You Ask, I Answer: Marketing Trends vs. Tactics and Strategies?

Oleksandyr asks, “What defines a trend versus a tactic or a strategy?”

Mathematically speaking, the definition of a trend is a sustained change in a metric over a period of time that can be proven with a statistical test.

In the context of this question, I assume we’re talking about usage of particular channel, tactic, or strategy and whether or not to align them to marketing trends.

The key to understanding trends is in the statistics. Once you have enough data to prove the trend is real, you act on it.

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Machine-Generated Transcript

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In today’s episode, Alexander asks what defines a trend versus a tactic or strategy? They’re, they’re totally different things.

A strategy is why you do something, what’s the purpose of it? tactic is what you’re going to do, right execution is how you’re going to do the thing.

Let’s do strategy, tactics and execution, that’s pretty straightforward stuff.

A trend is something totally different.

mathematically speaking, a trend is a sustained change in a metric over a period of time, that has been proven with some sort of statistical test.

So again, a sustained change in a metric over a period of time, that can be proven, with a statistical test of some kind.

That’s what a trend is, when you look at a chart of, you know, dots or lines or whatever, if you can use some sort of mathematical test, like, for example, linear regression, a logarithmic regression, polynomial regression, exponential regression is something that can fit a line to the data, and have that be reasonably statistically sound meaning there’s a correlation, there is something that you can mathematically show Yes, there’s an increase in this.

There’s a cyclicality to this.

That’s a trend, right? I’m guessing by the intent of this question, we’re talking about what is the usage of a particular channel or tactic or strategy? And whether you should be doing those things? Right? So is Tiktok.

a trend? Or an anomaly? Well, depends on the period of time and the data you’re using to make that assessment.

How many users are on it? How quickly is the rate of use changing? It would be things you could test out, you could also test out, for example, how often people search for it, how often people talk about it.

And in that sense, you’re looking at a chosen metric of some kind, probably some measure of popularity, and whether there’s enough of it there to warrant you participating in it.

There’s a new social network or social media app nearly every day, most of them don’t survive.

But also, there are other trends, people try to take a look at what is the usage of Facebook, how many news media outlets are there? Pretty much any number that occurs over time, can be measured to see if there’s a trend.

Here’s the challenge for a lot of marketers.

most marketers do not have any kind of statistical background.

Mathematics was, for some, the reason why they got into marketing, because they didn’t want to do math, and statistical assessment and analysis is definitely not something they signed up for.

So in a lot of cases, marketers are making decisions on very qualitative data, like, hey, five of my friends just signed up for this new thing, it must be popular, as opposed to actually looking at the data and using some form of statistics to make that determination.

So how do we understand this? Well, the key to understanding trends is in the statistical test, when you look at any time series data, any data that occurs over time, and you fit a line to it of change over time.

Do you see in the given period of time that you’re trying to assess a meaningful, sustained change in that metric? If you were to take a chart, and it had the dots all over the place, and you know, for each individual day and drew a straight line through it, and it was just completely flat, there’s no change, right or going down would be would be bad.

As opposed to going upwards, either as a straight line or maybe a curve.

Those would be the tests you would run to determine is this thing, an actual trend.

And there’s three different kinds of things you’re going to see right you’ll see anomalies, which are where, you know, you’ve got dots that are way above or below whatever line you’re drawing on the chart.

Those be things that are odd, but definitely not indicative of a trend because remember, a trend is a sustained change.

A breakout would be the beginning of a trend where the dots or the lines on the chart, slowly start to go up and then stay going in that direction.

And then the trend is the sustained momentum.

In that direction of that change, trends can go up and down, right.

So you can have things that are D trending or becoming less and less popular.

There are, you know, for example, bell bottoms were a trend, upwards in the 1970s have been on a trend downwards ever since you have not really seen them come back.

So, you’ve got to be able to run the statistical tests.

Now, the good news is many, many software packages can do basic trend analysis very well, Microsoft Excel does it very well.

Tableau does it very well, IBM Watson Studio does it very well.

You don’t need like heavy duty machine learning software to find, you know, the four basic trend types.

But you do need to know how to, to run them.

And you do need to know be how to interpret them.

And that’s the challenge that again, a lot of folks will run into.

But remember, the four basic trend types are linear trends, which is a straight line.

logarithmic, no logistic, sorry, logistic trends, which is where let’s have an S shaped curve, exponential where it’s a straight up or straight down curve.

And polynomial, which can fit a line to waves.

most marketers are going to run into polynomial trend curves, with cyclical data, especially if you are a b2b company.

You work with polynomial trends every single day, you just don’t know it.

Because your traffic or your leads, or whatever goes up Monday through Friday and goes down pretty sharply, Saturday, Sunday.

So your chart looks like this every week, right? So you have a polynomial curve.

When you fit a trendline to that, you’re obviously looking for the inter day or inter week changes, but then you’re going to add an additional trendline on top of it to say, okay, in general, is my website traffic going up? Or is my website traffic going down to determine what the trend is? So when we’re talking about identifying a trend, in order to apply marketing strategies or tactics about it, we’re talking about doing the data assessment first, and then making a decision is something that we want to be part of.

And you’ve got to do this frequently.

It’s not something you can do just once and make a decision.

For example, a year ago, well, more than a year ago, Tiktok was like, Yeah, okay.

The trend data was starting to, you know, move upwards, but it wasn’t really as hot.

Fast forward six months ago, it takes off, right.

And so if you’re not measuring trends frequently, or looking for trends frequently, you may miss things.

This is, again, why a lot of really good marketing analytics, departments or groups have automated software that pulls the data in and looks at it very frequently to say, yes, is there they’re there this week? You know, are you starting to see Oh, it’s merging upwards, you know, real ugly version of this.

Look at the number of coronavirus cases, there are trends up and down and up and down.

And you’ve got to be keeping a careful eye on it.

Because it can change rapidly, it can change, you know, within days and see a change in that the velocity was called an inflection point.

That’s something that gets out there’s a new trend to starting.

So we’ve got to have the tools to to look for them frequently, and be able to react to them.

The most important thing when it comes to trends is being able to make a decision from it.

You look at a trend change and say yep, it’s now changed enough that we should do something about it.

And again, you need to be monitoring constantly for that.

So in this context, that’s what a trend is sustained change in a metric over a period of time that can be proven with a statistical test of some kind.

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