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← Monday's Prompts

Automate Retention System 🚀

Turn Monday's 3 prompts into production-ready churn detection

October 21, 2025
30 min read
📈 Growth🐍 Python + TypeScript⚡ 100 → 10K users/day
On This Page:ProblemDemoCodeScaleGotchasCost

The Problem

On Monday you tested the 3 prompts in ChatGPT. Sweet! You saw how cohort extraction → churn scoring → intervention recommendations works. But here's reality: you can't manually analyze 500 users daily. One growth PM spending 15 hours per week running retention reports? That's $375/week in labor costs ($19,500/year). Multiply across a 5-person growth team and you're burning $97,500 annually on spreadsheet hell. Plus the lag time means you miss churn signals until it's too late.

15+ hours
Per week on manual cohort analysis
3-5 days
Lag between churn signal and action
Can't scale
Beyond 100-200 active users

See It Work

Watch the 3 prompts chain together automatically. This is what you'll build.

Watch It Work

See the AI automation in action

Live Demo • No Setup Required

The Code

Three levels: start simple, add reliability, then scale to production. Pick where you are.

Basic = Quick startProduction = Full featuresAdvanced = Custom + Scale

Simple API Calls

Good for: 0-100 users/day | Setup time: 30 minutes

Simple API Calls
Good for: 0-100 users/day | Setup time: 30 minutes
# Simple Retention Analysis (0-100 users/day)
import openai
import json
import os
from datetime import datetime, timedelta
from typing import Dict, List, Optional

# Set your API key
openai.api_key = os.getenv('OPENAI_API_KEY')

def analyze_user_retention(user_data: Dict) -> Dict:
    """Chain the 3 prompts: extract behavior → score churn risk → generate interventions"""
    
    # Step 1: Extract and score user behavior
    extraction_prompt = f"""Analyze this user's behavior data and extract key retention signals.
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When to Level Up

1

Simple Scripts

  • Direct OpenAI/Claude API calls
  • Synchronous processing (one user at a time)
  • Basic try/catch error handling
  • Console logging
  • Manual result review
  • No caching or queuing
Level Up
2

With Reliability

  • Async/concurrent processing (5-10 users at once)
  • Exponential backoff retries (3 attempts)
  • Winston/structured logging to files
  • Redis caching (24hr TTL)
  • Rate limiting and throttling
  • Error alerting to Slack
  • Basic monitoring dashboard
Level Up
3

Production Framework

  • LangGraph state machine with checkpoints
  • Queue-based processing (Redis/RabbitMQ)
  • Conditional routing and error recovery
  • Database persistence for historical tracking
  • Real-time alerting for high-risk users
  • Automated CSM task creation
  • Datadog/Sentry monitoring
  • A/B testing intervention effectiveness
Level Up
4

Multi-Agent System

  • Specialized agents: Behavioral Analyst, Risk Scorer, Intervention Planner
  • Distributed task queue (Celery/SQS)
  • Custom ML models for churn prediction
  • Real-time event stream processing (Kafka)
  • Automated intervention execution (email/in-app/SMS)
  • Closed-loop feedback and model retraining
  • Multi-region deployment for global scale
  • Advanced analytics and reporting dashboards

Growth-Specific Gotchas

Real challenges from building retention systems. Save yourself the debugging time.

Data Freshness vs API Costs

Tiered approach: real-time for critical signals (payment failed, support ticket), hourly batch for engagement metrics, daily for cohort analysis. Use Redis to cache recent analyses.

Solution
# Tiered analysis timing
import redis
from datetime import datetime, timedelta

redis_client = redis.Redis()

def should_analyze_user(user_id: str, trigger_event: str) -> bool:
    """Decide if we need fresh analysis based on event type"""
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Cohort Comparison Accuracy

Multi-dimensional cohorts with fallback hierarchy. Start specific (industry + plan + tenure + team size), then broaden if sample size <50 users.

Solution
# Hierarchical cohort matching
from typing import Dict, List, Optional

def find_comparable_cohort(user: Dict, min_sample_size: int = 50) -> Dict:
    """Find most specific cohort with enough data"""
    
    # Try from most specific to most general
    cohort_definitions = [
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Intervention Fatigue

Track intervention history and enforce cool-down periods. Prioritize high-impact, low-friction interventions first.

Solution
# Intervention throttling and prioritization
from datetime import datetime, timedelta
from typing import List, Dict

class InterventionManager:
    def __init__(self, redis_client):
        self.redis = redis_client
        self.max_interventions_per_week = 3
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False Positives in Churn Prediction

Add temporal context and behavioral baselines. Look for *changes* in patterns, not just absolute values.

Solution
# Temporal pattern analysis
from typing import Dict, List
import numpy as np

def calculate_risk_with_context(user: Dict, historical_data: List[Dict]) -> Dict:
    """Adjust churn risk based on user's historical patterns"""
    
    # Calculate baseline metrics from historical data
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Intervention Attribution

A/B test interventions with control groups. Track user state before/after intervention. Use holdout groups to measure natural retention.

Solution
# Intervention A/B testing and attribution
import random
from datetime import datetime, timedelta
from typing import Dict, Optional

class InterventionExperiment:
    def __init__(self, redis_client, db_client):
        self.redis = redis_client
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Adjust Your Numbers

500
105,000
5 min
1 min60 min
$50/hr
$15/hr$200/hr

❌ Manual Process

Time per item:5 min
Cost per item:$4.17
Daily volume:500 items
Daily:$2,083
Monthly:$45,833
Yearly:$550,000

✅ AI-Automated

Time per item:~2 sec
API cost:$0.02
Review (10%):$0.42
Daily:$218
Monthly:$4,803
Yearly:$57,640

You Save

0/day
90% cost reduction
Monthly Savings
$41,030
Yearly Savings
$492,360
💡 ROI payback: Typically 1-2 months for basic implementation
📈

Want This Running in Your Growth Stack?

We build custom retention systems that integrate with your analytics platform, CRM, and communication tools. From churn prediction to automated interventions, we handle the complexity so you can focus on strategy.

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2026 Randeep Bhatia. All Rights Reserved.

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