Churn prediction
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Using usage data, support patterns, and engagement signals to identify customers likely to cancel before they actually do.
Churn prediction is the practice of identifying at-risk customers before they churn. The signals are usually there months in advance: declining login frequency, fewer active users, increasing support tickets about basic functionality, reduced feature adoption, executive sponsor departure.
The best churn prediction combines quantitative and qualitative signals. Quantitative: usage data, login frequency, feature adoption rates, support ticket trends. Qualitative: CSM notes from conversations, sentiment analysis from email exchanges, changes in the buying committee.
Predicting churn is only useful if you act on it. A churn prediction model that identifies 50 at-risk accounts is worthless if the CS team does not have the capacity to intervene. Pair prediction with a playbook: what does the CSM do when an account moves from green to yellow? From yellow to red?
Examples
A health score model predicts churn.
The model flags 15 accounts as high churn risk for next quarter. Common signals: less than 3 logins per week (down from 15), zero support tickets (disengaged), and no executive sponsor on record. The CS team launches targeted intervention.
Early intervention saves an account.
The model flags a $150k account. The CSM calls the primary contact and learns they are evaluating a competitor. The CSM escalates to a VP, arranges an executive-to-executive meeting, and offers a customized success plan. The customer stays.
Churn prediction reveals a product problem.
The model flags 25 accounts that all share one characteristic: they use feature X. Investigation reveals feature X has degraded performance after a recent release. The product team fixes the issue. 20 of the 25 accounts stabilize.
In practice
Read more on the blog
Frequently asked questions
What are the best predictors of churn?
Declining product usage (login frequency, feature adoption), decreasing number of active users, increasing support tickets about basic functionality, executive sponsor departure, and failed onboarding or implementation. The strongest predictor varies by product but declining usage is almost always number one.
How far in advance can you predict churn?
The best models identify at-risk accounts 60-90 days before renewal with 70-80% accuracy. Some signals appear much earlier. A customer who stops using key features at month 3 of a 12-month contract is at risk from month 3, not month 10.
Related terms
A composite metric that combines usage data, engagement signals, and support patterns to indicate whether a customer account is healthy or at risk.
The rate at which customers cancel or do not renew. Measured as logo churn (customers lost) or revenue churn (dollars lost).
The team and practice of ensuring customers achieve their goals with your product. Owns retention, expansion, and advocacy.
The percentage of revenue retained from existing customers before counting expansion. Measures pure customer stickiness.
When an existing customer extends their contract for another term. The foundation of recurring revenue.
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