How we reduced churn by 35% for a B2B SaaS company using predictive analytics and customer health scoring.
A B2B SaaS company providing project management software was experiencing 18% monthly churn, well above their industry benchmark of 5-7%. They had no systematic way to identify which customers were at risk before they cancelled, and their customer success team was reactive rather than proactive.
By the time CS reached out to struggling customers, the decision to leave had already been made. The company had usage data, support ticket data, and billing data, but no way to synthesize it into actionable insights about customer health.
We built a predictive churn model using machine learning that analyzed usage patterns, support interactions, and billing history to identify at-risk customers before they cancelled. The system generated customer health scores and automated alerts to the CS team.
Consolidated usage data (login frequency, feature adoption, active users), support data (ticket volume, response time, sentiment), and billing data (payment history, plan changes) into PostgreSQL. Created 30+ features for model training.
Built a Random Forest classifier using Python and scikit-learn trained on 18 months of historical data. Achieved 85% accuracy in predicting churn 30 days before cancellation.
Created real-time health score dashboard in Metabase showing each customer's churn risk (0-100 scale), key risk factors, usage trends, and engagement patterns.
Set up automated Slack alerts when customers entered "high risk" category, with recommended interventions based on specific risk factors (e.g., low usage, support issues, billing problems).
Within 4 weeks, the CS team went from reactive firefighting to proactive retention. They could identify at-risk customers 30 days before cancellation and implement targeted interventions. Over 3 months, churn dropped from 18% to 11.7%—a 35% reduction.
This system completely changed how we do customer success. Instead of reacting to cancellation requests, we now reach out proactively to struggling customers before they've even decided to leave. Our churn is down 35% and our CS team feels like they finally have the tools they need to succeed. This has been the best investment we've made this year.
Included model development, dashboard, alert system, documentation, and 1 month of model tuning support.