Lead Scoring Is Broken. Score Fit, Intent and Timing.
Great signal scoring combines fit, intent and timing into one priority number. Learn how to weight each dimension and avoid the common scoring mistakes.
Mert · Founder, AiporateBUILDS THE SYSTEMS HE WRITES ABOUTMay 26, 2026·7 MIN READ·- Score fit, intent and timing together, never just one.
- Stacked weak intent signals often beat a single strong one.
- Apply recency decay so stale intent fades from the top.
- Calibrate the model against real outcomes and keep it transparent.
Three dimensions, one score
Good scoring answers one question: who should we work next? To answer it well you need three dimensions. Fit: does the account match your ICP. Intent: is it showing buying behavior. Timing: is that behavior fresh. A score that uses only one of these misleads.
An account can fit perfectly and be ice cold, or be on fire but completely wrong. Only the combination tells you who is both right and ready now.
Scoring fit and intent
Score fit from firmographics and tech stack against your ICP. Keep it simple and explainable: industry, size, stack, region. Score intent from behavior weighted by strength: a pricing-page repeat outweighs a single blog read by a lot.
Stack intent signals rather than relying on one. Several weak signals from one account often predict better than a single strong signal, because breadth of engagement is itself a signal.
Do not forget timing
Timing is the dimension most models ignore, and it is decisive. Intent decays fast. A pricing visit today is worth far more than the same visit a month ago. Apply a recency weight so stale intent fades and fresh intent rises.
Without decay, your hot list fills with accounts that were interested last quarter. With it, the top of the list is always who is active right now.
Calibrate against outcomes
A scoring model is a hypothesis. Test it. Correlate scores with real outcomes: did high-scoring accounts convert more? Reweight the inputs that predicted wins and cut the ones that did not.
Keep the model transparent so reps trust it and you can debug it. A black-box score that no one believes gets ignored, no matter how clever it is.
- Score fit, intent and timing together, never just one.
- Stacked weak intent signals often beat a single strong one.
- Apply recency decay so stale intent fades from the top.
- Calibrate the model against real outcomes and keep it transparent.
Frequently asked questions
What is signal scoring based on fit, intent and timing?
Signal scoring combines three dimensions into one priority number: fit, does the account match your ICP; intent, is it showing buying behavior; and timing, is that behavior fresh. A score using only one dimension misleads, because an account can fit perfectly and be ice cold, or be on fire but completely wrong. Only the combination tells you who is both right and ready now.
How do you score fit and intent for an account?
Score fit from firmographics and tech stack against your ICP, kept simple and explainable: industry, size, stack, region. Score intent from behavior weighted by strength, where a pricing-page repeat outweighs a single blog read by a lot. Stack intent signals rather than relying on one, because several weak signals from an account often predict better than a single strong one.
Why is timing the most important scoring dimension?
Timing is decisive because intent decays fast: a pricing visit today is worth far more than the same visit a month ago. Most models ignore it, so apply a recency weight that lets stale intent fade and fresh intent rise. Without decay your hot list fills with accounts that were interested last quarter; with it, the top of the list is always who is active right now.
How do you know if a scoring model works?
Treat the model as a hypothesis and calibrate it against real outcomes: correlate scores with conversions, then reweight the inputs that predicted wins and cut the ones that did not. Keep the model transparent so reps trust it and you can debug it. A black-box score that no one believes gets ignored, no matter how clever it is.
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