So What? Marketing Analytics and Insights Live
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In this week’s episode of So What? we focus on Instagram hashtags. We walk through what they are and how to determine if they help or hurt your social posts. Catch the replay here:
In this episode you’ll learn:
- What data is available on Instagram
- How to explore your Instagram data
- How to analyze your data to make the right decisions
Upcoming Episodes:
- Out of the box attribution – TBD
- How do you benchmark a website’s performance? – TBD
- 202 recap – TBD
Have a question or topic you’d like to see us cover? Reach out here: https://www.trustinsights.ai/insights/so-what-the-marketing-analytics-and-insights-show/
AI-Generated Transcript:
John Wall 0:25
Hello, everyone, welcome to so what the marketing analytics and insights live show from Trust Insights. Today, Christopher Penn and I are here, Katie Robbert is off, her husband works on the front lines, we appreciate his service. And she’s taking advantage of a little pre-holiday time to go off and just have some fun and have a little bit of vacation. But yeah, Kristen, we’ve got a lot of stuff up today. Um, we’re talking about Instagram. In the past, well, I’ll let you even tell the tale about some past research on Instagram, but we’re going to talk about the core of his hashtags. And if they work for Instagram, and pretty much with that I will throw to you.
Christopher Penn 1:02
Right a reminder, folks, if you’re watching on Facebook, you need to authorize it stream yard comm slash Facebook so we can see your comments. Otherwise, we get nothing at all. So yeah, a while back, our friends over Agorapulse put up his blog post. And it’s a really it’s, it’s a well thought out post on do Instagram hashtags really lead to more engagement. This is about three years old now. So it’s a little older side. And I like stuff like this, I like stuff like this, because I like to see people trying science. The challenge that I think we run into sometimes is we don’t necessarily have either the right data, or the right methodology to really do the science in depth. So if you read through this article, it’s a fairly long one here. They come up, they do some testing, they look at for Instagram accounts, they post some stuff, hashtag some stuff with not they conclude that Instagram posts with hashtags, increased likes by 70%. So we I said, you know, it’s 2020, the suppose a few years old, a little bit has changed Instagram since then. And now the question is like, what, what would this look like if we did it with more data, larger data, and current data. And that’s where this whole idea for today’s episode came from is the fact that we don’t have any of those things with this older post. So the first place we want to start is where are we going to get the data. Now, there’s a couple different places you can get data. But the one that we use a lot is a service from Facebook called CrowdTangle. We got, we were grandfathered into this. Now we should be paying customers until they turned into a free tool. But then they closed the doors. It’s only open now to like journalists and stuff. It’s really intended for journalism and academic research, which we actually do a fair amount for. What’s cool about it is you can load up a list of accounts, and then export all the posts for that account for as you know, up to two years worth of backup data, which is really, really handy. And as long as the account is public, you can get the data and it works for Facebook, Instagram and Reddit, they used to Twitter, but then Facebook and Twitter had a little falling out. So that’s the first step. If you don’t have access to this tool, your best bet is going to be working with Facebook’s API directly. They have an Instagram Graph API, you can write software to connect to it. It’s kind of cumbersome, and requires a lot of hoops to jump through this pretty soon from loads. But if you have access to a tool like CrowdTangle, that’s a good place to start. And you can also do what Scott did in his post, which is, you know, just run a bunch of tests. If you happen to work at an agency or something, you can run a bunch of tests on client accounts with their permission to get the same thing. But I like to have a large amount of data. So that’s that’s step one. The second step is we got to explore this data set. How large is it? So we pulled together a list of actually a couple lists inside the tool. We have 4200 brands and all the stuff that they are are sharing. And then we have a list from CrowdTangle of influencers. And this is a crazy, crazy long list of like 11,190 influences. And there’s a lot of duplicates and stuff here but there’s so much data to be had. So the next step is in the processes. We got get the data out of here and start figuring out what it looks like. Now, john, when you do social media analysis, what are the stuff that you typically look at? Like when you’re looking for, you know, conversations and things when you’re looking at like marketing over coffee? What is it that you look for?
