Introduction to the Viral Economy

Not knowing if a video will succeed or flop is the bigger nightmare for content creators, and for the past few months, we’ve been working on solving it.

And it seems we cracked the code.

When you come up with a video idea, you might have a gut feeling about how well it will do.

Problem: There's no way to know if you're right until after you've produced and posted the video.

Why is this an issue? Because what feels right in your head hasn't been checked against what viewers actually want.

With over over 500,000 votes from 4,000 people, our data shows that creators' gut feelings are only correct about 57% of the time.

In other words, it is barely better than relying on a coin flip.

What about you? Have you ever poured your heart into a video, only to watch it flatline helplessly in YouTube Studio?

You can’t diagnose without clear feedback. Guessing games don't lead to improvement, they lead to frustration.

It doesn't have to be your reality anymore. What if you could know before even producing the content?

Say hello to The Viral Economy.

A tool (currently in V3.1 alpha) that we have been building since April 2024 in The Investor’s Kitchen (my Discord community).

To understand where we come from, here’s some context. On YouTube this is how it works (simplified):

A viewer encounters several video thumbnails then quickly assesses based on their current interests and eventually chooses a video to watch.

Our goal was precisely to measure this silent and (almost unconscious) ranking. And spoiler: we’ve cracked it.

We’ve developed a method to translate these subtle levels of interest into concrete, measurable data.

The method in question, is based on a concept called “Crowd Wisdom” or the collective intelligence of a diverse audience. That’s exactly what we’re doing with the Viral Economy tool to predict the success or failure of you next project.

Crowd Wisdom explained: Historically, large groups have shown a remarkable ability to make accurate predictions. A famous example of that was an observation made by Francis Galton in 1906 at a country fair contest where a crowd of villagers tried to guess the weight of an ox. None of the individual guesses were exactly right, but the average of all 800 guesses was only one pound off the actual weight.

This accuracy emerges because diverse opinions within a group can balance out individual biases and often outperform even the most informed individual guess.

Applying This to YouTube: Inspired by this idea, we’ve designed an experiment. A free game (link in bio) where participants choose between two video concepts, mimicking the YouTube algorithm’s process of video selection but on a scale manageable for precise data collection:

The results have been clear. The wisdom of the crowd outperformed the average user 93% of the time.

This controlled setting allowed us to gather clean and direct feedback.

This validation has given us the confidence to start building a more complex algorithm that not only captures but quantifies viewer interest into a predictive view count that we coined “pricing”, in other words, turning that quantified interest into a view range.

Here’s what it currently looks like in v3.1 (alpha):

We chose “pricing” because measuring interest is very similar to measuring the price of an item. Even if it’s not precise, we all have the ability to evaluate the price of common items, for example, that a bottle of water isn’t worth $800.

Similarly, most of us can easily recognize whether a video concept is a “don’t care” or “super interesting”. What’s tougher is evaluating what’s in between.

That’s what the Viral Economy does, putting a price tag on your packaging’s potential success.

Now the question that probably arises for some of you is: how can you predict views without watch time?

It’s simple, we don’t. That’s what YouTube does.

Instead of waiting to see if people watch your video, we measure their initial interest right from the thumbnail and title.

What we want is to have an accurate idea of a video’s virality potential (and the magnitude) before the video is even produced.

For that, low precision but high accuracy is more than enough.

Knowing it’s a 50k, 500k, or 1M concept compared to just hoping for the best is already huge.

If your video lives up to the hype suggested by its packaging, the VE should accurately predict its virality potential.

We measure interest based on what viewers expect from the thumbnail and title without watching it. More specifically, we measure interest based on the first impressions of new viewers who have never seen your videos before, as they can decide without any past bias.

It’s essentially “browse traffic” (the king of traffic on YouTube) minus active watchers.

The name "Viral Economy" isn’t just for show, there’s an actual economy running inside it.

To ensure the quality of the votes, we have a system with coins that members earn when their vote is correct, but they also lose coins if it’s wrong.

After voting for other members' packaging, they can use these coins to price their own. It is a positive win-win loop where they vote for yours and you vote for theirs.

There’s a constant flow of real people voting on ideas every day.

We’re still in the alpha phase with plenty of room to grow and fine-tune but this version is quite strong.

Feel free to jump into The Investor’s Kitchen if you’d like to try it, your feedback is welcome.

Over the next weeks, I’ll keep you posted with fresh updates and dive into some cool case studies to show just how powerful this tool can be.

This is project is still a baby.