Navinspire-ia Lead Qualification
Qualification protects selling time. Score honestly, disqualify fast, document why. Use the deterministic helper for ICP totals and CSV validation.
The live book is Studio #/leads. leads action=rescore writes BANT-F on the desk. The desk loop and leads action=watch re-score locally. Heartbeat never hunts and never sends a sequence.
When to use
- Ranking a prospect list before outreach
- Routing A/B/C after enrichment or discovery
When not to use
- Pure research sheets without scoring (
account-research) - Inflating scores to hit activity quotas
Scoring model (BANT-F + ICP)
| Dimension | Questions | Signals |
|---|---|---|
| Budget | can they pay? | size, funding, current spend |
| Authority | decider or tourist? | role, buying process |
| Need | real pain? | trigger, cost of inaction |
| Timing | why now? | deadline, renewal, project |
| Fit / ICP | can we serve them? | sector, size, geo, stack |
Score each dimension 0-5 with evidence. Weighted default: Fit 30%, Need 25%, Timing 20%, Authority 15%, Budget 10%.
Tiers from total 0-100:
| Tier | Range | Action |
|---|---|---|
| A | ≥70 | contact now |
| B | 40-69 | nurture |
| C | <40 | discard / revisit condition |
Disqualification triggers
- No identifiable pain we solve
- Budget an order of magnitude off
- Fit failure (out of ICP)
- Deciders unreachable after agreed attempts
Log reason + revisit condition ("re-check after FY").
Helper script
# Validate CSV columns / URLs / confidence
python navin/skills/lead-qualification/scripts/score_leads.py sales/prospects.csv --validate-only
# Score rows that already have bant columns (fit,need,timing,authority,budget)
python navin/skills/lead-qualification/scripts/score_leads.py sales/prospects.csv -o sales/prospects-scored.csv
Expected optional columns for scoring: fit,need,timing,authority,budget (0-5 each) or a single icp_score.
Workflow
- Input leads + discovery/enrichment notes.
- Fill BANT-F; unknowns become next-call questions (do not invent).
- Run the script; route A/B/C.
- Update after material changes;
pipeline-analystconsumes tiers.
Rules
- Every score cites evidence or stays unknown.
- Optimism is not a data point.
- Never mark email
verifiedwithout enrichment proof / public source.
Anti-patterns
- Scoring everyone A
- Dropping disqualified rows without reason codes