What information do I need to provide to use this skill?

Ideally, you should provide your business model, current retention metrics, user segments, and any available qualitative data like churn surveys or interview notes.

How does it help with Product-Market Fit (PMF)?

The skill analyzes the shape of your retention curve; a curve that flattens indicates PMF, while one that trends to zero suggests a fundamental product-market fit problem.

What metrics does this skill analyze?

It focuses on D1, D7, and D30 retention, DAU/MAU ratios, churn rates, and Net Revenue Retention (NRR) to provide a holistic view of product health.

What does the Retention Analysis skill do?

It provides a structured framework for Claude to analyze user behavior data, identify why users are churning, and suggest specific interventions to improve product stickiness.

Product Retention Analysis

Name: Product Retention Analysis
Author: mohitagw15856

bymohitagw15856

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295

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Analíticas y Monitorización

Performs deep-dive retention and churn investigations to diagnose product-market fit and identify actionable growth interventions.

The Retention Analysis skill provides a structured framework for product managers and developers to diagnose why users leave and identify the specific behaviors that keep them engaged. By moving beyond surface-level metrics, it helps teams distinguish between onboarding friction and fundamental product-market fit (PMF) issues through retention curve analysis. It produces detailed retention snapshots, identifies 'aha moment' correlations, and generates prioritized, testable interventions to improve long-term user stickiness.

Características Principales

01Actionable intervention roadmaps with prioritized impact and effort scoring

02Cohort segmentation by acquisition channel, user type, and feature usage

03Retention curve diagnosis to distinguish between onboarding issues and PMF gaps

04Aha-moment correlation analysis to identify key behaviors driving long-term retention

05295 GitHub stars

06Automated retention snapshots with industry benchmarking for D1, D7, and D30 metrics

Casos de Uso

01Developing a data-driven retention improvement plan for a post-launch product

02Identifying core features that correlate most strongly with high-value user retention

03Investigating a sudden spike in churn or declining DAU/MAU ratios

Características Principales

01Actionable intervention roadmaps with prioritized impact and effort scoring

02Cohort segmentation by acquisition channel, user type, and feature usage

03Retention curve diagnosis to distinguish between onboarding issues and PMF gaps

04Aha-moment correlation analysis to identify key behaviors driving long-term retention

05295 GitHub stars

06Automated retention snapshots with industry benchmarking for D1, D7, and D30 metrics

Casos de Uso

01Developing a data-driven retention improvement plan for a post-launch product

02Identifying core features that correlate most strongly with high-value user retention

03Investigating a sudden spike in churn or declining DAU/MAU ratios