and data-driven customer analytics in modern business environments.