Most B2B tools give you single-dimension scoring. "This lead is qualified" or "this lead scored 73/100." Then you're supposed to figure out what that means and what to do about it.
That's not how buying works.
Buying happens across 3 dimensions simultaneously: company fit, persona match, and engagement level. A perfect-fit company with zero engagement isn't ready. A highly engaged person at a terrible-fit company won't close. A decision maker who's just browsing shouldn't get a sales call.
Single-dimension scoring forces you to guess which dimension matters most. We don't make you guess.
Before we explain the philosophy, just watch it work:
https://demo.arcade.software/9OwmnbEs9d42nf526SwB
What you'll see is the product doing exactly what we're about to explain: taking messy, multi-source data and turning it into clear prioritization across company fit, persona match, and engagement timing.
Now let's talk about why we built it this way.
Here's what happens when you collapse 3 dimensions into 1 score:
You import 10,000 contacts from Sales Navigator. Your scoring tool gives you a ranked list: 87/100, 85/100, 82/100, 79/100...
You call the top 50. Some conversations go nowhere. Some people say "not interested." Some say "call back in 6 months." A few actually book meetings.
You have no idea why. The score told you they were qualified. But qualified for what? Are they the right company? The right person? The right timing?
You're flying blind with a single number.
We started Unstuck Engine because we kept seeing this pattern. Companies invest in lead scoring, get a ranked list, then still waste time on bad-fit leads or miss opportunities because they can't tell the difference between "high score because perfect fit but cold" and "high score because terrible fit but very engaged."
The solution isn't better single-dimension scoring. It's separate scoring for each dimension, then intelligent combination.
In who we build for, we explained that you can run up to 8 ICPs and up to 8 Personas simultaneously. What we didn't explain is what happens when those definitions meet real data.