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client-retention

Analyze engagement metrics to score churn probability and trigger re-engagement campaigns.

3|Updated Aug 3, 2025
Search Tags:#retention#churn-prediction#customer-success#cohort-analysis#churn#user-engagement#ai-prediction
One-click install
npx skills add https://github.com/CleanExpo/Synthex --skill client-retention
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: client-retention
Source: https://github.com/CleanExpo/Synthex/tree/main/.claude/skills/client-retention
Command: npx skills add https://github.com/CleanExpo/Synthex --skill client-retention

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a data-driven system to monitor user engagement, identify churn risks, and optimise retention so teams can proactively re-engage at-risk accounts and improve long-term user value.

Core Features & Use Cases

  • Engagement Monitoring: Collects and computes DAU, WAU, MAU, session duration, and feature adoption to surface usage trends.
  • Cohort & Churn Analysis: Runs cohort retention curves, flags disengaged segments, and scores churn probability using an AI model with rule-based fallbacks.
  • Intervention & Reporting: Designs and triggers personalised re-engagement campaigns, measures impact, and generates weekly retention reports for stakeholders.
  • Use Case: A customer success manager runs cohort analysis for last quarter, identifies at-risk users with falling feature adoption, scores churn probability, and launches targeted email sequences to recover revenue.

Quick Start

Analyse the last 30 days of engagement for the new-user cohort, score churn probability, and produce a summary with top re-engagement recommendations.

Frequently Asked Questions about client-retention

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

FAQPage Schema
How do I predict user churn and identify at-risk customers?▼

Predict user churn by analyzing engagement metrics like DAU, MAU, and session duration to score at-risk customers. The system uses AI churn probability scoring with rule-based fallbacks to flag disengaged user segments for proactive retention.

What is the best way to run cohort retention analysis for tracking user engagement?▼

Run cohort retention analysis by grouping users into cohorts and computing retention curves to track user engagement over time. This surfaces feature adoption trends and flags disengaged segments so customer success teams can target interventions.

How can I automate personalized re-engagement campaigns for churned users?▼

Automate personalized re-engagement campaigns by scoring churn probability for at-risk users and triggering targeted email sequences. The system designs interventions based on falling feature adoption and measures the impact to recover revenue.

How do I generate weekly retention reports for customer success stakeholders?▼

Generate weekly retention reports by computing DAU, WAU, MAU, and session metrics, then summarizing cohort retention curves and churn scoring results. The output provides stakeholders with actionable insights on user engagement trends and re-engagement impact.

Does churn prediction work without connecting to AI models like OpenRouter?▼

Yes, churn prediction works without AI models like OpenRouter because the system includes rule-based fallbacks for scoring churn probability. It computes engagement metrics and flags disengaged segments using baseline rules when AI model integration is unavailable.

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