This project investigates the evolving ride-hailing landscape in Sri Lanka by conducting a comparative need state analysis of Uber and PickMe customers in Colombo. The research addresses a critical gap in understanding localized customer needs in ride-hailing, which is essential for designing data-driven engagement strategies. Using an unsupervised clustering approach, the study segments 384 ride-hailing users based on categorical behavioural and demographic attributes collected through a bilingual survey. The K-Modes clustering algorithm was employed to identify four distinct customer personas: (1) Flexible Weekly Dual-App Users, (2) PickMe-Focused, Low-Frequency Cash Users, (3) Occasional Price-Sensitive Uber Users, and (4) Loyal Female Dominant Daily Riders with Subscriptions. The model’s performance was validated using several techniques, confirming four as the optimal number of clusters. These insights were visualized using an interactive Microsoft Power BI dashboard, allowing stakeholders to filter, compare, and explore behavioural trends across user groups. Key findings revealed that price sensitivity, app-switching behaviour, subscription adoption, and ride purpose vary widely across clusters. Actionable business recommendations were formulated for both PickMe and Uber to tailor engagement, pricing, and service quality improvements accordingly. The study contributes methodologically by demonstrating the application of K-Modes in an industry setting and practically by providing local ride-hailing providers with insights for a segmentation-driven strategy. The project also sets a precedent for future research in mobility data analytics within developing urban contexts. Keywords: clustering, customer segmentation, K-Modes, ride-hailing, user behaviour