Scoring & Inference Engine
Health scoring and churn prediction system
This engine evaluates the health of every customer account and predicts the likelihood of churn using behavioral signals from the product, support activity, customer engagement, and renewal timelines.
The scoring process runs daily and helps Customer Success teams identify at-risk accounts early and take proactive action.
Health Score (0 – 100)
Each account receives a Health Score between 0 and 100. The score represents how healthy the account is based on product usage, adoption, support activity, engagement with the success team, and renewal timeline.
Health Score Interpretation
How the Score is Calculated
Each factor contributes a percentage (%) to the total score. The percentage represents the importance of that signal in determining account health.
Example: If Product Usage has a weight of 35%, it contributes up to 35 points out of the total 100 score. More important signals have higher weights.
Health Score Signals
Product Usage
Measures how actively the customer is using the product.
- • Daily / Weekly / Monthly active users
- • Product activity trends
- • Feature usage frequency
Since product usage is the strongest indicator of value realization, it has the highest weight.
Feature Adoption
Measures whether customers are using the product's most important features.
- • Key feature adoption
- • License or seat utilization
Higher adoption usually means customers are receiving more value from the product.
Support Experience
Measures customer support activity and product friction.
- • Number of support tickets
- • Open or unresolved tickets
- • Support sentiment
Frequent issues may indicate problems with product usability or stability.
Customer Engagement
Measures how actively the customer interacts with the Customer Success team.
- • QBR meetings
- • Customer success calls
- • Email interactions
Higher engagement generally correlates with stronger customer relationships.
Renewal Timeline
Considers how close the account is to its renewal date.
Accounts approaching renewal with low engagement or adoption require attention.
Machine Learning Churn Prediction
In addition to the health score, the system predicts the probability that a customer may churn. A machine learning model analyzes historical behavioral patterns across customer accounts to estimate churn risk.
The model looks for patterns such as:
- • declining product usage
- • low feature adoption
- • increasing support issues
- • reduced engagement
- • approaching renewal dates
Using these signals, the model calculates a churn probability score.
Model Output
Each account receives a Churn Probability score between 0% and 100%.
The higher the probability, the more likely the customer may churn without intervention.
Why Machine Learning is Used
Rule-based scoring provides a structured health score. Machine learning improves this by:
- • identifying patterns across large datasets
- • detecting early signals of churn
- • improving prediction accuracy over time
As more historical data becomes available, the model continuously improves.
Automatic Alert Generation
The system automatically generates alerts when risk signals are detected. Alerts help Customer Success teams take proactive action.