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So What? Marketing Analytics and Insights Live

airs every Thursday at 1 pm EST.

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In this week’s episode of So What? we focus on influencer identification. We walk through what data you can use, types of influencers and how to use the analysis. Catch the replay here:

In this episode you’ll learn: 

  • where to pull data from
  • how to identify influencers
  • common mistakes made in the identification process

Upcoming Episodes:

  • Video SEO – TBD
  • How do you benchmark a website’s performance? – TBD

 

Have a question or topic you’d like to see us cover? Reach out here: https://www.trustinsights.ai/resources/so-what-the-marketing-analytics-and-insights-show/

AI-Generated Transcript:

Katie Robbert 0:25
Well, hey, Happy Thursday, it is Thursday already. I can’t believe it. I feel like it was just Thursday. So welcome to so what marketing analytics and insights live show. As always, I’m Katie joined by Chris and john. This week, we actually do a walkthrough of GFI 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 GFI. And Chris is going to dig into that now, Chris, knowing upfront, 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?

Christopher Penn 1:14
It’s absolutely the day anyway. Okay, before we do the GFI 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 influence identification, because there’s different schools of thought about how you do this. So let’s go ahead and flip over to our fancy whiteboard here. When we’re talking about influences, there’s a whole bunch of different ways to think about this. Let’s do, let’s do three notes here. Let’s do a Katy node here, a Chris node and a john node. Now, let’s say that, as we’ve joked in the past that Katie talks to me, john talking to Katie, and then I talk to Katie, and then john talks 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?

Katie Robbert 2:18
Well, it’s definitely not john, because no one’s talking to him. As expected,

Christopher Penn 2:28
and I think you captured a really important point in that statement, not that no one talks to john.

Katie Robbert 2:35
Talk to you later.

Christopher Penn 2:38
But it’s not who does the most talking. It is who is most talked to, I think that’s an important distinction. So let’s just do a very, very simple bit of math here. I have one incoming connection, or two incoming connections, I have a score of two, right, Katie has one incoming connection, and other associated score two and port john has as a zero for john. But if we were to also say, you know, if Katie’s got this one incoming connection here, and she passes a little bit of that, like, like, point five of that, to me, that would bring my score up to 2.5. And vice versa, right? So if you think about the old days of SEO, when all everything we talked about was PageRank. Right? How does this is effectively the kind of very simplified algorithm of how PageRank works, who links to who, and those inbound links Jolla to talk about SEO are actually a network graph. Now, we’re talking about influencers in social media. We’re talking about the exact same thing, a lot of very primitive influencer 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. And so in a very primitive influencer marketing system, you might say all 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 everybody else talks about? Not necessarily, at least in this very simple example. 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, to and from your followers and talking to each other. And they all start talking back to you. Now you start to see, 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 an 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. And I’ve not seen a single company save one that tells you which actual algorithm to use. And they all say, it’s a proprietary blah, blah, blah, blah, blah, you’re the usual b2b sales pitch. 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 he’s got the biggest mouth, even though he’s might be talking to the air. And then we can change, change other names around here. But it Katie is a Chris, who’s who in their algorithm would be would get the nod?

Katie Robbert 5:41
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 broadcast, or the person who sends out the most information, then you would want john as your influence that regardless of if the information is coming back?

Christopher Penn 6:04
Exactly. So let’s actually go ahead and do a different version of this of Katie, 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. And we talk 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, I’m fine hiring a Kardashian or any other big mouth influencer, 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, would be an example of one type a connector, somebody who can make the connection between you. So if you are a b2b marketer, and you are trying to get an appointment with the VP of Marketing at like, I don’t know, 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.

Katie Robbert 7:39
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 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?

Christopher Penn 8:10
Exactly. And that’s what, you know, this graph database of this network graphing technology can do is to help illustrate those connections 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. And with LinkedIn, you have to because LinkedIn does not do any form of data export, period. But other networks like Twitter and Instagram, in particular, 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. We’re going to use Instagram today, because Instagram is sort of the the darling Damon’s paying attention to with Instagram, there’s two places you can get your data, you can get it from the API directly, or you can get it from a third party service. The one that we use is a service called CrowdTangle. It’s by Facebook, they have really restricted who can get an account, but they do still technically accept applications for it. And what you get out of that, let’s go ahead and move our whiteboard out of the way, what you get is just a big gigantic pile of data from them that contains things like your account, the account, the username, the followers, the posting, 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, and then links to all these things. So our first step in the process of doing influencer identification is to extract out from all this stuff, the actual connections, right? So what we need is we need this information. We needed the machine readable format. To do that. We write our own code for it. I have not seen a system That does it for you, that’s an off the shelf piece of software that you can buy. There are some influencer marketing services that 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 we ended up writing our own for this.