John Wall 5:17
Well, the biggest thing is, you know, the stuff that you’ve done, the Trust Insights does, where you’re not just looking at pure volume, you know, how much junk has been posted, you’re not looking at just raw followers, likes, you’re starting to get to some actionable stuff. But really, the machine learning stuff you’ve done, if you can look at what content is being shared by others, that’s the real measure of you know, what’s effective? And what is moving the needle and where the true influencers are, you know, it’s not just that millions of followers is that when they put something out there, it actually gets liked or shared.
Christopher Penn 5:50
Yeah. Now, the challenge with that is, Instagram doesn’t have a whole lot of that data. They do have handles like, you know, that same as Twitter handles. But for the most part, we can’t see what happens after somebody leaves Instagram. That’s true of everything except pity, obviously, sites we own and Google Analytics. So that’s not visible. So for this experiment, we’re probably going to want to look at just engagement and kind of hope that engagement leads to results later on. I mean, the challenge with engagement is you can get a lot of engagement for all the wrong things.
John Wall 6:25
Right? Well, and when you do you know, the good news is that if you’re measuring something you’re caring about, you can close the link, you actually do see how many folks come in the door and convert. So it’s not, it’s not as if we’re just kind of praying that large numbers equals success, we can at least see where they convert.
Christopher Penn 6:41
Yeah, so let’s go ahead and look at a bit like a cooking show. Because loading 3 million posts and Instagram into any kind of data processing software is a bad idea. It will take a really long time. So we did, we pulled it in, we pulled in, we have 1.3 million brand posts, and 1.6 million influence posts for total 3 million posts in total. Now, the first thing you had to do with all data, but especially with Facebook’s data, because Facebook is not great about this, because you got to clean it up. One of the things that we have found is that their data tends to be very messy, and what users put in, into like, you know, comments and just, you know, posts, titles, things tend to be even more garbage like is astonishing just how much garbage is in these. So the first step in any of these processes is okay, let’s go in, let’s clean things up. To have a really long function to basically says let’s create things like comment rates, engagement rates, video rates, Mark things with this paid not and critically, for our purposes today, count the number of uses of the pound sign as part of a hash tag, you know, with words and numbers after that. So our first run through, go through and clean all this stuff up. What we end up with is like I says, 3 million posts. And we want to ask us the very naive question, is there a relationship of any kind between hashtags and what we are going after? The original post was liked? Let’s do to do likes or should do engagements like all engagements?
John Wall 8:16
Hmm. Your call either way, the crushing the article is always interesting to me.
Christopher Penn 8:22
I let’s do likes, let’s do likes, comments and engagements. Right. I think that seems like a safe and comprehensive look. So we’re going to do a little bit of data processing here. All this is going to do is put our data together and get those measures into one spot. And then we’re going to do what’s called a correlation plot. Now, a correlation plot is real straightforward. It uses simple correlation. And it answers the question, is there a relationship between two sets of variables? Let’s see what we come up with here. We have our numeric with seven variables. We have our Spearman correlation. And let’s draw a plot. Oh, there we are. Okay. Let’s zoom in on this. So what we’re looking for here is we want to know, is there a relationship between the all these variables and this is where correlation plots is super handy, because you can see across the board whether relationships are so we see for example, there’s a perfect, almost perfect correlation between engagement and likes, if that makes sense, right? Because this is, this is actually a case of what’s called a high correlation. Oh, God. Sounds linear dependency is basically saying you have one number, it’s made up about the number so of course, you’re gonna have a perfect correlation. If you were doing machine learning, this would be a really bad thing. You’d want to knock out the things that are composed of each other. But so yeah, we’re perfect correlation between engagements and likes. You have a very strong correlation between engagements and the number of followers you haven’t posting, you have a very strong correlation between engagements and comments. If a weaker relationship between engagements and engagement rate, you have no relationship between engagement and views. And here’s where we are. There’s no there’s actually a slight negative relationship, not enough to be statistically relevant. But there’s basically saying no relationship between the number of hashtags. And the number of engagements. Same is true for likes, even less so for comments and things. So our first pass at this, we’re like, we don’t see it. We don’t see that that experiment. Design showing up where Yeah, there’s a, there’s this negative relationship. So at this point, john, what do we do?
