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retention-analysis

Analyze activation, cohort trends, and usage patterns to identify churn drivers.

20|4|Updated Oct 4, 2025
Search Tags:#activation#habits#data-analytics#retention#experimentation#cohort-analysis#churn
One-click install
npx skills add https://github.com/coalesce-labs/catalyst --skill retention-analysis-coalesce-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: retention-analysis
Source: https://github.com/coalesce-labs/catalyst/tree/main/plugins/pm/skills/retention-analysis
Command: npx skills add https://github.com/coalesce-labs/catalyst --skill retention-analysis-coalesce-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This analysis framework helps product teams diagnose why users churn and where retention drops occur, enabling targeted interventions to improve long-term engagement.

Core Features & Use Cases

  • Cohort diagnostics: compare retention across signup cohorts and channels to reveal trend patterns.
  • Activation and habit formation: identify activation bottlenecks and habit-building opportunities to improve D7/D30 retention.
  • Data-driven interventions: generate hypotheses, prioritize experiments, and guide win-back or onboarding improvements.

Quick Start

Run a retention analysis with your signup data to surface the top drop-offs and recommended actions.

Frequently Asked Questions about retention-analysis

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

FAQPage Schema
How do I analyze churn drivers and identify where user retention drops occur?▼

To analyze churn drivers and retention drops, you evaluate cohort trends, activation bottlenecks, and usage patterns. This framework tracks D1, D7, D14, and D30 metrics to pinpoint specific drop-offs and generate prioritized intervention hypotheses.

What is cohort analysis and how does it diagnose SaaS product retention?▼

Cohort analysis diagnoses SaaS retention by comparing user behavior across signup cohorts and channels. It reveals trend patterns to identify activation bottlenecks and habit-building opportunities for targeted interventions.

How can I improve D7 and D30 retention through habit formation analysis?▼

Improve D7 and D30 retention by identifying activation bottlenecks and habit-building opportunities. Analyzing usage patterns helps generate data-driven hypotheses and prioritize experiments to boost long-term engagement.

Can I use this retention analysis framework for onboarding and activation scenarios?▼

Yes, this framework specifically applies to SaaS onboarding and activation scenarios. It tracks D1, D7, D14, D30 cohort trends and resurrection rates to guide onboarding improvements and win-back strategies.

What is the best way to segment churn analysis by feature usage and signup channel?▼

The best way to segment churn analysis is by cohort, channel, and feature usage. This segmentation surfaces top drop-offs, enabling data-driven interventions and prioritized experiments for targeted retention improvements.

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