He owns the dataset. That is the whole advantage.
Everyone has a theory about the LinkedIn algorithm. Richard has 1.3 million posts and 1,400 hours of research, and that ends the argument.
Richard van der Blom publishes the Algorithm Insights Report, a 200-page annual study now in its seventh edition, built on 1.3 million posts and more than 1,400 hours of research. When the LinkedIn industry makes a claim about reach, this is the document it cites. Across the 97 posts we looked at, published between April and July 2026, he posted 6.2 times a week and averaged 273 reactions. 56 of those posts mention the algorithm directly.
The account is really a delivery mechanism for numbers nobody else has run. Proprietary-data authority is when your credibility rests on a measurement only you have run, so every claim you make is checkable and nobody can contradict you without doing the work themselves. He does not argue about the algorithm. He just tells you what 1.3 million posts did.
'Here's what I think the algorithm rewards.' Plausible, unprovable, and forgotten by Friday.
'In 2024, 70% of distribution went to your connections. In 2026, just 10%.' Go ahead and check it.
“Harsh? Maybe. But the data doesn't lie.”
— From his most-reacted post, on connections no longer driving distribution (1,458 reactions)
Five things that kept showing up across 97 posts
- A deeply engaged audience. A median of 242 reactions on 272,000 followers, and a 42.1% comment ratio, roughly seven times the norm. They stop and reply, they don't just scroll past.
- Interest is his second-most-common reaction at 14.4%, the highest in this series. His audience shows up to learn something specific.
- Discussion over distribution. A mean of 7 reposts, because his readers reply in the comments rather than reshare, which is exactly where that 42.1% ratio comes from.
- He works six and a half days. 89 of 97 posts land Sunday to Friday, and Saturday is the only lighter day at 8.
- The research is the spine. 56 posts mention the algorithm and 38 reference the report itself.
The numbers behind the account
His audience looks modest on paper and behaves like a front row: they read, they reply, they come back. His own research explains why that matters more than a raw follower count now.
Richard's median post earns 242 reactions and his mean is 273, on an audience of 272,000. One post in 97 cleared 1,000. 75 landed between 100 and 500, and 10 finished under 100. What those reaction counts hide is the conversation underneath them: his comment ratio runs about seven times the platform norm, so a 242-reaction post routinely carries a few hundred replies.
If you take one thing from this teardown, take that shift. We cover the mechanics in our guide to how the LinkedIn algorithm works.
How the reach is distributed
When he posts
Where the engagement comes from
The top posts
| # | Post | Reactions | Comments | Reposts |
|---|---|---|---|---|
| 1 | Your connections don't matter anymore | 1,458 | 471 | 81 |
| 2 | Not all LinkedIn posts die the same way | 755 | 145 | 31 |
| 3 | LinkedIn: We're fighting AI slop | 711 | 358 | 6 |
| 4 | I called my dad yesterday for Father's Day | 664 | 172 | 1 |
| 5 | 5 trends, based on 12,000+ creators since March | 645 | 276 | 19 |
| 6 | The Algorithm Insights Report 2026 is LIVE | 603 | 216 | 39 |
Curious how your own reach compares to your follower count? Run your profile through our free LinkedIn analyzer.
The six things he posts about
Four come straight out of the dataset. The other two are what keep the account from reading like a spreadsheet.
One number from the research that overturns something the industry takes for granted.
A pattern named and split into types, so readers can sort their own posts into it.
A scripted back-and-forth with LinkedIn itself, usually about something that happened to him.
Rare, nothing to do with the research, and among his highest reach.
What has changed since the last report, with the sample size stated up front.
The annual publication, sold on scale: pages, hours, editions, posts analysed.
Pillar 1: The headline finding (the reach engine)
Why it works: His biggest post by a factor of two, with 471 comments. The three-year series does all the persuading. 70%, 54%, 10% is a line nobody can argue with, and 'Read that again' is the only editorialising in the whole thing. The data is confrontational, so he does not have to be.
Pillar 2: The taxonomy (the most useful)
Why it works: Naming the patterns is what makes the research usable. 'The Rocket, 45% of posts' hands the reader a word for something they had seen a hundred times but never named, and the percentage tells them how common their own experience is.
