Creating a Customer Health Score That Predicted Churn With 85% Accuracy

The Challenge

We were losing customers without warning. Post-churn analysis consistently revealed warning signs that had been visible months before the churn event but were never aggregated into a coherent signal. We needed a predictive model that would flag at-risk accounts early enough to intervene.

The Approach

I built a customer health score incorporating seven signals: product usage trends (30% weight), support ticket volume and sentiment (20%), NPS scores (15%), stakeholder engagement frequency (10%), feature adoption breadth (10%), payment timeliness (10%), and executive sponsor stability (5%). Each signal was scored 0-100 and weighted based on its historical correlation with churn.

I validated the model against three years of historical data, testing whether the score would have predicted actual churn events. After tuning the weights and thresholds, the model achieved 85% accuracy in identifying accounts that would churn within 90 days. I then built automated alerts that triggered when an account's health score dropped below threshold.

The Result

In the first six months of deployment, the health score identified 22 at-risk accounts. Proactive intervention saved 18 of them, preventing $1.4M in churn. The four accounts that churned despite intervention had fundamental fit issues that no amount of engagement could resolve. The model became a core tool for the entire customer success team.

Key Takeaway

Customer health is not a feeling — it is a measurable metric. Combining multiple signals into a weighted score transforms vague intuition into actionable intelligence. The key is validating the model against historical data and continuously tuning the weights based on actual outcomes.