So What? Marketing Analytics and Insights Live
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In this week’s episode of So What? we focus on Durable and non-durable trends. We walk through which trends are reliable, how to determine if a trend is durable and what actions to take.
Catch the replay here:
In this episode you’ll learn:
- which trends are reliable
- how to determine if a trend is durable
- what actions to take
- UTM Governance in GA4 5/26/2022
Have a question or topic you’d like to see us cover? Reach out here: https://www.trustinsights.ai/resources/so-what-the-marketing-analytics-and-insights-show/
Katie Robbert 0:22
Well, hey there happy Thursday, everyone. Welcome to so what the marketing analytics and insights live show I’m Katie joined by Chris and John. I will never get that. This. This week, we’re talking about durable and non durable trends. We’ll be talking about which trends are reliable, we’ll be talking about how to determine if a trend is durable, and what actions to take once you figure all of this out. So, Chris, I have a feeling I know where you want to start. But where would you like to start this week?
Christopher Penn 0:56
Gosh, I don’t know. A lot of the stuff we talked about when it comes to trends specifically relates to forecasting, right? So if you have a trend that you can forecast is, I guess, maybe we should probably start with some definitions.
Katie Robbert 1:09
I should have put money down on it. I had a feeling you were gonna say, Katie, tell me what you think a durable and non durable trend are?
Christopher Penn 1:18
Katie, what do you think a durable and
Katie Robbert 1:22
we’ll be here all day, folks. So in my opinion, a durable trend is something that’s consistent and reliable. So something like in the United States, the holidays have the holiday season being every October, November, December. That’s a constant. And so you can always bank on Halloween and the trends around that for a couple of weeks with knit costumes, you can always bank on us Thanksgiving, and you can always bank on us Christmas and the coinciding shopping and gatherings and meals and things around that. So to me, that’s a durable trend. So regardless of you know, what else is going on in the world, those things continue to happen. Whereas a non durable trend is something that it’s not consistent. And so for the past couple of years travel has been inconsistent vacations have been inconsistent. And so data that you had prior to the pandemic about that might not be valid in terms of planning, because everything has changed. So to me that’s a non durable versus a durable, how close or off am I
Christopher Penn 2:30
I think that was terrific. You’re writing durable. I mean, by definition something that is durable is is lasting, right? You you you buy an automobile hoping it’s durable, and you’re not taking it to the shop every week, at least for a little while. Something’s non durable. It’s not. What’s happened, though, as you pointed out, is that over the past few years, a lot of things that were previously durable, for one reason or another have not been so everything from the availability of wheat, you know, thanks to the Russian invasion of Ukraine is no longer a durable trend is some incredible disruptions in the marketplace. AB formula, right? That’s, that’s this week’s crazy thing. But there are absolutely things which are 100%, nearly guaranteed around. So let’s let’s look at a couple of very quick examples here. Just to point out those things Katie was talking about our favorite piece of software for doing basic trend analysis is of course, Google Trends. So let’s put in gift ideas, which is a incredibly popular term. And here we’re looking at the last 12 months, right? So we have this this big bump here starting in October. And of course it peaks there. And then we see a bump here around Mother’s Day. And we see a bump here around graduations. So some pretty common things. Now, if we were to take a bigger view of this, may we zoom out here for like five years. Now you’re really starting to see a pattern. There’s a pattern, it looks like a heartbeat, almost. That’s a indication of a pretty durable trend. The key measure of durability is those patterns, right? It’s is that ability to see sustained patterns in your data at whatever timeframe you’re looking at. There’s three things we have to take into account mathematically, when it comes to trends, seasonality, cyclicality and the trend itself. So a trend is nothing more than a mathematically provable change, increase or decrease in your data over time. As opposed to an anomaly where you know, something happens, you hit the lottery. Somebody invade somebody, there’s those are the anomalies, whereas trends are things which are sustained. The second thing is what’s called seasonality. And this is a pattern with a fixed frequency. What we’re looking at right here, this is a this is a trend has seasonality. Make sense of literally the holiday season. That’s a that’s an easy way to remember it. But seasonality can occur at any timescale. So let’s take a quick look. I’m going to put in dinner ideas, which we talked about previously. What kind of pattern do you see here?
