Modern surveys reveal diminishing of socio-demographic segment descriptors, and evolution of dramatic increase of online services and customers. These conditions attract both researchers and decision makers to enhance market segmentation to gain customer loyalty and prevent customer attrition. This research contributes in developing a minor expert system to automate the evaluation of clustering process to enhance the Clustering as a Service (CaaS) through customer behavior segmentation case study. It comes as a part of the software development process to develop Customer Loyalty Intelligent Personalization (CLIP) system. The proposed expert system has been successfully implemented and tested over four months in two different dataset to proof the flexibility of implementation . The used data is a real customer data, it consists of 1659 customers, 146 products, and 5685 orders. The other datset consists of 668 transactions of real data in restaurant. The clustering is applied using the hierarchical clustering and it reached a good results with high efficiency. The proposed solution aims to be integrated with a plug and play product as it will be configured in different domains.