What Is Customer Health Score? Building and Tracking the Metric

What this export contains

What usually goes wrong with it

• Health scores built from too few signals are unreliable predictors of churn

  A health score based only on login frequency misses customers who log in but don't use key features — and misses the CRM and support signals that often precede churn by weeks before usage visibly drops.

  DataMimi Start with four to six signals: product usage rate, days since last login, support ticket volume, NPS, and days until renewal — weight them based on which correlated with actual churn in your historic

• Weighting signals correctly requires historical data on what actually predicted churn

  Giving equal weight to NPS and product usage without knowing which one correlates more strongly with actual churn in your customer base produces a score that feels rigorous but isn't predictive.

  DataMimi Build a weighted score in a spreadsheet using SUMPRODUCT to combine normalized signal values with your weights, then assign thresholds: above 70 is healthy, 40–70 is at risk, below 40 needs immediate

• A health score is only useful if CSMs act on it before the renewal window closes

  A health score that isn't reviewed until 30 days before renewal gives CSMs almost no time to address the underlying issues — the score needs to trigger action 90 to 120 days out when there's still time to change the trajectory.

  DataMimi Upload your customer data spreadsheet to Datamimi and ask 'which customers have a health score below 50 with renewals in the next 90 days' to get a prioritized at-risk list without building manual fil

Common questions