Katie Robbert 5:24
Not a really good one. It’s one that and this is still the past five years. It’s one that to me, I’m like, it seems kind of with a few anomalies, it seems kind of flat, for the most part, like, there doesn’t seem to be a strong trend, like there was with gift ideas where it was very consistent of like, Valentine’s Day, you know, etc, etc.
Christopher Penn 5:49
Right? Now, watch what happens if I go from five years to seven days, we’re back to a heartbeat. So even though the big picture doesn’t have a trend, doesn’t have doesn’t have seasonality to it. The micro definitely does. Because every single day, as you saw this week’s Trust Insights newsletter, which if you don’t get go to trust, insights.ai/newsletter, every day around 11am is when people start going, huh? dinner ideas. And so you have that seasonality. And then the third aspect is what is called cyclicality. And these are very large scale trends. So let’s take a look at term recession was look at all time, everything available. And here, there are periods of time there are cycles, there are business cycles. Now this only goes back to 2004. For to really see this, we would need to zoom out to something along the lines of maybe last 75 years to see those business cycles. And there are these macro cycles that occur all the time. There’s the business cycle, if you’re in whether there’s El Nino, right. If you are in investments, there’s recession, stock market booms and stuff. And our our knowledge of these terms is important because we need to know what, if anything we’re going to be predicting.
Katie Robbert 7:18
So I guess the question for marketers is, how do we know if our data is a durable or non durable set of data that we can use for forecasting? Because we’re so I mean, we’re talking about, you know, dinner ideas and consumer trends and gift buying. So for some marketers, you know, gift ideas, those listicles those things are route are relevant to their jobs. But that’s not true for all marketers. So what what should I be looking for? What should John be looking for in our data sets to see if it’s a durable or non durable set of data?
Christopher Penn 7:59
You are looking for the first two of those items, trends and seasonality. So let’s go over to Google Analytics, right. In Google Analytics, just got some our basic traffic report here. Let’s go and maybe look at the last the last 90 days. And what we’ve got here is users by default session default channel grouping over time. So the first question to ask is, is there a trend? Right, and you can eyeball it, it’s better to use some kind of statistical software, even something as simple as Excel will work just fine. And there’s a mathematical test you would use called The Man candle test to determine is there a trend? I will tell you just straight up when you look at this data, there is no trend, right? When we look at the traffic to the Trust Insights website, by channel cooking, there is no trend here. And the second thing to ask is, is there a seasonality to this? And again, there really isn’t. So if you wanted to say can I accurately forecast our traffic for like the next quarter from the state of the answer right now is no.
Katie Robbert 9:19
So but you’re looking at it by channel grouping, what if you just looked at just the overview, you know, total number of visits to the website. Because I have I was gonna say I have a theory, but that theory is just proven because I feel like anecdotally, we always get bumped to our website, right after the newsletter. And so, I would imagine I would see by day of week, you know, a little bit more of that heartbeat, but this it’s hard to see With this,
Christopher Penn 10:01
exactly. And so this is where the tool that you use also matters, or the ability for that tool to visualize. One of the things that is a lot of folks have found appropriately challenging with Google Analytics is the fact that the new version of Google Analytics freely makes that that ad hoc, just quick looks very, very difficult to see it’s not obvious from here. Let’s, if we were to pop on over maybe to Data Studio, let’s go ahead and edit this and go into Star cells a new page here. And let’s go ahead and just put our put in a nice little line chart.
And now, let’s, that’s views. Yeah, that’s good enough. And just for safety sake, put in a little date, Ctrl, pop on over to view mode. And let’s take a look at our year to date. And there you do see that little sawtooth pattern. Right. So you would be correct in saying like, yeah, there’s there is a sawtooth pattern. There’s also a really big anomaly here on March 30. And then resumption that sawtooth pattern. So from this perspective, again, you want to run a test on it, but there is at least something of a sawtooth pattern that would indicate some level of seasonality. There’s not a clear, bigger picture version of this, though.
