Case Study

Signal Intelligence System

A continuous monitoring system that tracks 6 signal sources, matches events to TAM companies in real time, and surfaces actionable intelligence for sales teams — before competitors notice the same opportunities.

1,451

Signals matched

Sources monitored

24/7

Coverage

Real-time

Alert delivery

The Problem

Sales teams operate in information darkness. A prospect raises a $50M Series C, hires a new VP of Logistics, and announces a facility expansion — and the sales team finds out 3 weeks later from a LinkedIn post. By then, every competitor has already reached out.

The gap is not data availability. The data exists. The gap is detection speed: matching relevant signals to your specific TAM companies and surfacing them before the window closes.

Six Signal Sources

Hiring Patterns

~380 signals

Job postings on LinkedIn, Indeed, Greenhouse. New roles = new priorities. A VP of Digital Transformation hire signals tech-buying intent.

Funding & M&A

~220 signals

Crunchbase, SEC filings, press releases. Capital events create buying windows. Acquired companies inherit new budgets.

Press & News

~310 signals

Industry publications, company blogs, PR wires. Expansion announcements, leadership changes, partnership deals.

Tech Stack Changes

~180 signals

BuiltWith, G2, vendor announcements. When a company switches TMS providers, that is a buying signal.

Event Attendance

~200 signals

Conference registrant lists, speaker lineups, sponsor directories. Physical presence = active buying research.

Content Engagement

~161 signals

Website visits, content downloads, email opens. Direct intent signals from your own properties.

How It Works

Ingest

Automated scrapers and API integrations pull data from all 6 sources on a continuous cycle.

Match

Each signal is compared against the TAM database. Company name, domain, and key personnel are used for matching.

Score

Matched signals are scored by recency, relevance, and signal type. A Series C announcement scores higher than a job posting.

Enrich

Matched signals are appended to the company profile, enriching the account record with temporal context.

Alert

High-priority signals trigger real-time alerts. Sales teams get notified within minutes, not weeks.

Trend

AI analysis identifies patterns: companies with 3+ signals in 30 days are flagged as active buying cycles.

Trend Detection

Individual signals are useful. Signal clusters are powerful. When a company shows 3+ signals in a 30-day window, the probability of an active buying cycle jumps dramatically.

Example cluster:

• Day 1: VP of Operations hired (LinkedIn posting)
• Day 8: Series B extension announced ($25M)
• Day 14: Attended FreightWaves LIVE conference
• Day 19: Visited your pricing page twice

→ Combined signal score: High priority. Recommended action: personalized outreach within 48 hours.

Impact

Without signal intelligence

Sales teams discover opportunities 2–4 weeks after competitors. Outreach is generic because there is no temporal context. Reply rates hover around 1–2%.

With signal intelligence

Detection within hours. Outreach references specific events the prospect just experienced. Reply rates reach 8–12% on Tier 1 accounts.

What earlier signals change operationally

Timing improves

Teams can reach out while the account is in motion, not weeks after the buying window cooled off.

Outreach gets more specific

Messages can reference real account movement instead of generic cold-email assumptions.

Prioritization gets cleaner

Signal clusters help the team decide which accounts deserve immediate attention and which can wait.

Keep reading

Playbook

Want signal intelligence for your pipeline?

We build monitoring systems that detect buying signals earlier, prioritize them more intelligently, and turn them into a cleaner action queue.

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