So What? Influencer Identification Walkthrough

Well, hey, happy Thursday. It is Thursday already. I can't believe it. Uh, I feel like it was just Thursday. So welcome to So What, the Marketing Analytics and Insights Live Show.

As always, I'm Katie joined by Chris and John. Uh this week, we actually gonna do a walkthrough of Gefi. Um, the past couple of weeks within the live stream and our podcast, we've been talking about influencer marketing. And one of the ways that Trust Insights identifies influencers is by using a software package called Gephy. And Chris is gonna dig into that.

Now, Chris, knowing up front, um, I've seen you walk through it and I've seen you describe it, I will be interrupting you to ask you to explain what the heck it is you just said. So please be prepared for constant interruption from me. It's a day ending and why. It's it absolutely is a day ending why. Okay, before we do the Gephy thing, before we dig into network graphing and graphing databases, which are the technology, we should probably talk about the process itself and the concept of influencer identification because uh there's different schools of thought about how you do this.

Um so let's go ahead and flip over to our fancy whiteboard here. When we're talking about influencers, there's a whole bunch of different ways to think about this. Um let's do uh let's do three nodes here. We'll let's do uh a Katie node here, a uh a Chris node, and a John node. Now, let's say that uh as we've joked in the past that uh Katie talks to me, and John talks to Katie, and then I talk to Katie, and then uh John talks uh to me.

Now, the question I have here is based on just this very simple three node network, how would how would you think about influence? Like, what are the kinds of things that that you would see as useful here to say, like who's influential? Well, it's definitely not John because no one's talking to him as expected. Um and I think you captured a really important uh point in that statement. Not that no one talks to John.

Uh John, I'll talk to you later. Um but it's not who does the most talking, it is who is most talked to, right? I think it's an important distinction. So let's just do a very, very simple bit of math here. Um, I have one incoming connection, I have two incoming connections, so I have a score of two, right?

Katie has one incoming connection and another one, and so she has a score of two, and and poor John has a zero right now. Um poor John. But if we were to also say, you know, um, if Katie's got this one incoming connection here, and she passes a little bit of that, like like 0.5 of that to me, that would bring my score up to a uh a 2.5, and vice versa, right? So if you think about in the old days of SEO, uh when all everything we talked about was page rank, right? How does this is effectively the kind of very simplified algorithm of how page rank works?

It's who links to who, and those inbound links, which we all love to talk about in SEO, are actually a network graph. Now, when we're talking about influencers in social media, we're talking about the exact same thing. A lot of very primitive influencer uh tools out there will do very straightforward things like saying who does the most talking, right? In this case, John does the most talking, right? He's talking to two different nodes.

Um, and so in a very primitive influencer marketing uh system, you might say, Oh, John's the most influential. He can he talks to everybody. Yeah, but is he the person that everybody else listens to? Right? Is he the person that everybody else talks about?

Not necessarily, at least in this very simple example. Um now, if you start to add in things like, you know, let's say you have start adding little followers along the way here, and then you have all those communications going back and forth uh to and from your followers and talking to each other, and they all start talking back to you. You now you start to see uh essentially a bigger picture of influence. So though, this is kind of how we think about influence, and when we're choosing the kinds of algorithms we want to work with in influencer market, we have to give some thought as to what does that algorithm look like when we're doing it, you know, on screen. And this is one of the big bones I have to pick with influencer marketing software that's out there on the market.

Nobody tells you what the algorithm is on the inside. There are 43 different methods of choosing influencers. There's 43 different algorithms you could use. Um, and I've not seen a single company save one that tells you which actual algorithm they're using. They all say it's a proprietary, blah, blah, blah, blah, blah, you know, the usual B2B sales pitch.

Um, but if you don't know that algorithm, you don't know what kind of logic they're using. Are they using the logic that says John is the most influential person because he's got the biggest mouth, even though he might be talking to the air? And we can change, we can change other names around here. Um, but is it Katie? Is it Chris?

Who's who in their algorithm would be would get the nod? Right. And I think that's an important distinction because it all starts with what is your goal of using an influencer. So if you want the person who we consider the broadcaster, the person who sends out the most information, then you would want John as your influencer, regardless of if the information is coming back. Exactly.

