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retention-analysis

Analyze D1, D7, D14, and D30 retention curves to identify churn drivers.

Updated Mar 11, 2026
Search Tags:#activation#retention#cohort analysis#user behavior#churn#resurrection#retention dashboard
One-click install
npx skills add https://github.com/pisithrps/yapzee --skill retention-analysis-pisithrps
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: retention-analysis
Source: https://github.com/pisithrps/yapzee/tree/main/.claude/skills/retention-analysis
Command: npx skills add https://github.com/pisithrps/yapzee --skill retention-analysis-pisithrps

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill diagnoses where users drop off in the onboarding and habit-formation funnel and identifies the behavioral and product drivers of churn so teams can prioritize retention interventions.

Core Features & Use Cases

  • Cohort diagnostics: Analyze D1, D7, D14, and D30 retention curves, visualize retention curve shape, and identify the largest drop-off points.
  • Retained vs churned behavior: Compare early behavioral signals to isolate actions that predict long-term retention and surface activation gaps.
  • Resurrection & win-back analysis: Evaluate dormancy windows and win-back campaign performance to prioritize re-engagement tactics.
  • Use case: A product manager supplies retention rates, product usage frequency, and churn research to receive a prioritized set of hypotheses, experiments, and dashboard templates for improving stickiness.

Quick Start

Provide D1, D7, D14, and D30 retention rates along with expected usage frequency and known churn reasons and request a retention diagnostic and prioritized interventions.

Frequently Asked Questions about retention-analysis

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

FAQPage Schema
How do I analyze cohort retention curves to identify where users drop off?▼

Cohort retention analysis examines D1, D7, D14, and D30 retention curves to visualize the retention curve shape and pinpoint the largest drop-off points in the onboarding and habit-formation funnel.

What is the best way to identify behavioral drivers that predict long-term user retention?▼

To identify behavioral drivers of user retention, compare early behavioral signals between retained and churned users to isolate specific actions that predict long-term stickiness and surface activation gaps.

How do I evaluate win-back campaigns and resurrection tactics for dormant users?▼

Evaluating win-back campaigns requires analyzing dormancy windows and resurrection performance to prioritize re-engagement tactics for churned users and improve overall retention.

What data do I need to perform a cohort retention diagnostic and prioritize interventions?▼

Performing a cohort retention diagnostic requires user-supplied D1, D7, D14, and D30 retention rates, product usage frequency data, and optional churn research to produce prioritized hypotheses and dashboard templates.

Can I use cohort analysis to diagnose churn factors for specific product features or channels?▼

Yes, cohort analysis applies channel and feature cohorts to diagnose churn factors, isolating retention drivers and identifying behavioral gaps specific to those product features or acquisition channels.

When should I not rely solely on cohort retention analysis for reducing user churn?▼

Cohort retention analysis identifies drop-off points and behavioral drivers but should be supplemented with direct churn research to understand qualitative reasons behind user dormancy and inform effective interventions.

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