John Wall 10:47
Yeah, well, this is it’s a big negative. Now I’m just wanting to be clear, too. So engagements, and likes are even and engagements or comments are less though now every comment isn’t engagement. But that’s because it’s shorter, because the majority of engagements are just likes with no comments. Is that right?
Christopher Penn 11:04
That’s correct. Yeah, the vast majority of engagements are people smashing the like button.
John Wall 11:09
And then as far as where the statistically relevant mark is, is this everything is statistically relevant, you know, as like a P of two or is it have to be over point two here for us to consider it relevant.
Christopher Penn 11:23
So this is a Spearman correlation. And that means that generally speaking anything between minus point two five and plus point two, five is really weak. There’s not much there. Anything between point two, five and point five is moderate, on both positive and negative, anything above point five is strong, saying Yep, there has some kind of relationship now, obviously, does not look at all anything like causality and stuff, just saying there’s a mathematical pattern matching between these two things. And the first step in this kind of investigation would be like, Is there a pattern match? Like, do we see any relationship at all between hashtags? And these measures? And the answer really is no, there? There isn’t?
John Wall 12:01
Yeah, it did. Not only we’re not even a point to like you said it’s actually negative, it could be reversed. So what is first order to the marketing team is just don’t bother with hashtags?
Christopher Penn 12:10
Well, I would say that the initial conclusion the article came up with that, you know, you have to be using hashtags to get engagement. In the first pass, we’re coming up empty. So what we should do now is dig into this a bit more. And the question is, how would we test this a bit more to see, is there something else at work here, one of the challenges that you run into with very naive tests like this is that there could be very, very badly matched samples. If a million posts if 3 billion pose is 3 million posted two and a half million, don’t use hashtags. And only half a million use hashtags, then you’ve got a really unbalanced sample set. And that means that you may be drawing conclusions incorrectly, right, the ideal, again, this is where hats off the Agorapulse T, the ideal way to do it is to do that controlled AB test like that’s, that’s really a really good way of doing things. The challenge you run into is, obviously if you only have a handful of accounts, and they’re not and you know, different industries and things like that, then you’re going to still you’re gonna come up with an underpowered test. So there’s a way to simulate an AB test with a technique called propensity score matching. So we’re gonna go ahead, and we’re gonna say some say even just split the group all these posts into two groups. One group uses hashtags. One group doesn’t, right, let’s worry about the, the number of hashtags later just went out, called a group that uses hashtags. We call it like a treatment, right? They’ve used that they’ve done thing and call the group out hashtags. But control, they didn’t do the thing. What we want to do is we want to do a sample, we can’t do the whole dataset, because my computer will melt. And the show will be like, well, 11 hours long, like, you know, 10 and a half hours, which will be just staring at the screen. So we’re gonna take a sample of the of the 3 million. Now, again, this is where a lot of people tend to go wrong. The question is, how large a sample should you use? There are any number of really good simple sample size calculators on the web. This one I just, you know, like the first Google result, I said, I want a 99% confidence interval, which means that if I repeat this test, 100 times 99% of time will come out with the same answer. And I want a confidence interval 1% meaning that every answer I get will be plus or minus 1%. Correct? Like that’s a lot if you want to get really, really aggressive can do it. What half a percent. What size population would I need to survey out of out of my 3 million people in order to get that half a percent margin of error? I would need 65,000. So let’s go back here. In my sample, I’m going to do exactly that. I’m going to sample my data set and choose 65 at random 65,000. samples here, we got here it says,
Unknown Speaker 15:13
a file.
