What is Lead Scoring?
Definition: A system that assigns numerical values to leads based on firmographic fit, behavioral signals, and engagement data to rank prospects by their likelihood of converting to customers.
Lead scoring separates the signal from the noise. Not every lead deserves sales attention. A VP of Engineering at a Series B SaaS company who visited your pricing page twice is a better lead than an intern at a consulting firm who downloaded a whitepaper. Scoring quantifies that difference.
Two scoring dimensions: fit (does this person match your ICP?) and behavior (are they showing buying signals?). Fit scoring uses firmographic data: industry, company size, job title, geography. A Director at a 200-person SaaS company might score +30 for fit. Behavior scoring uses engagement data: pricing page visit (+10), demo video watched (+15), email opened 3+ times (+5). Combine both for a total score.
Implementation: HubSpot and Salesforce both support native lead scoring. You set rules (if title contains "VP" or "Director", add 20 points) and the CRM auto-calculates scores. For more sophisticated scoring, GTM Engineers build custom models that incorporate product usage data, intent signals, and enrichment data from external sources.
The common mistake is scoring on activity instead of intent. Someone who opens every email but never visits the pricing page is curious, not buying. Someone who visits the pricing page once and compares plans is showing purchase intent. Weight your scoring toward intent signals (pricing page, competitor comparison pages, integration documentation) over vanity signals (email opens, social follows).