The short version
Classic lead scoring is a pile of hand-tuned rules that nobody remembers writing. A decision model replaces the guesswork with decomposed judgements — intent, seniority, fit, timing — each returned with a probability.
You keep the weighting in your own code, so the scoring model stays explainable even though the individual judgements come from a model.
Why this matters
Scoring is where marketing and sales argue loudest, because the rules are invisible and the disagreements are about intuition. Structured judgements turn that argument into a formula you can version and review.
The other benefit is routing. With per-judgement confidence you can send obvious leads straight to a rep, drip the uncertain ones into nurture, and stop treating a 0.51 as a decision.
The question set
Ask atomic questions, never one overall quality score. The classic decomposition is intent, authority, need and timeline, plus firmographic fit if you have it.
- Intent — does this activity suggest active evaluation? (noul)
- Authority — what is this person's likely decision role? (choice)
- Need — how closely does the stated problem match what we solve? (score)
- Timeline — is there any signal of near-term urgency? (noul)
- Fit — how well does the account match the profile that converts? (score)
Weighting and routing in code
Combine the outputs in your own formula rather than asking the model for a final number. Something like intent times authority plus need, with a ceiling, is enough to start — the point is that changing priorities is a coefficient edit.
Then set two thresholds: auto-route above the top one, human review in the middle band, nurture below. Log the probabilities so you can recalibrate after a quarter of real outcomes.
Common mistakes
Most scoring projects fail on measurement, not modelling.
- Asking for one blended “lead quality” score with no decomposition
- Never validating against closed-won data, so the weights stay fictional
- Treating activity volume as intent without checking the signal
- Routing on the top pick while ignoring a near-even probability split
How PixaSocial Ai helps
PixaSocial Ai connects audience models to outbound and follow-up, so scoring decisions land next to the campaigns that generated the lead. See System One models for social automation.