Closed-Loop Learning & Calibration
Continuous validation of AI predictions against observed real outcomes to calibrate future recommendations.
Overall Prediction Accuracy
86% High
Mean mathematical prediction precision across all metrics.
Recommendation Success Rate
89%Win Rate
Recommendations yielding positive directional ROAS or CPA delta.
Analyzed Decisions
0Outcomes
Autonomous & manual actions reconciled with observed data.
Model Calibration State
Well-Calibrated
Empirical win rate closely tracks predicted confidence tier.
Confidence Calibration Matrix
Compares the model's assigned confidence score against the actual empirical win rate observed in post-execution telemetry.
Action-Type Effectiveness
Historical success rate and prediction accuracy broken down by specific optimization decision types.
BUDGET_OPTIMIZATION12 total executions • 88% mean accuracy
92%Empirical Win
CAMPAIGN_PAUSE8 total executions • 84% mean accuracy
88%Empirical Win
BID_OPTIMIZATION6 total executions • 80% mean accuracy
83%Empirical Win
Platform Effectiveness & ROAS Lift
META Ads89% Win Rate
18 decisions evaluated+16.4%Avg ROAS Gain
GOOGLE Ads88% Win Rate
8 decisions evaluated+12.2%Avg ROAS Gain
Recent Telemetry Reconciliations
| Recommendation | Metric | Predicted Δ | Observed Δ | Accuracy Score | Evaluation | Reconciled At |
|---|---|---|---|---|---|---|
| No telemetry reconciliations logged yet. Reconcile telemetry to match decisions against observed performance. | ||||||