So What? Determining What’s a Trend

All right, folks. Welcome to the marketing analytics and insights live show. Uh Katie is off this week, so uh we're going to behave irresponsibly. John, what are we doing this week? Today we are talking about uh extending on last week.

We were talking about trends, what's going on? So we've got the mathematics behind trend detection, you've got some methods to measure, and then also how to know when to act, when to actually put this data to use. So hopefully we're gonna deliver the formulas and the so what that Katie has demanded in lack of her presence. So here we go. Fair enough.

All right. So let's start with this because I think it's important. What is a trend? To you, what is a trend? Yeah, I don't know.

People talk about the zeitgeist. I love that term because it's inside baseball, but you know, it's just every once in a while certain topics gain some heat and some traction. And a lot of them can be black swans where you just, you know, something explodes out of nowhere, some random meme. But then there's other trends that are cyclical or have you know repeat over time or when certain things happen. And that's the kind of stuff that you want to jump on because there's many times where you know if you live and die by corporate goals or hitting certain targets, if you can find trends that drive that stuff, you can make things completely predictable, and that's where you'd really love to be.

Yeah. Let's look at uh a few different terms because I think we should probably clarify some of this. I'm gonna go ahead and flip over to our art easel. Got the got the whiteboard going today. Uh let's do some uh some basic art here.

If you were to look in your analytics and see something that looked like this, what would you call that? Uh, you know, first it would be a spike, I'd call that. So I that would be really that's uh my economics uh term would call that a spike. Exactly. Uh mathematically or statistically, we call that an anomaly.

Something weird happened here, right? And then we go right back to where we were, right? So we start here, and then if you if you actually were just like sort of just pretend uh this didn't exist here, essentially you would be kind of just the same thing, right? You start and ended the same place. So that's our first thing.

Anomalies. You we always have an anomalies, and there it can be anything like you know, a bot attacking your website. It could be in a popular post on Reddit that sent you know a thousand visitors to your landing page. It could be any number of things, it could be an ad that ran really well. So that's our first part as the anomaly.

Next, what would you what would we call this? Oh, that's the upward trend. Yeah, that's the one of the two holy grail. Exactly. So that's the upward trend.

So a trend is essentially in this case, a either an upwards or a downwards uh decline, uh advance or decline in the data. So we have this. Imagine the mathematics here. We have it, we have this sort of average here, and then we start to deviate from that average more and more and more. And a trend uh can be, you know, it doesn't, it doesn't necessarily need to stick uh like that.

So let's do let's draw this. Here's our trend, and back. So this section here would be a trend, right? Where something has happened, we've got a big difference. If we have that average line, this is kind of a big difference for a while, and then we start headed back towards the average.

So that's a trend. Oh, and that's something that's importantly as math mathematically important. And then there's a third category, and this is uh, I I think is the holy grail, which is looks like this. What would you call that? That a new plateau is the you know, usually how we describe that.

You're jumping up to the next level. Exactly. So it in mathematical terms, this is a breakout, right? So something it trended for a while, and then it's stuck, right? So I hopefully this is your website traffic.

Like this is the hope of every marketer is you run a big campaign, you get this big boost, you're happy, uh, and then the audience sticks around. They find so much value in you, like, yeah, like they they continue to give you the thumbs up. So that's what we want to talk about today. Today we're talking about trends, but the goal of our marketing, this the so what uh is to find trends that lead to a breakout, they find trends that lead to the sustained level of growth because it's not much fun if after your campaign is done, you go back to the to the beginning, right? That's that's no fun.

Um, what we want to do is we want to figure out how do we get how can we see things that will eventually lead to breakouts? And the bad news there is there really is no easy way to know that, right? It's one of those things that only happens in retrospective. You can't tell in advance that this is going to be the thing. Um, there are very, very few companies that have ever done that.

They uh the only one I can think of off the top of my head that's had three breakouts, uh, is Apple, right? With first with the personal computer, uh, then with uh the smart the the graphical interface, the Macintosh, and then with the smartphone, so like the other three, I guess four because they had that they had the iPod, the portical musical player. Not many companies, most companies are lucky if they get one breakout, much less four. Um, so it really is is something special. So, how do we determine this?

