YouTube engagement rate calculator

Subscribers accumulate. A channel that was big in 2019 keeps the number long after the audience has moved on, and a channel on its way up looks small. Views on recent uploads are the number that says what the channel is now, and the report puts them next to the subscriber count rather than behind it.

• Engagement rate from the last dozen uploads, against the YouTube range for that size.
• Average views, likes and comments per video.
• Views against subscribers, which on YouTube is the number that matters.
• Audience behaviour, a score with every reason listed.
• The recent uploads, so you can see the spread and not only the average.

A word on comments

Comments carry more weight on YouTube than anywhere else. A video is long, and a comment means somebody watched enough of it to have something to say. A channel with healthy views and almost no comments is not necessarily fake, but it is worth knowing before you brief it.

Engagement rate falls as an account grows. That is not a failing, it is arithmetic: a million people do not all react to one post the way a thousand close followers do. So a rate only means something next to the account’s size, and this is the range the tool compares against.

Rate is average likes and comments per recent post, as a percentage of subscribers. These are working ranges drawn from what is reported across the industry for each tier, rounded so nobody mistakes them for precision. As the checker builds up its own data, they will be replaced with measured medians and this page will say where they came from.

There are two ways to look for fake followers. The expensive one samples the follower list itself, thousands of accounts per profile, and grades each on whether it looks like a person. That is what the paid platforms do, and it is why they cost what they cost. The other is to read what a bought audience leaves behind in the public numbers, which is what this tool does. Bought followers do not react, do not comment, and do not watch, and those absences show.

The signals

• Engagement against size. The heaviest signal. An account with 200K followers whose posts get the reactions of a 5K account has followers who are not there.
• Comments against likes. Likes are cheap to buy in bulk; comments are not. Under one comment per two hundred likes, on an account big enough that the ratio means something, is a tell.
• Following against followers. An account that follows more people than follow it, past a few thousand, usually grew by following everyone back rather than by being worth following.
• Audience against output. Fifty thousand followers on twenty posts happens, but rarely.
• Consistency. Real audiences are uneven. A run of posts that all land within a few percent of each other looks purchased; one post carrying the whole average means the typical post does far worse than the number suggests.
• Views against followers. On video platforms, followers who never watch are the clearest sign of all.

What the score is, and is not

Each signal adds to a score out of 100, and every point is shown with its reason, so you can disagree with any of them. A quiet week on a small account trips the first signal honestly, which is why one signal on its own never gets past “a few signs” and the score only says bought when several agree.

It is a reading of behaviour, not a count. An account can carry a share of dead or bought followers and still pass every check, because the real part of its audience is engaging normally; the score sees what the audience does, not who is in it. Counting fake followers means grading the followers one by one, thousands per profile, which is what the paid audit platforms charge for. This page does not do that, so it never quotes a percentage.

Wiro keeps a creator database for each agency across Instagram, TikTok and YouTube, refreshes the numbers in batches, and carries them through to campaign deliverables and the report a client reads. This page is one channel; the product is the whole roster.