Retention

What This Skill Does

The Retention skill is an all-in-one analytical engine designed to help teams identify, measure, and optimize user longevity. It provides the frameworks necessary to transition from raw usage data into actionable growth strategies. By tracking core health metrics—such as D1, D7, and D30 retention—and providing tools for cohort analysis, the skill highlights precisely when and why users drop off. It also monitors early-warning churn signals, designs habit-forming engagement loops, and automates lifecycle communication strategies to maximize Net Revenue Retention (NRR).

Installation

You can integrate this skill into your environment using the following command: clawhub install openclaw/skills/skills/ivangdavila/retention

Use Cases

• Proactive Churn Prevention: Identify "at-risk" users by detecting patterns like dropped login frequency or stop-usage of core features, allowing for automated outreach before a cancellation occurs.
• Aha-Moment Optimization: Use cohort analysis to visualize the 'week 2 cliff' and adjust onboarding to ensure users reach the core value proposition faster.
• Strategic Reactivation: Deploy automated win-back campaigns at 7, 14, 30, and 90-day inactivity intervals using the built-in message formulas.
• Feature Prioritization: Analyze feature stickiness to determine which specific tools within your platform correlate most strongly with long-term retention, allowing you to prioritize them in your onboarding wizard.

Example Prompts

1. "Analyze the last three months of signup cohorts and tell me if the week 2 drop-off rate is improving compared to our previous benchmarks."
2. "Draft a 30-day inactivity win-back email that highlights our new AI-summary feature and offers an extended trial period to bring the user back."
3. "Which feature usage patterns are currently serving as the strongest predictors for our 90-day retention?"

Tips & Limitations

• Consistency is Key: Always track cohorts by signup week rather than calendar week to avoid seasonal noise skewing your data.
• Variable Rewards: When designing engagement loops, ensure rewards remain unpredictable; predictable rewards lead to boredom and user decay.
• Data Integrity: This skill relies on accurate event tracking. Ensure your platform correctly logs core feature interactions; otherwise, the churn signal indicators will be unreliable.
• Human-in-the-Loop: While the skill can generate reactivation templates, always review the tone of automated messaging to ensure it aligns with your brand voice.