Lead scoring
A method of ranking leads by their likelihood to convert, typically combining how well they fit the ideal customer profile with how much buying behavior they show.
In more detail
Lead scoring assigns each lead a value that estimates how worth pursuing it is, so a sales team spends its limited time on the best opportunities. Scores usually blend two dimensions: fit, how well the lead matches the ideal customer profile, and engagement, the behavior that suggests actual interest, like visiting pricing pages or opening emails.
A scoring model is only as good as the data and the assumptions behind it. Scoring on stale or incomplete attributes misranks leads, and a model that is never revisited drifts away from what actually converts. Good scoring depends on clean, enriched data and periodic recalibration against real outcomes.
Where this shows up at Ceven
Ceven can score leads as a workflow step, combining enriched fit data with behavioral signals and writing the score back to the system that owns the lead. AI steps can weigh messy signals a rigid rules-based score cannot, and because Ceven is not the CRM, the score lives in the customer's own system, with the calculation recorded in the audit trail.