So What? Network Graphs

So What? Marketing Analytics and Insights Live

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In this week’s episode of So What? we focus on network graphs. We walk through what they are, the basics of construction, and how marketers can use network graphs to find influencers. Catch the replay here:

 

In this episode you’ll learn: 

• what are network graphs
• how marketers can use network graphs to find influencers
• the basics of constructing a network graph

Upcoming Episodes:

• can you hear me now? 9/15/2022

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:

Christopher Penn 0:27
Well, happy Thursday, folks, it is time for another episode of so what the marketing analytics and insights live show from Trust Insights. This week, Katie is being chased by bears, I think, again, in the woods,

John Wall 0:38
out in the woods yet again, that’s great, hopefully, to bet she doesn’t have that new iPhone with the satellite rescue function on it.

Christopher Penn 0:47
So hopefully all that is going well. So this week, we’re going to talk about network graphs. I know we’ve covered the topic a few times before, but it’s seems like a timely since we’re basically headed right into conference season. So John, when you’re keeping an eye on events, what kinds of data are you looking at? And and what do you do with it?

John Wall 1:09
Yeah, the biggest thing for me is to be able to get the pulse of the event, you know, it can socialise really where you can get, of course, I’m extra bitter today ready to rant about the fact that I was all set to go to inbound for today and tomorrow and tested positive for COVID. And it’s been, it’s classic for me to in that, like I got over the symptoms in four days, I was actually very mild. So I’m incredibly lucky on one hand, yet I’m still testing positive. And so I cannot go to the show. So I’m watching all these virtual sessions really pissed off that I have to do this. But in classic, you know, virtual and backchannel Yeah, I’ve been able to kind of keep up with what’s going on. And you know, what are the hotter topics, you know, they’re talking about tracking the customer journey. And basically, Hubspot, making kind of a full automation play, you know, not just the marketing automation thing. But yeah, the biggest thing is to get access to the attendees, because the thing is, and I mean, you’ll go into this deep, but if you make contact with all the major people that are publishing via social during the event, that is probably better than going to the event, you know, you’re kind of handcuffed when you’re there in person, you can only do so much. Whereas you can come through the social data, you know, the entire run of the show, and even afterwards, if you need to.

Christopher Penn 2:23
Gotcha. And then what do you do with that information? Like what’s what’s the action you take with knowing who the most talked about people are, for example.

John Wall 2:32
For us, it’s really category categorization, you know, kind of knowing what kinds of people that because the big thing for us is, there’s probably four or five events where the people that go in are active at those events, they are just exactly the kind of people we want to be talking to, right, they’re cutting edge, they want to go out and find out what’s the newest and greatest are trying to understand what’s going on. They’re not afraid of upsetting the applecart, you know, they’re willing to take some risks to go to an event and learn some things. So they’re just, they’re exactly the kind of people that we want to talk with and learn from, and they ultimately are our customers. So it’s really the best prospecting that you can do. I mean, if we had, if we were 100, person firm, with millions of dollars, we just have a booth at these things. But that’s not an option for us. And it’s actually you know, the return is just far greater on engaging with the folks who are there. And we are still doing, the other thing is lining us up so that we can do sessions and be involved with, you know, other versions of these events, know what’s the people want to see and hear so that we can put that stuff together and create that content for them for the rest of the year.

Christopher Penn 3:32
Gotcha. Okay, so let’s talk about what network graphs are. Because it’s a term that we’ve talked about a bunch of times, I’ve done some episodes in the past on it, but again, it’s worth revisiting. In short, this is a network graph. So the you can see this on the Trust Insights, Twitter account. And a network graph is just a representation of, in this case, who’s talking about who. And so interpreting this chart. The people who have the biggest bubbles on it, are the ones who have been talked about the most. So, for example, in the tweet you see on the right hand side, Trust Insights is the handle that we’re broadcasting from on Twitter. And we’ve mentioned Hubspot and inbound. So those would be two accounts that were mentioning. So if you can imagine sort of a a little scoreboard will tick marks next to it. We would be essentially voting for the Hubspot account on the inbound account with this tweet was saying we are sort of implicitly endorsing them by mentioning them. And the more that people do this, the the more weight or authority you they get in a network graph like this one. For example, we see in the upper left hand corner there, dharma Shah, the co founder of Hubspot. A lot of people have mentioned him a lot of people have talked about him. At the show. We see Yemini Ron Gunn, the CEO is sort of in the middle there. Again, someone Oh lot of people talking about her, you’d see tons and tons of little dots of, even though they have lots of lines going through them. Those are cases where you have somebody who is doing a lot of talking. But not a lot of people are talking back to that person, I love people interacting back with that person. So this network map this, this network graph effectively says, here’s who is most talked about at this event. And to your point, it’s a way of seeing who’s got the attention of the crowd, because it’s not necessarily just going to be the speakers. It’s also going to be people who are there who are networking effectively, who are networking effectively online. And, to your point these some of these people, particularly people whose bubbles maybe aren’t the largest ones, but are decently sized. Might be folks we don’t know yet.