Katie Robbert 11:37
John, you typically release new marketing over coffee episodes, around the same time every week. So do you start to see Do you know anecdotally, if you have that kind of cyclicality with your traffic or your podcast downloads?
John Wall 11:54
Yeah, because so many of the pod catcher apps are all fully automated. So as soon as a post gets up, and it gets in the RSS feed, you know, we’ll see 30 to 40% of the total downloads will happen in the first day or two. You know, there’ll be this huge shine search, and then after that, it’s a little more spread out. But yeah, that’s a real solid. Be. And there’s we’ve come to the point where it’s, you know, the show has to be in the feed Thursday evening, so that everybody’s ready for it Friday morning, because people expect it Friday mornings.
Christopher Penn 12:26
Yep. So is there a trend here? Not really, right. There’s, there’s not a clear up or down, like there’s no change. There’s, there’s a lot of whipsawing back and forth. But if you were to slap a trend line on this, in fact, let’s go ahead and do that. Let’s go to our style here. And let’s add ourselves just a linear trendline. What you see is there’s that relatively flat, right? So there isn’t necessarily a trend, there is a seasonality for sure. But that trendline is not strongly going up in one direction. And if we zoom out a bit, maybe we go to 90 days. Let’s actually go up. Again, that line is pretty flat. So that that indicates that there isn’t really a trend in our data. And therefore we could probably forecast because of the seasonality. But we’re not going to be able to say like, okay, there’s there’s clear and definite trend that we’re moving up into the right, right, which is what of course every executive is going to be looking for. In this case, that’s not here yet. The second thing that we’d want to look for is is there a possibility for multiple seasonality. And so in this here, we’re looking at year to date. And so there isn’t really a ton to look at, if we let’s see if we change this up and crank it all the way back to January of last year.
And again, looking at this, there is this interesting kind of weird anomaly like this big spike happened March 17. We have another big spike here on March 31. So we might want to take a look at see what a couple of these larger spikes are. But again, even at this larger, bigger picture, there still isn’t a clear like multi month seasonality. It’s it really is just the week level seasonality is what we have, which makes sense we we do a lot of stuff during the week. We don’t do as much on the weekends. These concerns. So go ahead. I say these considerations about are there multiple seasonality is what kind of seasonality is it? Is it additive or multiplicative? Those things we care about because it determines what kind of forecast we’re able to do, if any.
Katie Robbert 14:53
Got it? Okay, so that was going to be my question. So based on this, there’s not a whole lot that we can for forecast from traffic to the Trust Insights website. Because it’s, the trend is relatively flat, like we know, day of week based on what we’re doing in our activities, we’re going to see a spike but not enough to really change anything.
Christopher Penn 15:16
Exactly. Now, the one thing that’s interesting is there is a hint of multiplicative seasonality here, which is, you normally don’t see with marketing. The difference between additive and multiplicative seasonality is the height of the teeth. So like when we’re looking here, for most 2021, the height from the top to bottom of the teeth was relatively small, right. And so any kind of forecast, we would use an additive seasonality forecast, because the height is not changing. Once you get into late 2021, like mid to late 2001, the teeth start getting bigger, right, the teeth start getting longer. And that indicates that something is different in our data since about September of last year where the teeth are getting bigger over time, which is good, right? That means that we’re getting more hits on our content, we’re getting more people to our website, even if it’s in fits and spurts. Because the teeth are getting bigger, we now have what’s called multiplicative seasonality. And that means that in general, over time your traffic is growing, that’s a good sign for growth. So when you’re looking at past data, if you see additive seasonality, it means slow and steady. And if the lines going up into the right, and it’s additive, that’s fine. But it means you’re not exponentially growing, if you’re starting to see bigger and bigger teeth as the chart goes on. Now you’re in the territory of HA, not only is the trend going in a certain direction, but the the volume of the trend is getting louder, which means that we have the possibility for faster growth.
