mittuled lead-scoring-model-builder
Agent: Marketing Operations Manager
L2 marketing operations manager (1x) responsible for martech stack, lead scoring, campaign analytics, attribution modelling, and email deliverability.
Department ethos: ideal-marketing.md
Skill Description
Designs and calibrates the lead scoring model that combines behavioral and firmographic signals to determine MQL threshold and automate marketing-to-sales handoff.
When to Use
- When building a lead scoring model for a new product line, segment, or market.
- When MQL-to-SQL conversion rates drop below acceptable thresholds, indicating miscalibration.
- When sales consistently rejects MQLs as unqualified, signaling a scoring-reality gap.
- When ICP definitions change and firmographic scoring weights need updating.
Workflow
- Analyze historical conversions: Pull closed-won and closed-lost data to identify which behavioral and firmographic attributes correlate with conversion. Use the two-axis scoring architecture from
references/framework.mdto structure the analysis. Deliverable: attribute correlation analysis. - Define scoring dimensions: Build the fit score using the BANT dimensions table and the engagement score using the behavioral signals table in
references/framework.md. Apply decay rules per signal. Deliverable: scoring model specification with attribute weights. - Set MQL threshold: Apply the calibration methodology in
references/framework.md— map historical closed-won leads against the model, set threshold at P80, and validate ≥ 25% MQL-to-SQL conversion. [GATE] — threshold requires sales sign-off before activation. Deliverable: MQL threshold with back-test results. - Implement in marketing automation: Configure the scoring model in the marketing automation platform. Set up automatic MQL status assignment, CRM sync, and sales notification workflows. Follow the governance standards in
references/framework.md. Deliverable: live scoring model with automation rules. - Calibrate and iterate: Follow the calibration cadence in
references/framework.md. After 30 days, compare actual MQL-to-SQL conversion against target. Adjust weights per governance process. Deliverable: calibration report with model adjustments.
Anti-Patterns
- Over-weighting content downloads: Assigning high scores to every content download regardless of content type or funnel stage. Why: TOFU downloads like general industry reports indicate curiosity, not purchase intent; treating them equally inflates MQL counts with low-quality leads.
- Firmographic-only scoring: Scoring leads purely on company attributes without behavioral signals. Why: a perfect-fit company with zero engagement is not a qualified lead; intent signals are essential for timing the handoff.
- Set-and-forget models: Building the scoring model once and never recalibrating. Why: buyer behavior, ICP definitions, and product positioning evolve; a static model degrades in accuracy every quarter.
Output
On success: Produces a lead scoring model specification, configured automation rules, and a calibration report. MQL-to-SQL conversion rates meet or exceed the agreed threshold. Delivered to VP Marketing, demand gen, and sales operations.
On failure: Report which data gaps prevented model building (insufficient conversion history, missing firmographic data), what interim scoring rules are in place, and recommend data collection steps to enable a full model.
Related Skills
demand-gen-planner— Defines the MQL criteria that this model operationalizes into automated scoring.marketing-attribution-modeller— Attribution data informs which touchpoints should receive higher behavioral scores.email-deliverability-manager— Email engagement signals are a key behavioral input to the scoring model.