Average LinkedIn engagement rate: benchmarks from 12,988 posts

The median post in our cohort of 12,988 LinkedIn posts scored 0.40 engagement per 1,000 followers, earning 122 reactions and 12 comments. The 75th percentile was 1.27 and the 90th percentile was 5.95, which means the gap between a decent post and a top-decile post is roughly fifteen times, not fifty percent. If you want to know whether your own posts are working, score them with (reactions + 4 x comments) divided by your follower count and compare against the table below. One warning before you do: this cohort is made of established creators with 1,000-plus followers, and smaller accounts routinely beat these numbers because the denominator is smaller.

What is the average LinkedIn engagement rate?

There is no single honest answer, which is why so many pages give a confident one. What we can give you is a specific, reproducible answer for a specific population.

We took a public dataset of 34,012 LinkedIn posts, filtered to 12,988 English posts from 65 creators with at least 1,000 followers and at least 25 reactions, and scored every post the same way. The distribution is heavily skewed, which is the first thing worth knowing. A handful of posts do most of the work, and averages mislead badly when that is true. So we report percentiles.

The shape of that table is the finding. The distance from the median to the 75th percentile is about 3x. The distance from the 75th to the 90th is about 4.7x. Engagement on LinkedIn is not normally distributed and never has been. Most posts land in a narrow band near the bottom, and a small number break out by an order of magnitude.

Practically, this means two things. First, a single strong post does not mean your account has changed; it means you caught the tail. Second, chasing a 20% improvement in your average post is the wrong target. The returns come from raising the frequency of breakouts, which is a content problem, not an optimization problem. Our study of what the top decile does differently is where we dug into which patterns actually travel with those breakouts.

How do you calculate your own LinkedIn engagement rate?

The formula we use, and the one every number on this page is built from:

engagement rate = (reactions + 4 x comments) ÷ followers, reported per 1,000 followers

Comments get four times the weight of reactions for a straightforward reason: they are much rarer and much more consequential. A reaction is a tap. A comment costs the reader real effort, opens a thread other people can join, and surfaces the post inside the commenter's own network. Weighting them equally would let a post with 300 reactions and no conversation outrank a post that started an argument, which is backwards.

A worked example

Say you have 4,200 followers. A post earns 58 reactions and 9 comments.

1. Score the post: 58 + (4 x 9) = 94.
2. Divide by followers: 94 ÷ 4,200 = 0.0224.
3. Multiply by 1,000: 22.4 per 1,000 followers.

That number is far above the 90th percentile in our table, and it is not because the post was extraordinary. It is because 4,200 is a small denominator. This is the trap in every benchmark article, including the ones quoting much friendlier averages, and it is worth understanding before you either celebrate or panic.

Why does engagement rate fall as your follower count rises?

Because followers are not viewers. LinkedIn shows a new post to a fraction of your audience and expands distribution based on early response. That fraction does not scale linearly with follower count. An account with 2,000 engaged followers can reach most of them; an account with 400,000 followers accumulated over eight years cannot, and a large share of those followers are inactive.

Our own data shows the effect cleanly when you split by what the author does for a living.

Look at the founders row against the executives row. Founders earn more reactions and comments per post (132 and 12 against 101 and 8) and a lower rate (0.19 against 0.46). Nothing about their content is worse. Their follower counts are simply larger, and rate is a ratio.

The lesson is not that rate is useless. It is that rate is only comparable within a similar follower band, and the only follower band you can be certain matches yours is your own. Which leads to the rule we would actually give someone: benchmark against your own trailing thirty posts first, and treat the table above as orientation rather than a target.

How many reactions and comments should a LinkedIn post get?

Rate is the right metric and the wrong one to feel. Most people want to know whether 40 reactions is good. Here are the absolute counts, which are easier to sanity-check against your own feed.

The two rows behave completely differently and that difference is the most useful thing on this page. Reactions roughly triple between the bottom half and the top decile: 80 to 235. Comments go from 5 to 72, more than fourteen times. Reactions are a weak, abundant signal. Comments are scarce and they track the thing you actually want.

This is why we weight comments at 4x and why we would tell you to watch them as your primary number. A post that earns 90 reactions and 2 comments and a post that earns 90 reactions and 25 comments are not the same event, even though the first number matches. The second one entered conversations you cannot see.

It also reframes what to write. Reactions come from agreement. Comments come from having something to say back. If your comment counts sit at the bottom of your own range, the fix is almost always in the post's ending and its specificity, not in its formatting.

