You Ask, I Answer: Spotting Trends and Hits?

In this episode, Michael asks, How do you spot trends and hits? What should you look for? I mean, if I was super super good at this, we wouldn't be having this conversation because I'd already be retired. It depends on the context. Within statistics, a trend is detectable once you start having some form of sustained growth, right?

So if you're looking at a numeric series of some kind, uh, you're you see a change in that that is sustained. You know, three, four, five, six different data points, it's going up, where it's going down, or something like that. But there's this something that is a sustained amount of momentum. Um, that's the mathematical answer of how you spot a trend. But more broadly, when people are asking, like, how do I know what the next big thing is going to be?

What should we be focusing on? You know, where what's gonna be the next big hit? We don't know. We have no way of knowing what the next big hit is going to be because it's it's so from a mathematical perspective, it is such a complex equation with so many inputs that there is no way to accurately predict that outcome. Right?

It's like trying to pick a stock in the stock market. If the stock market was the only place where you could do business buying and selling stocks, you could probably come up with some predictive algorithms that would work. But because you have all these other financial mechanisms, hedge funds, high frequency trading, shadow markets, uh, private trades, all this stuff that isn't fitted into the outcome data, you can't build a model. Many, many companies have tried building a working model to predict uh the stock market, and zero have succeeded. And I can tell you this with confidence because if one of them had succeeded, they would have all the money.

There would be no other buyers to talk, but they'd be one company, they would have all the money, and that would be that. More broadly, um, from a cultural perspective, from a a people perspective, again, we don't know. We have no way of knowing what's going to take off. What you can do is pay very careful attention and look at data very, very frequently. So there are some great listening tools, conversational intelligence tools out there.

Talk walker, for example, uh, a good friend of mine, uh, the folks over there, that provide really good data tools to provide information that you can then put through uh predictive algorithms, uh Markov chain models, neural networks. So you take the fancy uh forecasting software of your choice, and you can start to look for those patterns of growth. But even then, it's not necessarily going to be all that clear. Here's a simple example. If you were to go to Google Trends, go to trends.google.com and type in TikTok, switch the timeline to you know all time, you would see that it took a really, really long time, years for the service to catch on.

When you look at that graph, you looking at it, go, wow, it wouldn't would have been really, really hard to detect that this is a thing. So, what do you do? How do you how do you do this? The answer is in the target market that you care about, wherever that market is, you've got to be listening. You've got to be in the mix listening to what people are talking about and listening for conversations and trying to understand what it is that people what catches people's attention.

For example, as of the day of this recording, which is July 24th of 2022, a new video game, Stray, uh has hit the market, and in a number of Discord groups that I'm a member of, um, conversations are happening a lot about this game. Gamers are talking about it. It made a big splash, and more importantly, people seem to like it, people seem to enjoy it. And as a result, um it appears to it's going to be a hit. Now it's early.

Um it only released a few days ago. But when you're in multiple communities and you see the same thing being talked about, that's a pretty good early indicator that you should pay attention to it. Um should dig in a little bit, see if see if there's a there. We say the same thing about, for example, when a new social network pops up. If you see enough people in your target audience talking about the thing, that's when it's time to go, all right.

You know, we need to go at least sign up for an account, get our name reserved, maybe see who else is there and see what the general conversations are. That's um one of the easiest ways to not predict a hit or trend, but to catch it early enough that to the outside world, it looks like you predicted it. For example, um, in January of 2020, I started reading a lot, a lot on Twitter within a very specific community about this new disease. It's called NCOV 2019, novel coronavirus. And some of the folks who were in these disease communities were saying this is a this is going to be a thing.

The numbers at that point, like there were less than 500 cases, you know, uh around the world at that point. There was like a couple thousand, five thousand or so in China. That was about it. It was quiet. It was a relatively quiet thing, but it didn't look like a quiet thing, right?

It it exhibited signs of exponential growth. Two months later, lockdowns came, right? April, March, end of March, April uh 2020, it became a thing. And then you had the main wave, the alpha wave, the delta wave, Omicron, Omicron B A2, Omicron BA5, and so on and so forth. And here we are two years later.

And in those early days, uh, I went to an event, and I decided I was the only person wearing uh one of my my P100 masks, and people are like, what is wrong with you? Dude, you are what's up with that? Um, and I sent an email to events I was speaking at you know later in the year saying, hey, I think this might be a thing, so I'm offering to to you know record my session as video in case um in case it it becomes a thing because it was exhibiting mathematical signs, exponential growth signs of becoming a thing. We all know what happened, right? And I got emails later that year and the next year of people going, yeah, you're right.

How'd you know you were and let's see? I didn't know. I wasn't predicting it. I was just paying attention to what was happening in the moment. We're seeing the same thing happening now.

Uh again, it's July 24th of 2022 with stuff like monkeypox. It's picking up steam, right? The mathematical trends are in its favor. And so you got to pay attention to it. When the math shows that uh uh early signs of an exponential trend, which you can find mathematically, it's time to pay attention to it.

The challenge is you've got to know where to listen. And that's the part that marketers have, I think, the most trouble with. Marketers spend so much time listening to themselves and to the people within their companies, and not nearly enough time listening to their customers, not nearly enough time listening to the audience, spending time even in your audience. When you spend time in your audience, you hear stuff. When you hear stuff, you can start quantifying it and identifying what's going to be a thing before it's a thing.

Because it is still technically a thing. Exponential growth just doesn't look like it until it really looks like it. That's the way exponents work, right? Doubling every number that doubles every time, you know, it goes from one to two, woo, big deal, right? Two to four, oh big deal.

Then you get to 64, 128, 256, 512, 1024, and you're like, oh, this thing's growing pretty fast now. Um that's the challenge. Our brains, as people, we don't we don't do exponential calculations very well, if at all. We can't see it. So we've got to rely on our tools to do it for us.

So that is what I would suggest is good listening to identify topics and things to pay attention to, and then good analysis tools to look at the data and say, are any of these things exhibiting exponential growth? If so, should we be doing something about it? Really good question. It's a question that requires you to actually have A the time to listen carefully in all the right communities, and B the technology to um to analyze it to look for those trends. So good question.

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