What metrics does the Retention Analysis skill track?

It calculates D1, D7, and D30 retention rates, DAU/MAU ratios, churn rates, and Net Revenue Retention (NRR).

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

It analyzes the long-term floor level of your retention curve; if the curve flattens, it indicates PMF, whereas a curve trending to zero suggests a fundamental value proposition issue.

Does this skill provide specific improvement suggestions?

Unlike generic tools, it generates specific, testable interventions based on where the drop-off occurs (e.g., D1 vs. D30) and correlates them with specific 'Aha Moments' in the user journey.

Can I use this for both SaaS and consumer apps?

Yes, the skill includes industry-specific benchmarks for SaaS, marketplaces, and consumer apps to ensure your metrics are evaluated in the correct context.

Retention Analysis

Name: Retention Analysis
Author: mohitagw15856

bymohitagw15856

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295

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Analíticas y Monitorización

Performs deep-dive product retention analysis to identify churn drivers, calculate engagement metrics, and develop data-driven improvement plans.

The Retention Analysis skill empowers product managers and developers to diagnose why users leave and identify the specific actions that keep them engaged. It provides a structured framework to evaluate retention curves, distinguish between onboarding friction and product-market fit issues, and correlate early user behaviors with long-term value. By moving beyond vague suggestions, this skill generates testable hypotheses and prioritized interventions based on industry benchmarks for SaaS, consumer apps, and marketplaces, ensuring your product growth is backed by rigorous data analysis.

Características Principales

01Cohort segmentation analysis by user type, acquisition channel, and feature usage

02Standardized calculation of D1, D7, D30 retention and DAU/MAU stickiness ratios

03Automated retention curve diagnosis to distinguish between PMF and onboarding issues

04Strategic intervention planning with effort-impact prioritization and monitoring thresholds

05Identification of 'Aha Moment' correlations to predict long-term user retention

06295 GitHub stars

Casos de Uso

01Investigating a sudden drop in user engagement or an unexpected increase in churn rates

02Optimizing onboarding flows by identifying exactly where new users drop off in the first week

03Validating product-market fit for a new launch by analyzing the stability of the long-term retention floor

Características Principales

01Cohort segmentation analysis by user type, acquisition channel, and feature usage

02Standardized calculation of D1, D7, D30 retention and DAU/MAU stickiness ratios

03Automated retention curve diagnosis to distinguish between PMF and onboarding issues

04Strategic intervention planning with effort-impact prioritization and monitoring thresholds

05Identification of 'Aha Moment' correlations to predict long-term user retention

06295 GitHub stars

Casos de Uso

01Investigating a sudden drop in user engagement or an unexpected increase in churn rates

02Optimizing onboarding flows by identifying exactly where new users drop off in the first week

03Validating product-market fit for a new launch by analyzing the stability of the long-term retention floor