Pillar 3: The platform satire (the comment engine)
Why it works: 358 comments on 711 reactions, a 50% ratio. The dialogue format lets him complain about the platform without sounding aggrieved, and 'That's an infographic. With bar charts.' is the line everyone quoted back at him. Look at the 6 reposts, though. People replied instead of forwarding.
Pillar 4: The personal post (the deepest)
Why it works: Fourth-biggest post on an account made almost entirely of data, and it got a single repost. He puts the hardest facts in fragments, four years, two lost pregnancies, medical complications, and never expands on any of them. That restraint is exactly what stops it reading like a bid for sympathy.
Pillar 5: The trend update (the freshness)
Why it works: He opens the first trend with his own losses, down 22% on his own content. Leading with a finding that makes you look bad is the strongest credibility move a researcher has, and it costs him nothing, because the data was going to say it anyway.
Pillar 6: The report launch (the business end)
Why it works: The whole pitch is scale: pages, chapters, tactics, posts, hours, editions. Six numbers, zero adjectives. For a research product, that is the right way to sell, because the only thing a buyer actually wants to know is whether enough work went into it.
The hooks that earned the click
Most of them open with a finding that flatly contradicts something the reader already believes.
'Your connections don't matter anymore. Harsh? Maybe. But the data doesn't lie.'
'Not all LinkedIn posts die the same way.'
'LinkedIn: We're fighting AI slop. / Me: Finally. Let's go.'
'5 trends worth knowing, based on +12,000 creators since March.'
'LinkedIn owes you an explanation. Your follower count went up. Your reach went down.'
'The LinkedIn algorithm no longer is a black box.'
Almost every hook carries either a number or a named pattern. That is the line between research and commentary. For the mechanics of openers, our guide to writing LinkedIn hooks goes deeper, and you can pressure-test your own first line in the free hook generator.
A voice that lets the numbers do the talking
He states findings flatly, points at his own losses, and keeps himself almost entirely out of the way.
- Leads with the figure, not the take. Three-year series, percentages, sample sizes, all in the first two lines.
- States the sample every single time. 1.3 million posts, 12,000 creators, 40,000 carousels, 80,000 profiles.
- Publishes findings that make him look bad, including a 22% drop in his own reach.
- Writes at a moderate length. A 258-word median, room for a finding plus its caveat and not much else.
- Barely touches hashtags, 5 across 97 posts.
- Works Sundays. 14 of 97 posts land on a Sunday and only 8 on a Saturday.
The research-post structure is the part you can lift, and it works for anyone sitting on a measurement their industry does not have.
[The overturned belief, stated flatly: 'Your connections don't matter anymore.'] [The pre-empt: 'Harsh? Maybe. But the data doesn't lie.'] [The series, one line per year, so the trend is undeniable] [The counter-movement: what rose while that fell] [The implication, in one sentence, and then stop]
What he does, and doesn't, do
- Own the dataset, then report it plainly
- State the sample size in every claim
- Publish findings that hurt his own numbers
- Name his patterns so readers can classify themselves
- Sell the report on scale rather than adjectives
- Arguing about the algorithm
- Hashtags, in 92 of 97 posts
- Video, at 2% of output and his weakest format
- Overstating what the data shows
- Making the personal posts about the research
What this account really shows is that authority is built on trust, not on a follower count. He is the most-cited source on LinkedIn distribution, his audience replies to almost everything he publishes, and his own research explains why depth now beats raw reach. CaptureFlow is an AI content agent that turns your expertise into weeks of on-brand content for every platform. You capture one idea in 5 minutes, a finding and a chart, and CaptureFlow, trained on your voice and your past posts, reshapes it into a LinkedIn post, an X thread, a carousel, or a short video. So one piece of research lands on every channel, not just one. See how the AI content agent works.
The system underneath the posts
One study a year, cut into a year of posts, sold back as a single product.
The research funnel
- 1The study runs1.3 million posts, 1,400 hours, seventh edition.
- 2Findings come out one at a time56 of 97 posts reference the algorithm.
