Custom Builds

Lead Scoring With Machine Learning: A Practical Build

How to go from a spreadsheet of past leads to a model that tells your closer who to call first — and what it takes to keep it honest.

8 min read

Rule-based scoring hits a ceiling fast

Point systems encode last year's assumptions. They can't notice that leads from a specific campaign at a specific hour close at triple the rate, because nobody thought to write that rule.

What you need to start

Less than people assume:

  • A couple thousand historical leads with honest won/lost labels.
  • Source, campaign, timing, and geography for each.
  • Whatever the lead told you at intake — the form fields you already collect.
  • A consistent definition of 'qualified' that everyone on the team uses.

Keeping it honest

Models drift. Markets shift, campaigns change, and a score that was accurate in March quietly degrades by August. Retrain on a schedule, monitor calibration against actual outcomes, and never let the score run unwatched.

Also watch for feedback loops: if reps only call high-scored leads, low scores never get a chance to prove the model wrong.

Why Global Advanta is the cutting-edge option

Our scoring runs on live CRM data and writes its reasoning to a log you can read, so a rep can see why a lead ranked where it did. And the score feeds your ad platforms as a conversion signal, so better routing also buys you better traffic.

Takeaway

Start with clean outcome labels, retrain on a schedule, and always leave a slice of low-scored leads worked so the model can be corrected.

More on custom builds and adjacent topics from the Global Advanta team.

Next steps

Keep going — here's where to look next.

Ready to see what Advanta can do for you?

Get My Free Audit