Key finding
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.
What we measured
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.
Methodology
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).
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.
The findings
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.
By distinct posts commented on (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.
What this means
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.
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.
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.
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.
Limitations
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.
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.
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.