TL;DR: Across 132 matured LinkedIn posts, the top 10% of posts earned 57.79% of all impressions and the bottom half earned 4.69%.
Across 132 matured LinkedIn posts, the top 10% of posts earned 57.79% of all impressions and the bottom half earned 4.69%, a concentration that holds at 46.39% for the top decile even with the two strongest accounts excluded. Our analysis
Key Takeaways
- The top 10% of matured posts, 14 posts, earned 57.79% of all impressions.
- The bottom half of matured posts, 66 posts, earned only 4.69% of all impressions.
- The five biggest posts alone took 39.49% of all impressions, and the single biggest took 12.47% by itself.
- With the two strongest accounts excluded, the top decile still earned 46.39% of the remaining impressions.
- 38.64% of posts finished under 1,000 impressions, while 15.15% cleared 10,000.
What did we measure?
Everyone who posts on LinkedIn for a while notices the same thing: most posts do little, and then one does not. This study puts numbers on that shape.
We took every matured post from one workspace, our own, where about 10 Reachium team accounts publish through the platform's content engine, ranked the posts by impressions, and measured how the total is distributed across them. The question is not what a post earns on average. It is how much of the total the winners take.
How was it measured?
The dataset, the split, and one robustness check together set the terms for the concentration figures below.
Dataset. The matured cohort from this series' content benchmarks: 132 posts published by about 10 accounts between 12 November 2025 and 6 July 2026, each at least 30 days old at the 6 August 2026 pull so its impression count is near-final. Together they earned 728,127 impressions. Platform data
The split. Posts were ranked by impressions and divided into deciles of the ranking, top decile first. With 132 posts the top decile holds 14 posts. Shares are each group's impressions divided by 728,127. Our analysis
Robustness. Concentration at the post level could just be concentration at the account level wearing a disguise: if two strong accounts wrote all the winners, the lottery would really be about who posted, not what happened. So the headline split is recomputed with the two accounts holding the most total impressions removed entirely.
What did we find?
The concentration shows up first in the three-way split, then sharper still inside the top decile itself.
| Group of matured posts | Share of all impressions |
|---|---|
| Top 10% (14 posts) | 57.79% |
| Next 40% | 37.53% |
| Bottom 50% (66 posts) | 4.69% |
The bottom half of this team's output, 66 posts, together earned less than a twentieth of the total. The top 14 posts earned more than everything else combined. Our analysis
It concentrates further inside the top. The five biggest posts alone took 39.49% of all impressions, and the single biggest, at 90,777 impressions, took 12.47% by itself. One post out of 132 carried an eighth of the team's entire measured reach. Our analysis
The floor is crowded: 51 of the 132 posts, 38.64%, finished under 1,000 impressions, and 39 posts, 29.55%, finished under 500. Meanwhile 20 posts, 15.15%, cleared 10,000. There is not much middle. The median post earned 1,778 impressions while the average earned 5,516, and that 3.1x gap is this distribution expressed as a single number. Our analysis
Does it survive removing the two strongest accounts?
Two accounts hold the majority of the team's total impressions. Excluding both leaves 75 matured posts with 135,567 impressions and a median of 628. In that reduced set the top decile still earns 46.39% of the total. Our analysis
The concentration weakens but does not disappear, so the lottery is not purely an account effect. Even among the smaller accounts, a minority of posts earns roughly half the reach. For reference, on the unrestricted 211-post series the top decile's share is 59.34%. Our analysis
What does this mean for LinkedIn content teams?
The expected value of a LinkedIn content program lives in its outliers. In this dataset, deleting the best 14 posts would have deleted most of the results, and no one can reliably say in advance which 14 they will be. The bottom half of output is not failure; it is the ticket price. Our analysis
This is why judging a content program on two weeks of posts misfires in both directions. A fortnight that happens to contain an outlier looks like a breakthrough. A fortnight that does not looks like decline. Neither is signal at this sample size, and this team's own months swing the same way.
It also reframes consistency. Posting more is not primarily about the median post doing better. It is about buying more draws from a distribution whose payoff sits in the tail.
Teams that want help buying more draws from that distribution can compare plans on Reachium's pricing page.
How do you use this in practice?
A founder should count outliers per quarter, not impressions per post. At this team's rates, about one post in seven cleared 10,000 impressions (15.15%); a quarter of consistent posting gives that number a chance to show up. Our analysis
A team reporting on content should report the distribution, not just the total. "728,127 impressions" and "57.79% of it came from 14 posts" are both true of this cohort, and the second sentence is the one that predicts what next quarter feels like. Our analysis
And nobody should delete or judge a post at day two. In this dataset the median post at age 0 to 7 days had 39.5 impressions. The distribution needs weeks to reveal which draw you got. Platform data
For a look at one team's content output over a longer window, see the 81-day follower growth study, and for a worked example from a single client, see the Solq case study.
What are the limits of this study?
The single workspace behind this data, and the small size of a 132-post cohort, are the two limits worth stating first. Our analysis
One team, one workspace. All posts come from Reachium's own accounts publishing B2B lead-generation content. The exact shares will differ elsewhere. The shape, heavy concentration in a small top, is the durable claim.
Deciles of a 132-post cohort are 13 to 14 posts each. The 57.79% figure is exact for this cohort, but small-cohort decile shares move by points when a single large post enters or leaves. Our analysis
Impressions are LinkedIn's lifetime totals as of the pull, including repeat views. Concentration measured in unique viewers could differ.
The two-account exclusion is one robustness check, not an exhaustive one. Account strength, format and topic all vary at once in this data; the check rules out the crudest confound only.
Matured is near-final, not final. Posts continue to accrue slowly after 30 days, which could shift shares slightly over time.
Teams that want their own posting cohort checked for this kind of concentration can talk to Reachium's agency team.
FAQ
Is LinkedIn impression distribution evenly spread across posts?
No. In this dataset the top 10% of posts earned 57.79% of all impressions, while the bottom half earned only 4.69%.
Does a small number of star accounts explain the concentration?
Not entirely. Excluding the two strongest accounts still leaves the top decile earning 46.39% of the remaining impressions.
What share of LinkedIn posts get very little reach?
38.64% of matured posts in this dataset finished under 1,000 impressions, and 29.55% finished under 500.
How should a content team plan around this kind of concentration?
By counting outliers per quarter instead of judging single posts, and by posting consistently enough to get more draws from the distribution. Teams that want help with that cadence can talk to Reachium's agency team.