Lead Prioritization

Pipeline contract

Stage 11 of 20. Reads 10-scored.csv, 09-signals.csv. Writes 11-prioritized.csv (adds intent_score, engagement_score, priority_score, tier), sorted best first.

• Engagement for a cold list = audience evidence: source_type (engager > follower > member/attendee > search) and source_count.
• Keep the three component scores next to priority_score so anyone can see why a lead ranks where it does.
• Rows with screen=fail are carried through unranked, not deleted.
• Cap the outreach set here (default: top 500, or fewer if fewer qualify — never pad).
• On a net-new cold list engagement is 0 and intent is thin, so the absolute tiers below bunch at the bottom. The ORDER is still valid: work from the top. Say this in the stage report; if the human wants cold-run thresholds, propose them, do not edit the tables.

Files live in runs/<run>/. Column definitions: /prospecting-system. Standalone use: ignore this block.

When to Use

• Processing a new lead list and need to decide who to contact first
• Building a priority queue for SDR teams to work through daily
• Designing lead scoring models for CRM or marketing automation
• Optimizing campaign ROI by focusing outreach on highest-probability prospects

Framework

Why Prioritization Matters

Without prioritization, your team works leads in the order they were imported — alphabetically, randomly, or whoever the SDR happens to click on first. This guarantees that your best prospects get the same treatment as your worst ones.

With prioritization, your team contacts the highest-probability leads first, every day. The result: same effort, 2-3x more meetings.

Without prioritization:
100 emails/day × 3% reply rate = 3 replies/day (random quality)

With prioritization (best leads first):
100 emails/day × 8% reply rate (top leads) = 8 replies/day

The Lead Priority Score

A composite score that combines three dimensions:

Priority Score = (Fit Score × 0.30) + (Intent Score × 0.45) + (Engagement Score × 0.25)

Why intent is weighted highest: A perfectly-fit company with no intent is a cold target. A moderately-fit company with strong intent is likely in-market. Intent is the strongest predictor of near-term conversion.

Dimension 1: Fit Score (30% of total)

How well does this lead match your ICP? (Derived from your ICP Matrix)

Total Fit Score: 0-100

Fit Score Tiers

Dimension 2: Intent Score (45% of total)

How likely is this lead to be in-market right now?

Total Intent Score: 0-100

Critical concept: Signal Decay. Intent signals lose value over time. A funding round from last week is urgent. A funding round from 6 months ago is a fact, not a signal. Apply decay rates to keep your scoring model honest.

Intent Score Tiers

Dimension 3: Engagement Score (25% of total)

How has this lead interacted with your outreach and content?

Total Engagement Score: 0-100 (capped at 100 even if multiple activities exceed)

Note: For net-new leads with zero engagement history, the engagement score is 0, and the priority is determined entirely by fit + intent. This is expected — cold outbound starts with fit and intent. Engagement builds over time.

Computing the Final Priority Score

Priority Score = (Fit × 0.30) + (Intent × 0.45) + (Engagement × 0.25)
Maximum possible: 100

Priority Tiers

The Daily Priority Queue

Transform your scoring model into a working system:

DAILY PRIORITY QUEUE

Morning Routine (9:00 AM):
1. Pull all leads with Priority Score > 60
2. Sort by score (highest first)
3. Check for new signals on top 20 leads
4. Re-score any lead with a new signal detected

Outreach Allocation:
├── P1 (Hot): 40% of daily capacity → personalized, multi-channel
├── P2 (Warm): 35% of daily capacity → signal-based email
├── P3 (Active): 25% of daily capacity → campaign sequences
└── P4-P5: Not in daily queue (automated nurture only)

End-of-Day Review:
- Update engagement scores based on replies/opens
- Move any newly-engaged leads up in priority
- Log new signals detected during outreach

Dynamic Re-Scoring

Lead priority is not static. Re-score when:

Prioritization for Different Campaign Types

Common Prioritization Mistakes

Templates

Lead Scoring Model Template

# Lead Scoring Model: {{Company/Product Name}}
# Version: {{version}} | Updated: {{date}}

## Fit Score Components (30% weight)
| Component | Tier 1 (pts) | Tier 2 (pts) | Tier 3 (pts) |
|-----------|-------------|-------------|-------------|
| ___ | ___ | ___ | ___ |

## Intent Score Components (45% weight)
| Signal | Points | Decay Rate | Source |
|--------|--------|------------|--------|
| ___ | ___ | ___ | ___ |

## Engagement Score Components (25% weight)
| Activity | Points |
|----------|--------|
| ___ | ___ |

## Priority Tiers
| Tier | Score Range | Action | SLA |
|------|-----------|--------|-----|
| P1 | ___ | ___ | ___ |
| P2 | ___ | ___ | ___ |
| P3 | ___ | ___ | ___ |

Tips

• Simplicity beats complexity. A 3-factor scoring model that your team actually uses beats a 15-factor model that lives in a spreadsheet nobody opens. Start simple, add complexity only when data shows it improves outcomes.
• The best validation for your scoring model: run a correlation analysis between lead priority scores and actual close rates. If P1 leads aren't closing at 2-3x the rate of P3 leads, your model is wrong.
• Signal decay is the most overlooked concept in lead scoring. Without it, your "hot leads" list fills up with stale data that was hot 6 months ago. Build decay into your model from day one.
• Don't let perfect be the enemy of good. If you don't have intent data, skip that dimension and weight fit + engagement more heavily. Some prioritization always beats no prioritization.
• Review and adjust your weights quarterly. As your product, market, and sales motion evolve, the relative importance of fit vs. intent vs. engagement shifts.

Progressive disclosure: load CRM-specific scoring implementation guides and automation workflows only when building scoring for a specific tool.