Jen asks, "How often do you feel like ads should be changed out and freshened up?"
I don't feel anything. I look at the data. Look for diminishing returns, and use that as your determinant for when to make a change. How do you calculate that? With a moving average change indicator.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today's episode, Jen asks, How often do you feel like ads should be changed out and freshened up? Well, I don't feel anything.
I mean, when it comes to decisions like this about your marketing, you shouldn't be relying on your feelings.
Because your feelings, frankly, are a little on the bias side, you may have seen the same ad over and over again.
Or you may be looking at the same website design over and over again and you get tired of it, but you are not your audience.
And that's an important part to remember here is you are not your audience.
What you do in your marketing, probably, unless you're very much in the market for what your company sells, and not the customer.
And so your feelings about things like advertising really should be put to the side and focus on the data.
The data will tell you what's going on whether your ads are working, whether your ads or not.
And for this particular question, there is a very specific technique that I recommend, that can help you make the determination about when it's time for a change.
The technique name is something called a moving average change indicator.
And it is exactly what it sounds like, tells you if something's changing when you look at your moving averages.
So let's look at a practical example of this.
We're gonna flip this around here.
What I have here is basic ad data, right? You have the date of the ads, and you have the ad performance, could be clicks, could be conversions could be any number you want.
And this is important point.
When you're looking at something like this, this can apply to pretty much any kind of marketing data, Facebook ads, Google ads, email marketing, you know, list performance, it can look at website traffic, whatever it is, it can help you understand sort of the diminishing returns.
So the first thing you want to do when you're looking at any kind of data set like this is just to get a sense of What is the data tell us? I want to do a very quick chart here.
Now we're using Google Sheets.
But obviously you can use the the tool of your choice.
And already we can see without doing any substantial analysis that there is a general down into the right line, right, we can tell that things are not going well.
Now, here's the catch.
When it comes to ads, this particular look is fine and retrospective.
But if you started your ads at the beginning of the month, wouldn't it have been nice to know that like ads performance was not great, before we got to the end of the month and spent all that money and didn't get the results? So how do we do this? We're going to do what's called again, a moving average change indicator.
So let's make a new column here called moving average.
And a moving average is nothing more than exactly what it sounds like.
It's a window of time.
Let's do this.
We take the average of the previous seven days.
And why is this important? Well, as you just saw from the chart earlier, there's a lot of ups and downs in this overall series.
So let's go ahead and chart this now.
And you can see, but by using a moving average, we smooth out a lot of those ups and downs, and get a much closer look at what's actually happening with a seven day lag.
And again, you can choose any window you like, I typically like seven days, because for a lot of businesses, and in my business, in particular, Trust Insights.
We're a b2b company.
And so weekends will always throw off a shorter window moving average in seven days, just because people do less stuff on the weekends.
So here we see the moving average, and it's changed over time.
Now, what we really want to do is next, we're going to create a moving average change indicator.
And this one's a real simple calculation.
It's actually the same formula as our Hawai new minus old divided by old.
So in this case, we're going to take the new, which is the second in the series minus the old, which is the first in the series, divided by the old.
And drag that down.
And now let's go ahead and get rid of this and just chart these two columns.
What we see here is the zero line, which tells us when our change, our rate of change has gone into negative territory.
And then everything below this.
Now, what does this tell us? What is it telling us? Well, it's telling us that the moment our indicator goes below zero in this thing, it means our ads have really stopped being effective, and the longer it stays under zero, the worst performances or the worst performance has gotten by having this indicator in place.
You You could have you could be monitoring for, you know, changes in your ad performance and know that, yeah, you hit diminishing returns, you hit diminishing returns pretty early on actually.
And in this analysis, then you can see there's not a whole lot that's going on that's, that's good.
This is how you tell that you hit diminishing returns.
Once this indicator goes below zero, and it stays there for maybe two or three days, it's time to change reds and freshen them up.
Ideally, what you're doing is you're doing this sort of computation, a calculation on a bunch of different ads, and that as an individual ad drops below this line, and you say, Okay, it's time to retire you It's time to try you, you're still going well, you're still going well, you're still showing growth, but and and so on and so forth.
And you can cherry pick the the ads that are just have run their course they're there, they're out and done.
Now, how do you implement this, practically if you'd have no other technical capability And then maybe you download your ads data, once a week, say Friday afternoons and you do exactly what we did here and just run those computations.
And then you change up the ads and freshen them up and get them ready to go for Monday morning, if you don't run them on the weekends, or if you do, depending on your business cycle, change the ads out whenever your slowest a typically is.
If you have more technical capabilities, what we just did is relatively straightforward to put into a piece of software that you write something like in Python or R, that can connect to your different ad systems, download that data, and then run those computations.
That's a more sophisticated way of doing this maybe even sends you an email saying like, hey, ad 23 has hit diminishing returns, it's time to retire.
That would be the the ideal solution.
So you don't have to manually do this every Friday afternoon.
And it just alerts you Hey, something's going on with this ad.
This ads just lost steam.
And likewise, you could even configure it to say like, hey, this ad Picking up on usually maybe add some more budget to it, maybe do something else that is a, you know, can reinforce, maybe make some copies of interesting variations, you would think that ad systems would have this technology built in, but they, they don't really, I can't think of any vendor that includes this level of analysis.
And so it's really important to be doing it on your own.
However you need to whether it's manually or with, it's a more sophisticated way of doing it.
But this will get you improve return on investment of ads substantially over time, because, again, once it adds run its course you take it down.
And more importantly, to the original question, just because you're tired of it doesn't mean it needs to be changed, right? If the performance data is still great, you leave it alone.
You let it do its thing until it does start showing diminishing returns.
And ideally, you're running many variations and tests so that at any given time you're pruning those ads that are hitting diminishing returns and you're putting up new ones to see how they perform at Zillow, See how they run? That's a really good question.
Avoid using your feelings to make decisions about when to change things.
It's it's not a good indicator.
Yeah, the data is a much better indicator of follow up questions leave in the comments box below.
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