Glossary

Lead Scoring

Lead Scoring is a B2B sales and marketing process that assigns numeric values to leads based on their fit (who they are) and engagement (what they do) to prioritize sales follow-up. It typically involves marketing, sales, RevOps, and sometimes product or data teams, and comes into play from initial lead capture through qualification (MQL/SQL) and early opportunity stages. Related terms include MQL scoring, SQL criteria, predictive scoring, fit scoring, behavioral scoring, and lead qualification model.

Importance in B2B Sales

Lead Scoring is significant because it helps B2B organizations focus limited sales capacity on the leads most likely to convert and generate revenue. By aligning on scoring rules, sales and marketing reduce friction over lead quality and create a shared definition of what “sales-ready” means. Operationally, lead scoring powers routing, SLAs, and automation (e.g., when a lead becomes an MQL and gets handed to an AE or SDR). Strategically, it supports more accurate pipeline forecasting, better campaign optimization, and smarter budget allocation by showing which lead segments actually turn into opportunities and customers.

FAQ

How do we get started with Lead Scoring if we’ve never done it before?

Begin with a simple model: define your ideal customer profile (ICP), list 5–10 key fit attributes (e.g., industry, company size, tech stack), and 5–10 key behavioral signals (e.g., demo request, pricing page views). Assign basic points, validate with sales, then refine monthly based on conversion data rather than trying to get it “perfect” on day one.

Who should own Lead Scoring—sales or marketing?

Marketing usually owns the initial Lead Scoring model and ongoing configuration, but it must be co-designed and reviewed with sales leadership and RevOps. Sales provides real-world feedback on lead quality, and RevOps ensures scoring integrates cleanly with CRM, routing, and reporting.

What data should we use in a Lead Scoring model?

Use a mix of fit data (firmographics like industry, revenue, employee count; technographics; region; title/seniority) and behavioral data (website visits, content downloads, event attendance, product usage, email engagement). Over time, incorporate historical conversion data to see which attributes and behaviors actually correlate with opportunity creation and closed-won deals.

How does Lead Scoring relate to MQLs and SQLs?

Lead Scoring typically determines when a contact becomes a Marketing Qualified Lead (MQL) by hitting a point threshold, often combining fit and engagement. Sales then reviews MQLs against agreed criteria (e.g., need, timing, budget) to promote them to Sales Qualified Leads (SQLs), using the score as input—but not the only factor—in that decision.

How often should we update our Lead Scoring model?

Review Lead Scoring at least quarterly and after any major go-to-market changes (new ICP, new product line, shift upmarket). Use performance data (conversion rates by score band, win rates by segment) plus feedback from SDRs and AEs to adjust weights, thresholds, and signals.