Product Analytics: Evaluating Feature Changes with Data

How do you know if a new feature will drive growth or alienate your existing users? Making product changes without a structured, data-driven approach often leads to wasted development effort and missed business targets. This course equips you with the analytical frameworks needed to evaluate feature changes before, during, and after launch. You will learn to connect user behavior data to high-level business metrics, ensuring every product update delivers measurable value. What you'll learn: - Understand foundational product analytics concepts, terminology, and core metric frameworks. - Analyze historical user behavior to establish baseline performance before introducing changes. - Apply modern feature flagging and A/B testing concepts to mitigate risks during rollouts. - Track post-launch adoption, engagement, and retention using cohort analysis. - Link feature-level usage data directly to high-level business outcomes and key performance indicators. - Interpret analytical results to confidently decide whether to iterate, roll back, or fully launch a feature. This course begins with essential definitions and metrics, guiding you step-by-step through the lifecycle of a feature change from pre-launch planning to post-launch evaluation. Designed for beginner product managers, junior analysts, and aspiring product owners, this course requires no prior experience in advanced statistics or data science. Start reading today to make more confident, data-driven product decisions.