retention churn recovery planner

    1

    Diagnose churn and generate prioritized, data-driven action plans to improve customer retention and activation.

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    Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLI+17 more

    See it in action

    You say

    Analyze our 15% monthly churn rate. Onboarding completion is 40%. Users who use the 'Reporting' feature stay 3x longer, but only 10% find it in week 1. Suggest a P0 plan.

    Your agent does

    Executive Summary

    High churn is driven by an activation bottleneck: users fail to discover the 'Reporting' feature, which is your primary retention hook.

    Prioritized Action Plan

    | Priority | Action | Target Segment | Impact | Effort | |---|---|---|---|---| | P0 | In-app 'Reporting' Guide | New Signups | High | Low |

    Problem: Only 10% feature adoption despite 3x higher LTV. Intervention: Add a mandatory tooltip and 'Aha' moment nudge in Day 2 of onboarding.

    What you get

    Identify friction points in the onboarding funnel that lead to early churn.Prioritize retention interventions based on revenue impact and effort.Create structured win-back campaigns for specific lapsed customer segments.Map feature usage behaviors to long-term customer health and loyalty.

    About this skill

    The problem

    Customer churn often feels like a black box, making it difficult to distinguish between poor onboarding, product gaps, or wrong-fit customers. Teams struggle to prioritize retention fixes that actually move the needle on net revenue retention.

    What it does

    • Analyzes churn across the full lifecycle from onboarding and activation to long-term loyalty.
    • Categorizes root causes such as time-to-value delays, feature adoption friction, and ICP mismatch.
    • Generates a prioritized P0/P1/P2 action plan mapped to specific customer segments and success metrics.
    • Designs measurable experiments including A/B tests and holdout groups for win-back campaigns.
    • Identifies data gaps in the retention funnel where tracking or instrumentation is missing.

    Why this beats prompting it yourself

    General prompts often suggest generic "better support" or "email sequences." This skill forces a rigorous lifecycle diagnosis, requiring evidence for every root cause and ensuring every recommended intervention is tied to a measurable success threshold.

    Use cases

    • Identify why a specific cohort is dropping off during the first 14 days of a trial.
    • Build a win-back sequence for high-value accounts that have gone dark.
    • Map feature adoption trends to long-term customer health scores.
    • Prepare a retention strategy for a quarterly business review or board meeting.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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