Katie Robbert 16:50
This is a lot to take in a lot of consider. Well, no, in all seriousness, and so it’s a lot of considerations, because I can imagine, you know, someone sitting in my seat saying, you know, how fast are we going to grow? And then someone Chris sitting in your seat saying, if we keep doing what we’re doing not that fast, but then having to get into the mathematics behind why that is and what we know, historically. And so then, you know, it’s doing that deeper dive into well, what are we doing what’s changed, you know, I know, around September ish of last year, give or take, you know, we changed over the design on the website. And so that may account for some of the growth because it’s a nicer design, it’s the navigations, a little bit more clear cut those kinds of things. But we’ve also changed the way in which we’re doing our content planning. And so that may be changing things as well. And so it’s not just okay, how fast are we going to grow? There’s so many different layers to that answer.
Christopher Penn 17:55
Exactly, exactly. And so when we look at these different time spans, for forecasting purposes, for trying to figure out what whether there’s a trend here, and if it’s durable and non durable, we got to look at all these considerations and see what types of things happening under the hood. Now that there’s three things that we really can’t forecast. Number one, we can’t forecast what’s never happened, we can’t predict something that has no pattern. And we can’t predict with bad data. When marketers are dealing with any kind of data. A lot of the time, the data is not in great shape. You’ve set up your Google Analytics wrong or something like that. So that’s an issue. But the other ones are much bigger consideration. So let’s take a look at an example. Let’s look at inflation. So this is the consumer price index for the United States. Looking at this is since like 1950 this is not it’s interesting, when you look at this graph, something happened around the late 1990s, where it’s very similar to our Google Analytics, right. But teeth were relatively small for a while. I mean, the early 70s was the gas crisis. But the teeth were relatively small for a good long while and then suddenly the teeth just just gotten much much sharper, right inflation is spikier all around and that’s not counting the pandemic and stuff. This is just something changing economic policy, which makes this harder to forecast. If we look at something like Bitcoin, right, here’s the Bitcoin chart by the way, if you bought Bitcoin at 60,000 last year, I’m very sorry. This you know, trying to forecast bitcoin is almost impossible, because what’s happening is never happened before to it if you look at the all time chart for it, right It’s kind of a mess, you can’t really figure out what’s going on. You can’t forecast the next presidential election. We don’t even know who the candidates are. But even if we had to name brand candidates that were recognizable, maybe from the previous election, the election itself has never happened, the electorate has changed the population demographics have changed. The amount of Russian bribes to politicians has changed. So there’s a whole bunch of things going on under the hood, that makes it unpredictable. So when you’re talking about durable and non durable trends, a lot of trends like this one are non durable. This is a non durable trend, even though there’s a very clear trend to it. But you’re good at the macro picture. It’s not durable.
Katie Robbert 20:42
It’s it’s interesting, I think that the election, you know, without getting into specifics, is a really good example of that non durable, and it’s misunderstood, because I think and myself included, I think there’s a misunderstanding that we do have enough data to say, well, this is what happened before. And therefore we can predict the outcome of next time. You know, when looking at that Fred data, one could say, well, you have so much historical data, why can’t you just project forward what’s likely to happen? And I think that that may be one of the biggest misunderstandings is just because you have, you know, 1015 20 years worth of data doesn’t mean that there’s any kind of cyclicality or seasonality to it. And that’s, for me, that’s sort of the light bulb for me today of like, oh, okay, that makes a lot of sense.