John Wall 5:50
Yeah, and yet, we have to give a shout out to Kristina Garnett that Christina G at the bottom there. This is basically she just has to send this paper in and she can get her performance review for the year. She’s obviously overdue for a raise because she’s crushing it. So there you have it.

Christopher Penn 6:06
You’re exactly. So that’s what a network graph is. And and again, the use case for it is identifying people who are most talked about, if you’re doing any kind of influencer marketing or influencer identification, this is should be one of your premier tools, because we’ll talk about centrality measures in a little while, but just having access to this data dramatically simplifies. Who you can, who you want to approach first i Who are the people that like okay, this is somebody I need to speak with, because clearly they have the attention of the crowd. And we have our friend George B. Thomas there on the on the left hand side, somebody who has the attention of the audience. So if I was trying to figure out who is influential about the inbound conference, clearly, obviously, you know, Dharmesh being one of the cofounders and Yemeni being one of the being the CEO, there clearly influential about this event, but the probability of you getting on either person’s calendar, not terrific. Right?

John Wall 7:11
Yeah, no having to you know, it’s the same deal. We see this for Dreamforce, too. It’s like Marc Benioff is always dominating list. And you’re not just going to walk into the tower and talk to Mark tomorrow.

Christopher Penn 7:19
Know exactly, but other folks like Christina granite, or George Thomas and stuff, were Troy, they are folks that you probably would have a better chance of being able to talk to and then if there’s, you know, if you have something worth talking about, they might be helpful folks to help amplify your message. So you know, the basic influencer marketing. So let’s talk about how you build something like this. I’m going to start off by saying, to build it effectively, you have to use some tooling, you have to use a bit of coding and stuff you can do it by hand, is extremely painful to do it by hand, it would take you days to even put together one of these things, by the time you did it, the event will be over for months. You start with a data feed of some kind. So in this case, we’re using Twitter, you can also use Instagram, you can use Tiktok, you can use anything where you can get essentially a data feed where there are identifiable sources of content, and then targets of content. So in this example, here, we see here’s our screen name of the person doing the tweeting Keith Spiro. And then over in this column, we have the text of what he tweeted, and he’s tweeted at inbound. So in this case, this would be a you’d make a pair a spreadsheet of pairs, that shows Keith on in one column, and then inbound in another. If we scroll down here we have Christina do we have aka are a Neil would be the source. And then Christina G Yemeni Rangan would be the be the targets of that. And essentially, you’re going to create a large spreadsheet. Let’s go ahead and pull one open here, that shows here is how all these different entities interact. So here’s what the spreadsheet looks like. And you can see who did the talking and who they were talking about. And this goes on and on and on. And on this, just from a day and a half, you got 4600 pairs of conversations. So that’s step one, what’s called a network reference called an edge list, the number of conversations

John Wall 9:40
now, do you pull that stuff into Excel? Or is this just a dump of what you’re normally working with over an hour?

