SaaS Customer Success Automation

How It Works

1. 1
   Data Collection

   Fetch customer records from HubSpot using the CRM API, filtering for active customers and retrieving health scores, usage metrics, and account properties from the past 90 days

   HubSpot CRM API
2. 2
   Engagement Analysis

   Pull conversation and ticket data from Intercom API to analyze support interaction patterns, response times, and sentiment indicators for each customer

   Intercom Conversations API
3. 3
   Billing Context

   Retrieve subscription status, payment history, and MRR trends from Stripe API to identify billing-related churn risks and expansion opportunities

   Stripe Subscriptions API
4. 4
   Risk Scoring

   Apply machine learning model to combined dataset, generating churn probability scores and identifying top risk factors for each account

   scikit-learn RandomForestClassifier
5. 5
   Alert Distribution

   Update HubSpot contact records with churn risk scores and send targeted Intercom messages to customer success team members for high-risk accounts

   HubSpot Contacts API + Intercom Messages API

Implementation

# Churn Predictor Implementation

## File Structure
```
churn_predictor/
├── app.py
├── models/
│   ├── churn_model.py
│   └── feature_engineering.py
├── integrations/
│   ├── hubspot_client.py
│   ├── intercom_client.py
│   └── stripe_client.py
├── config/
│   └── settings.py
├── requirements.txt
└── Dockerfile
```

## Core Implementation

### app.py
```python
from models.churn_model import ChurnPredictor
from integrations.hubspot_client import HubSpotClient
from integrations.intercom_client import IntercomClient
from integrations.stripe_client import StripeClient

def main():
    # Initialize clients
    hubspot = HubSpotClient(os.getenv('HUBSPOT_API_KEY'))
    intercom = IntercomClient(os.getenv('INTERCOM_ACCESS_TOKEN'))
    stripe_client = StripeClient(os.getenv('STRIPE_API_KEY'))
    
    # Fetch customer data
    customers = hubspot.get_active_customers()
    
    # Enrich with engagement and billing data
    for customer in customers:
        customer['conversations'] = intercom.get_customer_conversations(customer['email'])
        customer['billing'] = stripe_client.get_customer_billing(customer['stripe_id'])
    
    # Generate predictions
    predictor = ChurnPredictor()
    predictions = predictor.predict_churn_batch(customers)
    
    # Update systems
    hubspot.update_churn_scores(predictions)
    intercom.send_risk_alerts(high_risk_customers)
```

## Environment Variables
```
HUBSPOT_API_KEY=your_hubspot_key
INTERCOM_ACCESS_TOKEN=your_intercom_token
STRIPE_API_KEY=your_stripe_key
DATABASE_URL=postgresql://user:pass@localhost/cloudmetrics
CHURN_RISK_THRESHOLD=0.7
```

## Cron Setup
```
0 8 * * 1 cd /app && python app.py
```

Data Flow

Prerequisites

• -Health Score Engine must be running and populating daily scores
• -HubSpot custom properties created for churn_risk_score and risk_factors
• -Intercom workspace configured with customer success team member access
• -Historical churn data available for model training (minimum 6 months)
• -Stripe customer IDs properly mapped to HubSpot contact records

Error Handling