Use case / Churn prevention
Catch churn signals before the cancel button.
Most churn emails are post-cancel win-backs. By then the decision is made. Behavioral churn prevention watches for declining engagement patterns, drop in logins, seats removed, feature abandonment, and intervenes while the user is still on the plan.
The problem
Churn is rarely a sudden decision. It's a 4-to-8-week slide in engagement that ends in a cancellation. Companies that only run post-cancel win-back campaigns miss the whole window where they could have done something.
The behavioral approach
Segment by behavioral risk signals. Trigger graduated interventions: a friendly check-in at mild risk, a use-case reminder at moderate, a founder email or call offer at high risk. Real-time segments catch the transitions so contacts exit interventions when engagement recovers.
Implementation
How to ship it, step by step.
Define risk signals
Track: login_count_last_14d, seats_active (drop from 5 to 3 is a signal), last_feature_used_at, support_ticket_count. Push as attributes.
Build graduated segments
Mild risk: logins_14d < 3. Moderate: seats_active dropped AND nps_score < 7. High risk: logins_14d = 0 for 21 days.
Design escalating workflows
Friendly check-in → resource email → founder email → call offer. Each escalation gated by continued risk signals.
Exit on recovery
Conversion goal: logins_14d >= 5. When engagement returns, the contact exits all active churn-prevention workflows immediately.
Segment examples
Actual segment rules you can copy.
Mild risk
plan != "Free" AND login_count_last_14d < 3 AND last_login more than 7 days ago
Moderate risk
plan != "Free" AND seats_active_dropped is true AND last_feature_used more than 21 days ago
High risk
plan != "Free" AND logins_14d = 0 AND support_ticket_unresolved is true
Recovered
logins_14d >= 5 AND previously_at_risk is true
// Send them a "welcome back" nudge
Email examples
Subject lines that actually work.
What good looks like
Typical results for teams who do this well.
// Directional ranges, not guarantees, depends on your baseline, audience, and product.
FAQ
Churn Prevention Emails, answered.
Depends on your product. Common signals: drop in logins, drop in seats, drop in feature usage, support tickets unresolved, NPS decline, failed payment. Start with 2–3 and expand as you learn what predicts churn in your product.
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