Every Reaction Tells a Story: The Koka Signal Model for Reading Buyer Intent

Your next deal already engaged with your content. You just didn't notice.

Most sales teams live in their CRM. They track email opens, demo requests, whitepaper downloads. They treat those as intent signals. Meanwhile, a VP at a target account has been liking your CEO's posts for three weeks. A director at another account just followed three of your executives in one day. A procurement lead hit the Insight reaction on your pricing post yesterday.

These are not vanity metrics. They are buying signals dressed as social engagement. And most companies have no system for reading them.

I built one. I call it the Koka Signal Model. It turns passive social engagement into a scored pipeline signal you can act on, route, and close.

Why Traditional Intent Data Fails

Third-party intent data tells you a company visited a pricing page. It doesn't tell you who. It doesn't tell you why. It gives you an account-level flag and a prayer that someone in your SDR queue picks up the phone.

Social signals are different. They are identity-tied. You know exactly who engaged. You know what content triggered the response. You know the reaction type — which, as I'll show you, carries dramatically different meaning depending on which emoji they chose.

A VP who clicks Like on your blog post could be skimming. A VP who hits Insight on your framework post is thinking deeply about your space. One of these is worth 5x the other. Traditional intent tools treat them the same. They're not the same.

The Signal Scoring Formula

Single signals are weak. Anyone can accidentally like a post. Compound signals — multiple engagement types from multiple people at the same account in a short window — are where pipeline lives.

Here is the formula I use to score every contact in my signal database:

Let me walk through a real example. A VP of Marketing at a Series B company follows you and your CEO. She's used the Insight reaction on two of your posts in the last week, and a Like on a third. She engaged with both your content and your CEO's. One other person from her company liked your post last week.

Follow Weight: follows 2+ execs = 3pts × 1.5 = 4.5. Reaction Diversity: Insight present = 5pts × 2 = 10. Recency: last 7 days = 3pts. Executive Coverage: 2+ execs' content = 3pts. Account Density: 2+ people = 3pts.

Total: 23.5. That's not a lead. That's a deal waiting to happen. And the signal didn't come from your CRM. It came from your LinkedIn notifications.

The Four Signal Tiers

Once you score a contact, you need to know what to do. I use a four-tier system that maps directly to action.

The key discipline: do not jump the tiers. A contact who scores a 3 today might be a 10 in three weeks. If you pitch them at a 3, you burn the relationship before it builds. The signal model forces patience. Wait for the compound. Move when the math says move.

Every Reaction Carries a Different Story

LinkedIn gives you nine reactions. Most people treat them as interchangeable. They are not. Each one reveals something different about the person behind it.

The Insight reaction is the one you're hunting for. When someone hits Insight on your framework post, they're not just scrolling. They're processing. They're applying your thinking to their situation. That is the signal of someone who will take a meeting — not because you pitched them, but because they already did the mental work of connecting your ideas to their problems.

The reaction pattern matters as much as the individual reaction. Someone who uses the same reaction on every post is skimming. Someone who uses mixed reactions across different content types is genuinely reading. Someone who reacts to posts from 30 days ago is digging into your profile — researching you the way a buyer researches a vendor before reaching out.

From Score to Pipeline: The Workflow

The model is useless if it stays theoretical. Here's how you operationalize it.

Step one: Build a watchlist. Identify 20-30 target accounts and map the key executives. Follow them. Monitor their engagement with your content and your executive team's content.

Step two: Score weekly. Once a week, run the formula against every contact who showed any signal activity. Update the scores. Watch for movers — people who jumped from Warming to Hot since last week.

Step three: Route by tier. Cold contacts stay tracked. Warming contacts go into a light nurture sequence — engage with their content, don't pitch. Hot contacts get outreach within 72 hours with a message that references the engagement. Urgent contacts get same-day outreach.

Step four: Track signal-to-meeting conversion. Measure how many scored contacts become conversations, then meetings, then pipeline. This is how you prove the model works — and how you justify building infrastructure around it.

The companies that win at this don't have better products. They have better signal detection. They see the deal before anyone else does. That's the advantage.