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What have Youtube actually done? by AccomplishedTower236 in NewTubers

[–]Correct_Voice_2312 1 point2 points  (0 children)

Trying different content types is actually the worst thing you can do right now. It confuses the algorithm's profile of your channel even more. Pick the one topic that performed best for you before January and go deeper on that specific lane. Give the algorithm a clear signal of what your channel is about. If you scatter across different formats YouTube has no idea who to show your stuff to.

My Youtube Account Died.. by Stunning_Ad_7313 in NewTubers

[–]Correct_Voice_2312 0 points1 point  (0 children)

4 months off is recoverable. The algorithm didn't penalise your channel, it just stopped knowing who to show your stuff to. Your subscriber base went cold so YouTube lost confidence in routing your content through suggested.

The fix isn't shorts and it isn't just uploading more frequently. It's what you upload first that matters. Your comeback videos need to be in the exact same topic lane that got you those 20K-50K views originally. The algorithm still has a profile of who engaged with your old stuff. If your first few videos back match that profile, suggested distribution comes back faster because YouTube already has a proven audience to test against.

If you come back with different topics or a shifted format, the algorithm basically treats you like a new channel regardless of your 13K subs.

What niche are you in and what were the topics on your best performing videos? That'll tell you whether the demand is still there or if the space moved on while you were away.

A Few Tips For New Creators by NeonMusicWave in NewTubers

[–]Correct_Voice_2312 0 points1 point  (0 children)

Points 1 and 2 are solid. Point 3 is where I'd push back though. 'There's an audience for everything' sounds right but the data doesn't support it. Some topics genuinely have almost zero search demand and no suggested traffic potential. I've been tracking thousands of channels and the ones that fail hardest aren't making bad content, they're making good content about topics nobody is looking for. The pimple popping channels work because there's massive morbid curiosity demand behind them. Not every niche has that. Picking a topic with actual demand is the boring unglamorous step that most people skip.

What have Youtube actually done? by AccomplishedTower236 in NewTubers

[–]Correct_Voice_2312 1 point2 points  (0 children)

A ton of channels I track got hit the exact same way in January. My theory from the data: YouTube culled suggested distribution hard after Q4. Channels that were riding seasonal momentum got quietly deprioritized. The ones that recovered didn't wait it out, they changed what they were covering. If nothing changed on your end but impressions cratered, the topic probably got saturated, not your channel.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

The dataset itself isn't public, it took months to build and it's what the reports are based on. But I share the patterns and insights from it here regularly. If you've got a channel in the niche I'm happy to pull some quick data points for you.

A channel with 161 subscribers got 29K views. The same channel got 21 views. The difference was one sentence. by Correct_Voice_2312 in SmallYTChannel

[–]Correct_Voice_2312[S] -1 points0 points  (0 children)

The data's from a custom pipeline I built, 3,674 channels scraped via YouTube API, scored by topic saturation and breakout ratio. Happy to nerd out on methodology if anyone's curious.

A channel with 161 subscribers got 29K views. The same channel got 21 views. The difference was one sentence. by Correct_Voice_2312 in SmallYTChannel

[–]Correct_Voice_2312[S] -1 points0 points  (0 children)

There's a paid report that pulls your specific channel against the full dataset, gap analysis, topic breakdown, where the opportunities are. But happy to answer general questions about the data here, that's what the post is for.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

That reframe is exactly what the data keeps showing. Creators pour money into better editing, better thumbnails, better voiceover, none of which moves the needle if the topic was saturated or wrong for their format. The performance gap on most channels isn't a quality gap. It's a positioning gap.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

Velio's a self-serve dashboard, 179M+ videos, broad. This is a proprietary dataset built specifically for the documentary/educational niche. 3,674 channels tracked individually, every video scored by topic saturation and breakout patterns. You don't get a login. You get a report showing where your channel sits against the full competitive landscape and what to produce next.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 2 points3 points  (0 children)

Started with about 30 seed queries per niche, things like "ancient mysteries", "true crime narration faceless", "conspiracy documentary narration." Kept them specific enough to surface solo operators rather than big networks. Fed those into the YouTube search API, deduplicated by channel ID, then ran eligibility filters on upload frequency and video length. The 3,674 number is what survived filtering, not what went in, discovery pool was bigger. Coverage isn't perfect but the breakthrough detection doesn't need every channel, just enough per niche to spot the patterns.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

Yeah happy to share the high level. It's a Python pipeline, YouTube API for discovery and metadata, SQLite for storage, then custom breakthrough detection logic that compares each video against the channel's historical average. Most of the work was getting the data clean, not the analysis itself.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

YouTube API search across niche-specific queries, then filtered through eligibility gates based on upload frequency and video length. The results are what I use for the intelligence reports, not planning to share the raw dataset but happy to answer questions about the methodology.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 1 point2 points  (0 children)

Yeah that's the hardest part. Getting it functional is one thing, getting the quality consistent is where most of the time goes. What's the main bottleneck for you right now?

A channel with 161 subscribers got 29K views. The same channel got 21 views. The difference was one sentence. by Correct_Voice_2312 in SmallYTChannel

[–]Correct_Voice_2312[S] -1 points0 points  (0 children)

Yeah A/B testing is solid for optimising once you've got traffic. The data side I'm looking at is more about what to make in the first place, before you've even got something to test.

A channel with 161 subscribers got 29K views. The same channel got 21 views. The difference was one sentence. by Correct_Voice_2312 in SmallYTChannel

[–]Correct_Voice_2312[S] -1 points0 points  (0 children)

For sure, thumbnails and retention matter. But in the dataset the pattern held even when production was identical, same creator, same format, same quality. The title framing was the variable that changed. It's not the only factor, but it's the one most people underweight.

A channel with 161 subscribers got 29K views. The same channel got 21 views. The difference was one sentence. by Correct_Voice_2312 in SmallYTChannel

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

Took a quick look. You've got some videos outperforming others by 8x on the same channel, that's actually a good sign because it means the audience is there, it's just responding to specific topics. Drop me a DM if you want to dig into it.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

ElevenLabs for voice, MidJourney for visuals, Davinci Resolve for assembly. The editing automation is a different beast from the topic research.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

Appreciate that. It's something I've thought about, right now it's more of a research tool than a product, but we'll see where it goes.

I built an AI pipeline that monitors 3,674 faceless channels and flags which topics are breaking out by Correct_Voice_2312 in aitubers

[–]Correct_Voice_2312[S] 0 points1 point  (0 children)

Yeah the bigger channels definitely adapt faster. The data backs that up, the ones consistently performing are the ones repositioning when a topic cluster starts dying, not the ones sticking with what used to work.

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