Retention Analytics

Skill Profile

(Select at least one profile to enable specific modules)

[ ] DevOps
[x] Backend
[ ] Frontend
[ ] AI-RAG
[ ] Security Critical

Overview

Retention is the lifeblood of SaaS and consumer apps. This skill covers cohort analysis, retention curves, churn prediction, and lifetime value (LTV) tracking to understand user behavior and improve product-market fit. By analyzing retention patterns, you can identify which user segments are most valuable, predict churn before it happens, and take proactive measures to improve user engagement.

Why This Matters

Revenue Growth: Retained users generate more revenue over time (LTV increases)
Cost Efficiency: Acquiring new users is 5-25x more expensive than retaining existing ones
Product-Market Fit: High retention indicates strong product-market fit
Predictable Growth: Stable retention enables predictable revenue forecasting
Competitive Advantage: High retention creates moats against competitors

Core Concepts & Rules

1. Core Principles

Follow established patterns and conventions
Maintain consistency across codebase
Document decisions and trade-offs

2. Implementation Guidelines

Start with the simplest viable solution
Iterate based on feedback and requirements
Test thoroughly before deployment

Inputs / Outputs / Contracts

Inputs:
- User event data (signup, activity, payment events)
- User metadata (acquisition channel, user type, geography)
- Historical retention data for baseline comparison
- Business targets (retention goals, churn thresholds)
Entry Conditions:
- Event tracking infrastructure in place
- Minimum 30-90 days of historical data
- User IDs consistent across systems
- Database with event table and indexes
Outputs:
- Cohort retention matrix and visualizations
- Churn prediction scores per user
- LTV calculations and projections
- Retention dashboards and reports
Artifacts Required (Deliverables):
- Cohort analysis SQL queries or Python scripts
- Churn prediction model and feature definitions
- LTV calculation formulas and reports
- Retention dashboard configurations
Acceptance Evidence:
- Cohort retention matrix showing retention rates by cohort and day
- Churn prediction model with accuracy metrics (ROC AUC > 0.7)
- LTV calculations validated against actual revenue
- Dashboard accessible and displaying retention metrics
Success Criteria:
- Cohort analysis runs weekly with accurate retention rates
- Churn prediction model deployed with monitoring
- LTV calculations match actual revenue within 5%
- Retention dashboards provide actionable insights

Skill Composition

Depends on: kpi-metrics, sql-for-analytics, data-visualization
Compatible with: ab-testing-analysis, funnel-analysis, dashboard-design
Conflicts with: None
Related Skills: cohort-analysis, business-intelligence

Quick Start / Implementation Example

Review requirements and constraints
Set up development environment
Implement core functionality following patterns
Write tests for critical paths
Run tests and fix issues
Document any deviations or decisions

# Example implementation following best practices
def example_function():
    # Your implementation here
    pass

Assumptions / Constraints / Non-goals

Assumptions:
- Development environment is properly configured
- Required dependencies are available
- Team has basic understanding of domain
Constraints:
- Must follow existing codebase conventions
- Time and resource limitations
- Compatibility requirements
Non-goals:
- This skill does not cover edge cases outside scope
- Not a replacement for formal training

Compatibility & Prerequisites

Supported Versions:
- Python 3.8+
- Node.js 16+
- Modern browsers (Chrome, Firefox, Safari, Edge)
Required AI Tools:
- Code editor (VS Code recommended)
- Testing framework appropriate for language
- Version control (Git)
Dependencies:
- Language-specific package manager
- Build tools
- Testing libraries
Environment Setup:
- .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

Top Threats: Injection attacks, authentication bypass, data exposure

[ ] Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
[ ] Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
[ ] Authorization: Validate user permissions before state changes

2. Performance & Resources

[ ] Execution Efficiency: Consider time complexity for algorithms
[ ] Memory Management: Use streams/pagination for large data
[ ] Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

[ ] Design Pattern: Follow SOLID principles, use Dependency Injection
[ ] Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

[ ] Logging Standards: Structured JSON, include trace IDs request_id
[ ] Metrics: Track error_rate, latency, queue_depth
[ ] Error Handling: Standardized error codes, no bare except
[ ] Observability Artifacts:
- Log Fields: timestamp, level, message, request_id
- Metrics: request_count, error_count, response_time
- Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

Thinking Process: Analyze root cause before fixing. Do not brute-force.
Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
Self-Review: Check against Guardrails & Anti-patterns before finalizing.
Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

[ ] Tests passed + coverage met
[ ] Lint/Typecheck passed
[ ] Logging/Metrics/Trace implemented
[ ] Security checks passed
[ ] Documentation/Changelog updated
[ ] Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

⛔ Don't: Log PII, catch-all exception, N+1 queries
⚠️ Watch out for: Common symptoms and quick fixes
💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

Internal documentation and examples
Official documentation and best practices
Community resources and discussions

Versioning & Changelog

Version: 1.0.0
Changelog:
- 2026-02-22: Initial version with complete template structure