Lead scoring
leed SKOR-ing
Assigning numerical values to leads based on their fit and engagement to determine which ones are ready for sales follow-up.
Lead scoring assigns points to leads based on two dimensions: fit (do they match your ICP?) and engagement (have they shown buying signals?). A CTO at a Series C company who visited your pricing page three times scores higher than an intern who downloaded a whitepaper once.
Fit scoring uses firmographic data: company size, industry, job title, tech stack, revenue. Engagement scoring uses behavioral data: pages visited, content downloaded, emails opened, webinars attended, free trial activity.
When a lead's score crosses a threshold, they become an MQL and get routed to sales. The threshold should be calibrated so that 20-40% of MQLs become SQLs. Too many MQLs with low conversion means the threshold is too low. Too few MQLs means the threshold is too high or your scoring model is wrong.
Examples
A lead scoring model.
Fit scoring: VP or above (+20), company 100-1000 employees (+15), SaaS industry (+10), uses Kubernetes (+10). Engagement scoring: pricing page visit (+15), demo video watched (+10), blog post read (+2), email opened (+1). MQL threshold: 50 points.
Lead scoring reveals misalignment.
Marketing passes 500 MQLs per month. Sales accepts only 150 as SALs. The team analyzes rejected MQLs and finds most had high engagement scores but low fit scores: individual developers at small companies. They increase fit score weight. MQL volume drops to 300 but acceptance rate jumps to 60%.
Product usage replaces traditional scoring.
A PLG company finds that traditional scoring (content downloads, email opens) is less predictive than product usage signals. Users who complete 3+ API calls in their first week convert at 5x the rate of high-scoring MQLs. They rebuild scoring around product activity.
In practice
Read more on the blog
Frequently asked questions
How do you build a lead scoring model?
Start with your best customers. What firmographic traits do they share (fit)? What did they do before buying (engagement)? Assign points to each trait and behavior. Set a threshold. Test by comparing MQL-to-SQL conversion rates. Adjust quarterly.
Should lead scoring use fit, engagement, or both?
Both. A perfect-fit lead with no engagement is not ready to buy. A highly engaged lead that does not match your ICP will not close. The best scoring models require a minimum threshold on both dimensions before marking a lead as an MQL.
Related terms
A person or company that has shown interest in your product. The starting point of every sales pipeline.
A lead that marketing has scored and deemed ready for sales follow-up based on fit and engagement signals.
A description of the company (not person) most likely to buy, succeed, and expand with your product. Your best-fit customer.
Software that automates repetitive marketing tasks: email sequences, lead scoring, campaign management, and lead routing.
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