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

Claude Does	You Decide
Designs scoring model structure	Point values for your business
Calculates lead scores	MQL threshold for handoff
Identifies high-intent behaviors	Which behaviors matter most
Segments leads by score	Sales follow-up priorities
Suggests model improvements	Model weight adjustments

What This Skill Does

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

How to Use

For Model Design:

code

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:

code

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:

Criteria	Points
Company size matches ICP	+10
Industry in target list	+10
Geography in target regions	+5
Revenue in target range	+5
Company size too small	-10
Industry excluded	-20

Contact Demographics:

Criteria	Points
Title is decision maker	+10
Title is influencer	+5
Title is end user	+3
Student/Personal email	-15
Competitor domain	-40

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

Content Engagement:

Action	Points
Pricing page visit	+15
Case study download	+10
Product demo video watched	+10
Blog post read	+2
5+ pages in session	+5
Return visit within 7 days	+5

Direct Engagement:

Action	Points
Demo request form	+30
Contact sales form	+25
Free trial signup	+20
Webinar registration	+10
Newsletter signup	+3
Email opened	+1
Email clicked	+3

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

Third-Party Intent:

Signal	Points
Researching your category (Bombora/G2)	+15
Competitor comparison searches	+10
Job posting for related role	+5
Funding announcement	+5
Technology install matches	+5

Step 4: Apply Negative Scoring

Signal	Points
Unsubscribed from email	-10
Bounced email	-20
No engagement 90+ days	-15
Marked as spam	-30
Competitor	-100

Step 5: Set Thresholds

Score Range	Qualification	Action
80-100	Hot MQL	Immediate sales call
60-79	Warm MQL	SDR outreach 24hr
40-59	Marketing Qualified	Nurture sequence
20-39	Early Stage	Educational content
0-19	Not Qualified	Monitor only
Negative	Disqualified	Suppress

Examples

Example 1: Scoring Model Design

Input:

code

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:

code

## 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:

code

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:

code

## 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

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

Checklists & Templates

Lead Scoring Model Template

markdown

## [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