Lead Prioritization

Description

Rank leads by conversion probability using weighted scoring models. Stack signals, fit scores, and engagement data to build a dynamic priority queue that tells your team exactly who to contact next.

What this skill does

1. 1Computes a composite Priority Score using three weighted dimensions: Fit Score (30%), Intent Score (45%), and Engagement Score (25%), with explicit decay rates applied to intent signals over time.
2. 2Scores fit across 7 ICP attributes (company size, industry, revenue, persona title, geography, tech stack, growth stage), intent across 8 signal types with recency-weighted decay, and engagement across 9 past interaction types.
3. 3Assigns leads to 5 priority tiers (P1 Hot through P5 Cold) with defined action and SLA per tier (P1: contact within 24 hours, P5: review quarterly).
4. 4Produces a daily priority queue allocation: 40% of capacity to P1 (hyper-personalized multi-channel), 35% to P2 (signal-based email), 25% to P3 (campaign sequences).
5. 5Adjusts formula weights for different campaign types: ABM (fit and intent equal), trigger-based (intent dominant), re-engagement (engagement dominant).

When to use

• Processing a new lead list and needing to rank who to contact first before sending anything.
• Building a daily priority queue for an SDR team so the highest-probability leads are always worked first.
• Designing lead scoring models in a CRM or marketing automation platform with explicit weighting and decay logic.
• Optimizing campaign ROI by concentrating outreach effort on the top 20% of leads by conversion probability.

Tools used

Frequently asked questions