Lead Scoring

Lead Scoring

Prioritize leads using a systematic scoring model that combines ICP fit, engagement behavior, and buying intent signals.

When to Use This Skill

• Designing a new lead scoring model
• Prioritizing inbound leads for SDR follow-up
• Setting MQL thresholds for sales handoff
• Analyzing lead quality by source
• Optimizing marketing spend by lead score

Methodology Foundation

Based on HubSpot's Lead Scoring methodology and Forrester's B2B Buyer Journey research, combining:

• Firmographic/​demographic fit (who they are)
• Behavioral scoring (what they do)
• Intent signals (buying readiness)
• Negative scoring (disqualification)

What Claude Does vs What You Decide

What This Skill Does

1. Model design - Create scoring framework with fit + behavior + intent
2. Score calculation - Apply model to lead data
3. Threshold setting - Define MQL/​SQL qualification levels
4. Segmentation - Group leads by score for routing
5. Optimization - Analyze score-to-conversion correlation

How to Use

For Model Design:

Help me create a lead scoring model for [Business Type].

Our ICP:
- Company size: [Range]
- Industries: [List]
- Titles: [Target titles]
- Geography: [Regions]

Key buying signals we track:
- [List website pages, content, actions]

Current conversion rates:
- Lead to MQL: X%
- MQL to SQL: X%
- SQL to Won: X%

For Lead Scoring:

Score this lead:

Company: [Name]
Size: [Employees]
Industry: [Industry]
Title: [Contact title]
Location: [Geography]

Behavior (last 30 days):
- [List pages visited, content downloaded, emails opened]

Instructions

Step 1: Define Fit Score (0-40 points)

Company Firmographics:

Contact Demographics:

Step 2: Define Behavior Score (0-40 points)

Content Engagement:

Direct Engagement:

Step 3: Define Intent Score (0-20 points)

Third-Party Intent:

Step 4: Apply Negative Scoring

Step 5: Set Thresholds

Examples

Example 1: Scoring Model Design

Input:

Help me create a lead scoring model for B2B SaaS (marketing automation).

Our ICP:
- Company size: 50-500 employees
- Industries: Tech, E-commerce, SaaS
- Titles: VP Marketing, Director Marketing, CMO
- Geography: US, Canada, UK

Key buying signals:
- Pricing page, demo request, case studies
- Marketing automation category on G2

Output:

## Lead Scoring Model: Marketing Automation SaaS

### Fit Score (Max 40 points)

**Company Fit (20 points)**
| Criteria | Points |
|----------|--------|
| 50-500 employees | +10 |
| 500-1000 employees | +5 |
| <50 or >1000 employees | +0 |
| Tech/​E-commerce/​SaaS industry | +10 |
| Adjacent industry (Retail, Media) | +5 |
| Non-target industry | +0 |

**Contact Fit (20 points)**
| Criteria | Points |
|----------|--------|
| VP/​Director Marketing, CMO | +15 |
| Marketing Manager | +10 |
| Marketing Coordinator | +5 |
| Non-marketing title | +0 |
| US/​Canada/​UK | +5 |
| Other geography | +0 |
| Personal email (@gmail) | -15 |
| Competitor domain | -40 |

### Behavior Score (Max 40 points)

**High-Intent Actions**
| Action | Points |
|--------|--------|
| Demo request submitted | +25 |
| Pricing page visit | +15 |
| Case study download | +10 |
| Product tour completed | +10 |

**Engagement Actions**
| Action | Points |
|--------|--------|
| 3+ pages in session | +5 |
| Return visit (7 days) | +5 |
| Webinar registered | +8 |
| Blog post read | +2 |
| Email clicked | +3 |
| Email opened | +1 |

### Intent Score (Max 20 points)

| Signal | Points |
|--------|--------|
| G2 category research | +10 |
| Competitor comparison | +8 |
| Marketing hire posting | +5 |
| Series A/​B funding | +5 |

### Negative Scoring

| Signal | Points |
|--------|--------|
| Unsubscribed | -10 |
| No activity 90 days | -15 |
| Email bounced | -20 |
| Competitor | -100 |

### Thresholds

| Score | Status | SLA |
|-------|--------|-----|
| 70+ | 🔥 Hot MQL | Call within 5 min |
| 50-69 | 🟢 MQL | SDR contact 24hr |
| 30-49 | 🟡 Nurture | Email sequence |
| <30 | Monitor | No outreach |