Christopher Penn 9:46
This is just a dump. I would never attempt to do this in Excel, I lose my mind. So that’s part one is is taking all the tweaks you have and extracting out the source target pairs. The second part, if you want more information about who these people are, you can also pull out their user information. So in this case, here are the, the names, the locations and the descriptions of a lot of these accounts. This, once you’ve done the influence analysis, this actually comes in handy because you can get a bit more information about who the person is, and sort of what their importance are. So again, going back to our friend, Kristina Garnett, we see you know, this is this is your Twitter bio featuring Hubspot Academy, community and advocacy at Hubspot, MBA and so on and so forth. We see Dharma Shah here with with his co founder and CTO at Hubspot, and so on so forth. So these are the these are what would be called nodes or node information and in that in that chart, an edge. Let’s go back to this. An edge is essentially what happens when and misplace. When you have a line between two connections, that’s an edge, a node is the bubble itself. So in our spreadsheets, every one of those two column pairs represents one of these lines. And every essentially biography represents one of these one of these bubbles. So that’s that’s the raw materials, you need to make a network graph and you have to process that there’s any number of ways we use our I use the our computing language, you can use Python. I’m trying to think there’s any utilities that do it that don’t involve coding and I can’t think of any off the top of my head. So once we’ve got that data, we have to bring it into some kind of tool to do the visualization itself. The tool that I tend to prefer for casual network analysis is a tool called giffy Jeffie, you can get it yeti.org GP H pi, that organ is open source and is free of financial cost. It doesn’t have the world’s best interface. And it has some showstopping bugs that will bring it to a grinding halt. It is free and it processes the data really well. With Gaffey, the first thing you would do is you’d import those two spreadsheets you nodes and your edges. And it will create a nice little, little, essentially internal spreadsheet of the data. So that data you’ve brought in all the different handles and some of the basic metrics if you provided that in there. So that’s that’s a starting place. Once you’ve got that, it will start to show you and help you visualize this information. So here’s the raw, unedited, look at at inbound. And you can see there’s not a lot that’s very helpful

John Wall 12:45
here. Still early stage.

Christopher Penn 12:49
Exactly. So there’s some things that we want to know about this networks and things that would be helpful. And this is where we’re gonna start getting into all kinds of different measures. In any kind of network, this concept of degree, if you ever played Six Degrees of Kevin Bacon, you know exactly what a degree is. It’s how many jumps does it take to go from one node to another node. So in this case, we’re looking, we want to look at how many how well connected is every node in this network, the people who are more connected to have more incoming connections or outgoing connections will have higher degree numbers. The second thing we’ll want to know is how do these things relate to each other with something called modularity. Modularity means, essentially, how closely related are things. If people talk to the same people over and over again, you’re going to see connections within the network. And you can identify those with this thing called modularity and say, like, yeah, these people talk to each other. Therefore, we should probably pay attention to that. So let’s go ahead and just add a bit of coloring. I use modularity because I like to apply colors to it now. There’s this sort of purplish color, which is one community that’s about 24% of the network is a green color, which is about another 24% as a blue color is about 4%. So we’re starting to break up this huge cloud into individual pieces. The big question we have to ask though, is how do we decide who is important, and there’s a bunch of different measures for that, I’m gonna go ahead and run a network diameter here and run our one of our measures of centrality here. In network graphing, there are probably half a dozen different ways to measure someone’s importance in a network. There are things like something called closeness centrality, which is how close or how connected is a node with the average length of the shortest path between that node and all the other nodes on the graph. That’s good for seeing it. Just you know, who is I guess, popular. There’s a measure called between this, which is who is the shortest bridge between two nodes? Again, kind of a popularity thing. And then there’s the one that we tend to look at the most, which is called eigenvector centrality. If you’re familiar with Google’s PageRank. There it is, based on this, this measure. And what it says is, instead of who you talk, how much you talk, measure how many people talk about you. And then how many people talk about them? And how many people talk about them and so forth. And when you do that, you assign importance to who is talked about the most by people who are talking about the most, if that makes sense.

John Wall 15:47
Yeah, yeah. So again, it’s this is the big thing of having referenceable, you know, content behind you, right? You can’t swing that vote, your stuff has to be actually promoted by other people to even score on that

Christopher Penn 15:59
vector. Exactly. And that’s, that’s why I personally prefer that measure, because we all know, plenty of, you know, LinkedIn thought leaders, if you will, who just love talking to the air.

John Wall 16:14
Just like relentlessly published just presuming that like spraying the world will get them there.

