· ai, machine-learning
Predictive churn, explained
How TrackCrumb scores churn risk with XGBoost and explains every prediction with per-user SHAP attribution.
Knowing that a user churned is history. Knowing who is about to churn — and why — is leverage. TrackCrumb's predictive churn turns your event stream into a nightly risk score for every user and account, available on the Scale tier and up.
How the model works
- Features — we derive behavioural features from your events: recency, frequency, depth of use, and trend.
- Scoring — an XGBoost model produces a churn probability per user, retrained nightly so it tracks your product as it changes.
- Survival curves — a Cox proportional-hazards model estimates when risk rises over the next 30 days.
Every score is explainable
A black-box risk number is useless to a growth team. That's why each prediction ships with SHAP attribution — the specific signals pushing a user toward or away from churn. "This account is high-risk because weekly active seats dropped 40% and they stopped using funnels" beats a bare 0.82.
From insight to action
Predictive scores feed directly into campaign targeting, so you can trigger an in-app message or email the moment risk crosses your threshold — no export, no reverse-ETL.
