When is the best time to use the Retention Optimizer Agent?

This skill is designed for the post-PMF stage. It requires sufficient user behavioral data and historical cohort patterns to provide accurate analysis and predictions.

Can this skill help reduce user churn?

Yes, it identifies leading indicators of churn—such as usage drops or feature abandonment—and helps you design intervention triggers to re-engage users before they leave.

Does it provide implementation templates?

Yes, it includes standardized output templates for cohort retention matrices, churn risk scoring, and engagement loop workflows.

What kind of data does this skill analyze?

It analyzes user segmentation data, acquisition channels, retention curves (D1, D7, D30, D90), revenue per user, and feature adoption rates.

Retention Optimizer Agent

Name: Retention Optimizer Agent
Author: BellaBe

byBellaBe

Analytics & Monitoring

Optimizes customer lifetime value through data-driven cohort analysis, churn prediction models, and engagement loop design.

About

The Retention Optimizer Agent is a specialized skill designed for post-Product-Market Fit (PMF) applications where user behavioral data is available. It empowers Claude to act as a growth engineer or product analyst by identifying high-value user segments, predicting churn risks before they occur, and architecting habit-forming engagement loops. This skill provides structured frameworks for measuring retention curves, identifying 'Aha' moments, and designing automated intervention triggers to maximize retention and monetization during growth and scale phases.

Key Features

Habit loop architecture and engagement trigger design
Aha moment identification and feature adoption tracking
0 GitHub stars
Predictive churn modeling based on usage drops and feature abandonment
ROI-focused reporting templates for churn prevention programs
Comprehensive cohort analysis by acquisition channel, behavior, and revenue

Use Cases

Designing habit loops and reward systems to increase daily active usage
Analyzing month-over-month cohort retention to identify critical drop-off points
Developing a predictive churn scoring system to prioritize high-value user outreach

About

Key Features

Habit loop architecture and engagement trigger design
Aha moment identification and feature adoption tracking
0 GitHub stars
Predictive churn modeling based on usage drops and feature abandonment
ROI-focused reporting templates for churn prevention programs
Comprehensive cohort analysis by acquisition channel, behavior, and revenue

Use Cases

Designing habit loops and reward systems to increase daily active usage
Analyzing month-over-month cohort retention to identify critical drop-off points
Developing a predictive churn scoring system to prioritize high-value user outreach