Metrics can reveal adoption rates, but they often fail to capture user success. A design system may boast high adoption—like an 87% usage rate for a button pattern—but that doesn't indicate whether users can complete their tasks effectively. The article emphasizes the importance of combining product analytics with design systems to gain actionable insights. By using tools like PostHog and its Model Context Protocol (MCP), teams can connect user interaction data with design elements, shifting focus from internal metrics to real user behavior. For instance, one team discovered that a highly adopted filtering pattern had a 34% abandonment rate and users struggled significantly before giving up. Despite the metrics suggesting the pattern was successful, user behavior painted a different picture. The article details how analyzing session recordings and user feedback led to actionable design changes that improved task completion rates dramatically. Examples illustrate the effectiveness of using user behavior data to inform design decisions, such as switching from tabs to accordions on mobile, which boosted task completion from 23% to 71%. Connecting analytics tools to design platforms like Figma allows for a streamlined feedback loop. The MCP facilitates real-time queries that help identify design flaws based on user behavior data. For example, if users drop off during a checkout process, a designer can quickly identify the issue and receive context-specific feedback directly in Figma. This integration not only speeds up the design process but ensures that decisions are grounded in actual user experiences. The article highlights the gap between quantitative metrics and qualitative insights, stressing the need for both to understand user struggles fully and design effective solutions.