Does lead scoring actually work?
Yes — when it's built on real outcomes rather than guessed point values. Machine-learning lead scoring reaches 40–60% accuracy versus 15–25% for rule-based systems, and converts up to 75% better; adding behavioral signals lifts MQL-to-SQL rates by up to 40%. It matters because only 27% of leads handed to sales are actually qualified. Scoring sorts that 27% from the noise, so reps spend their limited selling time on accounts likely to close instead of working a list top to bottom.
Why unscored lists waste rep time
Only about 27% of the leads marketing hands to sales are actually qualified, yet reps work them as if they're equal. With reps spending only ~28% of the week selling, burning that time on the wrong 73% is expensive.
Scoring puts the accounts most likely to close at the top, so limited selling time goes where it pays off.
Rule-based vs machine-learning scoring
Hand-built point systems (+10 for a title, +5 for a download) drift fast and land around 15–25% accuracy. Machine-learning models trained on your closed-won data reach 40–60% accuracy and convert up to 75% better.
Behavioral signals — what an account actually does — lift MQL-to-SQL conversion by up to 40% over demographic-only models.
- Train on your real closed-won outcomes
- Combine fit + intent + behavioral signals
- Refresh as the model and market drift
- Write scores back into the CRM reps live in
Common questions
Less than people expect. Even a few hundred closed-won and closed-lost outcomes beat a hand-built point system. The model improves as more outcomes accumulate.