How to Set Up Lead Scoring
Prioritize your best leads with a data-driven scoring model
Not all leads are created equal. Lead scoring helps you prioritize follow-up by ranking leads based on their likelihood to buy. This guide shows you how to build a lead scoring model that separates hot prospects from tire-kickers.
Steps in this guide
Define Your Scoring Criteria
Lead scoring typically includes two dimensions: fit (demographic/firmographic match to ICP) and engagement (behavioral signals showing interest). Define criteria for both.
Tips:
- Fit factors: company size, industry, job title, location
- Engagement factors: page views, content downloads, email opens
- Weight factors by importance to conversion
- Include negative scoring for bad fit signals
Assign Point Values
Give each criterion a point value based on its correlation with conversion. High-intent actions (pricing page, demo request) should score higher than low-intent (blog view).
Tips:
- Pricing page view: +20 points
- Downloaded guide: +10 points
- Job title match: +15 points
- Wrong industry: -25 points
Set Score Thresholds
Define what score triggers each action. Typically: MQL threshold for marketing nurture, SQL threshold for sales handoff.
Tips:
- MQL: 50+ points (marketing qualified)
- SQL: 80+ points (sales qualified)
- Start conservative and adjust based on feedback
- Review with sales to align on thresholds
Implement in Your CRM/MAP
Configure scoring rules in your marketing automation or CRM platform. Most tools (HubSpot, Marketo, Salesforce) have built-in lead scoring capabilities.
Tips:
- Set up automation to update scores in real-time
- Trigger alerts when leads hit thresholds
- Log scoring changes for transparency
- Test thoroughly before going live
Add Website Visitor Behavior
Enhance scoring with first-party intent data from website visits. Identified visitors who view high-intent pages multiple times are your hottest leads.
Tips:
- Score based on specific pages viewed
- Weight repeat visits higher than single visits
- Use time on site as engagement signal
- Integrate visitor ID data into scoring model
Monitor and Refine
Lead scoring is not set-and-forget. Continuously analyze which scores correlate with actual conversions and adjust accordingly.
Tips:
- Track conversion rate by score range
- Survey sales on lead quality feedback
- Adjust weights quarterly based on data
- Remove criteria that don't predict conversion
Common Mistakes to Avoid
- ✕Making scoring too complex at the start
- ✕Not including negative scoring for bad fits
- ✕Setting thresholds without sales input
- ✕Never updating the model after initial setup
- ✕Ignoring behavioral signals and only scoring demographics
Pro Tips
- ★Start simple with 5-10 criteria, then expand
- ★Website visitor behavior is the strongest intent signal
- ★Recency matters—recent engagement should score higher
- ★Build separate models for different products/segments if needed
Tools You'll Need
HubSpot
Built-in lead scoring with automation
Salesforce
Einstein lead scoring with AI
Marketo
Advanced scoring for enterprise
Frequently Asked Questions
What is lead scoring?
Lead scoring is a methodology to rank leads based on their likelihood to buy. It assigns point values to lead attributes and behaviors, helping sales prioritize the most promising opportunities.
What factors should I include in lead scoring?
Include both fit factors (company size, industry, job title) and engagement factors (page views, content downloads, email engagement). Weight high-intent behaviors like pricing page visits heavily.
How do I know if my lead scoring is working?
Track conversion rates by score range. High-scoring leads should convert at significantly higher rates than low-scoring leads. If not, adjust your scoring criteria and weights.
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