Lead Scoring.

Lead scoring is the process of assigning numerical values to leads based on two dimensions: behavioural score (actions taken—pages visited, content downloaded, emails opened, webinars attended, free trial started) and demographic/firmographic score (profile fit—job title, company size, industry, location). The combined score determines lead priority and triggers specific marketing or sales actions.

Designing a Lead Scoring Model

Effective lead scoring models are built backwards from closed-won data: which behaviours and profile characteristics are most predictive of eventual purchase? Analysing past customers to find common patterns—content consumed, pages visited before converting, company characteristics—reveals which signals deserve higher scores. Models should be reviewed quarterly and recalibrated when MQL→SQL conversion rates drift.

Negative Scoring

Sophisticated scoring models also subtract points for negative signals: visiting the careers page (likely a job seeker, not a buyer), unsubscribing from emails, or having a company size or industry that falls outside the ICP. Negative scoring prevents sales teams from pursuing leads that look engaged on the surface but are fundamentally poor fits, improving their productivity and trust in the MQL handoff.