Abstract
E-commerce websites offer a multitude of features to their customers through visually appealing interfaces. A significant rise in interest in e-commerce websites has been seen since the onset of the COVID-19 pandemic. Therefore, there is a growing need to incorporate methodologies that can help understand user navigational behaviour on such platforms. This study presents a business process software architecture, which is designed to understand user behaviour through the clustering of similar patterns. To achieve this, graph-based embedding approaches have been utilised. A prototype implementation is presented to demonstrate the proposed business process's efficiency, and the approach's technical details are discussed. Furthermore, the performance of the prototype implementation is evaluated by analysing the quality metrics for clustering. The results of this study show that the proposed business process is successful in analysing and comprehending user navigational behaviour on e-commerce websites.