Lead scoring works by assigning points to leads based on two dimensions: fit (who they are) and engagement (what they do). Fit scoring evaluates attributes like job title, company size, industry, and geography. Engagement scoring tracks actions like email opens, page visits, content downloads, and webinar attendance.
A well-built scoring model helps sales teams focus on leads most likely to convert, rather than working a list from top to bottom. It also defines the handoff point between marketing and sales, typically when a lead crosses a threshold that qualifies them as a Marketing Qualified Lead (MQL).
Most marketing automation platforms include native lead scoring. Marketo, HubSpot, and Pardot all let you build scoring rules based on demographic and behavioral criteria. Some teams supplement platform-native scoring with predictive scoring tools like 6sense or MadKudu that use machine learning to identify buying signals.
The biggest mistake in lead scoring is building a model and never revisiting it. Scoring models need regular calibration. Pull a list of leads that scored high but never converted, and leads that scored low but closed. Adjust the weights based on what actually predicts revenue, not what feels right in a conference room.
Start simple. A basic model with 5 to 10 scoring rules will outperform a complex model with 50 rules that nobody maintains. Add complexity only when you have the data to justify it and the operational discipline to keep it calibrated.