Example 2: Individual Lead Scoring

Input:

Score this lead using the model above:

Company: GrowthTech Inc
Size: 180 employees
Industry: SaaS
Title: VP of Marketing
Location: San Francisco, US

Behavior (last 30 days):
- Visited pricing page (2x)
- Downloaded "Email Automation Guide"
- Attended webinar "Marketing Automation 101"
- Opened 4 emails, clicked 2
- Visited 8 pages total

Intent: Showing on G2 for marketing automation research

Output:

## Lead Score: GrowthTech Inc

### Overall Score: 83/​100 🔥 HOT MQL

### Score Breakdown

**Fit Score: 35/​40**
| Factor | Points | Notes |
|--------|--------|-------|
| Company size (180) | +10 | Perfect ICP |
| Industry (SaaS) | +10 | Target industry |
| Title (VP Marketing) | +15 | Decision maker |
| Location (US) | +5 | Target geography |
| **Subtotal** | **35** | |

**Behavior Score: 38/​40**
| Action | Points | Notes |
|--------|--------|-------|
| Pricing page (2x) | +15 | High intent signal |
| Content download | +5 | Educational engagement |
| Webinar attended | +8 | Active learning |
| 8 pages visited | +5 | Deep exploration |
| 4 emails opened | +4 | Engaged with nurture |
| 2 emails clicked | +6 | Taking action |
| **Subtotal** | **38** | |

**Intent Score: 10/​20**
| Signal | Points | Notes |
|--------|--------|-------|
| G2 category research | +10 | Active buyer research |
| **Subtotal** | **10** | |

### Qualification: HOT MQL
- **Action Required**: Immediate SDR call (within 5 minutes)
- **Talking Points**: Reference webinar attendance, pricing interest
- **Ask**: "What prompted your marketing automation research?"

### Next Best Actions
1. Call within 5 minutes (hot lead SLA)
2. Reference webinar + pricing page visits
3. Offer personalized demo with VP Marketing use cases
4. Connect on LinkedIn (warm outreach)

Skill Boundaries

What This Skill Does Well

• Structuring scoring models systematically
• Calculating scores from provided data
• Recommending thresholds based on best practices
• Identifying model gaps

What This Skill Cannot Do

• Access your CRM data directly
• Know your actual conversion rates
• Predict individual lead outcomes
• Account for offline interactions

When to Escalate to Human

• Setting final MQL thresholds (needs sales alignment)
• Weighting decisions (requires business judgment)
• Model validation (needs historical data analysis)
• Edge cases (unusual company profiles)

Iteration Guide

Follow-up Prompts

• "Adjust the model for enterprise (1000+ employees) leads."
• "What score would trigger an immediate call for us?"
• "Compare scores for these 5 leads and rank them."
• "What behaviors should we add to increase accuracy?"

Model Refinement Cycle

1. Build initial model → Deploy
2. Track score vs. conversion rate
3. Adjust weights based on data
4. Add new signals quarterly
5. Remove low-correlation factors

Checklists & Templates

Lead Scoring Model Template

## [Company] Lead Scoring Model v[X]

### Fit Score (Max X points)
| Criteria | Points |
|----------|--------|

### Behavior Score (Max X points)
| Action | Points |
|--------|--------|

### Intent Score (Max X points)
| Signal | Points |
|--------|--------|

### Negative Scoring
| Signal | Points |
|--------|--------|

### Thresholds
| Score | Status | Action |
|-------|--------|--------|

### Review Schedule
- Quarterly weight review
- Monthly threshold check

Model Audit Checklist

• All ICP criteria have point values
• High-intent behaviors weighted appropriately
• Negative scoring prevents bad leads
• Thresholds align with sales capacity
• Model reviewed in last 90 days

References

• HubSpot Lead Scoring Guide
• Forrester B2B Buyer Journey Research
• Marketo Definitive Guide to Lead Scoring
• SiriusDecisions Demand Waterfall

Related Skills

• icp-matching - Deep ICP definition
• pipeline-forecasting - Score aggregation to forecast
• deal-risk-scoring - Post-MQL deal health

Skill Metadata

• Domain: RevOps
• Complexity: Intermediate
• Mode: centaur
• Time to Value: 30-60 min for model design, 2 min per lead
• Prerequisites: ICP definition, behavior tracking capability