Saasdevenvironments
See who is leaving before they cancel.
Churn prediction analytics for app teams who need early risk scores, honest labels, and playbooks someone will actually run.
Churn Risk Instrumentation
Map the behavioural moments that foreshadow cancellation, wire them cleanly across your app surfaces, and keep prediction inputs trustworthy after every release.
Most teams book this first: a focused sprint that leaves you with a documented event dictionary, validated pipes, and a monitoring loop that catches silent gaps before Monday’s retention review.
What you can request
Churn Risk Instrumentation
Wire the behavioural events that feed early churn scores across your iOS, Android, and web companion apps.
Early-Warning Cohort Reviews
A structured read of which cohorts are drifting toward churn — and which interventions are worth testing first.
Retention Playbook Design
Turn churn-risk alerts into calm, owned responses — save offers, outreach, and product fixes with clear owners.
Prediction Readiness Audit
Assess whether your current data, labels, and pipelines can support reliable churn models before you invest in building one.
“We stopped celebrating vanity retention and started acting on the three cohorts already halfway out the door. Saves went up; the war room got quieter.”Head of Growth, subscription learning app · Leeds
Notes on predicting churn
Leading Indicators That Appear Before Cancellation
Most teams watch churn after the fact. Here is how UK product squads spot drift two to four weeks earlier — without inventing a dozen new metrics.
Labelling Churn Without Poisoning Models
Voluntary cancel, involuntary lapse, and long dormancy are not the same outcome. Treat them as one label and your predictions will lie politely.