So let's actually go ahead and uh do a different version of this of Katie, you have John, we have our friend and teammate Emily, we have me, right? And John talks to Katie, John talks to Emily, Emily talks to me, uh, Emily talks to John, Katie talks to me. In this case, to what you were saying, Katie, sometimes let's go ahead and if you want to get to influence this node here, you can't talk to John because John doesn't have a direct connection to me, right? You'd have to either talk to Emily, who's got a connection here, or talk to Katie. Right?

So to your point, do I want the loudest person? Maybe. You know, if I'm hiring a Kardashian or any other big mouth influencer, um, they're basically walking ad machines. You insert a credit card into some part of their body and ads come out, right? So that's you know, the broadcaster style.

You have the who is the most talked about, which is the most incoming connections, but you also have, in this case, Emily uh would be an example uh of one type, a connector, somebody who can make the connection between you. So if you are say a B2B marketer and you are trying to get an appointment with the VP of marketing at like I don't know, uh Cisco Systems, right? Do you talk to the biggest mouth in the room? Do you talk to the subject matter expert? Or do you talk to that middle node that says, 'Yeah, I know Katie, I can get you an appointment with her.' And those are kind of the three big models of influence.

Let me ask you this question, Chris. Because when I think about that, that strikes me as something that LinkedIn does, you know, sort of uh adjacently. So if I'm trying to get in front of the VP of marketing somewhere, but they're not a first or second degree connection. I start to go down my list and see, well, who else might be connected to someone who's connected to someone who can maybe introduce me to someone who can then, you know, sort of those six degrees of separation. Exactly.

And that's what um, you know, this graph database or this network graphing technology can do is to help illustrate those connections in at a bigger scale. Obviously, if you're just trying to get one appointment to one person, you can do that legwork on your own. Uh, and with LinkedIn, you have to, because LinkedIn does not do any form of data export, period. Um, but on other networks like Twitter and Instagram in particular, um, you can get that data and you can start building those maps. So let's look at an example of how you might do this.

So we're gonna use Instagram today because Instagram is sort of the darling that everyone's paying attention to. With Instagram, there's two places you can get your data. You can get it from the API directly, uh, or you can get it from a third-party service. Uh, the one that we use is a service called Crowd Tangle. It's by Facebook.

Um, they have uh really restricted who can get an account, but they do still technically accept applications for it. Uh, and what you get out of that, let's go ahead and move our our whiteboard out of the way. What you get is just a big gigantic pile of data uh from them that contains uh things like your the account, the username, the followers of the posting uh created, and this is at the post level. So every single post that an account creates over a specific time period, the comments, the likes they get, the number of views if it's a video, what kind of post it is, uh, and then links to all these things. So our first step in the process of doing influencer identification is to extract out um from all this stuff uh the actual connections, right?

So, what we need is we need this information, we need it in a machine readable format. Um to do that, we write our own code for it. Um, I've not seen a system um that does it for you that's an off-the-shelf piece of software that you can buy. Um, there are some influencer marketing services that uh can scrape the data and then use it in their own algorithms, but the challenge with those is that you have to then use their algorithm because they don't give you the data. So uh we ended up writing our own for this.

So that was going to be one of my questions, Chris. Was what if I'm a marketer and I don't have the resources to write my own code or build an API connector directly into Instagram? And so for those who don't know it, I'm sure there are a few. Um, an API, the way that I describe it, and Chris, let me know if this is correct, is basically an API, if you think about it, is like a tunnel that the data passes through. So you need to put that connector tunnel in between your system, and it might be you know, R or BigQuery or SQL or something like that, and the software itself, Instagram, you know, pick a software.

And so that tunnel basically says, okay, data, now you have a safe place to travel through from where you are to where I need you to be. Yeah, that's a good enough analogy. It's like, you know, it's a it's a plug, you plug something into it, your software plugs into it, and you get stuff out of it. Um to the question of how do you do this if you don't have that capability? You have to hire it out.

Um either a contractor or work with an agency of some kind that will do it on your behalf. Um, or again, you could you can you could there are some influencer marketing companies I think probably will do it for you. Um, whether they'll give you the raw data or not, I don't know. Um I've not yet run into the that I can think of off the top of my head. So that aside, um, once you have the data and it's processed, you it gets turned into a list, and it gets turned into a very simple uh kind of list.