- 3Every finding is checkableSample size stated, percentages given, no interpretation bolted on.
- 4The industry cites himThe report becomes the thing everyone quotes.
- 5The report sells on that reputationWhich pays for the next edition.
This is the same evidence-first model we mapped in the Lily Ray playbook, where her screenshots get reshared more than his prose, 30 reposts a post to his 7. Put side by side, the two of them show how far owning the finding takes you, and how much further a forwardable format carries it. For marketing teams sitting on their own data, that is the whole opportunity.
Your 30-day challenge
Twelve posts, each one reporting something you actually measured instead of something you just think.
- Days 1-3: Pick something in your field everyone has opinions about and nobody has measured
- Days 4-5: Measure it, however small the sample, and write the sample size down
- Days 6-7: Publish the headline finding with the number in the first two lines
- Days 8-10: Split your finding into named types with percentages
- Days 11-12: Give each type a label a reader can apply to themselves
- Days 13-14: Publish a finding that is inconvenient for you
- Days 15-17: Turn your best finding into a chart somebody could paste into a deck
- Days 18-19: Compare its reposts against the text version
- Days 20-21: Pre-empt the objection in one line, then decline to argue
- Days 22-24: Publish one post entirely unrelated to your research
- Days 25-27: Do not connect it back to your subject
- Days 28-30: Compare your reach-per-follower against last month, not your raw reach
The stop-doing list
| Stop doing | Do this instead |
|---|---|
| Opinions about your industry | Measurements of it, with the sample stated |
| Describing a pattern | Naming it, and giving it a percentage |
| Hiding findings that hurt you | Leading with them; it is your strongest credibility signal |
| Defending your data | Pre-empting the objection once, then stopping |
| Dense text for a chart-shaped finding | A chart people can forward into a deck |
| Measuring raw reach | Measuring reach relative to your follower count |
The metrics to track weekly
| Metric | Benchmark to aim for |
|---|---|
| Interest reaction share | 10%+ (his is 14.4%, the highest here) |
| Comment-to-reaction ratio | 20%+ organic (he sits at 42.1%) |
| Reposts per post | His 7 has room; a forwardable format lifts it |
| Posts stating a sample size | Every research post |
| Reach per 1,000 followers | The number worth watching as the algorithm shifts |
| Posts per week | 6, Sundays included |
The takeaways
- 01Own the measurement. Richard's whole authority rests on 1.3 million posts and 1,400 hours of research nobody else has run.
- 02State the sample every time. 1.3M posts, 12,000 creators, 40,000 carousels. The number is the credibility.
- 03Lead with the finding that hurts you. He opened a trends post with a 22% drop in his own reach.
- 04Name your patterns. 'The Rocket, 45% of posts' hands readers a word for something they had noticed but could never describe.
- 05Pre-empt the objection once, then stop talking. 'Harsh? Maybe. But the data doesn't lie.' is the entire defence.
- 06Watch reach per follower, not raw reach. His own research shows connection-based distribution fell from 70% to 10% in two years.
Frequently asked questions
- Who is Richard van der Blom?
- He publishes the LinkedIn Algorithm Insights Report, a 200-page annual study now in its seventh edition, built on 1.3 million posts and over 1,400 hours of research. When someone in the industry makes a claim about LinkedIn reach, this is the document they point to.
- What kind of post performs best for Richard van der Blom?
- Findings that overturn something people believe. His top post, reporting that connection-based distribution fell from 70% in 2024 to 10% in 2026, earned 1,458 reactions and 471 comments, roughly double anything else in the sample.
- How engaged is Richard van der Blom's audience?
- Unusually engaged. His comment-to-reaction ratio is 42.1%, roughly seven times the LinkedIn norm, and 'Interest' is his second most common reaction at 14.4%, the highest in this series. His own research shows why that depth counts more than raw reach now: connection-based distribution dropped from 70% to 54% to 10% across three years while the Interest Graph climbed to 50%.
- How do you turn one research project into a year of posts?
- Capture findings as the analysis runs, instead of mining a finished report months later. CaptureFlow turns one 5-minute capture into a week of native posts, carousels and infographics across platforms.