Katie Robbert 10:19
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 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 sequel 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.

Christopher Penn 11:06
Yeah, that’s good enough analogy. It’s

Unknown Speaker 11:07
like, you know, it’s

Christopher Penn 11:08
a, it’s a plug, you plug something into it, your software plugs into it, and you get stuff out of it. To the question of, how do you do this, if you don’t have that capability, you have to hire it out. Either a contractor or work with an agency of some kind that will do it on your behalf. Or again, you could, you can, you could, there are some influencer marketing companies, I think probably will do it for you. whether they’ll give you the raw data or not, I don’t know, I have not yet run into that. And that can pick up off the top of my head. So that aside, once you have the data, and it’s processed, it gets turned into a list and it gets into a very simple 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. 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 are build a map of that. But at a much larger scale. This is where you do need software. And the the tool, as Katie mentioned, at the top of the show is a package called giffy, g e p h ai gfi.org is where you can pick this up. It’s a very, very good tool. It’s open source, so it’s free of financial cost. So let’s go ahead and import our, our data that we just processed, and bring it in here and go find where it is on my hard drive.

Katie Robbert 12:37
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.

John Wall 12:51
Now, how about is this? A lot of folks use Tableau because they get that with Salesforce are these similar as far as what they’re solving?

Christopher Penn 13:00
No, none of those visualization tools do network graphs, it’s actually an interesting gap in the marketplace. And the reason for it is it’s a relatively specialized application, it’s not something that you would run into in your standard marketing. 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 that like, every time you log into Facebook or LinkedIn or or Google, you’re using a network graph, you just don’t know it. So we’ve loaded all these nodes. And then right now it’s Cruz’s unmanageable, big, black, black box, all these nodes, you have 24,000 individuals that we’re talking about, I think we choose travel as the segment here, and 27,000 connections between them. So our first step is, we got to weed out people who are talking to the air, right? People who are talking and nobody’s listening, and you’ll see that the centrality measure we’re going to use is this one called this eigenvector centrality, which is, mathematically, I won’t get into the math on it because it’s just ugly. But it basically is who’s who’s most talked about is the algorithm going to use, there are other ones available here. Gaffey supports four or five other ones, including, you know, who’s the, in the middle, who is the who is the shortest distance across the network, but I like the who is most talked about? Because again, I think that represents influence reasonably well. So didn’t run that

Katie Robbert 14:25
when you’re picking you know, eigenvector centrality or a different you know, anchor point. Is this something that like, when you first opened GFI, you were like, Okay, I need to do some googling, because I don’t know what the heck, eigenvector centrality even is.

Christopher Penn 14:44
Yes, 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 it. 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 has created these scores from zero to one, all these network scores are zero to one. And what we’re going to get rid of is essentially always got a zero score. A zero score is just somebody who is talking to the air and not doing a whole lot. All right,

Katie Robbert 15:25
make the cut.

John Wall 15:25
Now this is a john Jay, while

Unknown Speaker 15:29
you’re out

Christopher Penn 15:30
there also, because the way Instagrams data comes out, it comes out very, very messy, there’s always going to be some like weirdness, like people who have missed mistyped a handle or put it half of my handle. And those are going to be largely single letter handles, so it is safe to get rid of those. Now, just in doing this, we have thinned out that regretful a bit, it’s still a little bit more sparse. There are programmatic ways of doing this that don’t use Gaffey, you can actually do this inside of R or Python and and do multiple passes. But we’re not going to do that, because it’s not really much fun to look at. So we’re going to do this a couple of times. And let the software kind of weed through the network and say, Okay, let’s now 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 they’ll 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 pastes and people who are also talking to the air, you can see there’s this, it looks qualitatively different now, I’ll do one more pass through just to clean house here.

Katie Robbert 16:38
So why are you picking? 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?

Christopher Penn 16:50
It’s 1000. So how many iterations? How many times is is the software going to go through and recalculate the entire graph, so is a one time 10 times 100 times 1000 times? Generally speaking, you kind of go with sort of the graph size. So if the graph is actually it’s a graph size, inverse of the, it’s the inverse of the graph size, the larger the graph is, the fewer iterations you’re going to want to do otherwise, you’re going to be waiting for days for this. To finish up, I’ve actually had that happen. So now we’ve got a thinner graph, we’ve gone from 26,000, notes down to about 4000 of these things. The next thing we’re gonna want to do is we’re gonna want to color code this because right now is kind of a big, unmanageable mess. And so we’re going to color code it, we’re gonna use something called modularity, which is a type of clustering, it says, okay, who would if we go back to our, our network graph here, right? Who is in these clusters? So for example, I might draw a cluster around Chris, Emily, because Chris and Emily, talk to them most right? Am I have a cluster of cadian job, because they talk the most, we’re gonna do apply the same idea. But in a much larger scale, see who’s talking to who within the graph. And so we’re gonna go ahead and run our modularity calculation.

