mql-nurture - SKILL.md Agent Skill

name: mql-nurture description: >- Build MQL nurture programs — lead scoring, nurture tracks, email drip sequences, MQL→SQL conversion. Triggers on: "MQL nurture", "lead nurture", "nurture tracks", "MQL to SQL", "lead scoring". license: MIT compatibility: Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose metadata: version: "1.0.0" author: LeadMagic category: lifecycle tags: [lifecycle, nurture, MQL, lead-scoring, email-drips] frameworks: - "SiriusDecisions Demand Waterfall" - "Marketo Nurture Framework" - "Reforge — Lifecycle Marketing"

MQL Nurture Programs

Overview

Most MQLs aren't ready to buy — they're researching. Nurture programs keep your company top-of-mind during the 3-12 month evaluation window, converting MQLs to SQLs at 2-3x the rate of "send them to sales and pray." This skill covers nurture strategy, track design, and optimization.

Authoritative Foundations

SiriusDecisions Demand Waterfall — Named methodology governing recommendations in this skill's process.
Marketo Nurture Framework — Named methodology governing recommendations in this skill's process.
Reforge — Lifecycle Marketing — Startup operating cadence — default alive, talk to users, launch fast.

Lifecycle Stage

Acquisition (stage 2). Canonical index → references/gtm-lifecycle-stages.md.
Metrics → references/lifecycle-metrics-by-stage.md (Acquisition).
Monitoring → skills/analytics/gtm-metrics/templates/lifecycle-monitoring-dashboard.md.

When to Use

"Build an MQL nurture program"
"Lead nurture strategy"
"MQL to SQL conversion"
"Nurture email drips"
"Lead scoring for nurture"

Step-by-Step Process

Phase 1: Lead Scoring Model

Define what makes an MQL:

Fit Score (0-50):

Job title matches ICP (0-20)
Company size in target range (0-15)
Industry in target vertical (0-15)

Engagement Score (0-50):

Content downloads (5 pts each)
Website visits >5 pages (10 pts)
Webinar attendance (15 pts)
Pricing page visit (20 pts)
Demo request (50 pts — auto-SQL)

MQL Threshold: Combined score >40 → MQL → enter nurture. SQL Threshold: Combined score >70 → SQL → route to sales.

Phase 2: Nurture Track Design

Create persona-specific nurture tracks:

Track 1 — The Researcher (downloaded educational content):

Week 1: "Thanks for downloading [resource] — here's a related template"
Week 2: Customer story from same industry
Week 4: Webinar invite: "How [Industry] teams solve [problem]"
Week 6: Benchmark report: "2026 [Industry] benchmarks"
Week 8: "Ready to see how this works?" → soft CTA for demo

Track 2 — The Evaluator (visited pricing/comparison pages):

Week 1: "How [Company A] evaluated [category] tools" (buyer's guide)
Week 2: ROI calculator + case study with specific ROI numbers
Week 3: "[Your product] vs [Competitor]" comparison
Week 4: Demo invite: "See it in action with your data"
Week 5: "Still evaluating?" + customer reference offer

Track 3 — The Event Attendee (attended webinar):

Day 1: Recording + slide deck + "top questions answered"
Day 3: Related case study
Day 7: "The one thing most people miss about [topic]"
Day 14: Soft CTA: "Want to discuss [topic] for your specific situation?"

Phase 3: Email Content Principles

Value-first: 80% educational, 20% product. If every email is "book a demo," they unsubscribe.
Progressive profiling: Each email asks for slightly more engagement. Click → download → webinar → demo.
Personalization by source: Reference how they entered the funnel. "Since you downloaded our [guide]..."
Behavioral triggers: If they click on pricing → shift to Evaluator track. If they stop opening → shift to re-engagement.

Phase 4: Multi-Channel Nurture

Layer channels beyond email:

LinkedIn: Connect with MQLs. Share content they engage with.
Retargeting: Show case study ads to content downloaders. Show demo ads to pricing page visitors.
Direct mail: For high-value MQLs (>$50K potential ACV), send a physical asset (book, report, tool).
Sales calls: SDR calls MQLs at specific nurture milestones (after webinar attendance, after pricing page visit).

Phase 5: Optimization

Track performance: Open rate, click rate, conversion to SQL, conversion to opportunity, revenue influenced
A/B test: Subject lines, content formats (text vs video vs infographic), send frequency, CTA phrasing
Cadence optimization: Too fast = unsubscribes. Too slow = they forget you. Start at 7-14 day intervals and adjust based on engagement.
Exit criteria: Auto-remove from nurture after 6 months of no engagement. Move to re-engagement track.
Dead lead management: After 12 months of no engagement, suppress from all nurture. Maintain in database for reactivation plays.

Output Format

Nurture program design with: lead scoring model, track definitions, email sequences per track, multi-channel integration, and optimization framework.

Quality Check

Before delivering, verify:

All required sections are complete
Output matches the user's stated need
Named frameworks are cited for key recommendations
No vague claims — every recommendation has a specific action
Deliverable is ready for operational use, not just conceptual

Common Pitfalls

Skipping research. Building output without understanding the specific context. Fix: always gather required inputs before producing deliverables.
Generic output. "Improve your process" without concrete steps. Fix: every recommendation must include a specific action, timeline, and owner.
Missing framework citations. Advice without named authorities. Fix: ground every recommendation in a cited framework from a recognized authority.

Execution Artifacts

references/framework-notes.md — Named frameworks and reference tables
templates/output-template.md — Deliverable shell for agent output
scripts/check-output.py — Lightweight deliverable validator Canonical lifecycle (repo root): references/gtm-lifecycle-stages.md (Acquisition) · references/lifecycle-metrics-by-stage.md · references/lifecycle-skill-index.md

Related Skills

inbound-triage, lifecycle-drips, re-engagement, email-deliverability, campaign-analytics