Lead qualification guide
A lead-scoring rubric humans can actually trust
A score is useful only when it changes what you do next and you can explain where it came from. Replace opaque “AI fit” numbers with a small rubric tied to observable evidence and clear disqualifiers.
Reviewed by the GenPilot team · Updated August 20, 2026
What matters
- Make hard disqualifiers explicit before scoring
- Score evidence quality separately from commercial fit
- Do not mark a lead outreach-ready without a contact path
- Record uncertainty instead of turning guesses into facts
Use gates before weights
Some failures should not be averaged away. If the company is outside the permitted geography, is a competitor you excluded, or has no evidence for the claimed trigger, a high score elsewhere should not rescue it.
After hard gates pass, weighted criteria help order the remaining opportunities. Keep the rubric short enough that a person can review it in under a minute.
A practical 30-point rubric
The exact weights should follow your offer, but this starting structure keeps opportunity, evidence, and actionability visible.
- 0–8 problem fit: how directly the observed need matches the offer
- 0–6 trigger strength: recency, specificity, and likelihood of active work
- 0–5 company fit: size, geography, business model, and delivery constraints
- 0–4 buyer relevance: confidence that the identified role owns the problem
- 0–4 contactability: verified email or a source-traceable contact route
- 0–3 evidence quality: primary, current, and specific sources score highest
Define what “ready” means
A lead can be strategically interesting but not ready for outreach. Reserve “ready” for candidates that pass minimum fit, have supported claims, and include a usable contact route. Keep research-worthy leads in a separate state rather than pretending they are actionable.
Calibrate with decisions, not vibes
Review a small set of leads blindly, compare human decisions with the score, and inspect disagreements. When a low-value lead scores highly, change the criterion that allowed it through. When a strong lead fails, decide whether the rubric missed a real signal or the reviewer is relying on information the system could not see.
Questions
Should contactability affect lead score?
Yes, but it should also be visible as its own requirement. A perfect account with no responsible contact path may deserve research, but it is not ready for outreach.
Should AI decide which leads to contact?
AI can organize evidence and apply a rubric consistently. A person should retain the final decision, especially when evidence is ambiguous or outreach could affect reputation.
Put the workflow to work