Christopher Penn 21:38
The other thing is that you have to take into account whether there’s interfering factors. So if you look, this is very interesting. The gray bars on this chart represent recessions. Prior to 1990, we had recessions relatively frequently, you can see there’s like five bars here. And there, some of them are decently sized. And we’ve had we had the real estate implosion, and then relatively quiet, and even the pandemic was only, you know, two quarters, it was not a big recession. So the recessions are thinning out as the teeth get bigger. So the question you have to ask is why why would that be the case? We know that we can’t forecast office, we know that this trend. This is not necessarily durable. But is there something that could explain this? And the answer is? Let’s see it m two? The answer is this is the amount of money that the United States has created. It’s called the empty the money supply. If you look back in time, for a long time, there was relatively small amount of money in circulation. And then, like we were talking about that what happened about late in the mid late 90s, we started printing money, and printing and printing and printing and printing and printing until we’re at, you know, we’ve like, during the first two months of the pandemic, we printed $4 trillion poof, just made it appear out of thin air. That’s where the huge jump is. And you can see this chart is starting to basically hockey stick. We’re doing that. Because what we are trying to avoid are those gray lines, people are so politically sensitive to a recession that we’re essentially printing money to to prevent them. The downside is this creates inflation. And so if you are a marketer, this means that everything gets more expensive and your purchasing power your customers purchasing power is lower and lower. Every time this line goes up, your customers dollar goes less far. Even if prices seem to stay relatively stable, they go less far. And as a result, you have a harder time doing stuff. Case in point this morning, though, the earnings calls for Walmart and Target happened and both companies reported large losses because prices are going up so fast, consumers are buying less stuff, but the price of labor and supplies is going also going sky high. And as a result those two things combined, and have made it very hard for those two individual companies to make money. If you and your supply chain are impacted by that you’re going to see that in your own market, it’s gonna be harder to do your own marketing. This is a case where when we’re talking about cyclicality there is there used to be cyclicality to recessions? And now it’s getting unreliable. But there’s a very clear very easy mathematical trend. Like you don’t even need stat software, you can just get out of crayon. Just draw a ruler and draw what’s happening with this trend. So there’s very strong And hear that has substantial implications at a macro level for how your marketing is even going to work.
Katie Robbert 25:11
So, I don’t know about you, John, but my head is starting to hurt, not in a, I don’t understand it. But really when you dig deep into the mechanics behind it, just sort of wrapping your head around it is a task in unto itself. But, you know, what do we do with all this? Like, John, what do you make of all this? What are you as a marketer? Where would you go, Okay, now, now I know what to do? Or would you keep digging?
John Wall 25:40
Well, you can’t you know, the anomalies, you can dig into and find points and at least understand why those are happening. But no, it only gets worse as you continue to dig to because you find out that all of this is irrational behavior, right? I mean, so much of this right now is panic, you know, the money supply is huge, and people are running out to buy stuff, because they’re worried about it costing more six months from now, you know, which just is irrational, you know, if you, if you rational individuals trying to fight inflation, they would buy less, right, they would stop buying all the unnecessary purchases and let things boil down a little bit. So yeah, no, I don’t, again, it only gets worse. And even from a marketing standpoint, though, it’s also really interesting that the normal advice there is also counterintuitive, it’s yes, prices are going to go up. But so this is normally the time where the weakest firms actually fall off and die. So better prepared companies, this is the time to actually step up the marketing. And hopefully, what you can do is use this to negotiate better rates. So you’re actually spending less money for the same amount of reach. And but this is where you’re going to pick up the new customer says other organizations bail or go to, you know, other businesses that are more profitable or not struggling as much, and then your prime for when the economy turns around, and you become the market leader, and you’ve picked up a lot of steam. So but yeah, there’s no easy answers, that’s for sure. Because again, it’s all human behavior. That’s, you know, who knows what the heck is going to happen over the next six months?
Christopher Penn 27:13
Yep. And even. It’s interesting, everything just gets more expensive over time. Like when you look at professional services, like the the services Trust Insights provides, our sector, the price increase, has has gone up an astonishing amount. When you look at just the last 10 years, we’ve gone and added about 100% More pricing pressure on on on consumers of our services, because everything gets more expensive. And it’s not because well, I mean, it is somewhat because companies like ours say yeah, we’re going to charge more because we have to charge more to GA 4 employees or rent or things like that. But the underpinning of all of that is fact that we’ve printed so much money in the last 20 years. And that everything that’s ties to that goes up in price too.
