Services
Customer Success
Churn Signal Detection
Build an early warning system that identifies churn risk signals before customers decide to leave.
Performance
The signals that predict churn are almost always present before the account expresses dissatisfaction. They're just spread across systems nobody is looking at in combination. Email engagement drops in one tool. Product usage declines in another. Support tickets increase in a third. The CS team sees each signal individually and none of them cross a threshold that triggers action. The detection model reads them together, calibrated to your actual churn history, so the threshold is set at the point where intervention still works.
Churn doesn't happen all at once. Accounts disengage in stages, and each stage leaves a signal in the data. The problem is that the signals are spread across systems: product usage in one place, email engagement in another, support history in a third, CRM activity logs in a fourth. No single view captures all of them, so the early warning gets missed.
In our analysis of B2B churn patterns across multiple companies, the signals that most reliably predict churn appear 60 to 90 days before cancellation. By the time an account expresses dissatisfaction directly, the decision is usually already made.
What we do
We build a churn signal model calibrated to your specific customer base and data sources. That means pulling historical churn data, identifying which behavioral signals preceded it, and building a detection framework that can be applied to your current account portfolio.
We validate the model against your actual churn history before deploying it, so you know what confidence level to apply to the signals it surfaces.
For context on what this type of analysis typically surfaces, read the 7 data-backed patterns that predict churn.
Deliverable
A churn signal model with validated detection criteria, a scored view of your current account portfolio against those criteria, and a response playbook for each signal level. Plus implementation guidance for integrating signal monitoring into your CS workflow.
Outcome
Earlier visibility into accounts that are drifting toward churn. CS interventions that happen at the signal rather than at the complaint. A systematic way to prioritize CS attention that accounts for actual risk rather than account size or last interaction date.
How Stratum Group Cut Reporting Time by 50% and Detected Churn 60 Days Earlier — cut decision time by 50% and started detecting churn 60 days earlier.
See how it worked in practice: Harbor Group reduced churn by 34%.
Best Fit
For any company with recurring revenue where churn surprises happen more than once a quarter. Also appropriate for companies that are scaling CS headcount and need a way to allocate attention systematically rather than by gut feel. The model needs at least 12 months of historical churn data to validate against.