Turning engagers on your own LinkedIn posts into meetings
Anyone who likes or comments on your LinkedIn post is a warm lead. The playbook: table setup, the sequence as a graph, a CTA test and manual approval.
Not the wider feed, your own post
LinkedIn keyword monitoring watches the whole feed for buying signals around specific terms, anywhere on LinkedIn and regardless of who posted. This playbook is narrower and therefore warmer: it takes only the people who reacted to your own posts, liking or commenting, and turns them into an outreach list. No guessing about interest, because the person has already said something in public about the exact topic you are posting on.
What follows is the method, step by step. We deliberately quote no numbers here: rates depend on your industry, the sender’s reach and the offer, and a single sequence would prove nothing anyway. What you should measure is at the end.
Step 1: set up the engager table
The starting point is a table with a source that collects post reactions. For every new post by the sender, likers and commenters are added automatically as new rows. Run that for a few weeks with a sender who posts regularly and the table grows into four figures quickly, so build for volume from the start rather than for a handful of rows.
The column structure that turns raw reactions into leads you can address:
- Name, LinkedIn, position, company, headline, resolved automatically from the LinkedIn profile as soon as the reaction comes in.
- Reaction + post, recording what the person did and on what: “LIKE on the post from …”, “EMPATHY on …”, “PRAISE on …”. LinkedIn has several reaction types and in practice they all show up. This is the preserved context that later carries the opener.
- Qualification, an AI column that checks position, company and headline against the ICP and states in plain language why yes or no.
- Salutation, generating the correct form of address from the resolved name.
- Qualified?, the binary gate that decides whether a row may enter the sequence at all.
- Intent, total score, extra weighting if you want to prioritise beyond a plain yes or no.
- Enroll, the terminal column. Only running it actually writes the row into a sequence. Nothing happens automatically before that.
A practical note: some sourced rows always come back as “failed”, because enrichment does not work on every profile (private profile, deleted account, company that cannot be resolved). That is normal, not a misconfiguration. Plan it in as steady attrition.
Step 2: the sequence, a graph rather than a rigid list
The outreach itself runs as a graph, not a linear list of steps. That allows conditions (already connected? already replied?) instead of forcing the identical chain on every person.
The flow, node by node:
- Start, then the condition “no message in the history”. Anyone already contacted is not worked twice.
- Condition: “connected on LinkedIn”. Existing connections go straight to step 3, everyone else gets an invitation first.
- A LinkedIn invitation without a note, which usually has a higher acceptance rate than one with text.
- Condition: “connection accepted”, with a time window of, say, 14 days. Not accepted inside the window means it ends there, with no further touch.
- A thank-you message after acceptance, referencing the connection.
- The CTA message, where the A/B split runs (next section).
Step 3: test CTAs against each other, and take the sample size seriously
The last node is where a test pays off: two CTA variants addressing the same audience differently, for example a concrete content offer against introducing a solution. Both arms run in parallel and assignment is random.
The important part is the evaluation, and it is usually skipped. A two-arm test with a few dozen leads reliably produces a winner that is not one: at small sample sizes the confidence intervals of both arms overlap so much that the higher rate can simply be chance. As an order of magnitude, proving a doubling from a starting rate in the low single-digit percentages takes roughly several hundred leads per arm.
The lesson: an A/B test with a few dozen leads is a first indication of direction, not a verdict. Let it run, but do not make expensive decisions on it, and distrust any tool that declares a winner at 30 leads without stating the uncertainty.
What you should measure
A 30-day aggregate across the whole sequence says more than the A/B test alone. Worth tracking:
- First contacts, replies and reply rate over the window, the headline number but only the beginning.
- Positive rate within those replies: how many are interest rather than a decline?
- Conversion per node, not just at the end. In practice the strongest lever sits at the start of the chain: the invitation decides everything downstream, and a later message can only lose what the front end won.
- Errors and stopped leads as a metric of their own. LinkedIn automation has a real error rate: declined invitations, withdrawn profiles, rate-limit brakes. Anyone who does not measure them mistakes them for an outlier.
Your own cold baseline is the anchor to compare against: same audience, no post reference. The gap between the two is what this playbook is really claiming, and you have to establish it in your own market.
The built-in brake: manual approval
Before anything is sent, a hard gate belongs in between: new leads wait until a person releases them. Nothing sends unchecked. In the approval view the finished message sits there in plain text before a click lets it out.
Why this is not security theatre is shown by the most common failure of this design: an unresolved placeholder in the step text, for instance an empty salutation column. Without the manual check, a message goes out with a visible {{salutation}} where a name should be. That is exactly what the brake is for.
Plan honestly for rate limits
LinkedIn caps connection requests per account per day. That is not a quirk of any tool, it is the platform limit. In practice the queue grows faster than it drains as soon as a post does well. Count the backlog in weeks, not hours.
The consequence is editorial: prioritising by qualification score becomes a necessity rather than a nicety. If you can only reach a fraction of your engagers, it should be the right fraction.
How to build this yourself, with our stack or any other
The method is tool-independent, even if GTM Goat makes it quicker to set up.
With GTM Goat: create a table with a post-engager source, add columns for enrichment, qualification and salutation as AI columns, build the sequence in graph mode with condition nodes, and leave manual approval on until you trust automatic release.
Without GTM Goat, with generic tools: LinkedIn shows you who reacted under each of your own posts, which you can export manually or pull as a list with a LinkedIn automation tool (Expandi, Waalaxy, HeyReach and similar). A simple spreadsheet with the columns name, company, reaction and qualified yes/no replaces the AI columns with a manual review. Slower, identical logic: only approach people who showed interest in public, name the reference in the opener, never send automatically without a visual check.
The same limit applies either way: LinkedIn’s daily cap on connection requests holds regardless of the tool.
Next step
If you would rather have this loop run for you than maintain it yourself, look at GTM Goat or work through the pricing. For the broader, keyword-based variant across the whole feed, see LinkedIn keyword monitoring.
Next step
GTM Goat runs exactly what you just read.