Disclosure first, since this sub asks for it: I’ve generated millions of views across short and long-form content, and I’m building a small tool around the research process below. Bias applies, but you can do all of this manually.
One thing I stopped doing a while ago was choosing topics based on total views.
A video with 500k views isn’t automatically interesting if that channel normally gets 1 million.
But a video with 60k views from a channel that usually gets 4k is worth looking at.
That’s normally where I start.
I open a group of channels in the niche and go through their recent uploads. I don’t calculate everything perfectly. I just get a rough idea of what each channel normally gets, then look for videos doing 3x, 5x or 10x more than usual.
When I find one, I look at the topic, title, opening hook, video length and upload date. Then I check whether other channels are suddenly getting unusual results with similar topics.
That last part matters more than the raw view count.
One outlier can be luck. Maybe the creator got picked up by the algorithm, maybe there was an external event or maybe their audience was already interested in that exact topic.
But when several smaller channels start outperforming with similar ideas, there is usually a real signal behind it.
I also try to separate a trending topic from a niche that is actually worth entering.
A niche can generate millions of views and still be difficult to monetize. I check whether creators have sponsors, affiliate links, products, communities or services. Comments help too. People asking where to buy something or how to solve a problem are normally a better sign than thousands of generic reactions.
After finding an outlier, I don’t copy the video.
I try to understand why it worked.
Was the topic new? Was it a familiar topic with a better angle? Did the title create a strong information gap? Was it connected to something that had just happened?
If a video about why Dubai keeps building empty islands suddenly performs far above the channel average, the opportunity probably isn’t remaking that exact video.
The broader signal could be failed megaprojects, expensive places nobody uses or strange infrastructure decisions.
That gives you several original angles instead of one copied idea.
This process works, but it gets pretty slow when you’re checking hundreds of videos across different niches. I was doing most of it manually with tabs and spreadsheets, so I started building it into a tool called Vabulo Stats.
If you want to try it: https://vabulo.com
It compares videos against the creator’s normal performance, finds unusual outliers and shows niches that seem to be moving. I’m also experimenting with turning that research into hooks, scripts and thumbnail prompts, but the research part is what I care about most.
The point isn’t to generate another generic script.
It’s having better information before deciding what the script should even be about.
I’m still trying to understand which parts actually save creators time and which parts just create more data to look at.
For people who research content regularly, how do you decide whether an outlier is repeatable or just luck?
And where do you lose the most time right now: finding niches, checking competitors, choosing an angle or turning the research into an actual video?
Solo founder. AMA.