MQL Nurture Programs skill

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

• Forrester (SiriusDecisions) — Demand Waterfall — Define inquiry → MQL → SAL → SQL stages with explicit handoff criteria and SLAs between marketing and sales.
• Adobe Marketo Engage — Engagement programs — Run nurture as streams with cadence controls and exhaustion rules so leads are not over-mailed.
• HubSpot Knowledge Base — Lead scoring — Combine fit and engagement scores, and decay engagement over time so stale activity does not create MQLs.

Lifecycle Stage

Acquisition (stage 2). Canonical index → references/gtm-lifecycle-stages.md.
Metrics → references/lifecycle-metrics-by-stage.md (Acquisition).
Monitoring → the gtm-metrics skill (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:

• Fit + engagement scoring model is two-dimensional — no single-criterion MQL thresholds
• At least 3 nurture tracks defined with distinct entry signals, cadences, and exit CTAs
• Behavioral branching rules exist — pricing click → Evaluator track; no opens 30 days → re-engagement
• Multi-channel layering is specified (email primary, LinkedIn for MQLs, retargeting by stage, direct mail for $50K+ ACV)
• 80/20 educational-to-product ratio enforced across all nurture content
• Exit criteria defined: auto-remove from nurture at 6 months no engagement; dead-lead suppression at 12 months

Common Pitfalls

1. Single-track nurture. Every MQL gets the same drip regardless of source, persona, or buying stage. Fix: build at least 3 tracks gated by acquisition source — Researcher (content download), Evaluator (pricing/comparison page), Event Attendee (webinar).
2. Product-heavy email ratio. Every email asks for a demo and provides zero educational value — unsubscribe rates climb. Fix: enforce 80/20 educational-to-product ratio; the 5th email in a track should be the first hard CTA.
3. No behavioral branching. Contacts keep receiving email #4 when they never opened #1. Fix: implement engagement-based routing — no opens for 30 days triggers re-engagement track; pricing page visit triggers Evaluator track.
4. Forever nurture. Contacts in nurture for 18 months with no engagement drain sender reputation and ESP budget. Fix: hard exit at 6 months of no engagement; suppress from all nurture at 12 months; retain in database for reactivation plays only.
5. MQL definition drift. Without a locked scoring model, 'MQL' becomes whatever sales asks for this week. Fix: publish the scoring model, lock thresholds (MQL >40, SQL >70), and require RevOps sign-off to change weights.

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: 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