How do we know that what to find these things? Well, let's pull up our good old friend Microsoft Excel or the spreadsheet software of your choice, you don't have to use Excel, just know that most people do. The first thing you need for any kind of trend detection is the data itself. So I pulled some data here from Google Trends, which is uh if you have not used Google Trends, I don't know what you've been doing for the last few years. But it's it's one of the best public data sources for just general trend things.

So let's pick uh let's pick on have a little bit of fun here. Let's pick on the NFT, the non-fungible token. Just from eyeballing, you don't even have to use any math yet. What would you classify this as so far, John? You better have already short sold because those tokens are getting to be worth less every day, it looks like.

Well, so these are searches, these are not actual dollar values. And again, this is data that can be applied to just about anything, but we want to get data that it has two things. It has a numerical value, which uh Google Trends provides you, and it has a date. Those are the two most critical pieces that uh for trend analysis. If you don't have those two things, you really can't do trend analysis, at least you can't do it in any sensible way.

Now, as we were just showing, uh, the first thing you want to do is just do a bit of explore exploration. So I'm gonna put in a chart here. Oops, get rid of that. So let's do I'm gonna use Clubhouse too. Everyone's uh well, I don't know how to do it.

Favorite punching bag would be though. Exactly, favorite punching bag. And obviously, Clubhouse is a search term that's existed long before the app that we know, but we can see right away that there was so this background noise and then big spikes you know coming in here um last year and then this year for for NFTs. So our first step always take a look at the data, just see if there's even a there there. If there's not even a there there, if it's just a flat line or something, then you know you probably don't need to do a whole lot of extra exploration.

In this case, it looks like there's something that's happening. So our next step. Trends, remember we said are increasing or decreasing. So the thing we want to look at is how does what is the rate of change? So let's do which which one do you want to do?

More, John NFTs or Clubhouse. Oh, let's look at uh the NFTs. That's definitely more fact. So we're gonna do uh call column called change here, and I'm gonna do good old fashioned new minus old divided by old. That's our rate of change, and populate that down.

Now we have divide by zeros, that's fine. It's not fine, but it's fine in this case. Um ignoring those divide by zeros for the longest time here. Um, you know, in fact, I'm gonna do this for anything that is a zero, just to avoid let's do an advanced replace. If it's zero, I want to replace it with point zero zero one just so that we have something there.

Okay, close. And now we have our graph. That's not really all that helpful, is it? Yeah, let's undo that. Okay.

So for the longest time, we have there was no search volume for NFTs, right? Just wasn't, and then you start to see that rate of change really start to crop up. Like, okay, there's you know something happening there. Um, and then you get to this, you know, some pretty big changes, like two and a half, uh 2.5x changes, and then going back down. So we can already see that there is something there.

I'm going to didn't even have anything until row 230. So I'm gonna go ahead and just drag this down there, just to make the graph a little bit cleaner. And now our rate of change becomes zero. Now, the challenge with this is because we were talking about, you know, is something increasing or decreasing. Anytime you're working with day level data, it does get kind of messy, it gets very noisy because you have fluctuations happening all the time.

So let's make a seven-day average here. And it's gonna roll the seven-day average of our change. Go ahead and take away our initial chart and put our seven-day average chart up instead. And there we see our rate of change. Generally speaking, when you you know, when we're talking about like should you pay attention to a trend, once it starts that ascent, yeah, you probably want to be paying attention to it.

Uh, you know, you go up here to 14% change, you know, for rolling uh rolling average of 28% change, and so on and so forth. So you start to see this increase uh until it really explodes, until you get you know, you're well over a hundred percent change here, and then the we take the roller coaster ride all the way back down. So you almost have two trends here uh at the same time, you know, if you split it down the middle. So that's one of the easiest way to detect a tr a trend is just do a uh moving average and see you know, is there substantial increases? Is or is it flatlining?

