In the rapidly evolving field of software development, the ability to efficiently prioritize and enhance software features based on user feedback is crucial for maintaining competitiveness and development efficiency. Deciding which features to develop or update is often complex and inefficient. Developers and product managers typically use traditional feedback methods, which are slow, subjective, and hard to prioritize. The main issue is the overwhelming amount and subjective nature of feedback from surveys, bug reports, and customer interactions. This often leads to analysis paralysis, where teams struggle to determine the most critical issues to address. This thesis proposes a structured approach to usage analytics aimed at addressing these challenges by helping developers identify and prioritize features that have the most significant impact on users. This method enables developers to understand and assess the impact of updates and new features on user engagement. By creating a more efficient and responsive feedback loop, usage analytics can help teams prioritize development tasks based on actual user interactions rather than subjective feedback, reduce delays in addressing critical issues, and improve overall development efficiency. Case studies demonstrate the practical benefits of usage analytics in software development. The first study on the IBM Academic Cloud project identified challenges in feature prioritization, such as unclear feature definitions and time-consuming data preparation. The second study, on IBM Watson Workspace, revealed issues in data identification, analytics metrics, and feature-action mapping, stressing the need for systematic data collection and iterative testing. The third study applied usage analytics to Odoo Notes, showing the impact of changes on user behavior and helping prioritize future features. These studies demonstrate usage analytics offers actionable insights for data-driven feature prioritization, enhancing decision-making and improving software to better meet user needs.