scoring-md.md

Lead Scoring

You help users build lead scoring systems in Clay using formulas and enrichment data to prioritize outreach.

Reference

Read clay-enrichment-workflows.md for the scoring framework (Step 8 of the 9-step pipeline).

100-Point Scoring Model

Component	Points	What It Measures
Company size match	25 pts	Employee count fits ICP range
Recent signals	25 pts	Job changes, funding, hiring, tech adoption
Email deliverability	25 pts	Valid email, low bounce risk
ICP fit	25 pts	Industry, revenue, tech stack match

Tier Assignment

Tier	Score	Action
Tier 1	80-100	Immediate outreach, phone + email
Tier 2	60-79	Nurture sequence, email only
Tier 3	<60	Long-term nurture or discard

Building the Score in Clay

Use formula columns (0 credits) to calculate each component:

// Company size score (25 pts)
let sizeScore = 0;
let emp = {{employee_count}} || 0;
if (emp >= 50 && emp <= 500) sizeScore = 25;      // Sweet spot
else if (emp >= 20 && emp < 50) sizeScore = 15;    // Acceptable
else if (emp > 500 && emp <= 2000) sizeScore = 15; // Acceptable
else sizeScore = 5;                                 // Poor fit

// ICP fit score (25 pts)
let icpScore = 0;
if ({{industry}} == "SaaS" || {{industry}} == "Software") icpScore += 15;
if ({{revenue}} > 5000000) icpScore += 10;

// Signal score (25 pts)
let signalScore = 0;
if ({{recent_funding}} && {{recent_funding}} != "purple") signalScore += 10;
if ({{job_change_90d}} == "true") signalScore += 10;
if ({{hiring_signals}} == "true") signalScore += 5;

// Deliverability score (25 pts)
let delivScore = 0;
if ({{email_validation}} == "valid") delivScore = 25;
else if ({{email_validation}} == "catchall valid") delivScore = 15;
else delivScore = 0;

// Total
sizeScore + icpScore + signalScore + delivScore

Segmentation Formula (Tier Assignment)

let score = {{lead_score}} || 0;
score >= 80 ? "Tier 1 - Immediate" : score >= 60 ? "Tier 2 - Nurture" : "Tier 3 - Long-term"

Customizing for Your ICP

Before building, define:

Ideal company size range (e.g., 50-500 employees)
Target industries (e.g., SaaS, FinTech, Healthcare)
Minimum revenue (e.g., $5M ARR)
Must-have signals (e.g., recent funding, hiring for your target role)
Required tech stack (e.g., uses Salesforce, runs on AWS)

Adjust point values based on what matters most to your sales team.

Credit Cost

Scoring itself: 0 credits (all formulas)
Data needed for scoring: depends on enrichments run upstream
Scoring should be the LAST step, after all enrichment is complete

Examples

Example 1: "I want to score 5,000 leads before pushing to my sequencer" --> Build 4 formula columns (size, ICP, signals, deliverability). Sum into total_score. Add tier formula. Conditional CRM push: only Tier 1 + Tier 2. Cost: 0 credits for scoring itself.

Example 2: "My scoring is too simple -- I only use company size" --> Add signal-based scoring: Claygent for funding (2 credits), job change detection via LinkedIn (2 credits), hiring signals via careers page (1 credit). Feed all into scoring formula. Adds ~5 credits per row but dramatically improves prioritization.

Example 3: "How do I adjust scoring for different campaigns?" --> Create separate scoring columns per campaign. ABM campaign: weight company fit 40pts, signals 30pts, deliverability 20pts, size 10pts. Outbound campaign: weight deliverability 30pts, size 25pts, ICP 25pts, signals 20pts. Use views to filter by each campaign's tier.

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scoring.md