Christopher Penn 16:19
Exactly. And in simpler measures of influence, that would count right, you can, you can sort of throw the game, you can hack the algorithm, by just vomiting content all the time, when you use something like an eigenvector centrality measure, if you’re doing all the talking, but nobody’s talking about you, it pretty clearly indicates you haven’t actually achieved anything. So let’s go ahead and apply that. And we’re going to apply eigenvector centrality. And we’ll make the maximum size here 99. And now we’re starting to see this this network shape up starting to see the different groups. For clarity sake, we should probably apply some labels so that we’re not just squinting at strange shaped bubbles. And let’s turn down some of those so that it’s a little easier to see. And now we’re now starting to see essentially that network graph, we see some of these different colors. So we have DynaMesh. And inbound there. We have Hubspot. We have Yemeni, we have Christina, we have this black. At inbound community, we have Speth Spirent. Inbound, Christina Kay. So we’re now starting to see these these communities a little more clearly.

John Wall 17:40
Yeah, so next thing, see how that stuff breaks out, you know. Now, as far as the color coding here, though, is that the green is the highest level then is that the deal and then it’s purple after that.

Christopher Penn 17:53
Purple is it has the biggest community followed by green. Okay. It’s interesting, because what you’re starting to see is you starting to see like the Hubspot as themselves be sort of separated out in the green, and then the purple being just the event overall. And that that tends to happen to any event, which which has a strong social presence. Now, this is still a little confusing to look at. So the next thing we want to do is try to organize the data into some kind of layout. And there are gazillions of different choices. For an event like this, the one I use is an algorithm called multigraph. Multi gravity force Atlas. The names aren’t terribly important. But what is important is what they do. And this is going to essentially take this giant hairball and start to try and organize it and put similar nodes together. And we can see here, it’s already pulled some of the Hubspot stuff out. Let’s do a quick sword. There we go. And now you can definitely see just the coloration, this sort of this greenish area here, there’s a purple area, there’s sort of a blue area. It is it is moving those different communities into their own clusters so that it’s easier to see so it’s easy to explore. Let’s go ahead and put a just hit pause. And now if we zoom in, we can definitely see there are here. So the headline speakers, the keynote speakers, right you have

John Wall 19:25
right Jane Goodall, the James goat, Viola Davis,

Christopher Penn 19:27
former President Obama etc. In the green area, definitely some of the Hubspot is for sure. And then we have you know, some NFT folks down here doing their thing. But this is now more interpretable of a graph.

John Wall 19:46
Yeah, it’s that constant struggle of trying to show as much of the links as you possibly can, but still keep the thing readable.

Christopher Penn 19:55
Exactly. And so you can choose there’s, again, there’s a gazillion different algorithms you can use, I find that these sort of these force Atlas ones that essentially try to replicate almost a gravity do a pretty good job of taking a network and moving it around. And you could actually let this run for a while. And it’ll keep trying to find optimal sorts, it’s very relaxing to look at, if you need to a few minutes. But at this point, our network graph is pretty well sorted. And now at this point, what we could do is say, Okay, I want to now turn this, maybe it’s something a bit more publication ready. So let’s go ahead and close that. And let’s do our show labels. Let’s put our font to eight point and we scale our weights and show. Okay, that looks good. And let’s hit refresh. And now we’ve got a nice chart here. This is what shows up in screenshots is what we put up on Twitter and things like that. It’s a fun, sort of Where’s Waldo representation. And if you’re, if you’re being talked about at inbound, there’s a good chance that you’re on here somewhere.

John Wall 21:05
Now, how about taking this, you know, where else would you go with this data? Because this is great that we can hook people in with an example and show who’s effective and what’s going on there. But what other kinds of stuff would you pull out of here,

Christopher Penn 21:17
and we’ll see the visualization is fun, but it’s not where the good stuff is, the good stuff is in the spreadsheet. So I’m gonna go ahead and export this table here. And let’s go ahead and open up that table in Excel. If you have an agency, or a social media marketing team, let’s go ahead and sort and filter. This now is your checklist of the people that you should be engaging with. Right, so you now have this nice list of who these people are. And if there are accounts, specifically accounts, or topics that you think would be valuable for your company, to be involved in conversations, this is what you do with it. You take this and say your social team, go engage with these people go follow them, go retweet some of their stuff, direct message them if it’s wet, see if they have sponsorships available. Maybe if you if they’re a creator, professional creator, that you could sponsor. This is this is the useful output of network graphing is this this spreadsheet that says, Let’s go talk to these people. So if I was at the event, and I needed you to do some sales follow up, I would say here’s the list, see what you can get out of these people?

