About

Most platforms discover their best users too late.

This project builds an early behavioral intelligence engine that predicts high-value users within their first 3 days of activity — before churn happens and before monetization decisions are obvious.

Starting from 400K+ raw event logs, we engineered user-level behavioral features across:

Engagement intensity

Execution behavior (block runs, production signals)

AI agent interaction

Canvas & file operations

Credit consumption patterns

We uncovered a critical pattern:

Only 2.57% of users ever become monetized — but those users show measurable behavioral signals almost immediately.

Using clustering, we identified two clear populations:

98% low-engagement exploratory users

1% highly committed users who convert at 100%

We then trained a RandomForest model on only the first 3 days of user activity and achieved:

99.4% accuracy

100% precision on high-value users

77.8% recall

AUC: 0.889

Just three early signals — total_events, credits_used, and active_days — explain over 93% of predictive power.

But prediction alone isn’t enough.

So this system operationalizes insight into action:

Real-time API scoring for new users

Risk classification (Low / Medium / High Potential)

Automated onboarding nudges

AI feature trial prompts in first 24 hours

Premium exposure for high-probability users

Production monitoring dashboard with drift detection

This transforms passive analytics into a live decision engine that drives:

Early activation

Intelligent retention

Revenue acceleration

Targeted monetization

The result is not just a model — it’s a deployable product intelligence layer built entirely inside Zerve.

This project demonstrates how behavioral data can move from raw events to real-time strategic intervention in a single integrated pipeline.