Christopher Penn 15:19
There we go. Okay, so I now have a sample of 65,000 randomly chosen, that’s going to be a big enough sample to go, okay, there’s some statistical reliability here. Let’s go ahead and run our matching. And what we’re doing is we’re taking this sample, we’re splitting it into two groups. And then we’re looking at the software is going to go through and identify and try to match up and create a subset of this saying who, which posts are similar. So like, john, if you have 1000, Twitter, Instagram followers, and I have like 10,000 followers, there’s a good chance that we’re not a good match, right? Because there’s a, we’re different. But if you have 1000 followers in a scam, and I have 1200, it’s probably a closer match. Right? We’re more alike, than we are different numerically. And so comparing our results would make more sense as opposed to comparing, you know, either one of us with like, I don’t know, pick a celebrity choice. I don’t
John Wall 16:22
know, Kardashian, ultra Kardashian in there for
Christopher Penn 16:25
the suit, Dwayne Johnson, right, you know, Dwayne Johnson posts on Instagram, and you know, millions of dollars of things get sold. So, what we want to do with this technique, and this is a technique that’s super useful for all kinds of marketing data, is we want to do that when that sample and figure out okay, what does the What does it look like? So, we have here are balanced data set, here’s the control, here’s the treatment, what are the differences? Actually, in our, in our sample set, we see that people use hashtags have gotten substantially worse performance on stuff. So that now is now a really head scratching territory, like what the heck is going on here, out of this statistically valid sample, we’re getting, we’re seeing some substantial differences between these two groups. And the, in particular, the engagement rate is is much lower between the treatment control group for the hashtag. So now we’re like, I think we might have poked another hole in this in this thesis that you know, you must use hashtags for engagement is, at least for where we are. That’s that’s not the case.
John Wall 17:43
We had a fight, David chimed in with a question said he’s worked with a statistician that liked an ad. That said 85 was still a coin toss. And he wanted to have his clients to at 99% I you know, if that was the case, I’d love to do it have an 85% confidence coin toss and play for money. But that’s, you know, just me. I can see where he’s coming from with that, though.
Christopher Penn 18:07
Oh, yeah, totally. Because in 85%, compensated, if Think about it, you run a test 100 times and 85% of the time it comes up, okay, and 15% of the time, you can’t reduce the results. You’re like, uh, but that’s a lot of variability. Right? It’s like, you know, well, revolver has what, six, six things right, one out of six is 16%. So basically, you’re playing Russian roulette. At that point, if you have a one in six chance of being wrong. That’s not great odds, I would rather have the, you know, the one chance in 98 out of 99, that that thing’s gone wrong, certainly, for stuff like this. So what we’ve done here is we’ve done a simple test of correlation, and found that there was a relationship. And then when we did a dug into the sample and tried to do basically a retroactive AB test, we found that, actually, for this sample set, and this group of people, hashtags didn’t help things back hashtags might have made things worse. So what do you think, john? Should we just completely? Can using hashtags on Instagram?
John Wall 19:21
I don’t know. Is there any way to flip it to find out if there’s, you know, any things that did well, that do have more hashtags? Is there any way to look at it from the other angle? Or is it just better to call this dead?