The list looks a lot like this: it's just a list of who was talking and who they talk to, just you know, lots and lots and lots and lots of these. Um, and this is the raw goods that goes into the network graphing software because we want to build this, you know, the the drawing that we're doing, we want to build a map of that, but in a much larger scale. Now, this is where you do need um software, and the the tool that, as Katie mentioned at the top of the show, is a package called Gephi. G-E-P-H-I, Gephi.org, is where you can pick this up. Uh, it's a very, very good tool.

Uh, it's open source, so it's free of financial cost. So let's go ahead and import our data that we just processed and bring it in here. Go find where it is on my hard drive. And this is a section where I'll be interrupting you a lot. And John, even though nobody talks to you, apparently, feel free to also interrupt and ask questions.

Now, how about is this? Um a lot of folks use Tableau because they get that with Salesforce. Are these similar as far as what the they're solving? Uh no, uh none of those visualization tools do network graphs. It's actually an interesting gap in the marketplace.

Um, and the reason for it is it's a relatively specialized application. It's not something that um you would run into in your your standard marketing. Um yet it's interesting because there are a lot of places where they're starting to use network graphs, whether or not the users know them. Like every time you log into Facebook or or LinkedIn or or Google, you're using a network graph. You just don't know it.

So we've loaded all these nodes in, and right now it's this unmanageable big black black box of all these nodes. You have 24,000 individuals that were talking about. Uh, I think we choose travel as the segment here, and 27,000 connections between them. So our first step is we gotta weed out people who are talking to the air, right? Um people who are talking and nobody's listening.

Uh, and you'll see that the the centrality measure we're going to use is this one called this eigenvector centrality, which is uh mathematically, uh, I won't get into the math on it because it's just ugly. Uh, but it basically is who's who's most talked about, is is the algorithm going to use. There are other ones available in here. Uh, Gephy supports four or five other ones, including you know who's the in the middle, who has the who's the shortest distance across the network. But I like the the who is most talked about because again, I think that represents influence reasonably well.

So let's go ahead and run that. When you're picking, you know, eigenvector centrality or a different um you know anchor point. Is this something that like when you first opened Gephy, you were like, okay, I need to do some Googling because I don't know what the heck eigenvector centrality even is. Yes. Um, you should also pick up some linear algebra because it deals a lot with matrices and matrix math.

If you want to get into the guts of it, if you want to get into the guts, you don't need to know the math to use the tool. You just need to know what the buttons do. But to research that to know what the buttons even mean, yes, there's gonna be some Googling and there's gonna be some math. Okay. So our first pass will notice in our data um has created these scores from zero to one.

Uh, all these network scores are zero to one. And what we're gonna get rid of is essentially everyone's got a zero score, right? A zero score is just uh somebody who is talking to the air and not doing a whole lot. Uh all right, John. You didn't make the cut.

I know this is a John J. Wall at Twitter being flushed. You're out. There are also because the way Instagram's a uh data comes out, it comes out very, very messy. There's always going to be some like weirdness, like people who have missed uh mistyped uh a handle or put it a half of a handle in.

Uh, those are going to be largely single letter handles. So it's it is safe to get rid of those. Now, just in doing this, we have thinned out network graph a little bit. It's starting to get a little bit more sparse. There are programmatic ways of doing this that don't use Gephy.

You can actually do this inside of R or Python and do multiple passes, but um we are not going to do that because it's not really much fun to look at. So we're gonna do this a couple of times and let the software kind of weed through the network and say, okay, let's now that we've gotten rid of the people who just love to talk to the air and tag, you know, those there's always those jerks on Instagram who just like tag 80 people in their post and you know put 500 hashtags. Once you get rid of them, then the network the graph starts to really thin out because you're getting rid of basically people who are also talking to the air. You can see there's there's the it looks qualitatively different now. I'll do one more pass through just to clean house here.

So why are you picking um when you click eigenvector centrality and it you know you put in a number, you have in there what 1000, or is it 1.000? It's 1,000. So how many iterations uh how many times is is the software gonna go through and recalculate the entire graph? So it's one time, ten times, a hundred times, a thousand times. Um generally speaking, um you kind of go with sort of the graph size.