Because if you were to to take a this exact same data and actually let's do this with Clubhouse because Clubhouse is a good example of this. And I'm just gonna do new minus old by old. And do this, and we're only going to do a few days worth here because I because we know that Clubhouse didn't actually exist as a search, you know, an important thing for from at least for marketers, uh, for a while. I'm gonna plot this out here, our seven-day average. Now let's go ahead and answer our plot.

And what we see is kind of that ups and down, up and down volatility, kind of bouncing up and down, but it's hovering around that zero line, keeps coming coming back to zero eventually. That's a an example of something where there isn't a trend, right? Where you've got this up and down noise, there's no trend, there's nothing detectable saying, like, yeah, there's big changes from day to day. There really isn't. Uh, it's just you know some relatively minor noise.

Now, if we take that and extend this all the way down, up and down, up and down, up and down noise until you get into a little bit later on, then you start getting okay. Now we're we're starting to see some real uh stickiness to that increase. So it's not it doesn't stay near Z back to you know anywhere near zero for a while. When you've got your trend that kind of averages out to zero, hovering around that zero line, and then suddenly it pulls away from that and stays pulling away from that for a little while. Now you've got a trend.

Now you've got something like, okay, there's a trend there. So imagine doing this with, say, like your open rates for your email newsletters, for your your Twitter posts, uh, for views on your YouTube videos. What you're looking for is for it to start pulling away from the zero line on a regular basis. Like here, you can see these are these are fairly close to zero. And then you start saying that, okay, now we're you know above five percent uh pulling away.

Now we're above 10, 15, 20, you know, 25% just pulling away and staying pulled away. That's a trend. So from a mathematical perspective, that's a relatively easy way to figure out these trends. So here's the the next challenge. When would you take action, John?

When would you like say, you know, I got a you know, I'm I got some money, I'm ready to invest in you know an NFT of I don't know, uh bugs bunny with a helicopter on his head or I don't know. When when would you make that decision? Yeah, that's you know, uh unfortunately hindsight will always give you better, you know, results. So I mean, anybody here would be like, okay, let's see that. Um, you've kind of got that first surge.

You know, if you had your choice, you'd get at the lowest point before that first surge, right? I mean, that would be where you can make the most profit. But of course, at that point, you'd be sitting there saying, Oh my god, it's flying through the floor here. You know, we're not going to do that. Now you do have kind of a whipsaw there, which is interesting.

You know, it went up and then went back down again. And so that's you know, it's a question of like how brave are you or you know, when do you want to jump in? A lot of formulas I see will do, you know, you take a second larger moving average, and when it gets above a certain point over the moving average, you say, Okay, that's when we're gonna jump in and dive. Because I think you would definitely want to when that second peak went above the first peak or was up near that first peak, that's you know, when your moving averages should be yelling at you to buy in. Exactly.

So what you're describing was called a uh a moving average convergence divergence indicator, and all it really is is a shorter term average differenced by a longer term average. So we're going to put together a 30-day moving average here. And let's go down to sell 31 32 equals average. Right your rate of change. And now if we plot these two things together, let's make this nice and big so that everyone can see it.

The orange line is the longer term moving average. The blue line is the shorter term moving average. And what you were saying is exactly right. You're looking for when the blue line pulls away from the orange line and stays pulling away from it. Like because there's lots of points in here where there's not much of a difference between the two, uh, and it's bouncing back and forth.

And then this is the first point where there's a lot of area under that blue line that that wasn't there previously, and then even more so to the point where the orange line pulls up. So the the buy point for you really would have been uh probably about here, right? Where you see that big change in the orange line, the orange line starting to pull up, and then the blue line pulls away from it again. So that's the divergence where you say the short-term average, you know, things are popping, things are there's interest in this thing. That's when you buy in, so you catch all this upside.

And then the big question, of course, is when do you sell? Uh, you probably would have you know ideally like to this be the point where you don't lose anymore. So if you buy in here and you sell here, you've still made a decent delta, right? So now this is a seven to thirty-day moving average. You can change those two windows to be whatever is appropriate for the trend you're trying to analyze.

