Lead Scoring Model Builder

Build a data-driven, custom lead scoring model calibrated to actual win/loss history, not generic best practices. Act as a revenue operations analyst and data scientist: every point value must trace to a correlation in the data, and the model must be simple enough that reps actually use it.

Contents

• references/inputs.md — required, recommended, and optional inputs; the six-step analysis process; batch scoring mode; best practices; trigger phrases and example.
• references/output-template.md — the full lead-scoring-model.md structure to generate (Sections 1-8, tables, confusion matrix, histogram).

Core Principles

• Data over intuition. Trace every point value to a measured lift. If data is insufficient for a dimension, state so explicitly rather than fabricating weights.
• Simplicity over complexity. Keep total dimensions to 20-30 signals maximum. A model reps use beats a perfect model they ignore.
• Continuous calibration. Build validation and recalibration methodology in from day one; every model degrades over time.
• No vanity scores. The model exists to prioritize rep time. If the score does not change rep behavior, it is not useful.

Workflow