signal-scoring

name	signal-scoring
description	Score and prioritize prospects using buying signals — hiring, funding, tech stack changes, executive moves, and intent data. Use when the user wants to score leads, prioritize outreach, detect buying intent, rank prospects, or find the hottest accounts. Triggers on: "score leads", "prioritize", "signals", "buying intent", "trigger events", "who should we contact first", "lead scoring", "signal detection", "intent data", or any request to rank prospects by readiness-to-buy.
license	MIT
compatibility	Claude Code, Jesse, Codex, Hermes, Windsurf, OpenCode, Gemini CLI, Copilot, Zed, VS Code, Goose
metadata	{"version":"1.0.0","author":"LeadMagic","category":"prospecting","tags":["signals","scoring","intent","prioritization","triggers"],"related_skills":["icp-scoring","lead-enrichment","proactive-alerts"],"frameworks":["ColdIQ Signal Taxonomy","Explorium Signal-Based Selling","Aaron Ross — Predictable Revenue","Joey Gilkey — Phone Intent (reachability scoring)"]}

Signal Scoring

Overview

Most prospects aren't ready to buy. Signals separate the ones who are from the ones who might be someday. A signal-anchored outreach program consistently outperforms list-blast approaches — triggers beat blasts every time.

This skill builds a configurable signal scoring system. You define which signals matter for your business, assign weights, and produce tiered lead lists that tell your team exactly who to contact first and why.

When to Use

• "Score these accounts and tell me who to reach out to first"
• "Set up a signal scoring system for our ICP"
• "Which of our target accounts are showing buying signals?"
• "Prioritize this list by intent"
• "Build a trigger-based outreach prioritization model"

Authoritative Foundations

Signal-based selling as practiced by ColdIQ and Explorium demonstrates that trigger-anchored outreach achieves 2-3x higher reply rates than generic cold email. The core principle: reach out when something changed, not when you decided to run a campaign.

The priority rule is recency + specificity. A website pricing page visit from yesterday beats a Bombora intent surge from last week. The signal with the clearest, most recent tie to purchase intent leads the outreach.

For phone-led outbound, layer Joey Gilkey Phone Intent on top of buying signals — reachability is a separate dimension from purchase intent. High intent

• low phone intent → email/LI first. → references/joey-gilkey-bucketing.md

Prerequisites

• ICP defined (use icp-scoring first)
• Target account list to score
• Data sources: job boards, funding databases, tech stack monitors, intent platforms

Step-by-Step Process

Phase 1: Define Signal Taxonomy

Choose from four categories. Assign weights based on what correlates with closed-won deals in your sales data:

Signal Category	Example Signals	Default Weight
Hiring	Job postings for roles your product supports, team expansion, new leadership	0-25 pts
Funding	Recent round closed, new investor, funding amount	0-20 pts
Tech Stack	Added complementary tool, removed competitor, new technology adoption	0-25 pts
Intent	Website visits, content downloads, search behavior, review site activity	0-30 pts

Total possible: 100 points. Adjust weights based on your sales data — weight the signals that correlate with closed-won.

Phase 2: Configure Signal Detection

For each signal category, set up detection sources:

• Hiring: Job board search (LinkedIn, Indeed, Greenhouse). Sales hiring = highest signal. LeadMagic Jobs Finder for automated detection.
• Funding: Crunchbase, PitchBook, news monitoring.
• Tech Stack: BuiltWith, Wappalyzer, Clay technographics. Monthly snapshots with change detection.
• Intent: Bombora, G2, LeadMagic Company Search for firmographic signals.

Phase 3: Score Each Account

For each target account:

1. Check each signal category
2. Apply the weight for each detected signal
3. Sum to get total signal score (0-100)

Priority rule for multiple signals: score only the single most relevant signal per account. Recency + specificity determines which signal leads.

Phase 4: Tier and Route

Tier	Score Range	Action	SLA
A — Hot	80-100	Immediate personalized outreach. Multi-thread.	Within 24 hours
B — Warm	60-79	Enroll in sequence. Monitor for new signals.	Within 3 days
C — Cool	40-59	Nurture track. Re-score in 30 days.	Nurture drip
Discard	<40	Not yet. Re-evaluate quarterly.	No action

Output Format

Scored account list with signal breakdown:

Account: Acme Corp
Total Signal Score: 85 (Tier A — Hot)

Signals detected:
- Hiring: 3 new SDR roles posted this week (20 pts)
- Funding: Series B closed last month, $25M (15 pts)
- Tech Stack: Added Salesforce last quarter (15 pts)
- Intent: Visited pricing page 3x this week (25 pts)
- ICP fit bonus: Exact match on industry + size (10 pts)

Recommended action: Outreach within 24 hours. Lead with hiring signal.
Suggested angle: "Saw you're scaling the SDR team — most teams at your
stage struggle with ramp time. We helped [similar company] cut it by 40%."

Quality Check

• Signal weights calibrated against actual closed-won data
• Only one primary signal drives outreach per account
• Tier routing rules defined and automated
• Signal sources documented with refresh cadences
• Re-score schedule set (weekly for A tier, monthly for B/C)

Common Pitfalls

1. Acting on too many signals per account. A company showing 5 signals doesn't mean send 5 different emails. Pick one — the most recent and specific — and lead with it.

2. Weights not calibrated to your data. Default weights are a starting point. Review quarterly against your closed-won deals and adjust.

3. Stale signals. A funding round from 6 months ago is no longer a trigger. Signal freshness matters. Set expiration windows.

4. No action on signals. A signal without an immediate, automated action is just noise. Every detection should trigger a workflow.

5. Equal-weight scoring. Not all signals are equal. A company hiring for a role your product directly supports is worth more than a generic intent signal. Weight accordingly.

Execution Artifacts

• references/framework-notes.md — Named frameworks and reference tables
• templates/output-template.md — Deliverable shell for agent output
• scripts/check-output.py — Lightweight deliverable validator
• references/joey-gilkey-bucketing.md — Phone Intent scoring layer (repo root)
• references/cold-calling-experts-index.md — Phone vs email signal router (repo root)

Related Skills

• icp-scoring: ICP fit scoring runs before signal scoring
• proactive-alerts: Automated signal monitoring and alert routing
• cold-email-strategy: Turn signal scores into outreach sequences
• lead-enrichment: Enrich accounts to enable signal detection