Is it you know email open rates? Well, you might have to have like a 90 day average. If you send emails you know only once a week, a 90 day average is probably gonna be uh about right. If your sales cycle is two years, you're gonna need a a lot of data. Uh and and your your moving average might be like a five year moving average.

Um if your sales cycle is is that cripplingly long. But a moving average convergence divergence indicator is exactly the way to go. Because if you look at the the variance, you look at the differences and say, okay, hey, something's pulling away here. Now, yeah, obviously if if this has been something you've been keeping an eye on for a while, this would have been the point to buy in and then you know sell off earlier on here. Uh when you first start to know some big you know percentage change differences, okay, like let's sell off maybe here.

But even if you were to follow this what we've outlined so far, uh you still would have done pretty well. Yeah, yeah, you could have taken advantage of it. The other thing with that is you you do uh have to layer on just intelligence, like whatever you know. Like if you realize that hey, a new product dropped here at this point, that's something that could drive your decision making factor the next time the cycle comes around, or it's hey, we lost a CEO or you know, whatever any kind of black swan events can can help that. But those those moving averages are yeah, are just trends.

It's just you're picking up more population. And so that's the the easy stuff to spot. Exactly. So let's look at a another data series, one that is a lot more mundane and a lot less uh buzzy than uh NFTs and Clubhouse. Let's look at TSA passengers, people who've walked through a TSA uh checkpoint.

We're gonna do the exact same thing. So we're gonna put in our rate of change here first. So uh and if you want to get this data, you go over to TSA.gov. You can get this data from the transportation uh security administration. So mean minus old divided by old, and what we're looking for is passenger changes through TSA checkpoints.

Let's do our seven-day average, go down to here, make a nice seven-day moving average for percentage change in passengers. Now let's do our 30-day average. And now let's go ahead and plot our things because what you were just talking about is is so important. This is we'll play the game called spot the pandemic, right? Um what you're seeing here is that the the blue line is the seven-day moving average of number of passengers through the checkpoint.

We see that took a nosedive last year, and then you have the 30-day average follow following it, you know, following up behind it, uh, and then both of them coming back a little bit towards equilibrium. But what's interesting here is before the pandemic, you can see we're kind of hovering around the zero line, you know. It was, you know, there was growth, but it wasn't huge. And then after this, you know, we started the recovery period, you're seeing that there's a lot more space under both lines than there was prior to. Now, those big spikes downward, those are holidays, right?

Those are Christmas and new uh and Thanksgiving and so forth. People traveled a lot the days before and then didn't travel on those days again. So that again, it's a it's a good reason to uh to do that. It's kind of smoothing. But we're at a point now where as we start returning to the baseline, and where this here at the end looks very much like this here at the beginning, uh we can say with some level of confidence, hey, it looks like things might be getting back to normal.

And if you were to chart out the actual uh passenger throughput itself, let's put this up so it's visible here. We do indeed see, yeah, we've got that trend. It's a very clear trend that's happening here. Uh, and right here at this endpoint, you can see we're kind of even with you know, the sort of the bottom end of the of the pre-pandemic days. So we really are at a point where our moving average convergence divergence indicator is saying things may be closer to normal than we think if if we got passengers coming through.

So now the question is what do you do with that? Well, obviously, if uh if you were selling, if you're selling vacation packages, um, you probably should have started your marketing here, right? Uh, and you definitely should have your marketing uh you know going here. So this is uh this would have been about a month ago that we started that ramp, but you can definitely see just what the basic trend line here is you need you need it to be to get going. Um there's some software out there that can do a lot of uh this this trend analysis for you uh and save your you go you don't have to do it in Excel.

Uh one of my favorites for uh for doing that is a a piece of software called Tableau. Uh Tableau software is actually it's now owned by Salesforce, right? Yeah, yeah. Part of their uh well, I don't know if it's part of their digital 360, but yeah, it is definitely one of their clouds. Um so you could take this exact same data and uh try to put what are called trend lines onto these charts.

