Lead scoring is a system that assigns points or ranks to prospects based on their likelihood to buy. Each interaction-email opens, content downloads, website visits, job title-adds points. When a prospect crosses a threshold (50 points, for example), they’re handed to sales as a “sales-qualified lead” (SQL).
How Lead Scoring Works
Lead scoring combines two signal types. Explicit signals come from actions: downloading a case study, attending a webinar, requesting a demo. Implicit signals come from profile data: job title, company size, industry. A typical model might score: webinar attendance +15 points, email open +1, CFO title +20, company $50M+ revenue +10.
Most B2B platforms (HubSpot, Marketo, Salesforce) auto-calculate scores based on rules you define. Mature teams use predictive scoring, where machine learning models find patterns in won/lost deals and automatically weight signals. A prospect who resembles your best customers scores higher, even if they haven’t downloaded anything yet.
The goal: reduce the “sales-ready” definition from gut feel (“this looks good”) to data (“this scores 50+”).
Why It Matters for B2B Marketing
Lead scoring exists to answer one question: when should sales jump in? Too early, they chase unqualified prospects. Too late, competitors steal the deal. Scoring lets marketing nurture cold leads (30 points) without overwhelming sales, then hand off qualified ones (60+ points) automatically.
Departments stop fighting when scoring is transparent. Sales sees why a prospect was prioritized. Marketing sees which signals actually predict deals. Both optimize toward shared metrics.
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See the demo →Lead Scoring vs. Account Scoring
Lead scoring ranks individuals. Account scoring ranks companies. In ABM, you do both. A director at a target account gets high account score (company is in ICP) and a personal lead score based on their engagement. Sales treats them differently than a low-engagement person at the same high-potential company.
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Q: What’s a good lead-to-SQL conversion rate? A: Industry average is 25-35%. If your sales team converts 10% of SQL to opportunity, a 30% lead-to-SQL rate means 3% of all leads become deals. Tweak scoring thresholds up (higher bar, fewer SQL, higher conversion) or down (more SQL, lower conversion rate).
Q: Should I score inbound differently than outbound? A: Yes. An inbound lead who filled out a demo form should score much higher than an outbound prospect at the same company who downloaded a whitepaper. Inbound signals intent; outbound signals awareness.
Q: How often should I recalibrate my scoring model? A: Every quarter. Compare scored leads (who your model ranked highest) against deals won. If high-scoring leads didn’t close, your model is broken. Adjust weights and re-run.
Q: Can I use the same scoring model for multiple products? A: No. An Outreach buyer (Sales) and a HubSpot buyer (Marketing) behave differently. Separate models by product line or buyer persona.
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