What Makes a Good LinkedIn Engagement Signal

"Great post" is not an outbound signal.

It is LinkedIn activity. It shows the person exists and was online that day. It does not tell you anything about their current thinking on the topic, what they are evaluating, or whether they have any connection to a problem you solve.

Understanding the difference between a high-signal engagement and noise is the core classification problem in signal-led outbound.

The signal quality spectrum

Public LinkedIn engagement falls along a quality spectrum. At the low end: generic positive reactions and one-word comments. At the high end: specific questions that reveal evaluation language, comparisons, timelines, or stated problems.

Low-signal examples:

A thumbs-up reaction on a post about AI tooling. No text. No context.

A comment that says "Thanks for sharing!" on a post about LLM costs.

A comment that says "Agree!" under a take about vendor consolidation.

These show activity. They do not show what the person is thinking.

High-signal examples:

A comment asking "Has anyone benchmarked X vs Y? We're evaluating both this quarter."

A comment saying "We ran into this exact problem last month. Ended up switching off of Z."

A comment asking for vendor recommendations in a specific category with specific constraints named.

These show active engagement with a decision or problem. The person is not just consuming content. They are participating in a conversation about something specific.

What classification reveals

When you classify engagement by quality, you get a differentiated queue instead of a flat list. High-quality signals go to sequences that reference the specific conversation. Lower-quality signals go to sequences with a broader hook or get deprioritized for the current week.

Classification is not about excluding low-signal contacts entirely. It is about routing them differently. A generic reaction from a perfect ICP match is still worth a softer outreach attempt. It is not worth the same sequence as someone who posted an explicit evaluation question.

The engagement types and what they tell you

Comments are generally higher signal than reactions, because comments require more effort and reveal more context. A comment that names a specific problem, mentions a comparison, or asks a specific question is the highest evidence class.

Reactions (likes, celebrates, supports) are lower signal by default but still indicate the person saw the content and responded positively. They are evidence of attention, not evidence of active evaluation.

Shares carry moderate signal. Sharing content about a topic suggests enough agreement or interest to pass it to a network. It does not reveal where the person is in their thinking.

Why volume of engagement is not the same as quality

A person who reacts to 20 posts about AI tooling this month is not necessarily a better prospect than someone who left one detailed comment about a specific evaluation they are doing. The first shows high activity in the category. The second shows high engagement depth on a specific decision.

Most signal tools capture everything and let volume speak. A higher engagement count scores higher. This conflates activity with intent.

Good classification goes the other way: one specific evaluating comment outscores five generic reactions from the same person.

Richie Reach uses an AI classification step that reads engagement text and assigns a behavioral class before scoring. A "great post" reaction scores at the surface_engagement tier with a weight of 0.18. A comment that shows active vendor comparison scores at the evaluating tier with a weight of 0.82. Both enter the composite score, but they do not enter it equally.

If you classify by engagement depth and route accordingly, you send better sequences to the right signal tier.
If you treat all engagement as equal, you dilute your best signals with noise.