You Ask, I Answer: Action Steps After Algorithmic Understanding?

Warning: this content is older than 365 days. It may be out of date and no longer relevant.

You Ask, I Answer: Action Steps After Algorithmic Understanding?

In this video, Christopher Penn explains the importance of having a clear hypothesis to test when analyzing data for social media algorithms. He provides examples of how testing a hypothesis can help determine whether or not to take certain actions, such as what days to post on Instagram or which hashtags to use on TikTok. He also emphasizes the need to run statistical tests and the benefits of making data-driven decisions.

You Ask, I Answer: Action Steps After Algorithmic Understanding?

Watch this video on YouTube.

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

Christopher Penn 0:00

In this episode Robin asks, I watched the Trust Insights live stream that you did on social media algorithms.

And I was puzzled as to how you knew what to do.

I’m assuming the question is, how do you how do you make those determinations? The biggest thing that makes drawing a conclusion easy is having a clear hypothesis to test right? So if you are checking to see does the day of week matter to your posts on Instagram, and you have all the data that you’ve analyzed, you logically should have a proof or disproof of that hypothesis, you hypothesize that the day of the week matters.

And then you run your statistical test, and you’re done.

And you look at it and go, the day of the week does not matter, then you know, that you’ve disproven your hypothesis, and that you no longer have to take actions and or focus on we can only post on Instagram on these days of the week.

Or you can only maybe use pictures of this kind of dog, whatever the whatever the hypothesis is that you are testing.

That’s how you know what action to take.

Where people run into trouble is when they’re looking at data, and they don’t have a clear hypothesis, they don’t have a provably True or False single condition statement to their data.

Let’s take a look at Tiktok.

And you’re asking, for example, how many of the four year page hashtags should you be using? If you run a test, and you look at it and say, well, there’s no statistical relationship between for your page hashtags? And how how much reach your videos have, you know that it doesn’t really matter whether you put those hashtags in or not.

The converse may be true, right? If the converse is true, and it turns out that is very important, then you know that you need to take that action.

I’ll give you an example from my Twitter account, I was running that statistical analysis, trying to figure out what correlated to newsletter signups for my newsletter.

And I was stunned.

I was shocked that one of the correlates was Twitter followers, the number of Twitter followers that I gained on any given period of time during that the testing period.

And I was like, wow, that’s, that’s awkward, because for years, people have been said, Oh, don’t don’t believe in vanity metrics, right? vanity metrics, don’t matter how many people follow you.

That’s a vanity metric doesn’t matter? Well, that’s not universally true.

For some principals, you may find that a metric matters to you or your data, your business that may not matter, matter to others.

And the only way you do that, is by running those tests by testing those hypotheses.

But once you do, then you should have a pretty clear idea of what to do next, right? Say, the number of Twitter followers matters.

What do you do? You run a test, you go out and you get a bunch more Twitter followers, and you see, does newsletter signups go up commensurate with the amount of new followers you’d have? If the answer is yes, then you have causal impact, right? You’ve you’ve gone and done the thing, and seeing the proportionate rise in the outcome you care about, you have proven that that is that is real for your account.

Now, that may not be true for everybody, it may not even be true within your company.

But for that specific testing scenario, if you can first find a correlation and then prove its causation, then you have a pretty good idea of whether it’s working or not.

So that’s the secret to using this data to using this understanding of machine learning models to use an understanding of an algorithm understand what it does, you build a hypothesis, test that hypothesis, and then whether you prove or disprove it, that guides your next steps that creates the action that you need to take or the thing that you can stop doing because you know, it doesn’t really matter.

So if you are not in a position to run those kinds of statistical tests, ask someone who is right, there’s, there’s no, there’s no easy substitute for doing the work.

There’s no easy substitute for running the analysis yourself.

And if you just winging it, then you know, it’s kind of a flip of the coin as to whether you get a good results or not.

I would much rather someone invest the time to learn how to run these tests and then go, go run them if you can’t afford to hire somebody, teach yourself learn how to do it so that you can make decisions with confidence.

You can make decisions that are based in reality in the data in front of you that are not guesswork.

And most critically, when you have to defend your decision to your the powers that be whoever they are.

You can say here’s the approach that I took and the mathematics behind it.

If for some reason you Got a variance that doesn’t agree with the data that you can say, Yeah, this is an anomaly, because here’s the tests that I ran that prove that this decision I made was actually mathematically the correct one.

Whereas if you have a hunch, it’s a lot harder to prove that the hunch was good or bad.

So, really good question.

Thanks for asking.

If you’d like this video, go ahead and hit that subscribe button.

Machine-Generated Transcript

Christopher Penn 0:00

Comments

Leave a Reply Cancel reply

Pin It on Pinterest