onewave-ai/lead-scoring-model

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

1. Gather inputs. Request ICP definition, historical win/loss data (50+ closed deals minimum, 200+ preferred), and a CRM export of current leads. Accept whatever subset is available and note gaps and their accuracy impact. See references/inputs.md for the full input checklist.
2. Run the analysis process. Execute the six steps in order: data audit, win/loss pattern analysis, dimension construction, threshold calibration, validation, and implementation planning. Do not skip steps. See references/inputs.md for the detailed procedure.
3. Build the four-dimension model. Construct Firmographic Fit, Behavioral Signals, Engagement Depth, and Intent Indicators, plus negative signals. Assign point values proportional to measured lift and cap each dimension so no single factor dominates.
4. Calibrate thresholds. Plot won vs. lost score distributions, find the separation point, and define Hot/Warm/Cool/Cold tiers with expected conversion rates, SLAs, and volumes. Keep Hot small enough to work fully; keep Cold large enough to save rep time.
5. Validate. Hold out 20-30% of historical data, score it, and report precision, recall, F1, AUC-ROC, and a confusion matrix. Analyze false positives and false negatives and iterate.
6. Generate the deliverable. Write lead-scoring-model.md following references/output-template.md. Fill every placeholder with data-derived values. Include Section 7 only when a batch of current leads was provided.
7. Score current leads (when provided). Load the model, map fields, score each lead, assign tiers, and produce the Section 7 tables ranked by score with recommended actions. See the Batch Scoring Mode in references/inputs.md.

Guardrails

• Refuse to build a model on intuition alone. Without historical win/loss data, help the user set up tracking first and revisit in 90 days.
• Show the lift calculation behind every point value.
• Start conservative: under-scoring a few Hot leads beats drowning reps in false positives.
• Never include a signal the CRM cannot reliably capture.
• Insist on holdout validation before any model goes live.