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
signal-scoring.md for the complete scoring framework (weights, recency, thresholds, SLAs, plays)
- Read
gtm-plays.md for 11 executable GTM plays and multi-channel coordination
- Read
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
| Approach |
Reply Rate |
Contract Value |
| Cold outreach (no signal) |
6-8% |
Baseline |
| Single signal-based |
18-22% |
2-3x baseline |
| Multi-signal stacked (3+) |
35-40% |
3-4x baseline |
| Signal + ABM multi-touch |
36% meeting rate |
Highest |
Signal Scoring Framework
Tier 1 - Hot Signals (50-100 points)
| Signal |
Points |
| Demo/pricing request |
100 |
| 3+ pricing page visits in 7 days |
80 |
| Champion job change to target account |
75 |
| Multiple stakeholders from same account |
70 |
| Product trial signup |
65 |
| G2 comparison with competitors |
60 |
| 5+ website visits in 2 weeks |
50 |
Tier 2 - Warm Signals (20-49 points)
| Signal |
Points |
| Series A/B/C funding |
45 |
| Relevant job posting |
40 |
| Bombora topic surge (score 70+) |
40 |
| Case study download |
35 |
| LinkedIn engagement with your content |
30 |
| Webinar attendance |
25 |
| 3+ blog post visits |
20 |
Tier 3 - Cool Signals (5-19 points)
| Signal |
Points |
| Company news (expansion) |
15 |
| Single website visit |
10 |
| Industry report download |
10 |
| Email open (no click) |
5 |
| Social follow (no engagement) |
5 |
Recency Multipliers
| Recency |
Multiplier |
| Last 24 hours |
1.5x |
| Last 7 days |
1.2x |
| Last 14 days |
1.0x |
| Last 30 days |
0.7x |
| 30+ days ago |
0.3x |
Action Thresholds
| Score |
Heat Level |
Action |
SLA |
Owner |
| 150+ |
Red Hot |
Immediate manual outreach |
< 1 hour |
AE |
| 100-149 |
Hot |
Personalized sequence |
< 24 hours |
SDR |
| 50-99 |
Warm |
Automated nurture + SDR monitoring |
< 72 hours |
SDR + Marketing |
| 20-49 |
Cool |
Marketing nurture campaigns |
This week |
Marketing |
| 0-19 |
Cold |
Monitor for signal changes |
Ongoing |
System |
Compound Scoring Examples
| Scenario |
Signals |
Calculation |
Score |
Heat |
| Red Hot |
Pricing page (80) + Champion job change (75) + Bombora surge (40) |
80+75+40 |
195 |
Red Hot |
| Very Warm |
Funding (45) + Hiring (40) + 3 blog visits (20) |
45+40+20 |
105 |
Hot |
| Warm |
Website visit (10) + LinkedIn engagement (30) + Email click (15) |
10+30+15 |
55 |
Warm |
| Cool |
Blog visit (10) + Email open (5) |
10+5 |
15 |
Cold |
Building a Complete Scoring System
- Choose your signals - Pick 5-10 signals from Tiers 1-3 based on ICP and available tools
- Assign weights - Use the framework above as starting point, adjust based on your conversion data
- Set recency decay - Apply multipliers so stale signals do not inflate scores
- Define thresholds - 150/100/50/20 breakpoints, adjust after 30 days of data
- Map actions - Each threshold gets a specific play, channel, owner, and SLA
- Automate routing - Clay scores + Slack alerts + CRM updates
- 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.
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