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.

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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.

| Signal | Reliability | Urgency | Example | |--------|------------|---------|---------| | Visited your website (specific pages) | Very High | High | Viewed pricing page twice in one week | | Downloaded your content | Very High | Medium-High | Downloaded "Outbound Playbook" guide | | Attended your webinar | Very High | Medium | Registered and attended live | | Replied to previous outreach positively | Very High | Very High | "Not now, but reach back out in Q2" | | Free trial or demo request | Very High | Critical | — | | Opened previous emails (multiple times) | Medium | Medium | Opened 3 emails but didn't reply |

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.

| Signal | Reliability | Urgency | Detection Method | |--------|------------|---------|-----------------| | Researching your category on G2/Capterra | High | Very High | G2 Buyer Intent, Capterra alerts | | Searching for competitor alternatives | High | Very High | Intent data providers (Bombora, 6sense) | | Consuming content about your problem space | Medium-High | Medium | Topic-based intent providers | | Engaging with competitor content | Medium | Medium | Social monitoring | | Posting questions in relevant communities | Medium | Medium-High | Reddit, Slack communities, forums |

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.

| Signal | Reliability | Urgency | Source | |--------|------------|---------|--------| | Hiring signals | High | High | LinkedIn Jobs, Indeed, careers page | | Funding announcements | High | Medium-High | Crunchbase, TechCrunch, press releases | | Leadership changes | Medium-High | Medium | LinkedIn, press releases | | Product launches | Medium | Medium | Company blog, PR, Product Hunt | | Tech stack changes | Medium | Medium-High | BuiltWith, Wappalyzer, job descriptions | | Earnings reports | Medium | Low-Medium | SEC filings, press releases | | Awards/recognition | Low-Medium | Low | Industry publications | | Conference speaking | Low-Medium | Low | Event websites | | Office expansion | Medium | Medium | News, job listings in new locations | | M&A activity | High | Medium | News, SEC filings |

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 | Reliability | Urgency | Source | |--------|------------|---------|--------| | Posted about a pain you solve | High | High | LinkedIn, Twitter | | Shared a competitor's content | Medium | Medium | LinkedIn, Twitter | | Asked for vendor recommendations | Very High | Very High | LinkedIn, communities | | Commented on industry trends related to your space | Medium | Low-Medium | LinkedIn | | Changed their LinkedIn headline (new role) | High | High | LinkedIn notifications | | Published a thought leadership piece on your topic | Medium | Medium | LinkedIn, company blog |

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Signal Scoring Model

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

Signal Strength Matrix

| Factor | High Score | Medium Score | Low Score | |--------|-----------|-------------|-----------| | Recency | Last 7 days | Last 30 days | 30-90 days | | Directness | Explicit buying action (demo request, vendor search) | Implicit buying action (hiring, funding) | Tangential (content consumption) | | Frequency | Multiple signals in same timeframe | Single signal | Historical signal | | Specificity | Specific to your exact category | Related to your general space | Broadly related | | Source reliability | First-party or verified third-party | Public, verifiable | Inferred or aggregated |

Scoring Formula

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

Each factor rated 1-5:

| Score | Priority | Action | |-------|----------|--------| | 40-50 | Critical | Contact within 24 hours | | 30-39 | High | Contact within 3 days | | 20-29 | Medium | Include in next campaign batch | | 10-19 | Low | Add to monitoring list | | Below 10 | Noise | Ignore |

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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

| Combination | Confidence | Why | |------------|-----------|-----| | Funding + Hiring for target role | Very High | Money + action = active buying cycle | | Leadership change + Tech evaluation (G2) | Very High | New leader re-evaluating the stack | | Hiring SDRs + Content about scaling outbound | High | Building the function + researching solutions | | Competitor churn + Your website visit | Very High | Actively looking for alternatives | | Conference in your space + LinkedIn post about the topic | Medium-High | Problem is top of mind |

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.

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Building a Signal Monitoring System

Manual Monitoring (0 cost, 30 min/day)

| Activity | Frequency | Tool | |----------|-----------|------| | Check LinkedIn for job postings at target accounts | Daily | LinkedIn Jobs search, saved searches | | Monitor Crunchbase for funding in your verticals | Weekly | Crunchbase alerts (free tier) | | Track LinkedIn posts from target personas | Daily | LinkedIn feed, saved searches | | Check Google Alerts for target companies | Daily | Google Alerts (free) | | Scan industry communities for vendor questions | Weekly | Reddit, Slack groups, forums |

Automated Monitoring (Paid tools)

| Tool Category | Examples | What It Monitors | |--------------|---------|-----------------| | Intent data | Bombora, 6sense, ZoomInfo Intent | Category-level research signals | | Job monitoring | LinkedIn Recruiter, Otta, PredictLeads | Hiring signals | | Funding alerts | Crunchbase Pro, PitchBook | Capital events | | Tech detection | BuiltWith, Wappalyzer, HG Insights | Stack changes | | Social monitoring | Mention, Brand24, PhantomBuster | Social signals | | Website tracking | Clearbit Reveal, RB2B, Leadfeeder | Website visit signals |

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

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Signal-to-Message Mapping

Every signal should map to a specific outreach angle:

| Signal | Opening Line Approach | Campaign Angle | |--------|---------------------|----------------| | Hiring SDRs | "Building the outbound team?" | Scaling outbound | | Series B funding | "Post-Series B is when pipeline becomes a board metric" | Growth acceleration | | New VP Sales | "First 90 days in a new VP Sales role..." | New leader priorities | | Competitor evaluation | "Evaluating {{category}}? Here's what teams miss" | Competitive displacement | | Tech stack change | "Migrating to {{tool}} — most teams also rethink..." | Stack optimization | | LinkedIn post about pain | "Your post about {{topic}} resonated" | Thought leadership bridge | | Conference attendance | "Ahead of {{event}}, thought you'd find this relevant" | Event-based relevance |

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.

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Progressive disclosure: load signal provider integrations and API configurations only when setting up monitoring for a specific campaign.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: kenny589
• Source: kenny589/gtm-flywheel
• License: MIT

Install and usage instructions live in the source repository linked above.