So What? Marketing Analytics and Insights Live
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In this week’s episode of So What? we dig into four key findings about how LinkedIn works, based on engineering documentation and academic papers. You’ll learn how the newsfeed works and what upstream and downstream metrics are, how to improve your chances of being seen in People You May Know, and how the language you use trains LinkedIn’s AI to decide what you’re about. Catch the replay here:
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In this episode you’ll learn:
- How the LinkedIn algorithm decides what will be shown
- What drives organic growth
- What you need to do with your posts
Upcoming Episodes:
- Email statistics
- SEO
Have a question or topic you’d like to see us cover? Reach out here: https://www.trustinsights.ai/insights/so-what-the-marketing-analytics-and-insights-show/
AI-Generated Transcript:
Christopher Penn 0:45
All right, welcome back, folks, it is Thursday.
So what the marketing analytics insights live show, uh, Katie is on vacation this week.
So it’s just John and i, this week, we’re gonna dig into the LinkedIn algorithm, specifically.
One of the things that’s really interesting about LinkedIn is it’s you know, it’s it’s definitely the marketers, choice of social network, because a lot of business folks, particularly B2B.
But one of the things I think is really interesting about it is just how much LinkedIn tells us about how the network works.
So before we dig in, John, in terms of like, you’re used to LinkedIn for marketing and things.
What have you found? We found that works, especially recently?
John Wall 1:27
Yeah, well, and disclaimer first, I have to say they’re a sponsor of the marketing over coffee podcast and have been for many, many years.
So, you know, I am biased on that front.
But I mean, and we’ve talked about this on the show all the time, the fact that it’s very different from the other social networks for a number of reasons.
I mean, one is that it is business focused, right? None of the other ones are, everybody else is going for cat videos, or climb the milk crates, because that’s what the insane traffic comes from.
But LinkedIn is all about business.
And it’s amazing how it’s completely replaced the resume really, you know, I in fact, I, I don’t think I’ve seen a paper resume floating about in like, 10 years, I know, I haven’t bothered with one.
And so there’s that.
And then there’s a whole nother layer of them being acquired by Microsoft and the Lynda acquisition, then to be able to do training.
There’s some crazy stuff on the horizon, you can think about because they know how many companies have openings, how many people are looking where they all are in the globe.
I mean, there’s, there’s just an insane amount of data there.
So yeah, you know, I’m a huge fan.
I love posting stuff over there.
And you’ve been doing some cool stuff, too, about cleaning up your feed I measure.
We’ll talk about that, which is great.
But yeah, I’m, you know, all in is a tool.
Now, I guess, not to make it sound like it’s all rosy and perfect.
The one thing is you do pay to be there, you know, it’s ever.
I can’t think of an exception.
But every time we’ve done ad campaigns, people have said, Yeah, the leads are better and more qualified, but it’s more expensive to do business over there.
Christopher Penn 2:57
Yep, definitely.
Today, we’re gonna take a look at some things that we’ve found digging around in the academic papers and engineering content that LinkedIn publishes, because that, to me is where you’ll find really cool stuff about how the network works to a greater degree than I think any other social network period.
I mean, Facebook often has its like experts talking about, like operations engineering, whereas LinkedIn has its folks out there saying, Oh, yeah, here’s how this exact feature works.
And if anybody cares, and it’s always the machine learning books, it’s all the marketers are sitting here going.
John Wall 3:35
Math, math.
Christopher Penn 3:39
So let’s dig in.
Because we have four things that we want to talk about today.
The first is people you may know, so go ahead and bring up this blog post.
This is, again, from the engineering team.
Engineering has been really big on saying like, Hey, we want to make sure that everyone’s getting a fair shake.
And they talk a lot about this concentration problem where, you know, the sort of the rich get richer, right? So you know, the the popular people get more and more popular.
How do you deal with that? How do you have a balance for it? And they talk about some of the algorithms will one thing that stood out really interesting here is they have a decay metric for how often you’re shown in LinkedIn.
And I didn’t know this.
