Hanumanta Consulting implemented a machine learning-powered churn prediction system over a 3-month engagement:

1. Data Foundation & Feature Engineering

• Integrated data from product usage logs, support tickets, billing history, and CRM
• Analyzed 24 months of historical data covering 4,800 customer accounts
• Engineered 47 predictive features including login frequency, feature adoption, support ticket patterns, and payment history

2. Predictive Model Development

• Built gradient boosting model (XGBoost) achieving 87% accuracy in predicting 90-day churn risk
• Identified key churn indicators: declining weekly logins, unused premium features, delayed payments, and increased support tickets
• Created risk scores (0-100) for every customer account, updated daily

3. Actionable Intelligence for Customer Success

• Integrated churn scores into Salesforce CRM with automated alerts for high-risk accounts
• Built dashboard showing risk trends, account health metrics, and recommended interventions
• Created automated email campaigns for at-risk customers with personalized re-engagement content

4. Continuous Improvement & Monitoring

• Implemented model monitoring to track prediction accuracy over time
• Set up monthly retraining pipeline to incorporate new data and evolving customer behaviors