Now, trend lines can be a little bit tricky. Um you want to be using uh well, you want to look at the data and then try and find a reasonably close fit. So let's go ahead and pull up this this here. There's our our same TSA data. And I'm gonna change the trend lines to what comes out of the box.

So let's so out of the box, um you it comes with a linear model, right? This is not super helpful, right? That that line being drawn, you know, down to the right um doesn't really tell a good story of what's actually happening. So we want to find uh what are called a polynomial lines or curved lines and try and fit a line um to it as as reasonably as possible without you know going completely off the rail. So really about that third or fourth order polynomial you can see fits the curve pretty well.

And we can see here at that point where the curve line changes inflection, right? Um again, if this is uh a point where you're buying, you're like, yeah, I might want to start buying in here, start doing something here. By the time you get to here, what's going up and up and up, like yeah, you really have to have have been you know doing something and getting getting ahead of your marketing. Yeah, that's just like classic contrarian investing. You know, it's like when things are really bad, that's the time to go out and buy because you could ride that whole way up.

If you wait for that curve to turn, you're still what you're losing about almost five whole months worth of upswing there. Exactly, exactly. Uh a more practical example for a lot of retailers, if you were to put in gift guide uh here, uh this is pretty straightforward. You know, this is from from last year. Uh this point here, that inflection point in the trend is where you probably should have been doing something, right?

So at this point here, October 25th of 2020, uh, if you wanted to make the holiday season merry for your marketing, that would have been the point to get into your you know your marketing into markets so that you take advantage of people searching for gift guides here. If you work in public relations, for example, and you know that it takes what three months for a magazine to accept a pitch. Well, if it's October, then you had better be pitching for the you know the October issue back here in June. Right. So from like a PR perspective, if you're not pitching gift guide stuff now for 2021, the boat's leaving.

Yeah, yeah, you're not gonna be in the magazine. Exactly. The boat's leaving the dock right now today. You need to be you need to be on it if you want to hit this exact same curve for 2021. And this is again one of those things where if I zoom out to five years, there is a obvious pulse.

Exactly. It's a pulse, it's a retail pulse, right? So if you know uh when that's going to be happening, you know how to get in front of it. Uh one that's a lot more challenging because it moves, uh, is Prime Day, right? You know, you can see here there you it used to be semi-regular, then last year is a huge delay, and now it's coming back.

I think it's coming back what in a couple weeks, right? Yeah, I think July at some point. So back they're trying to get back to that like uh half Christmas date. Exactly. So in the past, Prime Day sort of peaked on July 14th.

Um, and it was a month beforehand that uh it would have done really well. So each of these times, um, you know, Prime Day here was in October 11th, and September 26th was where that inflection point. So, again, if you know from past experience, if you've got seasonality to a trend, um, you know when to get ahead of it. You've got in this case, you know, prime day it needs about a month worth of uh of uh advanced time if you want to hit it. And you know, like one of the things Katie always says, if you are not don't have plans written out and scenarios written out, this would be the time to do it so that if Amazon says, hey, it's prime day in a month, you're like, okay, pull out the prime day emergency binder and execute all the marketing in it.

Yeah, and this is I'm amazed there. It looks like it they really did bite them hard that they went that late with it. I mean, they lost two whole years of momentum uh over that, which is insane given how much e-commerce exploded during the same period. Exactly. Yeah, you would think in a year when everyone's sitting at home.

Yeah, yeah, it would have done well. But it, you know, it's it's like the old joke. We we always say, when when was Seinfeld on? You know, what day and time? And uh people still remember Thursdays at nine on C must see TV, yeah.

And why do we know this 25 years later? Because it was regular, predictable, and valuable. And when you were dealing with trends, you had if you have something that's regular and predictable and valuable, and then you break the predictable, you're in trouble. You lose yeah, you lose the momentum. Exactly.