The decay metric is how many invitations are sitting in your inbox that you haven’t answered yet.
So you need to get that list of like, yo, respond to these people.
I don’t know about you, I’m kind of lazy.
I’m like, I’ll go in like once a week just got cleaned out or whatever, like, Oh, we go yet another business develop manager who’s like, I want to connect with you.
And yeah, the decay metric says, the more you’ve got piling up, the less we show.
Yeah,
John Wall 4:46
that is so you know, that’s one of those things that that’s a discrete learning.
It’s just like once that’s explained to you like oh my god, of course it does that because they wouldn’t want to be showing you this stuff.
For some guy who has 150 invites buried in his inbox that he doesn’t look at you would send it to the people.
Okay, so yeah, to do clean inbox, that’s as soon as we get off this call.
Christopher Penn 5:06
Exactly.
So they do talk about other things in the what’s called the LinkedIn fairness toolkit or lift that looks at making sure that, for example, you’re shown a diversity of people like so people, obviously, you know, and things like that.
But then out of network people, there’s some randomization and stuff shown in there to make sure that no one gender or ethnicity or even, you know, a geography is being over shown to a person.
So that was, that was really cool.
But yeah, the big takeaway was, yeah, there’s a decay metric at work, and it’s under your control.
John Wall 5:41
That’s why it’s very interesting to see that too, because now I remember years ago, you know, you and I were both men in the same boat, we work at some of these places where there’s a lot of young 20 something women, and I’ve seen this more than once, where you go to a company and you’re like, looking at the executives, like people who looked at this exact looked at this exact, and it’s like a stack of supermodels like there’s no business relation between somebody just some Cretan trolling around.
So it’s good to see that that’s being the bias has been spun out of that.
Christopher Penn 6:08
Exactly.
So that’s number one.
So your homework, if you do nothing else on this show, make sure that you go and clean up at you know, outstanding invitations and your LinkedIn network that will that will help things out.
The second thing that I thought was really interesting is how LinkedIn uses language models.
So one of the things that they do is they try to analyze the text that is used and thing to make decisions about what you get shown.
And so they have this really, really in depth paper on a piece of software called D text, which is using the one of the most advanced language models out there BERT, the bi directional encoders for representing bi directional decoding representation for transformers.
And without getting into the bloody guts of this thing.
One thing that’s interesting is that they don’t use just one version of their model, they have split it out and trained it so that there’s four separate language models, one for searches, one for member profiles, one for job posts, and one for the Help Center.
And each language model behaves differently.
So something that you and they point this out in their paper, something that you type in, say for a search query will be treated differently language wise than if you’re in the job hunting section, because they don’t, you know, if you’re looking for, you know, job openings at Microsoft, in regular search, that’s different than if you have a different intent there in terms of what you’re looking for, then you’re in the job section.
And one of the things they talked about in this section is that they pre train this model on these different domain areas with what is something different than most implementations? Do, they train on multiple source fields? What that means is, what’s in your profile? What comments you leave, what interaction what you post on LinkedIn, but also what happens in your first degree network.
So with the comments that people leave there, all of this is used and essentially boiled down, you need to these four domain areas to try and give members a better experience.
And rank and rate what gets shown? Well, what’s interesting to me about this is if you are on LinkedIn all day, and you’re talking, I don’t know, what’s the next thing, you’re, you’re talking about some political politician, he is ranting on and on about this person, this model will start to interpret what you’re doing, and say, okay, you should be associated with this topic.
And we’re going to show you more for it, you know, in certain types of search and stuff for that thing.
So if you’re on LinkedIn, talking about stuff that is not related to what you want to be found for, you’re kind of, you’re hurting yourself.
John Wall 8:52
Right.
So you’re saying that the way you behave as far as your posts will actually ultimately determine the audience you’ve got access to.
Christopher Penn 9:00
Yeah, will determine what your some to be what your audience sees, right? So the kinds of things that they see, they will talk about some of the other performance metrics about the LinkedIn feed, too.
But the language that it’s interpreting is coming from multiple different places that you are providing.
