I found that one of the most successful methods for going viral on Tiktok, Instagram Reels, and other short form content social media, is to learn the importance of calibration when optimizing solutions for complex systems. It might sound complicated, but in reality it is really easy and one of the most powerful tools the most talented creators use to grow and become famous.
First, let's define two types of systems. Bear with me, this sounds complicated but the practical examples in the end will clear things up.
How can we model the outcome of a system? With modeling an outcome of a system, we mean that we are trying to create a methodology that consistently predict an outcome for that given system. In other terms; if we want to get famous on social media and go viral over and over, we want to create our own methodology that we can reuse on every video and get the same viral results.
There are two types of modelling that are relevant here:
Have you ever calibrated brightness on your TV? Maybe you have experienced that your TV is a bit dark and you struggle to see? Then perhaps you went into the settings and changed the brightness. This is an example of where we use a calibration process on a regular basis, and I will now compare the difference between using the two types of modelling, to show why calebration is the right way to go.
Set the right brightness with theoretical modelling: Let's try the first method to get the brightness right for our TV. With theoretical modelling we want to understand the system. So we could for instance start by measuring the distance between the TV and the couch, the light that is coming into the room from the window, the angle the light is appearing from and the amount of light that are coming from our lamps. If we do these complex calculations we might be able to create a formula, which if we do the math correctly will give us the optimal brightness setting for a TV. This doesn't sound that easy, right? This is not the recommended solution, and the method is not very practical. This is because the laws of physics that affect the perceived brightness of your TV is a complex system.
Set the right brightness with calibration: Let's try to get the brightness right with the second model, calibration, instead. This method is more appropriate for a complex system. Let's say we decide to do the calibration when we have average light in the room, that should be a good starting time. Not the brightest part of the day but also not during night and complete darkness outside. We then go into the TV settings, and we make a first guess. "I think brightness should be 56 / 100". We then sit in the sofa, look at the TV, and evaluate if 56 was a good setting. Hmmm, maybe it is still a bit dark. So we crank it up to 65. Now it turned too bright all of the sudden. We reduce to 61, but we notice quickly that that was too far in the wrong direction. We go up to 62 - that's it. Perfect. We have now used a calibration model to optimize the result of a complex system.
Let's translate this knowledge to social media and how people use it to become famous. The theoretical modelling approach, would mean we create our own theories and models of what we think the algorithms and human physchology of the social media apps look like. So we make a model of what we think will make our own videos go viral. For instance:
(Note: These are just example, and not necessarily true)
We now made a model that we think is true, and we can try to create videos where we plan very carefully so that each video fulfills all these rules, because then we increase the chances of going viral. The key here is that we are trying to understand the system, and then think that if we do lots of work upfront, we can predict the outcome.
But TikTok and Instagram are not linear systems. They are in fact very very complex system. They change over time, because people change over time. They are not predictable, because people are not predictable. There are also millions and millions of parameters that decide if your video will be successful or not on that given day. So we can not guess the outcome beforehand.
Instead, we should use calibration. When doing calibration for TikTok or Instagram, we can still have theories of what we think might work. For instance, I think the best video length is 7 seconds. But we should re-evaluate over and over, and we should expect the system to change over time. Let's use calibration to go viral:
With calibration we still create smaller theories, and can learn over time that, yeah, for me 7 seconds is probably the optimal video length. But we don't set these rules in stone, we keep re-evaluating them over time, since the system will change over time. We also use this knowledge to make our future content better, but we also don't spend loads of time on optimizing beforehand when we make new video concepts. We keep making small bets, and we keep re-evaluating.
Essentially, we don't write down a predefined set of rules that we think apply for TikTok or Instagram - instead we train ourselves and our own video making skills.
Making well planned out videos can be a good strategy, especially for already large creators. But when you are starting out, calibrate, calibrate, calibrate. Rather do a 100 quick videos than 10 well planned out ones. Keep doing micro-improvements, calibration and re-evaluation. Because instead of trying to understand and predict the complex system, you can iterate to get the desired result, which means you essentially train yourself into becoming a viral video machine that produces viral content over and over.