Understanding Lead Scoring: Quality Over Quantity

Not all leads are created equal. A proper scoring system helps you focus on the 20% of leads that drive 80% of revenue.

What Is Lead Scoring?

Lead scoring assigns a numerical value to each lead based on how likely they are to become a customer. Higher scores = higher priority.

Two scoring dimensions:

Fit Score — How well they match your ideal customer profile
Engagement Score — How interested they appear based on behavior

Combined, these tell you: "Should we pursue this lead, and how urgently?"

Why Lead Scoring Matters

Without scoring:

Sales wastes time on poor-fit leads
Hot prospects get lost in the noise
No objective way to prioritize
Marketing and sales argue about lead quality

With scoring:

Sales focuses on highest-potential leads
Fast response to buying signals
Clear, shared definition of "qualified"
Data-driven marketing optimization

Building Your Fit Score

Fit scoring evaluates static attributes that indicate potential.

Company Attributes

Industry (0-25 points)

Exact target industry: 25
Adjacent industry: 15
Unknown: 10
Excluded industry: 0

Company Size (0-25 points)

Sweet spot (e.g., 50-500 employees): 25
Acceptable range: 15
Too small or too large: 5
Unknown: 10

Revenue (0-20 points)

Ideal range: 20
Acceptable: 10
Outside target: 0

Geography (0-15 points)

Primary market: 15
Secondary market: 10
Serviceable: 5
Excluded: 0

Contact Attributes

Title/Seniority (0-25 points)

Decision-maker: 25
Influencer: 15
End-user: 10
Unknown: 5

Department (0-15 points)

Primary buying department: 15
Related department: 10
Unrelated: 5

Example Fit Scoring Matrix

| Attribute | Value | Points | |-----------|-------|--------| | Industry | SaaS | 25 | | Employees | 150 | 25 | | Revenue | $10M | 20 | | Location | USA | 15 | | Title | VP Sales | 25 | | Department | Sales | 15 | | Fit Score | | 125 |

Building Your Engagement Score

Engagement scoring tracks behavior that signals interest.

Website Behavior

Page visits (cumulative)

Pricing page: +20 per visit
Demo/trial page: +25 per visit
Case studies: +10 per visit
Blog posts: +5 per visit
Careers page: -10 (probably job seeker)

Session data

Return visit: +15
5+ pages per session: +10
10+ minutes on site: +15

Email Engagement

Actions

Email open: +5
Link click: +15
Reply: +30
Meeting booked: +50

Patterns

Opened 3+ emails: +20
Clicked multiple links: +25
Forwarded email: +35

Other Signals

Content engagement

Downloaded whitepaper: +20
Registered for webinar: +30
Attended webinar: +40
Requested demo: +50

Social signals

Connected on LinkedIn: +10
Engaged with post: +15
Visited company LinkedIn: +10

Decay Factor

Engagement loses value over time:

Last 7 days: Full value
8-30 days: 75% value
31-60 days: 50% value
60+ days: 25% value

Combining Fit + Engagement

Create a simple matrix:

| | Low Engagement (0-50) | Medium (51-100) | High (100+) | |--|----------------------|-----------------|-------------| | High Fit (100+) | Nurture | Prioritize | Hot Lead | | Medium (50-99) | Monitor | Nurture | Qualify | | Low (0-49) | Discard | Monitor | Investigate |

Lead Categories

Hot Leads (High Fit + High Engagement)

Action: Immediate sales outreach
Response time: Within 1 hour
Assignment: Senior rep

Priority Leads (High Fit + Medium Engagement)

Action: Accelerated nurture + sales touch
Response time: Same day
Assignment: Standard routing

Nurture Leads (High Fit + Low Engagement)

Action: Marketing automation
Response time: Within 48 hours
Assignment: SDR or automated

Investigate Leads (Low Fit + High Engagement)

Action: Qualification call
Response time: Within 24 hours
Assignment: SDR review

Implementation Steps

Step 1: Define Your ICP

Before you can score fit, you need clarity on your ideal customer:

Analyze your best customers
Identify common attributes
Document anti-patterns (who churns)
Get sales and marketing aligned

Step 2: Weight Your Attributes

Not all attributes matter equally. Assign weights based on:

Correlation with closed deals
Sales feedback
Churn patterns
Deal velocity

Step 3: Set Score Thresholds

Define clear handoff points:

MQL threshold: 75+ total score
SQL threshold: 100+ total score + sales verification
Hot lead threshold: 150+ total score

Step 4: Build Feedback Loops

Scoring improves with data:

Track conversion by score range
Adjust weights quarterly
Add new signals as you learn
Remove signals that don't predict

Common Scoring Mistakes

Mistake 1: Over-Complicating

Bad: 47 different scoring factors Good: 10-15 high-signal factors

Mistake 2: No Negative Scoring

Bad: Only positive points, scores only go up Good: Negative points for disqualifying behavior

Examples of negative signals:

Unsubscribed from emails: -50
Visited careers page: -15
Personal email domain: -20
Competitor company: -100

Mistake 3: Set and Forget

Bad: Build model once, never update Good: Quarterly review and optimization

Mistake 4: Ignoring Sales Feedback

Bad: Marketing owns scoring in isolation Good: Sales input on lead quality incorporated

Advanced Scoring Techniques

Predictive Lead Scoring

Use machine learning to identify patterns:

Analyze historical wins and losses
Let algorithms find correlations
Continuously improve with new data

Best for: High-volume businesses with 1000+ historical opportunities

Account-Based Scoring

Score accounts, not just contacts:

Aggregate contact scores per company
Track account-level engagement
Identify multi-threaded opportunities

Best for: Enterprise sales with multiple stakeholders

Intent Data Integration

Incorporate third-party signals:

Topic searches across the web
Competitor research
Technology installations
Hiring patterns

Best for: Companies with budget for intent data providers

Measuring Scoring Effectiveness

Track these metrics:

Lead quality metrics

MQL to SQL conversion rate (target: 30%+)
SQL to Opportunity rate (target: 50%+)
Score correlation with win rate

Efficiency metrics

Time to conversion by score range
Sales acceptance rate of MQLs
Score distribution across leads

Revenue metrics

Revenue by lead score range
Deal velocity by score
Customer lifetime value by score

Quick Start Guide

Week 1: Define ICP and gather historical data

Week 2: Build initial scoring model (start simple)

Week 3: Implement in your CRM/marketing tools

Week 4: Train team and launch

Ongoing: Monthly review, quarterly optimization

The Quality Foundation

Lead scoring only works with quality data. Invalid emails, wrong titles, and outdated company info corrupt your scores.

Scrappy ensures your leads start clean:

Verified email addresses
Validated contact information
Enriched company data
Deduplicated records

Quality in, quality out. Start with clean data.