Average TikTok Engagement Rate: Benchmarks From 460,483 Posts — buzzabout
Average TikTok Engagement Rate: Benchmarks From 460,483 Posts
The average TikTok engagement rate across 460,483 posts we measured is 5.38%, the median 3.95%. Full percentiles, breakdowns by intention, hook and length, and the one variable that actually explains a post.
The average TikTok engagement rate in our data is 5.38%. The median is 3.95%. We computed both on 460,483 TikTok posts, every post with a view count in the corpus buzzabout has collected, as likes plus comments plus shares, divided by views. The median lands in the same ballpark as the published per-view benchmarks, about half a point above them.
The number worth the article is the one underneath it. Who posted a video explains roughly six times more of its engagement than the strongest measurable thing about the video itself. So every benchmark on this page, ours included, can tell you whether a body of work is normal. None of them can tell you whether a video was good.
What is a good engagement rate on TikTok?
Above 3.95% puts a post in the better half of this corpus. But a single number is the wrong thing to compare against. Here is the whole ladder.
0.8p10 1.9p25 4.0p50 7.0p75 11.1p90 14.4p95 22.5p99 TikTok engagement rate percentiles (%, n=460,483)
The denominator is views: (likes + comments + shares) / views, on the 460,483 posts with a view count. The mean, 5.38%, sits well above the median; the tail section below explains why.
Read it as a ladder. A post at 1.94% is not failing; it sits at the 25th percentile. A post at 11.11% is not viral; it is in the top tenth. The middle half of all posts spans 1.94% to 7.01%, a gap of 5.07 percentage points, wider than the distance between most of the competing benchmarks published on this topic. The point: "good" is a range, not a number. Compare your work to a rung, not to an average.
What this corpus is, and how the labels were made
The 460,594 posts are the entire TikTok corpus buzzabout has collected: every post pulled by a research run since tracking began. They come from 212,029 distinct creators across 4,128 topic datasets. Together they carry 130.9 billion views, 6.72 billion likes, 596.9 million shares and 72.5 million comments.
This is a sample of topic-driven content, not a representative panel of TikTok. Posts enter because somebody ran a research query on a niche. The mix leans toward food, fashion, shopping and health: the five largest categories are food and drink (54,950 posts), style and fashion (46,679), shopping (46,040), healthy living (42,544) and medical health (28,098). No single account dominates.
The formula, computed rather than read. The database stores raw counts and no engagement-rate field. So every rate on this page is computed directly as (likes + comments + shares) / views, on the 460,483 posts with a nonzero view count.
The labels are model-assigned, and here is what that costs. Every post also carries fields the platform API does not return. The pipeline reads the post content and its comment thread, then extracts what the post is trying to do, how it asks, its tone, its narrative structure and its hook. Those are not metadata. They are the output of reading.
Coverage is two-tier, and every breakdown below says which tier it stands on. Content intention (442,171 posts, 96.0%), category (456,609, 99.1%) and duration (440,324, 95.6%) cover nearly the whole corpus. The craft labels do not. Call to action, hook, tone, narrative and language exist only on the roughly 47,000 posts (10.3%) that went through a full content-analysis run.
Why does every TikTok engagement rate benchmark report a different number?
Because they measure two different quantities and call both "engagement rate". We pulled the first page of search results for "average TikTok engagement rate" and read what each source actually reports.
| Source (position) | Published figure | Denominator |
|---|---|---|
| Phlanx (1) | 5.60% at 1K to 5K followers, 2.43% at 5K to 20K, 2.15% at 20K to 100K, 2.05% at 100K to 1M, 1.97% above 1M | per follower |
| Hootsuite (2) | 1.5% | not stated |
| Influencer Marketing Factory (3) | 8% median, 7.4% to 8.1% by follower band | not stated |
| Rival IQ (4) | 3.4% median | per view |
| Social Cat (5) | 4% to 8% | not stated |
| Influencer Gift Form (6) | 2.5% | not stated |
| Brandwatch (7) | 4.07% | not stated |
| Socialinsider (8) | 3.30% | per view |
| buzzabout (this study) | 3.95% median, 5.38% mean | per view |
Line the per-view sources up and most of the disagreement vanishes.
Socialinsider 3.3 Rival IQ 3.4 buzzabout 4.0 The per-view sources agree (median engagement rate, %)
Three samples inside a band 0.65 points wide, while the table as a whole spans 1.5% to 8%.
Average or median, and the shape of the tail
The mean is 5.38% and the median 3.95%, and 63.7% of posts sit below the mean. When nearly two thirds of a population sits below average, the average is describing something other than a typical member.
Concentration is the reason. A thin top layer of posts collects most of the engagement.
Top 1% of posts 59.8 Top 10% of posts 92.7 Share of all 7.39B engagement events held by the top posts (%)
Which variable actually explains a post's engagement rate?
Not the hook. Not the length. Not the hour. The account.
Creator 0.7 Topic dataset 0.4 Content intention 0.1 Category 0.1 Tone of voice 0.1 Call to action 0.1 Narrative structure 0.1 Hook 0.0 Share of the engagement ranking each variable explains (creator test n=156,651; craft variables ~12-14k)
Creator scores 0.662 against a chance floor of 0.048. That is nearly fourteen times its own floor and about six times the strongest content variable.
*** The strongest content finding in the piece. It survives most of the test that matters, but not all of it. Inside an account, most of the gap disappears, but not all of it.
How to use a TikTok engagement rate benchmark without fooling yourself
- Compute per view. Likes plus comments plus shares, divided by views. Compare that to a per-follower figure and you will draw a conclusion about nothing.
- Use at least 50 of your own posts. Fifty is not a round number. It is where the measurement error gets smaller than the effect you are trying to see.
- Compare your median to percentiles, not to an average. Place it against p25 1.94%, p50 3.95%, p75 7.01%, p90 11.11%.
- Segment your own posts before you compare them. Split by intention, by length, by whatever you actually vary, and compare each segment to its own history rather than to a global figure.
- Expect a small promotional penalty inside your own feed, and a large one from your feed's mix. Within a single account, promotional posts run about a third of a point below the account's own median.
- Read a single video against your own distribution, never against ours. One post above your p75 is a good post for you. One post above 11.11% is a good post for a topic-driven sample of somebody else's niches.