Early-stage engineering teams often prioritize speed, which, while advantageous, can result in numerous disruptive signals like stack traces and timeouts. Differentiating between these signals and identifying those that critically impact users and revenue is crucial. By integrating engineering telemetry data, such as logs and user sessions, with customer feedback from support tickets and reports, teams can form a cohesive feedback loop. This approach allows them to evaluate defects based on who was affected, where in the user journey the issue occurred, and its impact on conversion, engagement, or revenue. Implementing a feedback loop with tools like Datadog enables teams to prioritize issues based on their real-world impact, turning incidents into valuable product insights. Standardizing correlation identifiers like trace_id and user.id across telemetry data ensures seamless analysis and error tracking, while structured logging and customer feedback enhance the visibility and prioritization of impactful defects. By focusing on how errors affect core product flows, engineering and product teams can collaborate more effectively, ensuring improvements are based on evidence-backed insights rather than visibility alone, ultimately enhancing user experience and business outcomes.