How Lead Scoring Works: Turning Contact Behavior Into a Number

5 min read

Lead scoring translates contact behavior and attributes into a number that represents purchase readiness. Inside your automation platform, this score is a contact field that updates automatically as contacts take actions. When the score reaches a defined threshold, the platform can trigger a notification, move the contact to a new segment, or start an automation.

How the Score Is Calculated

Each scoreable action has an assigned point value defined in the platform's lead scoring settings. When a contact performs that action - opens an email, clicks a link, visits a page, fills a form - the platform adds the assigned points to their score. The score is stored as a contact field and is visible in the contact record and usable in segment filters and automation conditions.

Positive and Negative Signals

• Positive: email opens (+1), link clicks (+3), page visits (+5), form fills (+10), demo requests (+20)
• Negative: unsubscribe attempt (-10), bounce (-5), specific page exits (optional)
• Demographic match: job title matches ICP (+15), company size matches (+10)
• Inactivity decay: no activity for 30 days (-5 per week until re-engagement)

Setting the Threshold

The sales-ready threshold is a number that, when a contact's score reaches or exceeds it, triggers a handoff to sales. This number is calibrated to your data: look at past customers and find the average score they had when sales first engaged. Start with that number, and adjust based on results.

Score-Triggered Automations

Common automations triggered by score thresholds: enter a hot-lead nurture sequence at 30 points, notify the assigned sales rep at 50 points, send a direct meeting invitation at 75 points. The threshold triggers can be one-time or repeating depending on your setup.

Viewing and Managing Scores

The lead score field appears in the contact record and can be used in any segment filter or automation condition. Create a dashboard view sorted by lead score descending so your sales team can always see who is hottest at a glance. Review score distributions monthly - if most contacts are clustered at very low or very high scores, the model needs recalibration.