And you’re you essentially have a role in training LinkedIn, for what you are about.
So if there’s something that you’ve been talking a lot about on LinkedIn that you don’t want to be associated with, you might want to
John Wall 9:31
write say, save that for Facebook.
Unknown Speaker 9:34
Exactly.
Christopher Penn 9:35
Gotcha.
So that was one of the big takeaways from this, that and the fact that there isn’t a single LinkedIn language model.
There’s, there’s these four different domains.
And this goes back to an episode of this week in machine learning at AI, the podcast.
We’re one of LinkedIn engineers was on saying we look at a number of different success metrics like Facebook is very monolithic like we just will Aren’t you paying attention to the website no matter what, right? We’ll show you fake news will show you aliens will show you, you know, whatever it takes to keep you on site.
And Facebook’s algorithm, in many ways has has learned this and will show us stuff that makes us angry and upset all because we keep doing scrolling.
One of Lincoln’s balancing features is complaints, they look at to see how often a member complains and what and rebalances your fee feed.
So we’ve talked about this in the past, if you want to tune up your LinkedIn experience, not only hit the like button on stuff you want to see more of, but then choose that little three dots in the corner of a post, say, I don’t want to see this, and say, I’m not interested in this topic of this person, whatever, do that, you know, three times a day for 60 seconds at a time.
And within a week, LinkedIn is gonna be totally different for you.
John Wall 10:52
That’s really interesting.
And now as far as running these separate models, do you see this just as the enterprise plan going forward, that there’s not going to be a monolithic, single train model, but you’re going to want to train every that’s kind of crazy.
It’s almost like employees, like you’re gonna want specific ones for specific things.
Christopher Penn 11:09
Exactly.
Right.
Exactly.
Right.
Because Yeah, you want a model that is well tuned for specific domain tasks, you know, just like, you wouldn’t, I hope, put your HR person in charge of finance, right? At least not at a big company that that might not go so well.
John Wall 11:24
And now that you say that, now I see how that would work to what you would have to have is you would have yet another model.
On top of that, that model’s whole purpose would be, hey, which one does this go to?
Christopher Penn 11:35
Exactly? There’s actually, you know, Chairman terminologies like hypervisor networks that can essentially hand things out assignment.
So we’re in some ways we’re taking, like jobs, single task jobs, and having these models be very specialized in that because you don’t want the language that people use when they’re searching for a job to be determining what they see in their LinkedIn feed necessarily, right.
You want those to be discrete models.
And so what they’ve shown here is that they’ve actually done that.
And there’s even some more detail further on data about how they fine tune each of these language models based on the performance metrics that you know, they’re getting from users.
So it’s learning from what other experiences people have had on LinkedIn, like positive stuff like a business post about, you know, wily coyote on LinkedIn that nobody likes, overtime, if that happens enough, it’s actually going to start to get discounted further and further and language fall until you can put up a post about wily coyote and your LinkedIn feed and no one will ever see it.
John Wall 12:38
Right, you get shut down.
Christopher Penn 12:40
Exactly.
So that’s number two, as Be really careful about what you post on LinkedIn, and what you comment and things like that.
Not that you should censor yourself, but make sure that you’re talking about the things you want to be known for.
Okay, number three, is looking at content publication.
This I thought was fascinating.
LinkedIn is incorporated the idea of both upstream and downstream metrics.
So when you post something on LinkedIn, their downstream metrics, like who, who shares it, you know how far it is this content gets shared in your network downstream.
And there and obviously looking to see, the more that other people engage with it and reshare it, the more it engages the rest of the network, that’s a positive thing.
Like it’s like, cool, we like that.
But a couple years ago, they also added something, create your side metrics, they’re calling them upstream metrics.
Where if Richard that example to say is if Richard Branson posts something on LinkedIn, and you hit like on it, the impact to him is pretty much zero, right? He’s like, Oh, good.
I went from 8600 likes to 8600.
And there’s no impact.
But if you were if average people post on LinkedIn, and you’ll see one person likes, like, Oh, cool.
Like we actually see it, we like it, we engage it.
