Media · Marketing
Subscription renewal behavior
Spotting users who stop logging in before their plan expires.
Subscribers, by viewing in the last 14 days · % of each cohort still active
Finding
Under 2 hours of viewing in 14 days means a 67% churn probability; the signal shows 45 days before renewal.
Who it's for
Marketing teams predicting and preventing subscription churn.
What it solves
Identify at-risk subscribers early enough to intervene and save the account.
Questions it answers
Risk indicators?
Users with <2 hours viewing in last 14 days have 67% churn probability.
Prediction window?
Churn signals appear 45 days before renewal date with 78% accuracy.
What saves them?
Personalized 'we miss you' email with content recommendations recovers 23% of at-risk.
- Which users are at risk of not renewing?
- What engagement patterns predict churn?
- How far in advance can we predict non-renewal?
- What interventions improve renewal rates?
The impact
- Reduce churn by 30%
- Save $1.8M in annual subscription revenue
- Improve customer lifetime value
The data it needs
- Subscription renewal dates
- Login activity
- Content engagement events
- Historical churn data
Read in place in your warehouse. Nothing is copied out.
Subscription renewal behavior: frequently asked questions
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