A customer health score is a leading indicator of renewal probability. Done well, it tells your CS team which accounts need intervention 90 days before a renewal conversation starts -- and which accounts are expanding their usage and ready for an upsell conversation. Done poorly, it is a dashboard that nobody trusts because it does not predict anything useful.
The distinction between a health score that works and one that does not comes down to whether it is built from signals that actually correlate with customer outcomes, or signals that are easy to measure but tell you nothing about what the customer will do at renewal.
Step 1: Identify the signals that actually predict outcomes
Start with outcomes, not with the data you have. What do your churned customers have in common? What do your expanded customers have in common? The answers define your health score variables.
Step 2: Design the scoring model
These weights are a starting point, not a prescription. Calibrate against your actual churn data. If adoption correlates more strongly with churn than relationship signals in your product, increase the adoption weight. Validate the model quarterly against outcomes.
Step 3: Build the operational workflow
A health score that lives in a dashboard is not a health score -- it is a report. The health score becomes operationally valuable when it drives specific CS and sales actions automatically.
Step 4: Maintain and improve the model
Customer health scoring program checklist
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