So if the graph is, you know, uh actually it's the graph size inverse of the it's the inverse of the graph size. The larger the graph is the fewer iterations you're gonna want to do. Otherwise, you're gonna be waiting for days for this thing to finish up. I've actually had that happen. So now we've got a thinner graph.

We've gone from 26,000 nodes down to about 4,000 of these things. Um, the next thing we're gonna want to do is we're gonna want to color code this because right now it's just kind of a big unmanageable mess. Um, and so we're gonna color code it, we're gonna use something called modularity, which is a type of um clustering. It says, okay, who when it if we go back to our our network graph here, right? Who is uh in these clusters?

So, for example, uh, I might draw a cluster, um, as we are primitive, uh, around Chris and Emily, because Chris and Emily talk the most, right? And I might have a cluster of Katie and John, because they talk the most. We're gonna do apply this same idea, but at a much larger scale to see who's talking to who within the graph. And uh, so we're gonna go ahead and run our modularity calculation. So it sounds like that might be one of the misconceptions around some of this influencer identification, is not everybody is going to be connected to each other just because they appear on the same analysis.

So, in your example, Chris and Emily were connected, and Katie and John were connected, but Katie and Chris weren't connected, and John and Emily weren't connected. And so I think that that's an important distinction for this. Is just because two people appear on the same network graph analysis, doesn't mean that there's any connection between them, and therefore, that's why the color coding and clustering is a really important feature. Exactly. So I just turned the colors on based on that.

You can see about there's this this cluster four here, which is about six and a half percent of all the nodes, sort of the the tightest one. We're also gonna want to adjust the bubble sizes to be you know who's most talked about and who's doing the most talking, so we can get a better sense of like who is you know getting most talked about. Now we're still at a point here where this is not super useful yet, right? There's still a lot of clutter on the screen. So our next step is we're gonna want to use one of the sorting algorithms that are built into the product to how to arrange the network to be essentially more uh more visible as to uh who's in this box.

And you'll start to see as it runs, it's going to pull nodes apart and start pulling those clusters together to organize them. Let's go ahead and make this zooms out a little bit. And already you can see you know parts and pieces are starting to pull away, and you're getting you're starting to get a sense of okay, I can kind of see the network. You can see those big stars along the edge, those big triangles. Those are nodes that do are doing a lot of talking, but you can see there's not a real big bubble at the end of those, right?

Those are kind of you know uh people talking a lot but not being talked to. And then in the middle, you're starting to see you know these these uh larger nodes here. There's a lot of incoming conversation to those nodes. So those are the people that in this model of influence, those would be the influencers, the people like, yeah, I that's a person who everybody's talking about. And when they have something to say about travel, maybe we should pay attention.

I'm gonna go ahead and just for cosmetics, uh attach um names to these nodes so that uh can just get a better sense of who they these people are. So, Chris, I want to back up just a little bit because so you went from this sort of like you know, big cluster of things to this sort of more pulled apart. And uh on your layout, you clicked a bunch of buttons and magic started to happen. So, can we just, you know, at a high-level overview, just sort of walk through some of the options that you chose because it looks like you have you know force access to, and then you clicked a bunch of buttons and you change numbers. So, you know, I'm assuming that as you get more familiar with the functions in Gephi and you know what some of these things mean, such as like again, you picked Igang vector centrality because that was the algorithm you wanted.

I'm assuming the same is uh true of how you're organizing things. So, you know, force access to why did you choose that versus something else, for example. Yep. So these are all layout things. These don't fundamentally change the influencers themselves.

This is just a these are all visualizations. And you can choose, you know, uh a couple of dozen different versions. Uh the Force Atlas 2 algorithm, um, there's a whole mathematical paper on it. Essentially tries to balance out the prettiness of it with computational speed. There's some other ones in here that are are gener really beautiful graphs, but we'd be this would be a 10 hour live stream and nine and a half hours would be us waiting.

Um you've done this before. Just a few times. And then the thing to look at with with each algorithm is it it tells you like what are the different uh numbers and features you'd want to turn on to understand how they work. Some of them are pretty obvious, like dissuade hubs. This just says, please don't lump things on top of each other so I can't see what's going on, right?

