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Zerve Behavioral Digital Twin — From Events to Outcomes

Most product analytics explains what happened. This project asks a harder question: what behavioral state is a user in, what signals predict where they are heading, and what could happen if we intervene?

I built a reproducible Behavioral Digital Twin that transforms 408,919 product events into an actionable intelligence system:

Events → Behavioral DNA → Behavioral States → Early-Warning Engine → Opportunity Ranking → Transition Model → Digital Twin → Product Actions

Using 2,587 users reaching a first meaningful activity day, I defined transparent Behavioral Success as meaningful activity across at least two distinct observed days. Success increased from 5.22% in EXPLORING to 26.52% in VALUE, revealing a strong behavioral progression signal.

A leakage-controlled Random Forest predicts future behavioral success from first-day behavior, achieving 0.6939 ROC-AUC, 0.2333 PR-AUC, 65.67% recall, and 0.2924 F1.

The resulting opportunity engine identifies a VERY_HIGH segment with 26.89% observed success versus 2.94% for LOW — 9.15× separation.

Finally, the Digital Twin models observed state transitions and tests bounded intervention scenarios. A simulated 15-point shift toward EXECUTING produces a +3.94 percentage-point modeled improvement in VALUE attainment.

This is not a claim of causality. It is a framework for turning telemetry into measurable product decisions, targeted experiments, and continuously improving behavioral intelligence.

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