Which metric frameworks does this skill support?

It supports popular frameworks like AARRR (Pirate Metrics) for growth loops, North Star for strategic alignment, and HEART for measuring user experience quality.

Does it help with interpreting data or just collecting it?

It provides detailed interpretation guides for retention curves, helping you distinguish between healthy core audiences and onboarding mismatches.

Can I use this for early-stage startups?

Yes, it includes specific KPI guidance for pre-PMF products, focusing on activation rates, time-to-first-value, and qualitative success markers.

How does the metrics calculator work?

It provides a CLI utility that can process event data (CSV) to generate cohort tables, calculate retention rates by age, and perform funnel conversion analysis.

Product Analytics & KPI Strategy

Name: Product Analytics & KPI Strategy
Author: JantonioFC

byJantonioFC

•

Analíticas y Monitorización

Defines, tracks, and interprets product performance metrics across discovery, growth, and mature product lifecycles.

This skill empowers developers and product managers to implement data-driven decision-making by providing structured frameworks for defining KPIs, designing hierarchical dashboards, and conducting advanced cohort and retention analyses. It guides users through selecting the right metrics—such as AARRR for growth loops or HEART for UX quality—ensuring that analytics are stage-appropriate and actionable. By leveraging built-in calculation methodologies for retention curves and funnel conversion, this skill bridges the gap between raw event data and strategic product insights.

Características Principales

01Stage-appropriate KPI definitions for pre-PMF, growth, and mature products

02Advanced cohort analysis and retention curve interpretation

032 GitHub stars

04Hierarchical dashboard design from executive layers to feature-level health

05Metric framework selection including AARRR, North Star, and HEART

06CLI utility for calculating retention rates and funnel conversion from data

Casos de Uso

01Designing feature-specific dashboards to measure adoption, depth, and repeat usage

02Establishing a North Star metric to align cross-functional engineering and product teams

03Conducting cohort analysis to identify friction points in the user onboarding journey

Características Principales

01Stage-appropriate KPI definitions for pre-PMF, growth, and mature products

02Advanced cohort analysis and retention curve interpretation

032 GitHub stars

04Hierarchical dashboard design from executive layers to feature-level health

05Metric framework selection including AARRR, North Star, and HEART

06CLI utility for calculating retention rates and funnel conversion from data

Casos de Uso

01Designing feature-specific dashboards to measure adoption, depth, and repeat usage

02Establishing a North Star metric to align cross-functional engineering and product teams

03Conducting cohort analysis to identify friction points in the user onboarding journey