What is Lead Scoring?
A methodology for ranking leads based on their perceived value and likelihood to convert, using behavioral and demographic data.
Quick Definition
Lead Scoring: A methodology for ranking leads based on their perceived value and likelihood to convert, using behavioral and demographic data.
Understanding Lead Scoring
Lead scoring is a methodology used by sales and marketing teams to rank leads based on their perceived value to the organization. By assigning numerical values to various attributes and behaviors, lead scoring helps teams prioritize which leads deserve immediate attention and which need further nurturing.
Lead scoring typically considers two dimensions: fit and engagement. Fit scoring evaluates how well a lead matches your ideal customer profile (ICP) based on firmographic data like company size, industry, and job title. Engagement scoring measures how actively the lead interacts with your brand through website visits, content downloads, email opens, and other behaviors.
The value of lead scoring lies in sales efficiency. Without scoring, sales teams waste time on unqualified leads while hot prospects go cold. With scoring, sales focuses on leads most likely to convert while marketing continues nurturing others. This alignment improves both sales productivity and conversion rates.
Key Points About Lead Scoring
Lead scoring ranks leads based on fit and engagement factors
It enables sales prioritization and marketing-sales alignment
Scoring should be based on data about what actually predicts conversion
Both demographic/firmographic fit and behavioral engagement matter
Scores should trigger actions: sales handoff, nurture enrollment, etc.
How to Use Lead Scoring in Your Business
Define Scoring Criteria
Collaborate with sales to identify attributes and behaviors that indicate buying readiness. Fit factors: company size, industry, job title, budget authority. Engagement factors: pricing page visits, demo requests, content downloads, email engagement. Weight criteria based on predictive value.
Assign Point Values
Give higher points to stronger indicators. Example: Demo request = +30 points, Pricing page visit = +15 points, Blog visit = +5 points, C-suite title = +20 points, Small company = -10 points. Set a threshold (e.g., 50 points) for SQL status.
Implement Automation
Use your marketing automation platform to calculate and update scores automatically based on tracked behaviors. When leads cross threshold scores, trigger automatic notifications to sales or enrollment in specific workflows.
Refine Based on Results
Analyze which scoring criteria actually predict conversion. If leads with high scores don't convert, criteria need adjustment. If sales keeps rejecting leads, the threshold may be too low. Review and refine scoring quarterly based on closed-loop data.
Real-World Examples
B2B SaaS Lead Scoring
A SaaS company scores leads on fit (company size, industry, title) and engagement (website visits, content downloads, email clicks). A VP of Marketing at a 500-person tech company who downloads a case study and visits pricing = 85 points, exceeding the 70-point SQL threshold.
Predictive Lead Scoring
A company uses AI-based predictive scoring that analyzes patterns in their historical data. The model identifies that leads who visit the integrations page, have attended a webinar, and are from companies using specific technologies convert at 5x the average rate. These leads score highest.
Negative Scoring
A B2B company adds negative scoring: students = -50 points, personal email domains = -20 points, job seekers visiting careers page = -30 points, unsubscribers = -100 points. This prevents unqualified contacts from reaching sales regardless of engagement.
Best Practices
- Base scoring criteria on data about what actually predicts conversion
- Include both fit (who they are) and engagement (what they do)
- Use negative scoring to deprioritize poor-fit leads
- Set clear thresholds that trigger sales handoff
- Review and adjust scoring quarterly based on results
- Involve sales in defining and refining scoring criteria
Common Mistakes to Avoid
- Creating scoring based on assumptions rather than data
- Only scoring engagement without considering fit
- Not having negative scores to deprioritize bad fits
- Setting thresholds without testing against actual conversions
- Not refining scores based on closed-loop feedback from sales
Frequently Asked Questions
How do I know if my lead scoring is working?
Track MQL-to-SQL conversion rates and SQL-to-opportunity rates. If high-scoring leads convert better than low-scoring leads, scoring is working. If there's no correlation between score and conversion, your criteria need adjustment. Sales feedback on lead quality is also valuable.
What's a good MQL threshold score?
There's no universal number—it depends on your criteria and weighting. Set the threshold so that MQL-to-SQL conversion is 15-30%. If conversion is much higher, you're being too restrictive; if much lower, too loose. Adjust threshold based on sales capacity and conversion data.
Should I use manual or predictive lead scoring?
Manual scoring works well for companies with clear, established patterns. Predictive scoring (AI-based) is better when you have large datasets and want to find non-obvious patterns. Many companies start with manual scoring and evolve to predictive as they scale.
How many scoring criteria should I have?
Start with 5-10 high-impact criteria rather than dozens of minor factors. Too many criteria make the model complex and hard to understand. Focus on the attributes and behaviors that most strongly predict conversion. You can add nuance over time.
How often should leads be re-scored?
Behavioral scores should update in real-time as leads take actions. Demographic/firmographic scores can update when new information is captured. The overall score should be dynamic, not static. Set up automation to recalculate scores immediately when relevant data changes.
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