multi-signal

Multi-Signal Stacking and Scoring

Multi-signal stacking is the highest-performing outbound strategy: 3+ signals = 35-40% reply rate vs 6-8% cold. This sub-skill covers the scoring framework, recency multipliers, action thresholds, response SLAs, and compound scoring logic.

Reference Files

• Read {SKILL_BASE}/resources/signal-scoring.md for the complete scoring framework (weights, recency, thresholds, SLAs, plays)
• Read {SKILL_BASE}/resources/examples/signal-campaigns/gtm-plays.md for 11 executable GTM plays and multi-channel coordination
• Read {SKILL_BASE}/resources/signal-detection-tools.md for 30-trigger quick reference with detection tools, timing windows, Clay credit costs, signal freshness rules (when signals expire), reliability tiers, and signal sources by data party (1st/2nd/3rd)

Performance Benchmarks

Signal Scoring Framework

Tier 1 - Hot Signals (50-100 points)

Tier 2 - Warm Signals (20-49 points)

Tier 3 - Cool Signals (5-19 points)

Recency Multipliers

Action Thresholds

Compound Scoring Examples

Building a Complete Scoring System

1. Choose your signals - Pick 5-10 signals from Tiers 1-3 based on ICP and available tools
2. Assign weights - Use the framework above as starting point, adjust based on your conversion data
3. Set recency decay - Apply multipliers so stale signals do not inflate scores
4. Define thresholds - 150/100/50/20 breakpoints, adjust after 30 days of data
5. Map actions - Each threshold gets a specific play, channel, owner, and SLA
6. Automate routing - Clay scores + Slack alerts + CRM updates
7. Review monthly - Recalibrate weights based on closed-won attribution

Implementation Tools

• Clay: Custom scoring formulas with enrichment data
• Common Room: Built-in scoring across 50+ sources ($1K+/mo)
• Koala: Product + website signal scoring (Free/$750/mo)
• HubSpot/Salesforce: Native lead scoring with intent integration
• 6sense: AI predictive scoring ($35K+/yr)

Key Rules

• 3+ signals = always worth immediate outreach (35-40% reply rate)
• Recency matters more than signal count - 1 fresh Tier 1 signal > 3 stale Tier 2 signals
• Response speed is the #1 lever: 5-min response = 21x more likely to qualify vs 30 min
• 50% of signal value is lost after 7 days - speed wins
• Stack across categories (website + social + firmographic) for strongest compound signals
• Recalibrate weights monthly based on actual conversion data

Examples

Example 1: "Build me a complete signal scoring system" -> Design 3-tier framework with 8-10 signals, assign weights from the table above, apply recency multipliers, define 5 heat levels with actions/SLAs/owners, recommend Clay for scoring automation, set monthly review cadence. Map each threshold to a GTM play from gtm-plays.md (e.g., Play 5 for hiring signals, Play 8 for competitor bad reviews, Play 9 for champion job changes).

Example 2: "A prospect has 3 signals firing - what do I do?" -> Calculate compound score: sum points for each signal, apply recency multipliers, map to heat level. 150+ = AE immediate outreach within 1 hour (see Play 9: Champion Change). 100-149 = SDR personalized sequence within 24h. Include all 3 signals as context for personalization (without mentioning them directly).

Example 3: "How do I prioritize my signal queue?" -> Sort by compound score (highest first), then by recency of most recent signal. Red Hot (150+) always first. Within same heat level, prioritize accounts with freshest signals (24h > 7d > 14d). Assign capacity: AE handles top 5 Red Hot/day, SDR handles top 20 Hot/day. Use Play 10 (ServiceBell Allbound) for website visitor signals, Play 11 (Inbound Followers) for content engagement.