Katie Robbert 28:09
So then, we get to the big question, the so what what do we as marketers do so we can see, okay, prices are going up? Inflation? You know, maybe our website traffic isn’t a durable trends, like, what do we do? How do we take action? How do we use this information, or not to plan out our marketing.
Christopher Penn 28:35
So there’s a few different things. Just like you do KPI mapping, when you’re trying to work out your your KPIs and metrics for analytics, one of the things that you have to do is sort of supply chain KPI mapping, what costs and what economic variables impact your company’s operations. So for example, health insurance is an easy one to get your head around. The more that health insurance prices go up, the thinner your margins get, because you have to pay more as a company for for this commodity. And so the question you have to ask is, how much of your expenses as a company? How much your expenses as a marketing department hinge on outside actors? And can you forecast from that? So if you use SEO tools as an example, are the prices of SEO Tools going up? And if so, is that something you’re able to forecast? And if so, do you change your strategy if it suddenly becomes unaffordable? Like for example, when we started Trust Insights, we did not have any budget, right? So we kind of we did the best we could with essentially free services, and our own code and our own technology to be able to get up and running. Now fortunately, thanks to folks like you who are watching During this, we have some budget to do things. But those pricing pressures continue to increase, which means that we have to increase our costs, which in turn means that, you know, there’s, there’s impacts to that. So we may lose some customers who can no longer afford us, we may gain new customers, who suddenly say, like, hey, now you look reassuringly expensive, like you’re not too cheap that I’m worried them getting a poor quality product. So as a marketer, you have to not only look at the the trends, you have access from a customer perspective, but you also get the supply chain and the operations enter your company, and take that into account. Are there things that are going to impact your marketing budget, when you look at the CMO survey, you know, we joke that CMOS really have no actual idea how to forecast anything, because they always say our marketing budgets going up 15% really does. But in the entirety of the CMO survey history, that from the 10 years, we’ve been watching it or whatever, I have not yet once seen CMOS forecasts, their budgets were going to go down. Like every year, they say, Oh, I’m forecasting in the next three years, my budget is gonna be up 11%. Whether it actually is or not, doesn’t matter. It’s that they think it is, and therefore they’re going to behave like that. And thus, as a marketing, like in our case, as a marketing firm, we say, Okay, well, if you think your budget is going up, 11%, I’m going to ask you for 11% more fees.
Katie Robbert 31:24
So does that then qualify as a durable trend? Because they consistently say it’s gonna go up whether or not it does,
Christopher Penn 31:32
yes, as long as their behavior matches it, right? That’s the thing. If you search for gift ideas, but then you don’t buy any gifts, then you have a one trend decoupled from another. But if you search for gift ideas, and you continue to buy gifts every year, then yes, there’s, that trend becomes durable. If you’re a CMO. And you’re, you know, you’re you’re all in on programmatic advertising, and then you cut your programmatic ad budget to zero. It’s no longer durable, right? Because you had such a change to it, that it’s no longer something you can forecast once you get out of once, there’s a big enough change to disrupt the trend, it stops being durable.
Katie Robbert 32:13
So for marketers, let’s just sort of talk about the mechanics of what you can forecast on how much this is a question we get, how much data do you need, in order to be able to forecast from it because you know, one day’s worth of data obviously doesn’t tell you much one week’s worth of data, there’s no chance for real cyclicality, quality or seasonality. One month’s worth of data maybe so how much data is enough data?
Christopher Penn 32:40
It depends. And as we saw with the dinner ideas, want a week’s data is actually plenty fine. Right? When you type in, you know, dinner ideas, actually, Chinese food near me. And if I go into past the past seven days, that’s enough. Right there. I’ve got an I’ve got enough periodic enough of a heartbeat to forecast reasonably well. I know, I would like to have maybe 30 days of this hourly data. But seven days is enough for me to predict ahead a day or two and say like, Okay, well, you know, when tomorrow is this gonna, this term got to peak. It’s interesting, because you see some interesting little jagged points here like noon, and then you have the afternoon dip. And then this one is different. The the peak for dinner ideas earlier in the day, the Chinese food one seems later in the day, and I think it’s one of those where you you argue with your spouse, you’re like, What do you want for dinner? I don’t know. What do you want for dinner? I don’t know, that goes on for 20 minutes, like finals just ordered Chinese, and you type in Chinese food to me?
