Propose a pilot to compare ML lead scoring with the current rule-based model for [CompanyName] in [Region]. Define features (fit, engagement, intent, channel velocity) from [DataSources] and select an interpretable model (e.g., logistic regression with monotonic constraints) alongside SHAP explanations. Set evaluation metrics: AUC, lift at [TopPercent]%, calibration error, and downstream impact on [ConversionMetric] during [Timeframe].
Output format:
Experiment Design (control vs treatment assignment and guardrails);
Feature Spec Table (name, definition, type, expected direction);
Model Card (assumptions, limitations, fairness checks);
Deployment Plan (scoring frequency, latency, failover to rules);
Executive Readout template with lift charts and recommendations.
Include privacy-by-design, consent-aware features, and fallback when data is sparse. Provide criteria for full rollout if lift exceeds [LiftThreshold] with acceptable operational complexity.