kenny589/gtm-flywheel/intent-signals

Intent Signals

When to Use

• Building signal-based outbound campaigns (vs. spray-and-pray)
• Setting up monitoring systems for real-time prospect alerts
• Prioritizing which accounts to contact first from a large list
• Training SDRs or AI systems on what qualifies as a meaningful buying signal

Framework

What Intent Signals Are

An intent signal is any observable action or change that suggests a company or person may be ready to buy. It's the difference between "this company matches our ICP" (fit) and "this company matches our ICP AND is actively looking for a solution" (fit + intent).

Fit alone: "They're a 200-person SaaS company in our target industry." Fit + Intent: "They're a 200-person SaaS company that just posted 3 SDR job openings, raised a Series B last month, and their VP Sales is engaging with content about outbound scaling."

Intent signals don't guarantee a sale. They guarantee relevance — which is the single most important factor in cold outreach conversion.

The Intent Signal Taxonomy

All intent signals fall into one of four categories, each with different reliability and actionability:

Category 1: First-Party Signals (Highest Reliability)

Actions the prospect takes that directly involve your brand.

Challenge: You need website tracking, marketing automation, and CRM data to capture these. Many companies don't have this infrastructure.

Category 2: Third-Party Intent Signals (High Reliability)

Actions the prospect takes on other platforms that suggest buying intent.

Challenge: Requires paid intent data subscriptions. Quality varies wildly by provider.

Category 3: Public Signals (Medium Reliability, Free)

Observable events anyone can detect with the right monitoring.

Advantage: Free and widely available. These should be the backbone of any signal-based outbound strategy.

Category 4: Social Signals (Variable Reliability)

Content the prospect publishes or engages with on social platforms.

Signal Scoring Model

Not all signals are equal. Score each signal to prioritize outreach:

Signal Strength Matrix

Scoring Formula

Signal Score = (Recency × 3) + (Directness × 3) + (Frequency × 2) + (Specificity × 1) + (Source × 1)

Each factor rated 1-5:

Signal Combinations (Stacking)

Single signals are useful. Signal combinations are powerful. When multiple signals converge, the probability of buying intent increases dramatically:

High-Confidence Combinations

Signal Stacking Rules

1. Two signals from different categories = 2x the score. A hiring signal (public) + a G2 research signal (third-party) is much stronger than two hiring signals.

1. Recency trumps everything. A weak signal from yesterday beats a strong signal from 3 months ago.

1. Person-level signals > Company-level signals. The VP Sales posting about outbound challenges (person) is stronger than the company posting 3 SDR jobs (company) — because you know exactly WHO is feeling the pain.

1. Don't stack noise. Three weak signals don't equal one strong signal. "Posted on LinkedIn" + "website has a blog" + "company is in tech" = noise, not intent.

Building a Signal Monitoring System

Manual Monitoring (0 cost, 30 min/day)

Automated Monitoring (Paid tools)

Signal Pipeline Architecture

Data Sources (APIs, scraping, alerts)
    ↓
Signal Detection Layer
    → Parse raw data for relevant signals
    → Categorize (hiring, funding, tech, social, first-party)
    ↓
Scoring Engine
    → Apply signal scoring formula
    → Stack multiple signals per account
    → Calculate composite score
    ↓
Prioritization Queue
    → Tier 1 (score 40+): Immediate outreach
    → Tier 2 (score 30-39): Next batch
    → Tier 3 (score 20-29): Add to campaign
    → Below 20: Monitor or discard
    ↓
Outreach Trigger
    → Route to appropriate campaign
    → Match signal to personalization angle
    → Enrich contact data if needed

Signal-to-Message Mapping

Every signal should map to a specific outreach angle:

Templates

Signal Tracking Sheet

| Company | Signal Type | Signal Detail | Date Detected | Source | Score | Action | Status |
|---------|------------|--------------|---------------|--------|-------|--------|--------|
| ___ | ___ | ___ | ___ | ___ | ___/50 | ___ | ___ |

Signal-Based Campaign Brief

Campaign: {{campaignName}}
Target Signal: {{primarySignal}}
Supporting Signals: {{secondarySignals}}
 
Signal Detection:
- Source: {{dataSource}}
- Refresh frequency: {{howOften}}
- Estimated volume: {{leadsPerWeek}}
 
Personalization Map:
- Opening line template: "___"
- Value prop connection: "___"
- CTA: "___"
 
Qualification Threshold:
- Minimum signal score: ___
- Required firmographic fit: ___
- Maximum signal age: ___ days

Tips

• The best time to email someone is within 48 hours of a signal firing. After 2 weeks, the signal is stale and your "I noticed..." line sounds like a lie.
• Don't chase every signal. Pick 2-3 signal types that correlate with your highest close rates and build systematic monitoring around just those. Depth beats breadth.
• Hiring signals are the single most reliable free signal for B2B outbound. If a company is hiring for the role your product serves, they are actively investing in that function.
• Person-level signals always beat company-level signals. "The VP Sales posted about needing better pipeline visibility" is 10x more actionable than "the company raised a round."
• Create a feedback loop: track which signal types lead to the most meetings. After 3 months, double down on the winners and drop the losers.
• Signal + fit > fit alone > signal alone. A perfectly matched ICP company with intent signals is your #1 priority. A perfectly matched ICP company with no signals is Tier 2. A non-ICP company with signals is usually noise — don't chase it.