Katie Robbert 33:43
Well, so putting their ideas next to compare and see if I mean, because that really is an interesting, and that’s where you can start to get into that. Granular how
Christopher Penn 33:54
you see here, that peak starts to happen much sooner. Except here. It’s happened sooner here. It’s about power there. It’s about it’s still a little sooner than the second peak here. So there are differences. Let’s try
and this is kind of funny, because you now have McDonald’s near me so much later. That’s really Yeah. Okay, we’re finished. We’re just going to McDonald’s.
Katie Robbert 34:28
Well, and that’s probably you know, the people who have like after school activities and meetings and stuff like that, that run well past dinner time and there’s no time to cook. You don’t want to wait for takeout. So drive thru is a better option.
Christopher Penn 34:43
Exactly. Also, if you look at the timing on this, it’s two to 3am Are those peaks. So that’s also people’s going what’s open? Is anything even open McDonald’s is open. So it’s not a question of how much data it’s Question How many, how much seasonality is available. When you look at a week’s worth of data, at the hourly level, you have 168 periods of observation, that’s enough to make like an additional 42 days worth it, we generally say it’s a three to one four to one ratio for every one period you want to forecast for, you need four periods of data in the past to predict for it. So in this case, with seven days data, I could project forward at least a day, maybe two days, depending. But because this is so seasonal, so reliable, so durable, I could, I could stretch out a little bit further and it wouldn’t be out of the realm of reality. Right. Other things like projecting Gulfstream aircraft purchases, gonna be really, really hard because there’s so infrequent that you’re going to need a really long period of time to try and build a model around and you may not even be able to, because there may just may not be enough data. So it’s, you need an four to one for whatever seasonality you’re looking at. And it has to be durable, it has to look like this in order to do a good forecast. If you were to go to back to Bitcoin, there is no seasonality here. There’s, there’s no cyclicality. There’s nothing here that will let you reliably say, Okay, tomorrow, I’m pretty sure the price is going to be IC X, even if you had a year’s worth of data, right, you have 365 observations because there’s no seasonality to it. I wouldn’t even attempt to predict tomorrow’s price. This is also why there’s no such thing as a stock market predictor that works if there was whoever invented that would own the world.
Katie Robbert 36:51
Alright, so it sounds like at the end of the day, we need to really pay attention to what’s going on outside of our little marketing bubble. But within our marketing bubble, we can take a look at the data that we have available to see if there’s cyclicality to see if there’s seasonality, to see if any trends exist at all. And just because you might get that, you know, sawtooth, it doesn’t necessarily mean that there’s anything to predict. So also using, you know, the linear trend lines to help you see if it’s going up and to the right is going to help you know if it is even a trend at all.
Christopher Penn 37:32
Exactly. Now, here’s something to think about. Katie, since you are the chief decision maker, you now know, in our marketing data, there isn’t a trend. You know, there is there’s seasonality to it. There’s the sawtooth edge of the weekly newsletter, but you know, there is no trend. So we’re not like, you know, nose diving into the ground. There’s no reason hit a panic, but we’re not also taking off like a rocket ship. So how does that change strategy to you as the decision maker when you look at that.
Katie Robbert 38:03
So that’s what I have to you know, as John was mentioning, I have to start to really dig deeper and peel back all the layers of everything that we’re doing, to see if any one thing, whether it be the newsletter, or the content, or the live stream or something else, by itself has a trend. So that that way we could see like, Okay, we know, obviously we do live stream every Thursday at 1pm. Eastern. So we know that there’s going to be a trend there. We know that there’s going to be you know, people who get the recap on Fridays, but is there anything else to that data? So take picking apart every single marketing effort on its own, that’s the way that I would approach it to see, okay, if everything collectively doesn’t really give us that much of a trend, what individually might be working.