And one of the things they realized in their machine learning training is if you keep the creator engaged, they stick around longer.
They are more engaged on the network, and they create more stuff.
So they now have these these bi directional downstream metrics and upstream metrics.
So things like likes and comments and stuff are now factored to upstream metrics, the likelihood that a post to the creator publishes, that is likely to give them feedback to keep them sticking around is part of the way that LinkedIn analyzes content decides what’s out what you know, what it should and should not show, I thought was really interesting, because what it does is it tries to essentially solve for that, you know, when Bill Gates publishes, everybody reads it or when Richard Branson or Elon Musk and try and help out getting smaller creators get their stuff seen.
They went through and said, yeah, this thing works really well and we’re getting creators engaged.
So one of the takeaways from this Is that if you, if you publish something on LinkedIn, and somebody engages with likes to comment, you go back in and keep the conversation going, right? Because it’s going to basically say that that post is valuable enough that it got you, the creator engaged.
And because it’s a balance of upstream and downstream metrics, you want to make sure that you’re, you’re doing your part on the upstream side of like, Oh, yeah.
So if you see a post you like, on LinkedIn, you know, anytime somebody comments out, you know, like, or whatever, you know, engage with it, reply back, if it’s appropriate to even just say, Thank you, so that you keep boosting that posts, upstream metrics and saying, Yeah, this is keeping me around as a creator algorithm, please keep showing it to more people.
John Wall 15:43
Yeah, that’s really interesting to to think about how, you know, it just creates that feedback loop going and going over and over again.
And how those big names are actually getting filtered by the general public, you know, it’d be people are just gonna follow them, because they’re a huge name.
But if it gets reshared, at the lower levels, that makes a lot of sense, to actually test the content and see what it’s really like.
So really, like you said, the big takeaway from that is you’ve got to stay engaged as stuff as comments keep coming on, you’ve got to go on and, and keep those posts alive.
It’s also interesting to think about how you accounts that are just spamming that never go back and, you know, reply to comments, or likes or whatever, that makes it really easy to see who those bad actors are.
Christopher Penn 16:32
Exactly, yeah.
Cuz they get no engagement.
And one of the things they said here, and this is interesting, because it mirrors something I was reading in a different paper on a different topic about Google attribution, is that one of their metrics they look at is time to first engagement.
So if you post this thing, you know, how long does it take before somebody else engages with it to boost that creator metric? So one of the tactics that we’ve seen a lot of people do, and because it works, is they’ll put up a LinkedIn post, like tag 10 friends, right? You know, or whatever.
Why? Because you get that time to first like that much faster than a post where you have to wait for the algorithm to show it to people, if you can proactively hit hit up people that, you know, it’ll work better.
So I don’t know if we want to encourage like, pods, or the idea of of groups of people doing stuff, but certainly tagging people in that, you know, are just going to hit the like button might have some value.
John Wall 17:29
Well, and yeah, you and I both know, tons of people who have done those kinds of bad things that have just been able to generate insane amounts of traffic, but we won’t talk about that on the live stream.
I mean, we can,
Christopher Penn 17:42
but at the very least, I mean, it’s in easy obvious when is it particularly if you’re at a large organization, where you’ve got a marketing team? company puts out something every employee Go, go hit the like button, if it’s just the marketing team, like everybody, hey, and put the post up at nine o’clock, you know, 901, go hit the like button.
Yeah, do
John Wall 17:59
your likes to drive the action? And it’s, like you said that calling out to pushes notifications.
So that’s just, that’s a huge benefit, because it’s it’s getting forced in front of their face.
Exactly.
Christopher Penn 18:11
And it also, again, if we go back to what we were looking at, when we’re looking at the the NLP model, you’re your first degree network is what’s being hit.
So if your employees are also connected to people within the network, where you have a couple of influencers, who are you sure bets, that time the first leg is really going to matter.
Because then that influencers audience is more likely to see it because at one degree away from that post.