Katie Robbert 18:13
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.

Christopher Penn 18:51
Exactly. So I just turn the colors on based on that you can see about this is this cluster four here, so about six and a half percent of all the nodes, sort of the the tightest one, we’re also going to want to adjust the bubble sizes to be you know, who’s most talked about 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 going to want to use one of the sorting algorithms that are built into the product to how to arrange the network to be essentially more more visible as to 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 zoomed out a little bit. And already you can see the parts and pieces are starting to pull away. And you’re getting you’re starting to guess that’s okay, I can kind of see the network. You see those big stars along the edge, there’s 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, people talking a lot but not being talked to. And then in the middle, you’re starting to see, you know, these these large notes here, there’s a lot of incoming conversation to those notes. So those are the people that in this model of influence, those are the influencers that 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 cosmedix, attach names to these nodes so that I can just get a better sense of who they these people are.

Katie Robbert 20:34
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 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, and Gaffey and you know what, some of these things mean, such as, like, again, you picked eigenvector centrality, because that was the algorithm you wanted, I’m assuming the same is true of how you’re organizing things. So you don’t force access to why did you choose that versus something else, for example?

Christopher Penn 21:26
Yep. So these are all layout things, these don’t fundamentally change the influencers themselves. This is just a visual visualization. And you can choose, you know, a couple of dozen different versions, the force Atlas to algorithm, there’s a whole mathematical paper on essentially tries to balance out the the prettiness of it with computational speed, there’s some other ones in here that are really beautiful graphs. But we’d be this would be a 10 hour livestream, and nine and a half hours would be us waiting for just a few times. And then the thing to look at with with each algorithm is it tells you like, what are the different numbers and features, you’d want to turn on to understand how they work, some of them 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, like approximation and stuff, you do have to know what barns optimization is to know essentially, how it’s doing its computations, spreading things out in the graph. So there’s some features that 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 we want to switch over to preview mode just to make it look a little prettier, I’m going to change the fonts and things to tighten them up a little bit, and make a a prettier printable graph. Let’s go ahead and zoom in here.

Katie Robbert 22:51
And if you follow the Trust Insights, Twitter and Instagram accounts, then this visual is a visualization of influencers should look familiar to you, when events happen, you know, like, 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, at inbound, for example, you’d expect to see Hubspot squarely in the middle, for example.

Christopher Penn 23:27
Exactly. So what we’ve got here, no, just a quick sanity check. Do you do we see anything in here that would be relatively relevant to travel? Yes, Ritz Carlton canon USA, the camera company, right? We got a whole bunch of different resorts and things. Or if tourism companies, so we at least know from it just a quick sanity check. You know, visit California is a is a hub. Yes, that makes logical sense, right, though, we’re not seeing things like, you know, 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 you know, for example, a lot of these companies when they dig in, the sort of the travel, and hotel stuff seems to be in green. And you know, there’s other stuff like tourism and technology stuff, a cabin 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 up. 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 would take the spreadsheet and go ahead and export this because it’s it’s open up a spreadsheet.

Katie Robbert 24:49
So Chris, with that network graph, so it looks like it’s clustered, you know, all of the nodes or people or accounts, whatever you want to call them together. Gather that are in some way, shape or form connected. And that’s the color coding. 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 than I would have been cut off the list.

Christopher Penn 25:25
That’s exactly right. 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. 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 you how influencer marketing works. We when we did our extract of all the people who were essentially doing the talking we got things like their follower accounts and things you could 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 would be talked about so just in a quick look at this influencer Expo you can see there’s a whole bunch of accounts that if we just gone on who is loudest who had the most engagement and most followers, we would have missed so so so many people, 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 for work.

John Wall 26:32
Yeah, cuz another one you’ve had in the past I’ve seen in this report a factor for whether it’s a fake account or not, you know, whether it’s something that’s just published only to so to be able to separate the ones that where there’s a human versus this just some bot cranking out stuff.

Christopher Penn 26:46
Thankfully, we can show this one this one is actually the Waldorf Astoria in Los Cabos. 1700 posts 80,000 followers.

John Wall 26:55
There we go. Red rocker, Sammy Hagar, Cabo wabo.

Christopher Penn 27:04
And yeah, this is a very nice travel account. Right. What I think is interesting, though, look at the following numbers and stuff, not a huge following, right. They’re not a an organization, this guy 10 million followers. This is an account that has a good sized following certainly don’t want to be argued that but they’re talking about this is a destination, this is a clearly, you know, based on their their way of clearly see what you would want to go after being locked out for you, let’s click a place that we would want to go. But

Katie Robbert 27:36
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 talking 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.