Christopher Penn 19:35
That’s a good question. I would say the next step, because we’re talking about a massive list here. This is across 10s of 1000s of accounts on Instagram brands and individuals. I think that the next step if if this was something that you know, a client had engaged us to do would be okay, let’s get together a curated list of a couple 100 accounts just in, you know, the industrial concrete industry or whatever you whatever the clients interested in. Be and say, Okay, let’s see how this looks specific to your industry. Let’s see if there’s a different pattern because again, we’ve done here, we’ve loved Toyota and JetBlue, and the Republican Party, and you know, Smith and Wesson all together into the lobby for two tiny g’safni boss pizza, with just one giant gumbo pot. And everything kind of gets, you know, mashed together. Whereas if you’re looking at a specific industry, that industry may have very, very tailored things that are going on that could be like, okay, we need to maybe focus in on just the thing. In fact, we’ve got a little time Well, no, because we have to reload the whole data set. And that takes about 20 minutes. I would say, you know, one thing we could do in the future is identify are there are accounts that, you know, use hashtags sometimes, but all the time? And if so, you know, then can we look at the hashtags they use and say, Okay, can we now in on that industry, like, you know, women’s shoes, for example, without our favorite go to? For examples to say, Okay, what about just this in women’s shoes? Could we identify just that just those people and then build ourselves a data set and test it just for women’s use? Think about this, could we do that?
John Wall 21:26
How hard can we push our without it running for
Christopher Penn 21:32
lately? Um, let’s try.
What is the field name here for people foaming at the mouth? description? But pick a hashtag.
John Wall 21:58
Oh, that’s a tough one. Um, how about lb fashion?
Christopher Penn 22:03
fashion? I like fashion. Okay, let’s drink. We want this to be all this actually count. And we’re gonna need to replicate that for our influences as well. I bet you’re gonna get a ton more influencers, then? Let’s see how long this takes if this outer leaf melts down?
John Wall 22:36
Yeah, is there any? As far as I know, there’s no public hashtag data. You can’t just say like, okay, here’s the top 200 hashtags.
Christopher Penn 22:44
So there are data sets that say that, um, I don’t know how reliable they are, like, I don’t know where they’re getting the data from.
John Wall 22:53
Right? I said that must be scraped. I cannot it’s not from the API.
Christopher Penn 22:59
Okay, let’s see what we get here.
Out of those 3 million posts, and each we’re going to get 20 929,000 brand and 70,000 foot so that’s interesting, I would have expected the opposite to be true. Okay, let’s put together ourselves a
take our to fashion data sets here. trim those down. And now let’s rerun our correlation plot with just our smallest set. Let’s see we got for fashion. Interesting. So now, hashtag count and engage right there. So minor positive relationship. So in just this hashtags may work for, you know, for fashion.
John Wall 24:11
But it’s not a tripwire. Oh, well, it’s not going to triple your results. I
Christopher Penn 24:16
mean, we see a slowdown, but there’s a problem we filtered by a hashtag. So inherently every result contains a fat hashtag. Right? We don’t have any data in our set of accounts that were in this space that did not use a hashtag because by default, we counted the fashion hashtag
John Wall 24:35
right and so but with so we do see we don’t see a correlation between hashtag and likes or
Christopher Penn 24:42
engagements. Right. So let’s do this. Let’s take just the word fashion that will get us I think a little bit closer to where we want to be. Because that would occur whether or not use the hashtag
John Wall 25:07
It’s unfortunate I’m not strong enough on our to actually be able to do play by play here.
Christopher Penn 25:12
So we are at now, we now have 70,000 posts just using the word fashion or brands 45,000 not so now we’re much more, we have, again a correctly bounce dataset. I jump on in here to our chart and we see still not rocking the relationship. So even here for just a subset it there isn’t a there there. Yeah. So I think you know, where it’s, you could obviously repeat this over and over again for wine or sports or politics and probably get similar results, but I’m starting to feel like this might be you know, barking up the wrong tree for the hashtag side of things.
John Wall 26:04
Yeah, right. So that would be the last step for a company that you would take your the 1015 hashtags you think work and you could actually see for those if they literally work or not.
Christopher Penn 26:15
Exactly, and they do use the generic term and then see if there’s the skew between the generic and the thing. Um, let’s pick one more. What do you want to use? fashion?
John Wall 26:27
Oh, that’s I you know, oh, travel is huge. and stuff.
