Prediction Precision
78%
Churn recall rate
75%
Risk score refresh cycle
6
Hours
ML inference time per user
48
Personalized retention actions per at-risk user
3
Labeled profiles in training data
29,000+
Behavioral features analyzed per user
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Uzera's churn prediction engine scores every user 0–100% every 6 hours — with 78% precision — and tells you exactly why they're at risk and what to do next. Not a dashboard. A retention system.
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Here's the insight that changes everything: the behavioral signals that predict churn are visible in your data 4–8 weeks before any user cancels. Low session counts. Rising error rates. Admin absences. Feature adoption drops. The data doesn't lie — but only Uzera is watching it.
By the time a user cancels, the window to save them closed weeks ago. Manual "at-risk" lists built on gut feel miss the 48 behavioral patterns that actually matter.
Every user in your product gets a churn risk score updated every 6 hours. Classified as High / Medium / Low with clear action thresholds. When a user's score crosses 70%, your CS team gets an alert with the explanation already written. → 78% precision · updated every 6 hours
No black boxes. Uzera uses SHAP (SHapley Additive exPlanations) to identify the top 3 behavioral factors driving each user's risk — low session count, error rate spikes, dropped feature usage, admin absence, onboarding stall, and more. → Top 3 risk factors per user · always explained
Your CS team shouldn't need to interpret ML output. Uzera's LLM layer synthesizes every risk profile into a human-readable paragraph that explains what's happening, why it matters, and what to do — in plain English. → Act in 2 minutes, not 2 hours
Not generic playbooks. Every at-risk user gets 3 specific recommended actions generated from their individual behavioral profile — personal outreach, guided sessions, feature re-engagement nudges, and more. → Personalized per user · not per segment
Uzera analyzes session frequency, session depth, error rates, feature adoption breadth, onboarding completion, NPS scores, admin login patterns, rage clicks, dead clicks, and 39 more behavioral signals — continuously, per user. → The most complete behavioral picture available
Automatic PSI drift detection triggers model retraining when your user population shifts. Your predictions stay accurate as your product evolves — no manual retraining, no data science team needed. → Always calibrated · always improving
From Zero to Retention System in 4 Weeks.
Uzera ingests your behavioral data and extracts 48 features per user. The XGBoost model — pre-trained on 29,000+ labeled profiles — starts scoring churn risk within 24–48 hours of your first 50 users. No months-long data ramp. No manual configuration.
What you have at end of Week 1:
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Your CS team reviews the High risk accounts. Each one comes with a SHAP breakdown of the top 3 contributing factors and a plain-English summary. No guesswork about which accounts to prioritize or what to say.
What you have at end of Week 2:
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Your team acts on Uzera's recommendations. Personalized outreach to high-risk accounts. Guided sessions for users with adoption gaps. Executive sponsor alerts for accounts with admin absences exceeding 14 days.
What you have at end of Week 3:
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Track which interventions worked. Which accounts were saved. What ARR was protected. Uzera records per-account outcomes and feeds results back into the model for continuous calibration.
What you have at end of Week 4:
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Can’t find the answer you're looking for?
Email us any time: help@uzera.com
Predictions start after your first 50 users are tracked. Uzera's model is pre-trained on 29,000+ labeled profiles — there's no months-long data ramp. Most teams see their first risk scores within 24–48 hours of integration. No data science team required.
Predictions start after your first 50 users are tracked. Uzera's model is pre-trained on 29,000+ labeled profiles — there's no months-long data ramp. Most teams see their first risk scores within 24–48 hours of integration. No data science team required.
Predictions start after your first 50 users are tracked. Uzera's model is pre-trained on 29,000+ labeled profiles — there's no months-long data ramp. Most teams see their first risk scores within 24–48 hours of integration. No data science team required.
Predictions start after your first 50 users are tracked. Uzera's model is pre-trained on 29,000+ labeled profiles — there's no months-long data ramp. Most teams see their first risk scores within 24–48 hours of integration. No data science team required.
Predictions start after your first 50 users are tracked. Uzera's model is pre-trained on 29,000+ labeled profiles — there's no months-long data ramp. Most teams see their first risk scores within 24–48 hours of integration. No data science team required.
Predictions start after your first 50 users are tracked. Uzera's model is pre-trained on 29,000+ labeled profiles — there's no months-long data ramp. Most teams see their first risk scores within 24–48 hours of integration. No data science team required.
Join customer success teams already using Uzera AI to identify at-risk users 2 weeks before they cancel — with 78% precision, SHAP explainability, and 3 personalized retention actions per account.
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