TL;DR: Across 16,661 keyword comments on one team's lead-magnet LinkedIn posts, the 4.8% who commented five or more times produced 40.4% of all comments.
Across 16,661 keyword comments on one team's lead-magnet LinkedIn posts, 70.2% of the 7,582 commenters commented exactly once, while the 4.8% who commented five or more times produced 40.4% of all comments, and the single heaviest commenter left 200. Platform data
Key Takeaways
- 70.22% of the 7,582 commenters commented exactly once.
- The 4.77% who commented five or more times produced 40.36% of all comments.
- The top 10 individuals alone hold 11.34% of all comments, and one person commented 200 times.
- By distinct posts, the 5.04% of commenters who commented on five or more posts produced 42.12% of post-attributed comments.
- A typical lead-magnet post's comment count is roughly 60% first-and-light commenters and 40% regulars.
What did we measure?
The standard objection to lead-magnet posts is "it's the same people commenting every time". The standard defense is "no it isn't". Neither side ever brings a table.
This is the table. Our lead-magnet studies measured how many comments the format earns; this study measures who leaves them, across every keyword comment processed on our own team's posts: 16,661 comments from 7,582 distinct people across 206 posts, February through August 2026. Platform data
How was it measured?
Every comment in this study comes from one workspace's LinkedIn posts, processed between 21 February and 27 August 2026.
Dataset. One workspace: our own, about 10 LinkedIn accounts run by the Reachium team, the same dataset and public framing as the comment-funnel study. Comments are keyword comments (people commenting a word to request a resource) processed between 21 February and 27 August 2026, and "lead-magnet posts" here means exactly the posts running that comment-to-resource mechanic, the format defined in the lead-magnet study. A commenter is a distinct LinkedIn profile URL.
Two cuts, two denominators. The by-comments cut counts each person's total comments across all posts (all 7,582 people). The by-posts cut counts how many distinct posts each person commented on; 6,522 of the 7,582 have post-attributed comments (the other 1,060 predate reliable post attribution and are excluded from that cut only, disclosed here). Platform data
What a repeat comment is not. These are people asking for resources, on posts explicitly inviting exactly that. Repeat commenting is repeat interest in what the team publishes. Nothing in this data classifies anyone as a pod member, a bot, or fake, and this study makes no such claim in either direction.
What did we find?
Comment frequency splits into five buckets when counted by comments left:
| Comments left | People | Share of people | Comments | Share of comments |
|---|---|---|---|---|
| 1 | 5,324 | 70.22% | 5,324 | 31.95% |
| 2 | 1,272 | 16.78% | 2,544 | 15.27% |
| 3-4 | 624 | 8.23% | 2,068 | 12.41% |
| 5-9 | 255 | 3.36% | 1,582 | 9.50% |
| 10+ | 107 | 1.41% | 5,143 | 30.87% |
The 5-plus group is 362 people, 4.77% of commenters, holding 40.36% of all comments. The top 10 individuals alone hold 11.34%; the top 1% of commenters (76 people) hold 28.89%. One person commented 200 times. Platform data
Counted instead by distinct posts commented on, among 6,522 people with post-attributed comments, 72.77% commented on exactly one post, 14.40% on two, 7.79% on three or four, and 5.04% (329 people) on five or more. That last group produced 42.12% of post-attributed comments. Platform data
What does this mean for lead-magnet publishers?
Both sides of the argument are holding a real piece of the table. The audience is overwhelmingly one-time: seven in ten people who ever commented did it once, and the distinct-commenter count is a quarter larger than it was at the funnel study's earlier snapshot, so new people keep arriving. The volume is meaningfully concentrated: a group of 362 regulars produces two comments in five, and a hundred-odd superfans produce three in ten. Platform data
For measuring content performance, the concentration is a real distortion. A post's comment count blends breadth (how many new people it pulled) with depth (how many regulars showed up again), and two posts with identical counts can have very different reach into new audience. Comment counts inflated by regulars are still real engagement, but they are not the same signal as first-time demand, and dashboards that only show totals cannot tell them apart.
For the funnel, the picture is more benign. A regular who comments on five resources received five resources and stayed in the audience; that is what an engaged subscriber looks like on a platform without subscriptions. The concentration would be damning if the goal were reach-metric bragging, and is mostly fine if the goal is conversations and booked meetings, which single-comment newcomers and regulars both feed.
Teams deciding how much of their content budget to put behind lead-magnet posts can size the format's economics on Reachium's pricing page.
How do you use this in practice?
A content lead judging lead-magnet posts by raw comment counts should mentally deflate them: in this data, a typical post's count is roughly 60% first-and-light commenters and 40% regulars. Comparing two posts honestly means comparing their new-commenter counts, not their totals. Our analysis
A skeptic auditing someone's viral lead-magnet post can now ask the calibrated question. "How many of those comments are distinct first-time commenters" has a measured baseline: here, 31.95% of all comment volume came from people commenting for the only time. A post claiming thousands of comments and near-zero repeat share would be unusual against this data; so would one that is mostly ten regulars. Platform data
For a worked example on the same platform, read the Padsnap case study.
What are the limits of this study?
One workspace's posting habits and one niche's audience shape every number in this study, so its limits start with coverage.
One team's audience. All 206 posts belong to one workspace of about 10 accounts posting in one niche. A different niche or a larger creator would concentrate differently; this is the first denominator on the question, not the universal one. Platform data
Keyword comments only. Organic comments that do not request the resource are not processed by the pipeline and are absent. Concentration among organic commenters could differ.
Profile URLs are the identity. A person who changed their public profile URL counts as two people, which overstates the one-time share slightly.
The post-attribution gap is disclosed. 1,060 of 7,582 commenters predate reliable post attribution and are excluded from the by-posts cut only; their comments still count in the by-comments cut. Platform data
No quality classification. Nothing here measures whether repeat commenters convert to meetings at different rates; the deduplicated person-level funnel lives in the comment-funnel study, and joining the two person-level cuts is future work, not this page. Teams unsure how to weigh comment volume in their own content plan can talk to the agency team about a content review.
FAQ
What share of LinkedIn lead-magnet comments come from repeat commenters?
The 4.77% of commenters who left five or more comments produced 40.36% of all comments across 16,661 comments from 7,582 people.
Is most of the comment volume from the same people every time?
No. 70.22% of commenters commented exactly once, so the audience is overwhelmingly one-time even though volume is concentrated among the regulars.
How concentrated is the heaviest commenting activity?
The top 10 individuals hold 11.34% of all comments, the top 1% (76 people) hold 28.89%, and the single heaviest commenter left 200 comments.
Should a content lead trust raw comment counts as a performance metric?
Comment counts blend new-audience reach with regular engagement, so two posts with identical totals can differ in real reach. Teams sizing a lead-magnet content plan can compare options on Reachium's pricing page.