product-analytics

The Product Analytics skill empowers product managers, data analysts, and growth teams to systematically measure and interpret product health across different stages of the product lifecycle. Whether you are in the pre-product-market fit (PMF) phase, actively scaling, or managing a mature product, this skill provides the necessary frameworks to move beyond vanity metrics and focus on actionable insights. It enables users to define clear metric hierarchies, design effective dashboards, and implement rigorous cohort and retention analysis techniques to identify friction points and value moments in the user journey.

• Metric Framework Mastery: Apply industry-standard models including AARRR (Pirate Metrics) for growth loops, North Star for cross-functional alignment, and HEART for comprehensive user experience measurement.

• Stage-Specific KPI Selection: Curate dashboards tailored to product maturity, from early-stage activation and time-to-first-value to mature-stage net revenue retention and churn risk assessment.

• Advanced Cohort & Funnel Analysis: Segment data by signup cohorts or feature exposure to track long-term retention curves and identify inflection points where user engagement drops.

• Automated Analysis Toolkit: Utilize the included metrics_calculator.py CLI utility to process CSV event data for retention matrices, funnel conversion rates, and cohort analysis without requiring complex external BI tools.

• Dashboard Design Principles: Implement best practices such as pairing KPIs with decision rules, establishing clear ownership for metrics, and avoiding segment averaging to uncover hidden user behavior insights.

• This skill is best used when planning new feature releases, auditing product performance, or troubleshooting sudden shifts in user behavior.

• Expected inputs include CSV data formatted with user identifiers, event actions, and timestamps; the output is quantitative metrics, trend visualizations, and data-backed product action recommendations.

• The methodology strongly discourages reporting isolated point estimates, advocating instead for the comparison of trend curves over consistent time windows to filter out noise and seasonal fluctuations.

• Integrate this skill with experiment-designer and product-manager-toolkit for a cohesive, data-driven product development workflow that bridges the gap between raw analytical data and strategic decision-making.