Other ones uh like approximation and stuff, you do have to know what uh Barnes Hut optimization is to know essentially you know how it's doing its computations or spreading things out in the graph. So there are some features that you know are pretty obvious and some features are not. But this is all visualization. This is not computation, and that's a really important distinction to make. So we've got ourselves a nice network graph here.

And if we want to switch over to preview mode, just to make it look a little bit prettier, I'm gonna uh change the fonts and things just to tighten them up a little bit and uh make a prettier printable graph. Let's go ahead and zoom in here. And if you follow the Trust Insights uh Twitter and Instagram accounts, then this visualization visualization of influencers uh should look familiar to you when events happen, you know, like uh inbound or content marketing world or any other events. These are this is the analysis that we post showing, you know, everybody's posting on social media about this event, hashtagging it, who's most talked about at this event. And so you would expect to see um at inbound, for example, you'd expect to see HubSpot squarely in the middle, for example.

Exactly. So what we've got here, and you know, just uh uh a quick sanity check. Do you do we see anything in here that would be relevant, relevant to to travel? Yes, Ritz Carlton, Canon USA, the camera company, right? Uh, we got a whole bunch of different resorts and things, uh different tourism companies.

So we at least know from a just a quick sanity check. You know, visit California is a is a hub. Yes, that makes logical sense, right? You know, we're not seeing things like you know, uh financial technology companies in here. That would be kind of weird to have that in here.

So at this point, we now have a visualization that we can use to see, okay. Do some of these things clump together? Are they, for example, a lot of these uh companies when we dig in, um, the the sort of the travel uh you know and hotel stuff seems to be in green. And you know, there's you have other stuff like tourism and and technology stuff, like Canon uh in gray. So we're starting to get a sense of who these people are.

Now, this by itself is cool to look at. It's not very actionable, right? You can't just immediately look at this, go, ah, I know exactly who I'm talking to. But the nice thing about network graphing software is that with it, we can just turn this into a very, very simple spreadsheet. And then from there, you take the spreadsheet.

I'll go ahead and uh export this because it's let's open up the spreadsheet. So, Chris, with that network graph, so it looks like it's clustered, you know, all of the um nodes or people or accounts, whatever you want to call them together that are in some way, shape, or form connected, and that's the color coding. Um, and it sounds like you've cut out anyone who doesn't have a significant number of connections or two-way connections, for example. So if you know, I originally showed up in tourism and I was just talking at a bunch of people, but nobody was coming back and reciprocating, then I would have been cut off the list. That's exactly right.

So when you turn this into a boring old spreadsheet and you sort by that eigenvector centrality, you end up with, you know, a decent list. Uh and now at this point, you could take any of these individual human beings and go chase them down on Instagram, see if they're open to sponsorships and things like that. What's really interesting, I think this is very telling about how influencer marketing works. We when we did our extract of all the people who were uh essentially doing the talking, we got things like their follower accounts and things. You can see a few of these rows have those numbers in them.

Look at how many rows don't have any numbers in them. Why? Because they weren't the ones doing the talking, right? They were the ones who were being talked about. So just in a quick look at this influencer expert, we can see there's a whole bunch of accounts that if we had just gone on who was loudest, who had the most engagement and the most followers, we would have missed so, so, so many people.

Uh, I'm gonna do a quick spot check and see if this account is any good. I'm gonna do off-screen in case it's not safe at work because that happens on Instagram. Yeah, because another one you've had in the past, I've seen on this report a factor for whether it's a fake account or not, you know, whether it's something that's just publish only, too. So to be able to separate the ones that where there's a human versus it's just some bot cranking out stuff. Thankfully, we can show this one.

This one is actually the Waldorf Astoria in Los Cabos. Um 1700 posts, 83,000 followers. Um, there we go. Red Rocker, Sammy Hagar, Cabo Wabbo. Right on target.

Um yeah, this is a very nice travel account, right? Um, what I think is interesting though, look at the follower numbers and stuff, not a huge uh following, right? They're not uh an organization that's got 10 million followers. Um, this is an account that has a a good size following. Certainly, you don't want to be argued with it, but they're talked about.

