AI Lead Scoring: The Complete Guide for B2B Agencies

Lead scoring is the process of giving each lead a score to show how ready it is to buy. With AI, this process becomes automatic, dynamic and multi-dimensional.

The 4 pillars of AI lead scoring

1. Engagement Score

Measures how active the lead is. It tracks email opens, link clicks, website visits, resource downloads and social interactions. A lead who opens 5 emails in a week and visits the pricing page has a high engagement score.

2. Fit Score (ICP Match)

Compares the lead's profile with your Ideal Customer Profile. Factors: industry, company size (revenue, employees), the contact's role (decision maker vs. operational) and location. The CEO of a 50-person marketing agency has a much higher fit score than an intern at a non-profit.

3. Intent Signals

Detects behaviour that shows buying intent: visiting the pricing page, requesting a demo, replying to an email to ask for details, downloading a case study. AI recognises intent patterns even in sequences of seemingly random actions.

4. Recency & Frequency

A lead who was active yesterday is worth more than one who was active 3 months ago. Dynamic scoring updates scores automatically based on recency (how recent the last interaction is) and frequency (how often the lead interacts).

How to put it into practice

With QVADIS Marketing AI, lead scoring is automatic. When you import leads (via CSV, form or webhook), the system starts tracking every interaction. You can set promotion thresholds: when a lead reaches a score of 80+, it is automatically moved to the “Sales Ready” stage and the sales team is notified.

Metrics to track

• MQL→SQL conversion rate (target: 25–35%)
• Average qualification time (target: < 7 days)
• Score accuracy (do high-scoring leads actually convert?)
• False positive rate (high-scoring leads that don't convert)