Lead scoring is a method of ranking leads by how likely they are to convert, using a combination of firmographic fit (company size, industry) and behavioral signals (email opens, page visits, response speed) to prioritize where a sales team spends time.
Common lead scoring models
The simplest model assigns points for specific actions and attributes — a demo request might be worth more points than a newsletter signup, and a director-level title more than an individual contributor. Leads above a threshold get routed to sales.
More advanced models use predictive scoring, where a system learns from historical conversion data which combinations of signals actually correlate with a closed deal, rather than relying on manually assigned point values.
Common lead scoring pitfalls
Scoring models built once and never revisited go stale — the signals that predicted conversion a year ago may not predict it today, especially as a product or market changes.
A subtler problem: scoring based only on explicit actions (form fills, downloads) misses signal from actual conversations. A lead who's had three substantive calls but hasn't filled out another form may be more qualified than the score suggests, if the model only counts trackable web behavior.