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growth-analysis

Analyze post-launch product funnel performance across acquisition, activation, retention, referral, and revenue cohorts.

4|Updated Jun 1, 2026
Search Tags:#instrumentation#retention#product metrics#cohort analysis#growth analysis#funnel analytics
One-click install
npx skills add https://github.com/vmobifystudio/app-dev-team --skill growth-analysis-vmobifystudio
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: growth-analysis
Source: https://github.com/vmobifystudio/app-dev-team/tree/main/skills/growth-analysis
Command: npx skills add https://github.com/vmobifystudio/app-dev-team --skill growth-analysis-vmobifystudio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Growth analysis turns post-launch product data into trustworthy insights without confusing cumulative totals, shifting definitions, or unsupported estimates with meaningful evidence.

Core Features & Use Cases

  • Funnel Definition: Establish consistent definitions for installs, activation, retention, referral, and revenue.
  • Cohort-Based Reporting: Compare user cohorts with explicit denominators, date ranges, consent rates, and small-sample safeguards.
  • Confounder Analysis: Separate observed correlation from causation by documenting releases, seasonality, store features, and pricing changes.
  • Instrumentation Gaps: Identify the events and data needed to answer questions that current analytics cannot support.
  • Use Case: Compare D7 retention across monthly install cohorts, explain the most likely drivers of movement, and recommend the next instrumentation improvement.

Quick Start

Use growth analysis to append a dated funnel report to the analytics documentation using the available event schema and cohort data.

Frequently Asked Questions about growth-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze post-launch product funnel performance using cohort analysis?▼

Cohort analysis evaluates post-launch funnel performance by comparing user cohorts using explicit denominators, date ranges, consent rates, and small-sample safeguards to identify trustworthy changes in acquisition, activation, retention, referral, and revenue.

What is the best way to separate causation from correlation in growth analytics?▼

Growth analytics separates correlation from causation through confounder analysis, which explicitly documents releases, seasonality, store features, and pricing changes to isolate the true drivers of metric movement.

Why does my retention reporting show misleading changes in product metrics?▼

Retention reporting shows misleading changes when cumulative totals confuse actual performance or definitions shift; trustworthy growth analysis requires explicit metric definitions, fixed denominators, and small-sample thresholds to validate observed changes.

How do I identify missing event instrumentation for unanswered growth analysis questions?▼

Identify missing event instrumentation by mapping unanswered growth questions against the available analytics schema, then documenting the specific events and data needed to close the measurement gaps and support future diagnostics.

Can I compare D7 retention across monthly install cohorts without established analytics schema?▼

Comparing D7 retention across monthly install cohorts requires an established analytics schema with explicit metric definitions, consent rates, and small-sample safeguards; without this structured event instrumentation, the cohort-based KPI reporting remains unsupported.

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