Uncover Product Insights: Engagement Metrics

Master the engagement metrics and statistical monitoring techniques that distinguish high-performing product teams from those flying blind. This course tackles two critical analytical challenges: interpreting DAU/WAU stickiness ratios that reveal true user loyalty, and implementing control chart systems that detect performance declines before they escalate into business crises. This Short Course was created to help data analysis professionals accomplish systematic interpretation of user engagement metrics and implementation of statistical monitoring systems that transform reactive reporting into proactive performance management. By completing this course, you'll be able to move beyond surface-level user counts to engagement quality metrics that predict product success. You'll transform from reporting historical numbers to providing early warning systems that protect product performance—calculating stickiness ratios that Meta uses across 3 billion users and applying the same statistical process control methods that Netflix leverages for enterprise-scale monitoring. By the end of this course, you will be able to: Understand the formulas and contextual benchmarks for DAU/WAU stickiness Analyze control charts to detect significant declines in session-depth metrics This course is unique because it bridges advanced statistical methods with practical product analytics workflows, using real-world examples from Meta, Netflix, Spotify, and other industry leaders to show how engagement insights drive strategic business decisions. To be successful in this course, you should have a background in basic statistical concepts, familiarity with product performance metrics, and Python scripting fundamentals

• Interpret DAU/WAU stickiness ratios for real engagement insights
• Apply statistical process control to distinguish normal variation from true performance changes
• Set benchmarks and control limits for accurate engagement analysis
• Develop monitoring systems transforming reactive reports into predictive intelligence

• Basic familiarity with topic and terminology
• Readiness for applied exercises and case studies