MagicPost · LinkedIn Benchmark
March 2026
Impressions per post by follower count · 27,581 posts analyzed| Followers | Low | Median | High | Top 10% |
|---|---|---|---|---|
| 0-1K | 100+10% | 212+2% | 508+11% | 1,242+15% |
| 1K-5K | 220-1% | 482-3% | 1,139-2% | 2,963+3% |
| 5K-10K | 344-10% | 769-9% | 1,984-5% | 5,206-8% |
| 10K-25K | 767+16% | 1,673+9% | 4,196+12% | 12.4K+11% |
| 25K-50K | 1,161-6% | 2,676-18% | 7,116-20% | 20.6K-16% |
| 50K-100K | 2,732+4% | 6,422-9% | 17.2K-6% | 44.8K-11% |
| 100K+ | 5,686-24% | 13.4K-21% | 29.6K-29% | 75.6K-29% |
Low = bottom 25% · Median = middle post · High = top 25% · Top 10% = best decile
Six-month trend
Median impressions for a LinkedIn postimpressions on a image post
+NaN% vs the same author's other posts
+NaN% vs the same author's other weekdays
Posts built around repetitive bullet lists and a highly mechanical structure receive 5 % fewer impressions than the same author's usual average. This drop is consistent across a large subset of publications, suggesting that predictable, interchangeable content weighs on reach regardless of who is posting. The hypothesis that LinkedIn penalises content that feels AI-generated is not supported by the data. When comparing the most AI-sounding and the most personal posts from the same creator, no meaningful difference in reach appears: both perform almost identically for the same authors. The practical takeaway is straightforward: the issue is substance, not writing style. Replacing wall-to-wall bullet structures with a more direct, less formulaic approach remains the most concrete lever for protecting your reach.