The Math Behind Lead Scoring: How to Rank Without Guesswork

"Gut feel" is not a scoring model. If your team's definition of a "hot lead" changes depending on who's looking at the list, you don't have lead scoring, you have vibes with a spreadsheet attached.

Here's the actual math

Magnivo scores every lead on a 0–100 scale built from two additive components:

Firmographic fit — max 70 points:

• —Geography match: 18
• —Buyer title match: 18
• —Reachability (verified contact): 14
• —Quality signal: 10
• —Data completeness: 10

Aggregate signal score — max 30 points, computed as the sum of each detected buying signal's weight, multiplied by a freshness factor:

• —1.0x if detected within 14 days
• —0.7x within 30 days
• —0.4x within 60 days
• —0.1x floor beyond that

Then a hard rule kicks in: a bounce penalty of –30 if the contact's email is unverified or dead, and an automatic disqualification to score 0 if the company is already an existing customer — no amount of firmographic fit or fresh signal should ever route your CS team's own account back into new-business outreach.

The tiers

Clamp the total 0–100, and it sorts into three buckets: hot at 80+, warm at 50+, cold below that. Every score carries a version tag (Magnivo runs v2.0) so when the rubric changes, you can tell whether last quarter's "hot" leads were scored under the same rules as this quarter's.

Why "explainable" beats "black box"

The other thing this math buys you: every score comes with a breakdown. A rep can see why a lead is 84 instead of guessing — geography hit, buyer title hit, contact verified, one fresh funding signal at full weight. That's not just satisfying, it's how you catch a broken model before it burns 200 reps' worth of trust. An AI scoring system that can't show its work is a system nobody will believe the first time it's wrong.

Stop guessing. Start ranking on math you can defend in a meeting.

Firmographic fit tells you if a company could be a customer. Signal freshness tells you if they're ready right now. Multiply timing into your model, not just targeting, and your "hot" list actually means something.