What is Lead Scoring?
Lead scoring is a methodology for ranking prospects based on their likelihood to convert, using a combination of demographic fit (firmographics) and behavioral engagement (activity signals).
Lead scoring assigns numerical values to prospects based on who they are and what they do. A VP of Engineering at a 500-person SaaS company who visited your pricing page scores higher than an intern at a 10-person agency who downloaded one blog post. The score determines how and when sales engages.
Scoring Models
Lead scores combine two dimensions. Fit scoring evaluates demographic match: company size, industry, job title, revenue, technology stack. Behavior scoring tracks engagement: website visits, email opens, content downloads, event attendance, product trials. A simple model might give 0-50 points for fit and 0-50 points for behavior, with leads above 70 routed to sales. More sophisticated models use machine learning to weight factors based on historical conversion data.
Lead Scoring Pitfalls
The #1 mistake is scoring based on vanity engagement. Someone who downloads five ebooks might be a content enthusiast, not a buyer. Conversely, someone who visits the pricing page once might be ready to purchase. CROs should validate scoring models against actual conversion data quarterly. If high-scoring leads aren't converting at higher rates than low-scoring leads, the model is broken and reps will lose trust in it fast.
Lead Scoring Tools
Most CRM platforms (HubSpot, Salesforce) include native lead scoring. Marketing automation platforms like Marketo and Pardot offer more sophisticated models. AI-powered tools like 6sense and Demandbase combine first-party scores with third-party intent data for predictive lead scoring. The trend is moving from rule-based scoring (manually assigning points) to predictive scoring (ML models trained on your historical wins).
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