Christopher Penn 26:31
travels gonna be too big. Pick something a niche, something like, you know, a little bit obscure. Um,
Unknown Speaker 26:39
how about cosplay?
Christopher Penn 26:41
Oh, yeah, that’s a good one. Like that. Let’s see what we got here. Okay, so we ended up with 68. posts, by brands 234. By people? It’s a really small group.
John Wall 27:09
Yeah, that’s a lot more niche than I thought it would be.
Christopher Penn 27:11
Yeah. Well, keep in mind, these are brands and influencers. Broadly, these are not people specific to cosplay, like we did not put together a cosplay, influencer list. So you’re not gonna get a ton of that we look in here, we actually have now an actual negative correlation, which means that that’s hashtags go up in engagement goes down. So it’s a negative for for cosplay, which is as a term, this is really interesting, because now we’re at a point where like, Huh, hashtags actually hurt more than help there. And, again, we don’t know why, cuz we’re not looking at the underlying data to see if like, these are just, you know, maybe there are posts that were just, you know, bland or whatever. And obviously, this is not addressing things like post quality to the right audience. Did you boost it is that this is just the wall organic stuff, but definitely a case of there’s not a Bear Bear.
John Wall 28:05
Yeah, and just trying to, you know, as you start to think about what actually drives that, so my first thought would be that, if they’re spammy posts, you know, that’s why the hashtags are in there, and those aren’t doing well. So that would be one theory. And even to take it up another level to I was trying to think the only way hashtags can make a difference, right, is that you’re assuming that users have standing searches for those hashtags, right? I mean, is there any other use case where How else could hashtags help?
Christopher Penn 28:37
Oh, you know, I get hash tag is mainly a discovery function, right to use a hashtag, because somebody taps on it, cuz they want to follow it and see stuff in there. So there may be some opportunities to, to use that for discovery, but it doesn’t, it may or may not lead to engagement may just lead people find your stuff is one of the things that is not in here as we did not attempt to track you know, changes in follower accounts from post to post to see if like a certain if a certain number of posts generated more followers. So that’s also not in here. That’s something that has to be computed separately, but it’s definitely a case of, you know, there’s, there’s, it’s not looking good. It’s not looking good.
Unknown Speaker 29:23
So, alright,
Christopher Penn 29:24
so that pretty much wraps up today’s show for what data is available for Instagram. How to explore it using a utility, you know, like our you can use other tools. There are plenty of tools. IBM SPSS modeler is a fantastic tool. No disclosure, we’re an IBM Business Partner, but it’s a drag and drop clickable interface. Rather than writing code. It’s a lot easier for folks, not on a small enough data set. You could do this with Excel, right? There’s nothing wrong with good old fashioned Microsoft Excel, it gets the job done a lot of the time. And then from here, once we do the analysis, we walk through a couple of different steps, a couple of different tests. Look at the data and figure out, you know, what’s the next step? What should we do? And then in this case, we’ve pretty much ascertained and I think, you know, this, this, this may not there may not be there. So, if you got a follow up questions and stuff, by all means, you know, let us know, we’ll put up some information about where you can follow us. But, john, any, any parting thoughts on our great Instagram hashtag test?
John Wall 30:22
Yeah, I don’t need to hashtag any of this stuff when I post it into Instagram. So good.
Christopher Penn 30:29
I would say, if you’re gonna do it, test it, you know, test it on your account, because at the end of the day, the only count we really care about is yours. So test it and see if it works for you. If it does work for you. Great. You’re an outlier if it doesn’t work for you. Now, we kind of found that out. All right, well, let’s roll on out of here and we’ll we’ll talk to you folks 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 BERT Trust Insights podcast at Trust insights.ai slash ti podcast, and a weekly email newsletter at Trust insights.ai slash newsletter. got questions about what you saw in today’s episode. Join our free analytics for markers slack group at Trust insights.ai slash analytics for marketers. See you next time.
Transcribed by https://otter.ai
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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