John Wall 22:42
Yeah, I can see that that’s where you want to start, you know, as far as who’s worth talking to that kind of filters through all the junk.

Christopher Penn 22:48
Exactly. Because there will be any, you can see, when you scroll, you know, really, really, really far down the list. There’s folks out there who are just participating in the conversation, just listening or maybe tweet once or twice. But they’re not necessarily folks that are that are on everyone else’s minds. When you’re one of these sort of top 50 people. By definition, other people are looking to you for your take on things. And so if I had a product or service I wanted to sell, I might reach out to someone like Ashley FOSS from Atlassian and say, you know, hey, here’s our here’s our new paper on on private social media communities. Would you give it a read? And let me know what you think? Would you mind sharing it with your audience if it’s appropriate? This? And because we know these folks are thought leaders, we know these folks are of interest to the crowd. If they recommend it, then we’ll probably do better than you know, just hanging out randomly to somebody.

John Wall 23:56
And of course, we don’t want people just to bulk load this and start spamming the heck out of these people. That’s not the way you want to do this. Bad idea?

Christopher Penn 24:05
No, that is generally a bad idea. Although you can take the list. I believe Twitter at least still allows you to upload lists of handles. So you certainly could reach out to people who were you’ve identified as being involved in the inbound conversation. If you wanted to promote something via Twitter advertising, you could certainly do it that way. We’ve done that in the past somewhat, tongue in cheek, shared something saying like, you know, the missing inbound session, the one that wasn’t at the conference, you can check out here, but that’s something that you might want to do it quickly in a very timely manner. Particularly if you can do it maybe on day one of the event. So and by the time day rolls around, you know, people have gotten a chance to take a look at it.

John Wall 24:52
Yeah, and then how about working at it from a different angle. So showing? Obviously this proves the way To get some content that’s relevant how to create relevant content. So if you were going to an event, you know, what would you keep in mind as far as what you’re tweeting about, or who you’re tweeting with at the show, that would actually make it, you know, ultimately show up in the chart.

Christopher Penn 25:19
If you want to be seen at the event, you need to create stuff that other people are going to mention. So, you know, it’s one of those sort of classic things. When we do these sorts of explorations, they do tend to do very well, because people like talking about themselves, you know, this is probably what I believe this is our best performing tweet. So far, this this quarter, people like that stuff, people like hearing about themselves. At an event, if you’ve identified early on, maybe before the event, certain speakers are being talked about more often than not, those would be the sessions to go to, and then you live tweet, or you live share from those sessions, because if it’s already on the audience’s mind, you’re going to be able to provide them that information, especially if the speaker hasn’t planned any of their own pre scheduled social, and they’re relying on the crowd to share it. I mean, that would be to me an easy win for being at an event.

John Wall 26:18
Okay, so yeah, you can grab those the upcoming keynotes, the big ones, and make sure that you’re doing live tweeting on site for that.

Christopher Penn 26:26
Yep, are more on a more tactical level, following the event, hashtag downloading, you know, the, the last hours worth of tweets, the last two hours with the tweets, whatever, and just looking at the engagement scores, and just retweeting stuff that’s doing well, that’s, that’s low hanging fruit, that’s pretty easy thing, it may not add a ton of value. But if there are insights from the event that are trending, it never hurts to reshare them to your audience.

John Wall 26:52
Right, and well, and that’s just an even quick, easy hit, too. Because you know, as an event is going on, I always keep a running list of the accounts that are there and are doing things. And so by having this, you can automatically make sure that you’ve got those in your list. And so now you’re watching real time as the stuff goes through, because that’s great, my I’m able to, you know, keep a pulse on what’s going on at the show as far as what’s hot and what’s happening. Yeah, it’s even faster than if you were on the floor, because you’d be limited to one session on the floor. But by watching a feed like this, you can keep track of multiple sessions at the same time, like even Tamsin Webster tore it up at 11am. And her her session was virtual, she actually wasn’t even in the building. But she’s been getting a lot of chatter, you know, overall from the show. So, yeah, that’s a great way to kind of just be able to keep your finger on the pulse.