So I mean, for influencer marketing, you might even want to specify contract wise, like, Hey, you know, part of the part of the service level agreement for how much we’re paying you is that when we publish something you have to hit like on it and within 60 minutes,
John Wall 18:54
yeah, or even share to push it out? Yep.
Christopher Penn 18:58
Yeah, do the upstream and downstream metrics.
So you like to boost that creative score, and then you share it downstream to make sure it propagates? That’d be very interesting.
We should we should see if anyone wants to test that idea.
Okay, so that is number three.
When you’re looking at content, the upstream metrics and the downstream metrics matter, you’ve got to get that time to first like down.
And, as a creator, make sure that you are engaging with every single person that responds to your stuff, even if it’s just saying thank you, you know, over and over again, just to keep that engagement chain going.
And the last one that we want to take a look at today is this relatively new post this is from last year, on decay rate and and dwell time.
So I thought this was really interesting.
They don’t look just at clicks, because not that many people click on something, but they can absolutely measure how long you spend looking at an update.
They’ve got this thing called dwell time, where they look at the probability of you taking action, you know, upstream and downstream those metrics.
And they’re saying that clicks, or viral actions are not always measurable, they’re a very binary indicator of engagement.
They’re noisy, and positive signals a sparse dwell time, aka how fast you scroll by and update, or engage that is real valued.
It’s always measurable, it can be more reliable.
And there’s they got tons of data.
So every time you open up your LinkedIn app, or you’re on the website, and you’re scrolling through your feed, they’re looking at how fast you go by is something thumbs stopping or not.
And they built these models.
To look at just how fast something gets skipped.
One of the things I thought was very interesting in their model is they looked at, you know, things like is this a post about the job as a job anniversary post as a Job Change is a video is an article.
And the skip time is almost identical across them.
So it doesn’t matter, like the content type, what it does matter is whether it’s of interest to the person.
So as you’re creating content on LinkedIn, you’ve got to do something to stop that person’s thumb.
You’ve got to because the moment that they just cruise on by it, like you see that that sort of first thing that that negatively impacting how easily your content is seen.
John Wall 21:24
Yeah, that’s interesting.
And they are all very tight.
It’s very close.
But it is also interesting to see that images are just so you know, visual stuff grabs.
And the Job Change thing is interesting, too, if your people are interested in like, oh, who’s moving where what’s going on the soap opera is attractive.
Christopher Penn 21:43
This is why you see a lot of you know, when when you read how people structure LinkedIn posts, they start off with that provocative statement as that first sentence.
It’s not just because they’re they’re aspiring journalists or something is because they know, you know, the folks who get good engagement know whether or not they’ve been told that by reading these models enough that if they can get you to stop and get you past that skip threshold, then your your your post, your content is not going to get down ranked in how often show to other people.
On the other hand, everybody skips past it.
It’s going to show up less and less and less.
So you’ve got to do something to stop the thumb.
Unknown Speaker 22:24
tastefully.
John Wall 22:27
Everything goes to the sewer, eventually that Yeah.
Make it prove its quality, not just PT Barnum.
Christopher Penn 22:37
Exactly.
Now, the one thing they don’t say is they don’t tell you what the skip time is they’ve normalized it’s obviously because you know, if they if they know, it’s three and a half seconds and whatever, but it’s definitely short.
It’s a short period of time.
So what are some of the things you’ve seen people do at LinkedIn to stop the thumb?
John Wall 22:56
Yeah, that’s a great well, and it was interesting what you just mentioned about normalizing the time because yeah, I could totally see somebody hiring a bunch of targets, just saying, okay, spend four seconds here on this, you know, you could easily map that out and see how it goes.
LinkedIn content, it’s a lot like other social stuff, I think you get in this bubble of users, you know, there’s certain types of users like there’s a local incubator that I follow, and I read everything they put out, because they don’t put out too much.
And everything tends to be relevant and on target.
So that’s a big deal.
I think there is a lot to be said, you can, you know, some of the classic stuff of socialists have a good image, you know, a good image is key, if you don’t have an image, you’re just kind of choking yourself off.
