Lead Scoring

Not all leads are created equal. A VP of Sales at a 200-person SaaS company who has visited your pricing page twice is a fundamentally different opportunity than a junior analyst at a 10-person startup who downloaded a whitepaper. Lead scoring makes this distinction explicit and quantitative, so sales reps know where to invest their limited time. Scoring models typically combine two dimensions: fit score (how well the prospect matches your ICP based on firmographic data like company size, industry, and technology stack) and engagement score (how actively they have interacted with your brand through website visits, content downloads, email opens, and event attendance). The product of these two scores creates a prioritized list — top-tier leads with high fit and high engagement get the highest-touch, most personalized outreach, while lower-scored leads receive automated nurture. AI has made lead scoring substantially more accurate. Machine learning models trained on historical conversion data can identify non-obvious signals — like a specific combination of technology, company growth rate, and hiring patterns — that correlate with high purchase likelihood. Outvid integrates lead scoring into its outreach workflow, using prospect data to prioritize who receives personalized video outreach and to tailor the content of that video to the prospect's specific situation and score profile.