Post when your own uploads say to
Every “best time to post on YouTube” chart is an average of channels that are not yours. This reads your own public upload history instead and gives one window to test, in the timezone you pick. Free, no account needed.
Comparing platforms? The same read for Instagram and TikTok sits next to published benchmarks for those two.
A handle, a channel ID or a channel URL. Only public uploads are read — nothing here needs you to connect an account.
Your audience's timezone, not yours. A creator in Singapore making videos for the US has to think in US hours, and that conversion is where most published charts become useless.
A dozen uploads cannot establish a timing advantage, and the result says so when the sample is thin. Run the window for a few uploads, then check it against YouTube Analytics.
Compare tutorial uploads with other tutorials, not a mixture of Shorts, livestreams and announcements. A suggested window from that sample is a test candidate; public upload times do not reveal when your subscribers are online.
| Input or observation | Result or interpretation | How to use it |
|---|---|---|
| Sample A: 3 tutorials, measured 7 days after upload | Compare with tutorials at the same age | Review relevance and context |
| Sample B: 1 Short, 1 live stream and 1 announcement | Separate these formats before comparing timing | Leave this out |
| Public uploads cluster around 18:00 audience time | A candidate window to test; not the private audience-activity chart | Verify or test this next |
These are authored teaching examples, not a saved live response or measured performance. English sample briefs and outputs are quoted in their original example language. Calculations use the stated assumptions; unknown values remain unknown.
Our Instagram and TikTok pages show published research side by side, because for those platforms several studies with disclosed samples exist and we can name each source. For YouTube we do not have a public dataset of comparable quality, so there is no table here. Publishing a made-up one would be the easy move and the wrong one: a chart you cannot trace to a source is worse than no chart, because it looks authoritative while being unfalsifiable.
Write down the audience location, creator location and intended posting date together. Convert the actual offsets for that date and check whether the converted slot falls on the following or previous day.
Test two repeatable windows with comparable posts and the same measurement age. Keep the benchmark separate from your own observed result, then use your account data to choose the next window.
YouTube says, “Publish time is not known to impact a video’s long-term performance.” Publishing when your audience is most active may help early viewership, but it does not guarantee long-term performance. Treat the window from your public upload history as something to test against your own YouTube Analytics, not proof that posting at that hour causes more recommendations. YouTube Help: performance FAQ and troubleshooting
Open Analytics, then Audience, then “When your viewers are on YouTube.” That chart is built from your channel's own viewers and is the only authoritative answer for your channel. This page exists for the case where you want a read before you have enough Analytics history, or you want to compare what you have been doing against what you meant to do.
This page only reads what any visitor can see. Bring the channel into Kabo when you want the test tracked against your own numbers instead of re-running a public guess.
There is no single answer, and any page giving you one hour for all channels is averaging away the only thing that matters — who your audience is and where they live. The useful version is a window derived from your own uploads and read in your audience's timezone, which is what this page does.
No. It reads public uploads only. Nothing here asks for access to your channel, and it cannot see private metrics like click-through rate or retention — those exist only inside YouTube Analytics.
Because the sample was too small, or the view counts on recent uploads are too fresh to compare. Rather than dress a dozen uploads up as a schedule, the result labels the window as something to test. A window you know is uncertain is more useful than a confident-looking number you cannot check.
Treat each format as a separate timing test and compare the results in YouTube Analytics. This page reads your uploads as a whole; if you publish both, check whether your Shorts and long videos perform differently at the hours you test.
