SaaS
Churn prediction for a B2B software platform
- 34 days
- earlier warning
- 0.86
- model precision on at-risk accounts
- 2 quarters
- to a measurable churn drop
Problem
A subscription company learned about churn when the cancellation email arrived. Customer success had no early signal and could not tell which accounts were worth a call.
Approach
- 01Feature pipeline unifying product usage, billing and support data into a per-account health score
- 02Churn model trained on historical cancellations with a ranked weekly at-risk list
- 03Playbook automation that opens a success-manager task with the reasons behind each score
Outcome
At-risk accounts reached an average of 34 days before renewal. Churn on targeted accounts fell measurably within two quarters.
(Illustrative case; replace with a real engagement.)
Built with
- dbt
- BigQuery
- Python
- scikit-learn
- HubSpot API
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