So we're gonna leave you with this thought. Um, do this kind of analysis in your web analytics. Do it for like for traffic on your website. And the and the reason you want to do that is that you want to know what are sort of the extremes. Like how much does something change when you have uh an anomaly, when you have a breakout, when you have a trend happening, what extreme is that?

You know, so if we were to uh look here, let's go ahead and just uh go into basics of acquisition here. So I'm using Google Analytics 4 when and we want to do this in Google Analytics 4. Um for us the our big swings tend to be about a 200% difference day to day, you know, if you one day and seeing a lot more uh traffic coming the next day. We want that anomaly and trend detection knowledge. The you know what is the biggest percentage change because what we're gonna do is go into let's go back to home here in your Google Analytics 4 account.

You can do this in three, but the the software does a much better job of it in four. Go to insights, we're gonna go to view all insights, and now we're gonna create a new automated insight. And you can see there are there's already some anomaly detection built in here, but these are Google's settings. So I want to create a new one and evaluate it daily. I'm gonna do uh users, total users, and I want it to be a percentage increase more than 200%, right?

On a the previous day, and this is our own anomaly and trend detection. This is the call your lawyer because things are looking bad or good. Exactly. Well, so this is a percentage increase of more than 200%. So I know I know I want to see, I want to know.

It's fine if Google tells me uh, like, hey, we think there's an anomaly, but I know from our own our own data as in background, I want to know that specific thing. Tell me when that happens because it's important. We also do have another version um called you know who who broke this. Uh and who broke it is, you know, when you have a 90% decrease from the previous day, you know it's like the tracking code went missing. This is the so what of trend analysis.

When you have the ability to look at trends, digest the data, and then you set up alerts to say, like, I want to know when these we see some big spikes so that we can get when so we're not looking back in a week going, hey, we got a lot of traffic last week. Pity we didn't do anything with it. You know, staying in front of it isn't that's that's it. And if you can be there as it's happening, you can actually even stoke the fire if it's something that you could get involved with that, you know, you throw a discount on the pile or whatever. Exactly.

One of the things you can do is you could set for hourly. Uh now this is really getting you know, really, really uh tricky. If you want to do that hourly increase, like a 500% increase hourly compared to the previous hour, that would tell you, like, hey, something's really hitting us hard. The catch is you should do the math first because if your site has one visitor at 6 a.m. and 10 at 7 a.m., you're gonna get swamped with alerts because we get tripped with that all the time.

Yeah, exactly. Exactly. But yeah, you can do uh you can take action on these trends, and that is the so what is when you detect a trend or even an anomaly that you think might become a trend, have your software tell you, hey, I'm raising my hand here. You might want to take a look at this, you might want to see what's going on. You know, is a bot hitting your site?

Are customers hitting your site? Um, I don't necessarily know how to use traffic, but certainly like conversions. Like if you're an e-commerce company and you don't have like a 5x detector in your you know sales, like turn it on like today. Because you should know, hey, we made 5x more sales in the you know this hour than it did the previous hour, or whatever the the biggest jump is that you have in the past. That will tell you, yeah, we need to uh we need to to wake up and pay attention to what's going on on the website.

Do more of that, yeah. So that is uh determining what's trends. We talked about the mathematics, the change, seven day changes, changes of changes, the moving average convergence divergence indicator, and how to take action on it. Either for things that have high seasonality and cyclicality, measure the inflection points of trends in the past and figure out what is your lead time that you need to get in front of a trend. Um for things that you know are going to happen, and for things that you don't know what are going to happen, uh have built into systems like Google Analytics for Hey, I'm raising my hand.

Something's happening. Please do something about this. Any uh any parting thoughts on trend management job? No, again, it's the same as every week. Go grab some data and go dig in and find it because you know, unless you have these alerts set up, you're again gonna be doing that two weeks later looking at the numbers saying, geez, I wonder what happened right there.

And that's you're not in the driver's seat if that's the case. Exactly. All right, folks, if uh you've got questions and stuff, hit head on over to the Slack group and ask. Otherwise, we will see you all next week. And let's roll our Thanks for watching today.

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