Lead Scoring: how to score leads and know who's ready to buy
Lead scoring is a method for ranking contacts by likelihood of conversion. Learn about scoring models, behavior vs demographics, and when a lead is sales-ready.
Lead scoring is a method of assigning numerical points to contacts based on their attributes and behavior, so you can identify who is closest to buying — and prioritize your efforts accordingly.
Instead of treating all leads equally, lead scoring tells you: “This contact visited the pricing page three times, opened the last five emails, and downloaded a case study. She’s ready for a call.” Versus: “This contact signed up six months ago and hasn’t opened an email since. He’s not.”
Two dimensions: demographics and behavior
An effective lead score combines two types of data:
Demographic scoring (fit)
About who the contact is:
| Signal | Points (example) |
|---|---|
| Job title: C-level / VP | +20 |
| Job title: Intern / Student | -10 |
| Company size: 50-500 employees | +15 |
| Industry: target industry | +10 |
| Location: primary market | +5 |
Demographic scoring tells you whether the contact fits your ideal customer profile.
Behavioral scoring (interest)
About what the contact does:
| Signal | Points (example) |
|---|---|
| Visited pricing page | +20 |
| Downloaded whitepaper | +15 |
| Opened 3+ emails last 30 days | +10 |
| Clicked CTA in email | +10 |
| Watched demo video | +15 |
| No email opens in 30+ days | -15 |
| Unsubscribed from newsletter | -30 |
Behavioral scoring tells you whether the contact is actively interested right now.
Scoring models
Point-based model (simplest)
Assign points for each action and attribute. Sum them. Define a threshold (e.g., 80 points) that marks a lead as sales-ready.
Advantage: Easy to understand and implement. Disadvantage: Requires manual calibration and maintenance.
Tier model
Instead of precise points, place leads in tiers:
- Cold — signed up but no engagement
- Warm — engaging with content
- Hot — showing buying signals (pricing page, demo request)
Advantage: Simpler to operationalize for sales. Disadvantage: Less granular.
Predictive scoring
Machine learning models that automatically identify patterns in historical data, as described by Salesforce’s guide to predictive lead scoring. The system finds the signals that correlate with conversion on its own.
Advantage: More accurate, self-updating. Disadvantage: Requires volume (1,000+ conversions as training data).
Thresholds and actions
Lead scoring is only useful if it drives action. Define clear thresholds:
| Score | Status | Action |
|---|---|---|
| 0-30 | Cold | Nurturing via drip campaign |
| 31-60 | Warm | Targeted content, segmented communication |
| 61-80 | Hot | Sales-oriented content, personal outreach |
| 81-100 | Sales-ready | Hand off to sales with context |
Decay: scores that decrease over time
A lead that scored 85 six months ago but hasn’t interacted since is no longer sales-ready. Implement score decay — automatically subtract points from contacts who are inactive over time.
A typical decay model subtracts 5-10 points per 30 days without engagement. This ensures your pipeline always reflects current interest.
Getting started
Start simple, as HubSpot’s lead scoring guide recommends. Three rules are better than no rules:
- Opened email last 30 days: +10
- Clicked a link: +20
- Visited pricing page: +30
That gives you enough to distinguish active leads from passive ones. Refine over time based on what actually converts. Read more in our guide to engagement scoring.