Does the engagement rate benchmark change by post format?

Yes, and less dramatically than format-focused advice implies. Median rate by format across the cohort:

Document posts top the table at 2.42, and we would not build a strategy on that row alone: 60 posts from 13 authors is a thin sample, and carousels were novel during the period the dataset covers. Treat it as a signal worth testing, not a settled fact.

The rows we trust are the big ones. Native video (0.60) and plain text (0.58) sit comfortably above image posts and shared links (both 0.33). Note that text-only posts carry no media at all and still nearly match video. The format is not doing the work; the writing is. That is consistent with what we found on posts built around an external link, which sit at the bottom on both rate and comments.

How many impressions should a LinkedIn post get?

Our dataset does not carry impressions, so we cannot answer this from our own numbers and will not pretend otherwise. The best sourced figure we found is Rival IQ's, which puts brand accounts at roughly 8.63 impressions per 100 followers per post, and notes that smaller accounts do considerably better on that ratio than larger ones.

Call it a reach rate of somewhere under ten percent for a typical post. Two consequences follow. First, the arithmetic behind the denominator problem: if fewer than one in ten followers sees a post, a per-impression rate will always be roughly an order of magnitude higher than a per-follower rate for identical content. Second, follower count is a much weaker predictor of reach than people assume. Buying or accumulating followers who never see your posts moves the denominator without moving the numerator, which makes your rate worse while your account looks bigger.

Socialinsider's benchmark study, built on 1.3 million posts from 16,645 company pages, also reports median impressions rising with follower tier but not proportionally, and puts native document posts at the top of its engagement table. That is consistent with what our own format table shows, arrived at through a completely different denominator and a completely different population, which is about as much agreement as this field offers.

If you are trying to reconcile the impressions number LinkedIn shows you against anything on this page, the short version is that you cannot, and you should not try. Use LinkedIn's number to compare your posts against each other. Use a follower-based rate to compare yourself against published cohorts. Do not mix them.

Which topics earn the highest engagement rate?

Subject matter moves the number more than format does. These are median rates by topic, restricted to topics where at least 30 different authors contributed, so no single creator's habits drive the figure.

Personal branding at 3.72 is nine times finance at 0.20, and personal branding posts earn more than three times the median comments. The pattern running through the top of that table is that the subject is a person: their positioning, their career, their story, the people they know. The bottom of the table is subject matter about the world.

Be careful with the causal reading. Creators who write about personal branding also tend to be creators who understand the platform, so some of that 3.72 is selection rather than topic. What it does mean is that if you are posting about finance or technology and wondering why your rate looks weak against a generic benchmark, part of the answer is that the benchmark was never built for your subject.

Why do published LinkedIn engagement benchmarks disagree so much?

You will find numbers ranging from a fraction of a percent to well over five percent, all described as the average LinkedIn engagement rate. They are mostly not in conflict. They are measuring different things and rarely saying which.

The cleanest proof is a single report that publishes both. Rival IQ's LinkedIn benchmark study, built on more than 58,000 posts from brand accounts with at least 2,000 followers, reports a median engagement rate of 0.41% by follower and 4.73% by impression. Same posts. Same study. An eleven-fold difference, produced entirely by the choice of denominator.

The reason for the gap is in the same report: brands average “about 8.63 impressions per 100 followers on each post”. Most of your followers never see any given post, so dividing by followers and dividing by the people who actually saw it cannot give the same answer.

Here is how the commonly cited figures line up once you insist on a denominator.

Two things to flag honestly about that table. First, LinkedIn's own analytics define engagement rate as “the ratio of interactions per impressions on your post,” where “interactions include clicks, reactions, comments, and shares”. Clicks in the numerator and impressions in the denominator is why the number LinkedIn shows you looks generous next to anything follower-based. It is not wrong. It is a different measurement.

Second, our own 0.40 is per thousandfollowers, which is 0.04% per follower, an order of magnitude below Rival IQ's 0.41%. That is not a contradiction either. Our cohort is individual influencer accounts, many with six-figure follower counts, and per-follower rates fall as accounts grow. The near-identical digits are a coincidence of units, not agreement.

The most-quoted figure in this space is the one we would trust least. Hootsuite's 2% LinkedIn benchmark is attributed to an analysis of over a million social posts with a data science partner, but the page publishes no denominator, no date range and no per-platform sample breakdown, while separately listing six different engagement formulas without saying which produced the table. A different Hootsuite page puts the average at 3.4%. Both numbers circulate widely. Neither is reproducible.