This is a destination. This is uh clearly is you know, based on their their stuff, a place clearly we would want to go. Um, you know, after being on lockdown for you, this is like a place that we would want to go. But um one of the things that I want to ask, Chris, because what I'm not seeing, for example, and maybe this is just not included in this type of analysis, is whether or not these accounts are being talked about for good or bad reasons. And so as you're getting this information, you definitely want to do your due diligence.

So let's say, for example, you know, you were looking at the Waldorf Astoria in Los Cabos, okay, great. They're talked about a lot. Why? Was there a problem? Was there a legal issue?

You want to make sure that you are doing all of that checking before you're just going ahead taking listing, okay, go find these accounts and bring them on as influencers. And that's a really, really important point that has to happen before you use network graphing, right? So at this point in the process, when you are just slinging code, um, you will see here in in our in our filtration, we have a line here that says filter for just you know the term travel used in description. That's sort of the basic filter. You can add in, filter, you know, do sentiment analysis, do even something as simple and basic like check for swear words or check for you know racial slurs or anything that you would you would not want to have.

Yeah, bed bugs, yeah, uh things like that. Um that has to happen in the data extraction phase. So when we think about uh in the IT world, there's this concept of ETL, extract, transform, load, and it's a way to manage the process of data. We extract the data from a place, we transform it, and then we load it into a final form that a user can use. So this point here in R, this is the extract and transform phase uh where we have extracted the data and we're starting to transform it.

And those transformations need to happen. And you have to think about the business logic that you want to have at this point. By the time you get it over to the networking software, it's too late. You can't do those kinds of transformations in there. Now you have to do them at the extraction phase.

So a big part of doing influencer marketing really well the process and the requirements at this point. And this is where influencer marketing software really kind of hoses it because a lot of the software packages don't offer those capabilities to say, yeah, I don't want to include anybody who uh you know supports a certain politician uh or shares images of I don't know, uh cat or a dog person, they don't want to see any cats. Um you'd have to do that here. You can't do that in the graphing section. By that time, you get what you get.

And that's what you kind of happens with the with off-the-shelf influencer marketing software is you get what you get. So, John, I have a question for you. So, Chris, if you can bring up the visual again, you know, so you focus a lot on business development, John. So, one of the things that I'm always wondering is, you know, if someone from the outside, outside of our organization is looking at this, you know, what is their initial reaction? What do I do with this information?

So, from you know, from a business development perspective, like let's say you're looking for, you know, new sponsors for marketing over coffee, you know, how do you start to think about this and bring this up? And is there information that you know you wish was included in this, you know, keeping the Excel spreadsheet in mind that comes out of it? Like, what do you look for from that perspective? Yeah, right. So as Chris had talked about, the chart is great.

Because we use that for trade shows. Every time we do it for a trade show, all the speakers there are like, oh, I want to see my dot on there. I need to know that I'm important. So that happens all the time. But yeah, the juice is right here in the spreadsheet.

Actually, this is where you're gonna do your marketing work. Um, and uh it's been a while since we've validated this, but what we had seen last time we had run numbers on this, I know was that you know, those top three accounts, that's you're gonna find Kardashians and people up there where you could pay, you know, $250,000 for 10 Instagram posts or whatever. But once you get down to, you know, number 14, 15, 16, you're gonna find people that you can work with for you know a thousand bucks or 10,000 bucks who have almost the same degree of influence as those others. And in fact, what you'll see in a lot of times, the big names are always watching those names around the 70% marker. And that's where they're getting all that's where they find out about the cool.

They found the, you know, the the couple of tastemakers. And even though those people don't have the, you know, have never done a reality TV show, so they don't have the millions of followers. Um, but they're necessary. And so yeah, you're basically cutting out the celebrity um by doing this kind of stuff. And uh yeah, it's you know, the magnitude of expense if you're gonna do some influencer marketing programs, it's just you know, usually takes it from absolutely impossible to, yeah, you could probably afford to do it.

The other thing that's really important there is that you know, to what John was saying, in this phase, in the extraction phase, we chose travel as a super broad term. And of course, we get tons of travel stuff, right? I was looking at one of the other things when they listened. There's you know, this travel person, 106,000 followers, they travel and they do stuff, they go places and things. Is that right?

