Customer Churn Prediction Case Study

The Challenge

A rapidly growing tech startup was experiencing significant customer churn, losing approximately $1M annually. The company needed a data-driven approach to identify at-risk customers and implement targeted retention strategies.

My Approach

1. Data Collection and Analysis

• Analyzed 100,000+ customer records
• Identified 25+ key features affecting churn
• Performed extensive exploratory data analysis

2. Feature Engineering

# Key features engineered
features = [
    'days_since_last_login',
    'support_tickets_count', 
    'feature_usage_score',
    'payment_delays',
    'engagement_trend'
]

3. Model Development

Built and compared multiple models:

• Random Forest (chosen model)
• Gradient Boosting
• Logistic Regression
• Neural Networks

4. Implementation

Created a real-time scoring system that:

• Processes customer data daily
• Generates churn probability scores
• Triggers automated retention campaigns

Results

• 90% prediction accuracy on test data
• 30% reduction in churn rate within 6 months
• $500K annual savings in retention costs
• 15% increase in customer lifetime value

Business Impact

The churn prediction model became a core part of the company's customer success strategy, enabling proactive retention efforts and significantly improving business metrics.