Lead Scoring Models: Prioritizing Your Best Prospects

Lead scoring assigns numerical values to leads based on their likelihood to convert. Instead of treating all leads equally, sales teams focus on the highest-scoring prospects first, improving conversion rates and efficiency.

Demographic scoring assigns points based on who the lead is. Job title, company size, industry, and geographic location indicate fit. A VP at a 500-person SaaS company scores higher than an intern at a small nonprofit if you sell enterprise software.

Behavioral scoring assigns points based on what the lead does. Visiting your pricing page (high intent), downloading a case study (consideration), attending a webinar (engagement), and requesting a demo (decision) each receive different point values.

Negative scoring subtracts points for disqualifying signals. Competitors visiting your site, students downloading resources for research, and leads from excluded geographic markets should score lower, not higher.

Scoring thresholds define when leads are ready for sales. A lead scoring 80 points might be Sales Qualified, while a lead at 40 points needs more nurture. Define thresholds collaboratively between marketing and sales.

Decay rules prevent stale scores from misleading sales. A lead who scored 90 points six months ago but has not engaged since is not the same as a lead who scored 90 points this week. Reduce scores over time for inactive leads.

Calibrate your model regularly. Compare lead scores against actual conversion rates. If high-scoring leads are not converting at higher rates than low-scoring leads, your model needs adjustment.

Start simple: 5-10 scoring criteria with clear point values. Complexity can increase as you gather more data about what actually predicts conversion.

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