We are not picking on Hootsuite specifically. Socialinsider's benchmarks page says 5.20% while its own blog says 3.85%. Rival IQ's general social media industry report is cited constantly for LinkedIn figures and contains no LinkedIn data at all. And the report most often quoted for LinkedIn algorithm claims sits behind a paywall, which means every secondhand number attributed to it is unverifiable unless you buy it.

Beyond the denominator, four more things vary and rarely get stated.

• Pages versus profiles. Most large published benchmarks are built from company page data, because page data is available through official APIs at scale. Personal profiles behave differently and generally earn more engagement per follower. Comparing your personal profile to a page benchmark is comparing two populations.
• Mean versus median. Engagement is extremely skewed. A mean is dragged upward by a small number of breakout posts, so mean-based benchmarks read high and almost nobody hits them. We report medians and percentiles for this reason.
• What counts as engagement. Some methodologies count reactions, comments and reshares. Some add clicks, which can double or triple the number. Some weight comments, as we do. Few state it.
• Time window. Engagement counts in our dataset are lifetime totals at scrape time, so older posts have had longer to accumulate. Benchmarks measured at 24 or 48 hours will read lower.

The practical instruction: before you compare yourself to any published figure, find the denominator, the population, and whether it is a mean or a median. If a page does not tell you all three, the number is decoration.

This is also, incidentally, the same discipline that the AI content debate needs. A lot of confidently repeated LinkedIn statistics dissolve when you chase the source, which we went through in detail in what is actually known about AI content detection on LinkedIn.

How do you benchmark your own account in twenty minutes?

A procedure you can run today with nothing but your own analytics page and a spreadsheet.

1. Pull your last 30 posts. For each one record the date, the format, the reactions and the comments. Thirty is enough to see a distribution and short enough that your follower count has not moved much.
2. Score each post. (reactions + 4 x comments) ÷ followers x 1,000. Use your current follower count for all of them; the small drift over thirty posts matters less than the consistency.
3. Sort and find your own percentiles. Your median is the 15th value. Your top decile is the top 3 posts. You now have a personal version of the first table on this page.
4. Compute your spread. Divide your top-decile median by your overall median. In our cohort that ratio is roughly 25x. If yours is under 5x you are probably posting safe, consistent content and never breaking out. That is a diagnosis, and it points at the hook rather than the schedule.
5. Look at what the top 3 have in common. Not the posting time. The opening line, the specificity, whether you were in the story. Our breakdown of hook patterns and guide to opening a post cover what the top decile does in the first 200 characters.
6. Re-run it monthly. The only benchmark that survives every methodology argument on this page is your own trend. If your median is rising while your follower count rises, you are genuinely improving.

One optional refinement: track comments per post separately. It moves earlier and more clearly than rate does, and it is the number that predicts whether a post is going to travel.

What are the limits of these benchmarks?

We would rather you know where this breaks than quote it back at us later.

• Correlational, not causal. Nothing here shows that changing a format or topic causes a rate change. It shows which posts sit where.
• The cohort skews established. 65 creators, all with 1,000-plus followers, many with far more. If you have 800 followers these numbers are orientation, not a target, and your own rate will likely look better than the table.
• Lifetime counts. Engagement figures are cumulative at scrape time and post ages vary, which inflates older posts relative to newer ones.
• Visible hook only. The source captures post text up to the see-more fold, so any text finding describes the opening, not the full body.
• One platform era. The dataset reflects the LinkedIn of its collection period. Distribution mechanics change. The distributional shape (heavy skew, comments as the scarce signal) is the durable part; the exact decimals are not.
• Segment samples vary. Every segment figure on this page carries its post count and author count next to it. Where the author count is small, as with document posts, we have said so rather than quietly rounding it into a headline.

The average LinkedIn engagement rate, restated

Median 0.40 per 1,000 followers. 75th percentile 1.27. 90th percentile 5.95. Median post: 122 reactions and 12 comments. Those are the numbers to compare against if your account looks anything like this cohort, and the formula to compute yours is (reactions + 4 x comments) ÷ followers x 1,000.

The more useful conclusion is structural. Engagement on LinkedIn is a skewed distribution where comments are the scarce currency and the top decile lives an order of magnitude above the median. You do not close that gap by tuning. You close it by writing posts that give people something specific to answer, which is what the whole of the 34,000-post study is about.