Lead Scoring

Not all leads are created equal. A VP of Operations at a 500-person manufacturer who has visited your pricing page three times is far more likely to buy than a student who downloaded a whitepaper for a class project. Lead scoring quantifies this difference so sales teams prioritize effectively. Scoring models typically combine two dimensions. Demographic or firmographic scoring evaluates whether the lead fits your ideal customer profile. A lead from a target industry gets more points than one from a non-target industry. A decision-maker title scores higher than a junior role. A company in your sweet-spot size range scores higher than one too small or too large. Behavioral scoring evaluates engagement and intent. High-value actions like requesting a demo, visiting the pricing page, or attending a webinar receive more points than low-value actions like opening an email or visiting the blog. Negative scoring is equally important: if a lead has not engaged in 90 days, points should decay. The output is a prioritized list. Marketing qualifies leads above a certain threshold as "Marketing Qualified Leads" (MQLs) and passes them to sales. Sales further qualifies them as "Sales Qualified Leads" (SQLs). This handoff process prevents sales from wasting time on unready prospects while ensuring hot leads get immediate attention. Traditional lead scoring uses manually assigned point values. AI-powered scoring analyzes historical conversion data to determine which attributes and behaviors actually predict purchase. This data-driven approach often reveals non-obvious patterns and outperforms manually tuned models.