Step 1

Systematic Signal Capture

We collect engagement signals from company posts, executive profiles, seller content, and competitor-adjacent activity , normalizing everything into a single structured dataset with profile URL, engagement type, post URL, date, and source.

• Company page post engagement: likes, comments, shares, reactions
• Executive and seller profile post activity
• Competitor and category-adjacent engagement monitoring
• Timestamps for recency scoring and follow-up prioritization

Step 2

ICP Enrichment & Filtering

Every signal gets enriched with title, company, firmographics, and ICP fit data. Only contacts that match your ideal profile move forward , the noise gets filtered out.

• Job title, seniority, and department classification
• Company name, size, industry, and location appended
• Verified business email addresses
• ICP score based on your defined role and firmographic criteria

Step 3

Signal Scoring & Network Mapping

Each signal gets scored by relevance , considering signal type, recency, repetition, and account value. We also map engaged networks to identify accounts where multiple contacts are showing interest.

• Signal type weighting: comment > share > like > follow
• Recency scoring for time-sensitive outreach windows
• Repetition detection: multiple engagements from same account
• Cross-account network mapping for buying group identification

Step 4

Context-Rich Activation

Signals route to the right motion , seller outreach, nurture, retargeting, or watchlist , with messaging that references the exact behavior the prospect took. No more “I saw you liked our post” generic outreach.

• Signal-specific cadences: pricing comment vs. culture like have different plays
• Context-aware messaging referencing the exact post and engagement
• Multi-channel routing: LinkedIn, email, or both based on signal strength
• CRM-ready contact records with full engagement history

What counts as a “buying signal” in this system? ▼

Four categories: content engagement (comments, reactions, shares on your posts), competitor motion (engagement with competitor content or announcements), role and company triggers (new hires, funding, leadership changes), and network behavior (consistent interaction patterns across multiple people from the same account). Not every signal means “book a meeting now” , but every signal creates context for smarter outreach.

How is this different from just checking who liked our posts? ▼

Manual checking is ad-hoc and doesn’t scale. Our system captures signals systematically across company pages, executive profiles, seller posts, and competitor-adjacent activity , then enriches, scores, and routes them automatically. A rep might notice 5 likes; the system finds 200+ signals they’d never see.

Do we need a large LinkedIn following for this to work? ▼

No. Even smaller followings generate valuable signals , especially when you add competitor monitoring and seller profile activity. For companies with smaller organic presence, we lean more heavily on competitor signals and engaged network mapping.

How often are signals captured and delivered? ▼

Weekly , typically a Monday morning drop so your SDRs start the week with a fresh signal list. Timing keeps outreach close to when engagement happened, which is when intent is highest.

What’s the CMO’s role in making this work? ▼

You define the ICP, approve the signal sources, and set the strategy. We handle the collection, enrichment, scoring, and playbook design. The output is a revenue-ready system your team operates independently , with dashboards that show exactly how signals convert to pipeline.