Iain asks, "Fact or fiction. Using third party schedulers for social media posts get a kick shins as they haven't come from the native platform tools?"
Some testing has been done on this front, but none of the tests, from what I've read, have been done as rigorously as they should have been. Watch the video for an explanation of how to run the test for yourself.
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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, Ian asks factor fiction using third party schedulers for social media posts, get a kick, kick in the shins as they haven't come from the native platform tools.
This is a good question, because it highlights some of the issues that we have in marketing with analytics and statistics.
Specifically, some companies have done some testing on this Agorapulse has done some testing buffer has done some testing a few other social media posting tools have done some testing.
And obviously, one of the challenges there.
Well, there's several challenges.
One is there's an inherent conflict of interest if a third party company is testing to see if third party companies to penalize but that's not reason enough to disqualify their efforts.
Because as long as it's done in a statistically valid way, and methodologies are disclosed, and data is made available, then it's totally fine, right? It's the same with any kind of academic research, you disclose your funding, you disclose your conflicts of interest, if any, so that people know that the study may or may not be as neutral as it could be.
Here's the issue with the testing that's been done.
It's not that it's been by third parties, it's that it's not been done in a statistically rigorous way, not rigorous enough.
And the prot, the reason for this is that a lot of the data that's out there is it's not well structured.
So there's two ways that you can statistically test for something like this one is to run true A B tests where you're running the same content, well, one from one tool, one from whatever the control is, and it has to be the same content, or has to be very similar to the content so that a post that contains cats, people hit like cats, does well.
And it shouldn't matter which method you've posted by.
So you have to post one with cats in your treatment group and one with cats in your control group.
That tends not to be the case when a lot of testing goes on.
And understandably so because if you're doing that, you're essentially double posting your content.
And it gets tricky, it gets tricky to manage that.
The second methodology that you can use is a stats technique called propensity score matching, where you take a whole bunch of data, and you group it, you cluster it and then you try to find data that is similar in each of the groups is kind of like building a, an A B test retro actively.
The challenge there is you need a lot of data, you need a thousands of data points so that you can match control groups create essentially a control group, a control group and a treatment group of similarly performing content, so that you can see if the variant is because of the treatment or not, in this case, using a third party tool.
So how would you go about doing this? Well, again, if you're going the AV route, you create a bunch of social content, half of it, you basically duplicate it, you make a copy of everything, right.
And then you put one copy in your native platform and one copy in your Scheduler.
And after a while you run the numbers and you see which content perform better.
accepting the fact that you're going to have double posted content.
And second method is using again, the data that you get out of your systems, you will need to tag if the social platforms in their analytics don't give it to you, you'll need to tag which was third party scheduler, and which was native.
And then run a propensity score match.
So let's take a look at what this looks like here.
So this is Twitter.
Twitter, very kindly provides weather a post was well what tool a post was from, which is super, super handy.
And so if I look in my data frame here, let's go ahead and take a look at our sources.
We have 18 different possible sources.
We have a whole bunch of individuals and then we have the Twitter family official clients and then some more individuals there.
So what I've done is I've extracted the last 3200 tweets from my account, removing retweets, because retweets.
We're trying to test stuff that is native and then assigning if it was used, if Twitter the native platform was the source Want to go with in this case I'm going to make that the treatment doesn't really matter is just assign the groups.
And then if it was a third party tool, make it a zero.
No, it makes more sense to logically do this, let's, let's back this up.
So treatment is going to be using the third party tool.
Let's go ahead and run our matching test.
Okay, and we have as a result, the treated group meaning a third party tool, on measures of favorites, retweets and engagement, when we look at the main difference, third party content in for my account on Twitter does slightly better than the stuff from the native platform.
Now, again, this is really important.
This is my account.
So you can't generalize this to anybody else, you should do this on your own account.
And this is for Twitter only.
So you'd want to perform similar tests, with similar data on the social platforms, you care about Instagram, YouTube, Facebook, whatever.
But this is the methodology for how you do this, you can either retro actively do it with a propensity score match, or you can do a true AB test.
Let's take a look at one more let's do I know it's in the data set.
So let's test this.
Oh, and did not work because I need to switch this to nearest? There we go.
So in this case for this account, similar the treatment slightly better.
So is there a difference in third party tools? Yes.
And actually, in this case, a couple of tests here, we've seen that it's a slight improvement.
Should you generalize this to your social media strategy? No, no, I would test this with your own data, make sure that this idea applies to you specifically, because that's what you care about, you don't really care about my account, you care about your account.
So you'd want to to run this similar type of testing for you.
That said, based on the less statistically rigorous tests we've seen, based on doing something with a bit more rigor, so far, haven't seen any strong evidence that third party tools cause harm, right, if anything, scheduled content, at least in my tests, performs slightly better.
So does that mean that you should change your strategy now? run the test for yourself first, get the answer for yourself first, and then build your strategy on that.
And if you don't have the capacity to do this, then I would say frankly, it doesn't hurt to use third party tools so far.
Again, the various tests have been out there kind of proven to one degree or another, there's not substantial harm.
There's certainly nothing that makes you go Oh, wow, that was That's terrible.
Like, let's let's stop using these immediately.
It's not the case.
And again, you have to test this for each platform you're on because each platform has different algorithms.
So your follow up questions, leave them in the comments box below.
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