Lead Scoring
Lead scoring is a methodology for ranking leads based on their likelihood to buy, using a combination of demographic/firmographic attributes (who they are) and behavioral signals (what they've done). High scores get routed to sales; low scores stay in nurture. Done right, it's the filter that keeps sales focused on qualified prospects.
A lead scores: +25 for Director+ title, +15 for 200-1000 employee company, +20 for visiting pricing page, +15 for downloading case study, +10 for attending webinar, -20 for gmail.com domain = 65 total. They hit MQL threshold at 70, so they get one more nurture email. After opening it (+5), they're routed to SDR for outreach.
Lead scoring is either your revenue engine's quality control or a complete fiction — there's rarely an in-between.
The failure mode: marketing sets up scoring in year one, never validates it, and two years later nobody knows why downloading a whitepaper is worth 15 points. Sales ignores the scores because they've learned they don't predict anything useful.
The fix: score based on what actually predicts conversion. Pull your closed-won data, find the attributes and behaviors that correlate with wins, weight accordingly. Then validate quarterly. If leads scoring 80+ aren't converting 3x better than leads scoring 40, your model is broken.
Best practice: separate demographic score (fit) from behavioral score (intent). A perfect-fit company that never engages shouldn't score the same as a meh-fit company that's consuming every piece of content.
Define ItOther Definitions
“Lead scoring is a methodology used by sales and marketing departments to determine the worthiness of leads by attaching values to them based on their behavior and profile.”
“Lead scoring is a shared sales and marketing methodology for ranking leads based on a scale that represents the perceived value each lead represents to the organization. It uses both explicit data (firmographics) and implicit data (behavior) to prioritize leads.”
“Lead scoring is the process of assigning values, often in the form of numerical points, to each lead you generate for the business based on multiple factors including professional information and engagement behavior.”
Lead scoring assigns numeric values to leads based on their fit (who they are) and engagement (what they've done). HubSpot frames it as determining lead 'worthiness' for sales attention. Marketo emphasizes it's a shared sales and marketing methodology using explicit and implicit data. Salesforce highlights the multi-factor approach.
Scoring typically combines: (1) Demographic/Firmographic scoring — job title, company size, industry, location match; (2) Behavioral scoring — website visits, content downloads, email engagement, webinar attendance, pricing page views; (3) Negative scoring — deductions for competitor companies, students, unsubscribes, or inactivity.
Modern lead scoring often uses predictive models (machine learning on historical conversion data) rather than manual point assignments. The output is typically a 0-100 score with thresholds that trigger routing: 70+ to sales as MQL, 40-69 to nurture, under 40 to long-term drip.
MistakesCommon Mistakes
Setting up scores once and never validating against actual conversion data
Scoring based on assumptions rather than analysis of what closed-won leads did
Not separating fit scores from intent scores — both matter, differently
Over-weighting single actions (one webinar shouldn't make someone sales-ready)
No score decay for inactivity — a lead who scored 90 six months ago isn't still hot
Lead scores that sales ignores?
We rebuild lead scoring from conversion data — so high scores actually mean high likelihood to buy.
Experience across
