journeys-scoring
SKILL.md
Lead Scoring & Customer Journeys
Design intelligent lead scoring systems and automated customer journeys that identify sales-ready prospects, nurture leads through the funnel, and maximize conversion rates through behavioral triggers and personalized automation.
Core Objectives
- Identify sales-ready leads through behavioral and demographic scoring
- Automate nurturing journeys based on lead behavior and interests
- Segment audiences for personalized messaging and offers
- Reduce manual work through intelligent automation
- Maximize conversion rates through timely, relevant touchpoints
Mandatory Elements
1. Lead Scoring Model
- Demographic Score: Firmographics (company size, industry, title)
- Behavioral Score: Engagement actions (email opens, page views, downloads)
- Scoring Thresholds: Minimum scores for "Marketing Qualified Lead" (MQL) and "Sales Qualified Lead" (SQL)
- Negative Scoring: Deduct points for unengaged behavior (unsubscribes, inactivity)
2. Journey Mapping
- Entry Points: Where leads enter the journey (form submission, webinar, etc.)
- Stages: Awareness → Consideration → Decision → Retention
- Triggers: Behavioral events that advance leads through stages
- Content: Stage-appropriate content and offers
3. Automation Rules
- If/Then Logic: Conditional automation based on score or behavior
- Timing: Appropriate delays between touchpoints
- Personalization: Dynamic content based on lead attributes
- Suppression: Rules to prevent over-communication
Structure & Frameworks
The "Score & Nurture" Framework
- Score Leads: Quantify lead quality and readiness
- Segment: Group leads by score, behavior, or attributes
- Automate: Trigger journeys based on segments and triggers
- Optimize: Test and refine scoring and journey performance
Lead Scoring Example
"Scoring Model (Total: 100 points):
Demographic (40 points max):
• Company size: 10-50 employees (+10), 50-200 (+15), 200+ (+20)
• Job title: Manager (+5), Director (+10), VP/C-Level (+15)
• Industry match: Target industry (+10)
Behavioral (60 points max):
• Email open: +2 per open (max +10)
• Email click: +5 per click (max +15)
• Page view: +3 per view (max +12)
• Content download: +8 per download (max +16)
• Demo request: +20 (one-time)
• Pricing page visit: +15 (one-time)
Thresholds:
• MQL: 40+ points (send to marketing nurture)
• SQL: 70+ points (notify sales team)"
Voice & Tone Guidelines
- Strategic & Data-Driven: Focus on metrics and optimization
- Process-Oriented: Clear workflows and decision trees
- Automation-Focused: Emphasize efficiency and scale
- Formatting: Use flowcharts for journeys, tables for scoring models
Concrete Examples
Customer Journey Example
"Welcome Journey (New Subscriber):
Day 0: Welcome email + lead magnet delivery
Day 2: Educational email (if opened Day 0)
Day 5: Case study email (if clicked Day 2)
Day 10: Product demo offer (if scored 40+)
Day 14: Sales outreach (if scored 70+)
Exit Conditions:
• Unsubscribe → Remove from all journeys
• Purchase → Move to customer onboarding journey
• Inactive 30 days → Re-engagement journey"
Quality Checklist
For every scoring/journey plan, ask:
- Are scoring criteria aligned with ideal customer profile?
- Do journey stages match the buyer's decision process?
- Are automation triggers specific and measurable?
- Is there suppression logic to prevent over-communication?
- Will this system identify and nurture leads effectively?
Weekly Installs
5
Repository
mikefilsaime-gr…n-cursorGitHub Stars
3
First Seen
Feb 7, 2026
Security Audits
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