Short answer
Lead scoring ranks prospects by how likely they are to buy, using points for fit (such as job title, company size or assets) and engagement (such as email clicks, page visits and form fills). Sales teams use the score to decide who to contact first.
Lead Scoring explained
A scoring model usually combines a fit score, based on how closely a lead matches your ideal client profile, with an engagement score based on recent activity, often with decay so old activity counts less. HubSpot and Salesforce Account Engagement offer rules-based scoring, and both also offer predictive scoring that uses machine learning on historical conversions.
Scoring works when sales and marketing agree on what a qualified lead is and review the model against real outcomes every quarter.
How Vantage Point helps: we design scoring models with your sales team, build them in HubSpot or Salesforce, and tune them against closed business.
Frequently asked questions
What is the difference between an MQL and an SQL?
A marketing qualified lead (MQL) meets marketing's scoring threshold; a sales qualified lead (SQL) has been accepted by sales as worth active pursuit.
Should we use predictive lead scoring?
Predictive scoring needs enough historical conversion data to learn from. Many firms start with rules-based scoring and add predictive later.