Lead Scoring

Lead scoring is the practice of assigning numerical values to leads based on how well they match your ICP (fit scoring) and how engaged they are with your brand (intent scoring). The combined score helps sales teams prioritize outreach by focusing on leads most likely to convert, rather than working leads in the order they came in.

Why it matters: not all leads are equal. A VP of Marketing at a 200-person SaaS company who visited your pricing page three times and downloaded a case study is a fundamentally different lead than a student who signed up for your newsletter for a class project. Without scoring, sales reps either waste time on low-quality leads or cherry-pick based on gut instinct. Lead scoring creates an objective, data-driven prioritization system that ensures the best leads get attention first.

How to build a scoring model: the two dimensions are fit and behavior. Fit scoring assigns points based on how well the lead matches your ICP attributes: job title (VP/Director gets more points than intern), company size (in your sweet spot), industry (in your target verticals), and technology stack (using complementary tools). Behavior scoring assigns points for engagement actions: visiting the pricing page (high intent), downloading a guide (medium intent), attending a webinar (medium-high intent), requesting a demo (very high intent), or simply opening an email (low intent).

Point values should reflect actual predictiveness. Analyze which attributes and behaviors historically correlate with conversion. If pricing page visits predict conversion 5x better than blog visits, they should be worth 5x the points. Most CRMs (HubSpot, Salesforce) and marketing automation platforms (Marketo, Pardot) have built-in lead scoring features. Start simple: a scoring model with 8-10 attributes outperforms no scoring at all.

Lead score thresholds: define what happens at different score levels. Below 30: marketing nurture. 30-60: marketing qualified lead (MQL), add to targeted campaigns. 60-80: sales qualified lead (SQL), assign to SDR for outreach. Above 80: high-priority, fast-track to account executive. These thresholds should be calibrated based on your actual conversion data and adjusted over time.

Common mistakes: building overly complex models with 50+ scoring criteria that nobody understands. Not calibrating scores against actual conversion data (many teams set point values based on intuition). Not including negative scoring (lead goes cold, unsubscribes, or has a disqualifying attribute like being a competitor). Setting it and forgetting it: lead scoring models degrade over time as your product and market evolve. Review quarterly.

Practical example: a SaaS company implements lead scoring in HubSpot with 12 scoring criteria. Within the first month, they discover that 40% of their MQLs came from leads who would have scored below 30 under the new model. Sales reps confirm these leads rarely converted. By routing only leads scoring 50+ to sales, the team reduces SDR time spent on unqualified leads by 35% and increases their SQL-to-opportunity rate from 22% to 41%.