Christopher Penn 38:57
The other thing I think that tells you is that it’s safe to make riskier bets, right. There isn’t a strong upward trend that says, Oh, my goodness, if we do this, we’re going to upset the applecart and the train is gonna go off the rails and you know, the gravy train ends, because there is no trends to begin with. There’s nothing to disrupt, right, there’s no and that means that yeah, if you want to try something wild and new, like okay, we’re going all in on Tiktok that might be okay, because you’re there’s not we’re not in a position where a trajectory of growth would be imperiled by doing so.
Katie Robbert 39:33
Alright, John, what what are we going to risk it all on?
John Wall 39:36
Right? We could rent out a Superbowl ad. That’s the marketing classic, just throw it down in 60 seconds and burn it all on one huge pile.
Katie Robbert 39:46
All right, we’ll start playing for that now.
Christopher Penn 39:50
at a at a more macro level, though, for us. What we just talked about a few episodes ago about our strategy is actually the right strategy, which is we said okay, Let’s stop being slaves to the predictive calendar, right, and just do what we want. Because in this case, there is not enough seasonality in our data that would indicate that what we were doing was having a strong positive effect, we didn’t create a trend with it. The absence of that trend means that it’s okay to vivid.
Katie Robbert 40:22
And to clarify that it it, we are still creating the same amount of content. We’re just not using the predictive calendar to determine what content to create. And so it is still a guideline, but it’s not something that we are, you know, it has to be this, it has to be this because we’re not seeing that work for us necessarily, partially, because it’s such a competitive landscape with the types of keywords that we want to be known for. We’re not the only ones trying to rank for those things. So we are trying to think outside of those traditional keywords.
Christopher Penn 40:56
Yep. And the third thing is, it’ll be a research project for us is to say, Okay, we know what industry we’re in, we know what our work customers are, we know what things they care about, all of those things. Is there something that has a durable trend? And if there is, can we then take advantage of that? Can we find something that has that durability to it, that could be part of our content, heartbeat, our marketing heartbeat? Because it’s entirely possible that we’re focused on things that may not have that heartbeat, and as a result, we are not generating the results we’re after? And that’s where your idea of doing channel level analysis like, is there a heartbeat? In our organic search? Is there a heartbeat in our social media and so on and so forth? That’s where that’s gonna be a super valuable analysis.
Katie Robbert 41:49
No, right. Well, John, you know, what’s coming? Add that to your list.
John Wall 41:54
All right, drill down we go.
Katie Robbert 41:58
Any final thoughts?
John Wall 42:02
No, it’s you. You’re just always keep digging. That’s, you know, there’s always another layer, it doesn’t matter what you’re doing, it’s almost impossible to get to the bottom.
Christopher Penn 42:11
Keep digging or take a step back and look at the macro trends to because like Katie said, we’re not living in a marketing fishbowl, right? There’s a whole world out there that is having substantial impacts on us and our ability to do business.
Katie Robbert 42:26
And, you know, without getting too far down the rabbit hole, I think that that’s a mistake that a lot of companies are making right now is looking at what’s going on outside of their company versus just, you know, pounding their fists on the table, and crying. Why are you know, why aren’t we getting more? Where’s all the customers? Where’s all the new members to our really great thing that nobody knows about? Because everybody’s got a million other things happening in their lives? Yeah, I’ll get off my soapbox.
Christopher Penn 42:56
I folks, thanks for tuning in. We’ll talk to you next week. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources. And to learn more. Check out the Trust Insights podcast at trust insights.ai/t AI podcast, and a weekly email newsletter at trust insights.ai/newsletter Got questions about what you saw in today’s episode. Join our free analytics for markers slack group at trust insights.ai/analytics for marketers, see you next time.
Transcribed by https://otter.ai
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