Data Analytics and Product Metrics
Description
Understanding and application of data analytics, key performance indicators, and measurement frameworks to drive product decisions and measure success.
Learning Objectives
Learners will develop proficiency in product analytics, understand key metrics and KPIs, implement measurement frameworks, analyze user behavior data, and make data-driven product decisions.
Topics (8)
Foundation concepts of product measurement including metric types, KPI selection, measurement frameworks, and establishing data-driven culture in product teams.
Metrics and methods for measuring user acquisition including CAC, channel attribution, acquisition funnel analysis, and acquisition optimization techniques.
Comprehensive approach to measuring and improving user retention including cohort retention analysis, churn prediction, and retention optimization techniques.
Advanced analytical technique for grouping users by shared characteristics and analyzing their behavior patterns over time to inform product strategy.
Methodology for analyzing user flows through product funnels including funnel design, conversion optimization, and statistical testing of funnel improvements.
Essential statistical concepts and methods for product managers including hypothesis testing, statistical significance, correlation vs causation, and proper interpretation of data.
Best practices for creating product dashboards and data visualizations including chart selection, dashboard design principles, and storytelling with data.
Methods for measuring and analyzing user engagement including DAU/MAU, session metrics, feature usage analytics, and engagement optimization strategies.