With the growing demand in platforms that serve as a medium to connect businesses with clients, as well as for users to interact with each other, there is a high desire for quality user interface (UI) and user experience (UX). System usability plays a vital role in ensuring UX and UI quality. A widely accepted standard for evaluating usability is the system usability scale (SUS) survey which provides an overview on how usable a given system is. Since data-driven techniques and algorithms abound in today's fast-paced world, it is pertinent that system owners leverage on such tools to understand their users and provide quality UX and UI. The data-driven methodology popularly known as segmentation techniques have been applied in numerous fields such as market segmentation, customer relations management, supply chain and production, to better understand the requirements for providing quality customer experience. This study proposes the use of one of such tools, usability segmentation, achieved by applying the k-means clustering algorithm to SUS data. The study contributes to theory and practice.