GTM Engineering · RevOps Systems · AI in GTM
A Practical Guide to Lead Scoring That Sales Doesn’t Hate
How to build transparent lead scoring that sales can actually use — without overfitting or endless tuning.
Lead scoring fails when it’s opaque, brittle, or misaligned with how reps actually sell. A practical model is simple, observable, and adjusts with feedback.
1. Start with a few score inputs
Keep the model understandable and tied to behaviors sales trusts.
score_inputs:
- title_seniority
- account_tier
- buying_signal
- meeting_booked
2. Make the logic readable
If a rep can’t explain the score, it won’t be used.
select
lead_id,
case
when meeting_booked = true then 50
when buying_signal = 'high' then 35
when account_tier = 'A' then 25
when title_seniority in ('Director', 'VP', 'C-Level') then 20
else 0
end as score
from leads;
3. Use lightweight automation
Publish scores where sales lives, and keep enrichment lightweight.
async function pushScoreToCRM(leadId, score) {
await crmClient.updateLead(leadId, {
lead_score: score,
score_updated_at: new Date().toISOString()
});
}
4. Build feedback loops
The best scores are calibrated with real outcomes.
def score_accuracy(qualified, total):
return round((qualified / total) * 100, 2)
Final takeaway
Lead scoring is an enablement product. Keep it transparent, pair it with feedback, and ship in small iterations.