Or should you be incorporating things like from your SEO work? Like, is travel too broader term? You know, in our case, it would podcast be right, or would marketing podcasts, people talking about marketing podcasts. I might want to be much more thoughtful about the data that goes into the network graph so that I get a much more focused list because then I don't have to worry about spending a hundred thousand dollars on an influence, right? If I've narrowed down not just travel, but maybe we want travel in you know New York City.

Okay, who out of this whole travel field, how would how quickly would that list thin out if it was only people talking about traveling in New York City? So that extraction phase matters a whole lot when it comes to influencer marketing. Well, and I think that that's an interesting point because you're talking about it from the perspective of us trying to find those influencers based on what they're posting. But then, you know, it's also really good reciprocal advice for those accounts that are trying to be found for those things. And so, you know, we typically think of SEO as just you know, search on um search engines like Google, Yahoo, that kind of thing.

But it's also just as important for what how you're posting on social media. So if you have a travel account on social media, but you never say the words you know that you want to be found for of like, you know, travel in New York or travel abroad, or you know, you never use those terms, you're not gonna be found in an exercise like this. And that's a big miss because maybe you have a million followers, but no one's ever gonna find you because you're not using the right key terms in order to be found. So it's a reciprocal, you know, SEO exchange. Exactly.

One of the things that we discovered in our uh Is the Gram Doing Itfor You uh paper, which you can get over trust insights.ai, is that yeah, some influencers see decent performance, you know, with 10 or 15 hashtags in their posts on average. Like this is not, you know, once or twice every now, but like they're there's you know, shoveling in the hashtags all the time. But it's focused, right? Like to your point, it is focused, it is on target with what um people should be doing. And the the thing that we tell people is, you know, if you're a brand starting up, look at one to three hashtags, but power tip, take your SEO keyword list that you've you've done for optimizing your search side and start using some of them in Instagram.

Because if someone's willing to search for, you know, Bali travel or New York City travel on search, logically they're going to be doing the same thing on Instagram. Because uh, Katie, as you've pointed out many times, hashtags are how people get discovered for specific topics. And if you've found a niche that works really for you, the old expression still holds true. In niches are riches. I've literally never heard that expression.

Come on. I've saying that for years. So I digress. Exactly. So if you got some comments or questions about this, leave them in the uh comments on the on the platform where you're watching this.

We'll see it in here on the show. But to recap data is differentially available on different platforms, right? Twitter is robust with data, and you you know you don't need much to extract data from it. Instagram, Facebook, Reddit, uh, there are pieces of software that can get you some some decent data out of those services. Uh, we mentioned uh Facebook's crowd tangle.

Some platforms like LinkedIn, just not happening. Um, you can, and there's a way to do this, you can proxy um some of that with uh some very clever coding. If you've got some very clever coders on staff, uh you can look at people who are linking to LinkedIn posts as a way to go to identify some interesting content, uh, people who might be worth sharing uh use the SEO tool of your choice for that. So that's where the data comes from. We talked about some of the common mistakes, like, hey, you know, stop looking at the biggest mouth biggest mouth in the room.

Um, and then a couple of the uh identification algorithms with the warning that there's so many packages of software out there that claim to do the thing, but they don't tell you how they do the thing. And so you may not be getting what you're you're ordering. So, any other thoughts, uh, Katie or John on the fun of influencer identification. Well, I think that, you know, on in terms of the black box of the algorithm, Chris. You pointed out a really good um, you know, potential mistake that some of these algorithms have is you don't have the opportunity to say, and I don't want to include the following terms or topics, you know.

So that's just going to, you're gonna pay all of this money for influencer marketing software, and you're not gonna get what you want. Um, also like we always say, start with, you know, what's the question you're trying to answer? What's the goal? What kind of influencer are you after? You know, John, you gave some really good examples of what to do with the information.

And if you're looking for someone who's maybe not as well known, but is influential so that it fits within your budget, you know, there are ways to be looking at that information. That was your cue, John. It's true. You're right. This is why nobody talks to John.

I know. Well, and I'm here trapped, I'm like thinking of fake adages that I can use. I mean, like the monkey doesn't dance where the beaver goes. But I I don't know what that means, but that's okay. These are adages we have.

All right. Thanks for watching, everyone. We will talk to you next week. Bye. Thanks for watching today.

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