- Hero content is big events, which you can advertise in a big way. It gets huge attention on the day it’s happening, and then quickly becomes uninteresting again, such as the E3 presentations.
- Hub content is regularly scheduled content, to keep subscribers (and viewers you’ve reached through the other content) interested in your channel. This content gets watched by your subscribers in the first couple days after upload, and then basically never again.
- Help content (originally named: hygiene) is helpful content teaching users how to do stuff, ie tutorials. This content gets found at any time via search, but doesn’t add much value to subscribers to your channel.
Now, this model kinda makes sense if you have a product you’re making videos about. But it kinda breaks down once you put it into the context of a normal YouTuber: It doesn’t make sense to make a big event which only is relevant for a week, so Hero content is out. Hub content is more in line to what YouTubers do, but YouTubers do so much more than make videos which just are consistent and appeal to their current subscribers.
So, out of this model, only a few bits actually are usable for YouTubers, and even these only are so with caveats. So I thought about it a bit and came up with a new model instead:
The SEE–NTS Model
SEE-NTS is short for the following aspects:
- Subscriber Content. Ie content made primarily for subscribers, featuring funny in-jokes, references to previous videos, stories that make the creator more relatable to their fans and such.
- Evergreen Content. Ie content which will stay relevant to the world for the (forseeable) future.
- Event Content. Ie content which is tied to certain events.
— with their counterparts —
- New Viewer Content. Ie content which is accessible and fully understandable to someone who never has seen any of your content before.
- Timely Content. Ie content which is relevant during a specific window of time only, and then basically never again, eg news.
- Serial Content. Ie content which you can sure you’ll see more of next week anyway.
The individual aspects make predictions on whether the view distribution will be flat over time, or have a spike shortly after publication:
- Subscriber content is watched by subscribers, so it’ll get most of it’s views within the first week of publication, while New Viewer content may get discovered by potential new subscribers at any time.
- Timely content is only relevant shortly after publication, after which it’s old news. Evergreen content is ever relevant.
- Event content is most watched during the event (→ Tentpoling), while Serial content is watched all year round.
As such, the model explains why Hero-Hub-Help makes the predictions that it does: Hero content is minmaxed for spikeyness (Event/Timely, with a lot of advertising thrown at it so that talking about the Subscriber/New Viewer axis kinda is pointless), Hub content is Subscribers/Serial/Timely content (and doesn’t nearly spike as high), and Help content is minmaxed for flatness.
SEE-NTS also allows for other content to be categorized sensibly:
- Mr Beast’s content is no doubt Serial (it’s not really a surprise what he’ll do next), but features some Event-like qualities (he basically makes his own events in each video by giving away a lot of money). His videos are accessible for New Viewers, yet appeal for Subscribers as well. And the stunts he pulls generally age well, so: His content sits pretty much in the middle and manages to more or less cover all bases.
- A band doing a concert live stream is an Event for everyone who already knows the band (ie Subscriber-ish), but since music doesn’t really get outdated, it also is strongly Evergreen.
- Videos like “how to decorate your house for Halloween” and similar seasonal content is Evergreen while the (yearly repeating) Event is going on. This kind of content technically could still work for Subscribers primarily, but realistically it’s probably gonna be a optimized for New Viewers.
Using SEE–NTS for Content Programming
SEE-NTS can be used to assess a channel’s current standing to make decisions for future content programming.
Most obviously, if the vast majority of views a channel has come from subscribers and all formats on the channel are made for subscribers, that channel may want to develop a format which is meant to appeal to non-subscribers and draw them in.
If a creator feels like they’re grinding away in a hamster wheel, but can’t afford to take a day off because all their subscribers will lose interest, maybe Evergreen Subscriber content would be able to bridge these gaps in the future.
If a musician can only realistically make one big Event/Evergreen-type video a year and struggles to re-activate subscribers in-between uploads, them making Subscriber/Serial/Timely content in-between to fill the gaps and keep people engaged throughout the year may be useful.
Of course, as always: It’s hard to recommend any specifics without knowing the actual channel. I hope however it can help creators, at a glance, find out where they are with their current programming, and where they have potential left to explore.
SEE-NTS as a model doesn’t predict how successful content is going to be, it only can predict the rough shape of the view curve. The real world (and “The Algorithm”) of course can always throw a spanner in the works by having your viewers receive the video differently than what you designed it for.
Unlike Hero-Hub-Help, SEE-NTS doesn’t do content recommendations. For example, it’s not entirely clear to me what Subscriber/Event/Evergreen content would even look like, while for Help content, the hint already is in the name, and thus are the strategies you should take (ie SEO on your customer’s troubles).
SEE-NTS is untested as a tool for content programming. The questions that need to be answered in the future are:
- Is SEE-NTS useful to accurately describe different channel programming strategies?
- Is SEE-NTS complete, or are there more factors which are essential for programming?
- Do creators who use SEE-NTS understand their programming better than those who don’t?
- Is SEE-NTS useful to find gaps in the content programming?
From what I can tell so far, the SEE-NTS model seems promising. Even if it fails as a “practical” tool that can tell creators “do this”, it may still be a worthwhile academical tool as it categorizes content way better than Hero–Hub–Help.
Of course, I’d love even more for it to be useful as a practical tool. I guess time will tell how good this thing is.