Christopher Penn 28:15
And that’s a really, really important point that has to happen before us network graphic, right. So at this point in the process, when you are just slinging code, you will see here in our in our filtration, we have a line 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 do sentiment analysis, do even something as simple a basic like check for swear words or a check for, you know, racial slurs or anything that you would you would not want to have. Yeah, bedbugs. Yeah, things like that. That has to happen in the data extraction phase. So when we think about 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 place, we transform it, and then we load it into a final form that a user can use. So this point here, in our, this is the extract and transform phase, where we have extracted the data and we’re starting to transform 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 those kinds of transformations in there. Now you have to do them the extraction phase. So a big part of doing influencer marketing really well is documenting 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 this capability is to say yeah, I don’t want to include anybody who you know supports a certain politician or shares images of I had a no cat or a dog person and was he any cats? 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’s you get what you get.

Katie Robbert 30:17
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?

John Wall 31:03
Yeah, right. Because, as Krista talked about, the chart is great eye candy, and that that has marketing, Paul, you know, because we use that for trade shows, every time we do it, first trade show all the speakers, they’re like, Oh, I want to see my dad 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 going to do your marketing work. And 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 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 1516, you’re gonna find people that you can work with for, you know, 1000 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 off. 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. But they’re necessary. And so yeah, you’re basically cutting out the celebrity by doing this kind of stuff. And yeah, it’s, you know, the magnitude of expense. If you’re going to 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.

Christopher Penn 32:31
The other thing that’s really important there is that, you know, 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, I was looking at one of the other things and they listen, they say, Oh, this travel person 106,000 followers, they traveled, they do stuff, they go places and things. Is that right? Or should you be incorporating things like from your SEO work? Like his travel to broader term? Shoot, you know, in our case, it would podcast be right? 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 $100,000 on an instance, right, if I’ve narrowed down, not just travel, but maybe we want travel in New York City. Okay, who out of this whole travel field? Well, how would it how quickly would that list than 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?

Katie Robbert 33:34
Well, and I think that that’s an interesting point, because you’re talking about it from their 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 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 on social media, but you never say the words, you know, 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 going to be found in an exercise like this. And that’s a big mess, 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.

Christopher Penn 34:38
Exactly. One of the things that we discovered in our is the grand doing it for you paper, which you can get over TrustInsights.ai dot AI is that Yeah, some influencers, see decent performance, you know it with 10 or 15 hashtags, in their posts, on average, like this is not, you know, once or twice every now bellick Bear does, you know, shoveling in the hashtags all the time. But it’s focused, right like to your point is focused it is on target with what people should be doing. And the thing that we tell people is, you know, if you’re a brand starting out with 123, 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 and 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, Katie, as you’ve pointed out, many times hashtags are how people get discovered for specific topics. And if you found a niche that works really for you, the old expression still holds true in niches or niches.

Katie Robbert 35:38
I’ve literally never heard that expression.

Christopher Penn 35:41
Come on, let’s say that for years, I’ve,

Katie Robbert 35:45
there’s a lot of expressions that you tell me that you’ve been saying for years that I’ve never heard before. So and I digress?

Christopher Penn 35:53
Exactly. So if you had some comments or questions about this, leave them in the comments on the on the platform where you’re watching this, we will see it here in the show. But to recap, data is is differentially available on different platforms, right? Twitter is robust with data and you don’t need much to extract it for Instagram, Facebook, Reddit, there are pieces of software that can get you some some decent data out of those services, we mentioned Facebook’s CrowdTangle, some platforms like LinkedIn, just not happening, you can and there’s a way to do this, you can proxy some of that with some very clever coding, if you’ve got very clever coders on staff, you can look at people who are linking to LinkedIn posts as a way to to identify some interesting content, people who might be worth sharing with us 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, biggest mouth in the room. And then a couple of the identification algorithms with a warning that is 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 ordering. So any other thoughts? Kate, your john, on the fun of influencer identification?

Katie Robbert 37:12
Well, I think that, you know, in terms of the black box of the algorithm, Chris, you pointed out a really good, you know, potential mistake that some of these algorithms have as 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 going to get what you want. 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 influence area 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 fits within your budget. You know, there are ways to be looking at that information.

Unknown Speaker 38:04
That was your cue, john.

John Wall 38:07
It’s true. You’re right.

Katie Robbert 38:09
This is why nobody talks to john.

John Wall 38:11
I know, well, I’m here. I’m like thinking of fake averages that I can use. I mean, like, the monkey doesn’t dance where the beaver goes. I don’t know what that means. But that’s okay. These are outages we have.

Christopher Penn 38:26
Thanks for watching everyone. We will talk to you 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 the Trust Insights podcast at Trust insights.ai slash t AI 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 pro marketers. See you next time.

Transcribed by https://otter.ai

 


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