Christopher Penn 27:38
Yep. The other thing you can do with this data, is, if there are accounts and and personalities that you know, you probably are not going to get a shot at reaching out to directly. Once you’ve identified them using this kind of data, you can then again, using the API extract, who are all the accounts they follow, and do an intersections set, basically, are their accounts in common that Dharmesh and Yemeni and Brian and George and Ashley all follow? If there are, those might be additional sources of opportunities for you to pitch right? If there are certain publications maybe, that maybe they all read, Scott Brinker is Chief martec Well, okay, if they do if they are all following that account, or they’re all engaged with that account, then that’s pretty obvious place where you should be trying to land some content because you know, that the eyes the people who are most influential to the crowd, are on those places. That’s where they’re getting their information from.

John Wall 28:41
All right, that’s solid. How about what I was gonna say, What have you seen over because you’ve run these multi day is there anything that you know, multi day running teaches you or gives you reason to make it important that you do run it over multi day.

Christopher Penn 29:00
Running multi day is important, particularly when you look at early influencers, to see if they manage to sort of hold on to their engagement. This is especially important for speakers like if you speak on one day, earlier on to see how much pull through you have how much influence you have for the rest of the event, if people keep talking about you, or if you know, the next day you just vanish from the charts. That gives you a sense of how impactful what you had to say was for people who were behaving as influencers. It’s a simple measure of saying, Okay, how engaged are you? So for somebody like Kristina Garnett, who is trying to build ambassadors for the Hubspot brand, this kind of data would be extremely useful to see who continues to have poll day after day who continues to have a voice that other people are talking about over time, especially if you run this say a week after the event. And you see other still conversations there’s still people talking about the event? Well after it’s over, if they’ve maintained mindshare, then those people that you want to put on the shortlist of your influencers.

John Wall 30:11
Alright, so yeah, the folks that actually have the ability to hang on to it for more than one day, that’s huge.

Christopher Penn 30:16
Exactly. Whereas you may have somebody who speaks one day, and then poof, they’re gone. And nobody references that session the following days, okay, maybe something happened, maybe something that didn’t land as hard as it should, as he would have liked to do. So that’s network graphing, we covered what they are, how to use them, and, and the basic constructing them again, in terms of the tools needed, you do need to access the data from some kind, probably from some kind of API, or from a really good social listening tool, something like Talkwalker, for example, you need to then export that data, turn it into those lists of the nodes of the edges, and then use a piece of free software like Gaffey to visualize it. And to turn it into a network graph. The thing about network graphs is that it doesn’t have to just be social media data, it can be any data where you can identify connections from one point to another. So for example, Microsoft has what’s called the academic graph, they have built some machine learning that scans academic papers to see who is the most cited authors. Right. So if you’ve, if you wrote a paper on RNA sequencing, and everybody in their cousin for next 20 years has been referencing that paper, you’re going to show up in the academic graph as being one of the most one of the thought leaders on RNA sequencing. So it this is agnostic to how you do the connections that can be any sets of connections that you can put together. You could do it with your own social media followers, you could do it with, you know, Slack groups, if you if you administer a Slack group, and you can, you can see who’s talking to whom you can measure those conversations, any set of interactions between groups of people, it’s something that you can use network graphing for, so you really is the sky’s the limit on what you can do with it?

John Wall 32:15
That sounds good. Yeah, of course, if you want to run these or have any questions about how it could be run, if you don’t want to spend you know, the next three weeks digging around in our and trying to figure out how to use gafi, you could just give us a call that would be a much faster and easier way to get that done.

Christopher Penn 32:31
And after after inbound is over, we’ll share the export spreadsheet in the analytics for marketers group if you want to play with the data itself and see, you know, sort of the catalog of what, what inbound had to say. Actually, that would be kind of fun for one of our monthly makeovers is hand that out and see what folks can do with the data, see if there’s any interesting visualizations or any interesting insights, but we’ll put that in the analytics for marketers group. So I don’t see any comments in the queue. So we’re gonna go ahead and roll on out of here and we will 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 the Trust Insights podcast at trust insights.ai/t AI podcasts, and a weekly email newsletter at trust insights.ai/newsletter Got questions about what you saw in today’s episode. Join our free analytics for markers slack group at trust insights.ai/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.