Yeah, and then tagging people as the other, I pretty much get looped into stuff that I’ve been tagged on or that other people have found interest.
Interesting.
That goes right to the top of the pile.
And then after that, it just becomes kind of morning or afternoon news check.
You know, I just kind of scroll through the pile to see.
And, yeah, in fact, I think this is good for you to talk about tuning, you know how you’ve tuned the algorithm a little bit, because I think that’s huge.
There’s a couple people who show up all the time, because obviously, the algorithm knows I like their stuff, and I’m gonna engage with it.
And so I get fed that stuff on a regular basis.
Christopher Penn 24:11
Yeah, exactly.
It really does come down to reinforcing that network, right.
So as as the consumer telling the algorithm, this is what I like, this is what I don’t like, show me more of this, show me less of this through your actions.
Making sure that you just are outright blocking people that you just don’t want to see.
A block is essentially a complaint.
We know that in discussions about a multi objective optimization.
LinkedIn does take that into account because 40% of their revenue comes from their their talent management software, their hrs the ability for them to plug into an enterprise companies HR system and say, Here’s 500 candidates that that you know, all match the job description you’re looking for.
And if they get people leaving because they’re pissed off at you know, seeing, you know, So and So politician in their feed, that that’s not a good thing.
So to the extent that they will D prioritize things like that, to reduce complaints, that’s a good thing.
But yeah, from a consumer perspective, you want to make sure that you’re doing that, from a marketers perspective, that first headline really matters, the image does matter.
It was interesting looking at this, that video performed relatively poorly, you know, people didn’t stay engaged with it as much as, like you said, an image or a job change as things.
In fact, articles seem to be, you know, outperforming video.
So if you’re spending a lot of time on LinkedIn video, you may want to do some AV testing of your own, you know, to publish an article, publish an image, publish a video and see if what kinds of performance you get, because right now, it’s looking like your images sort of winning the winning the battle there.
John Wall 25:51
Yeah, that is interesting.
Well, and how about to what more Have you heard, because we had seen some modeling and some stuff talking about how, like for company pages, you only want to post a post a day, it’s not like other networks, where you want to be spamming seven, eight times a day, Is that still the case? Or is there anything? You know, what do you suggest on that front?
Christopher Penn 26:08
I think based on what we’ve seen, if you can keep the upstream and downstream metrics solid, on every post, then yeah, post as as often as you can.
If on the other hand, you’re posting seven times a day, and it’s getting skipped every single time in the feed, you’re just shooting yourself in the foot by itself, that’s no good.
So post only as often as you have the bandwidth to, to do what you need to do to keep that post alive.
Getting that time to first like that we were talking about having somebody with your community management software, interacting with people all the time, tagging in people, and making sure that nobody scrolls past it to the best you can, if you have that that might mean you might not post maybe more than twice a week, right? If that’s all you can afford to to marshal your resources to get that engagement, you might be resource constrained.
John Wall 27:02
Yeah, but that’s a great point.
So then it comes down to make it engaging and keep it keep the engagement going.
So that keeps running, you probably don’t want to be pumping up your press releases four times a day on that, that would be a really bad idea.
Christopher Penn 27:15
Exactly.
In fact, you know, after this, one of the things I’m thinking about doing is I’ve been curating content and putting it up on LinkedIn, it gets occasional engagements here that I think I’m just gonna pull the plug on that and just, you know, do an original post every couple of days, you know, something that where I can spend the time to feed it.
That way, when I do have an announcement, I’ve still got essentially that social credit, you know, built up a social currency stored up that the algorithm is likely to help show it.
As opposed to just a curated stuff that doesn’t get a ton of engagement.
That’s fine for Twitter.
But clearly, based on everything we’ve talked about today, with on LinkedIn, it’s getting scrolled by,
John Wall 27:51
yeah, it’s not the kind of stuff you want up there.
Christopher Penn 27:54
Yep.
So recapping, people, you may know, clear out your inbox soon, sooner rather than later.
As a creator, as a participant on the network, make sure that you are writing and commenting and discussing and interacting with stuff that you want to be known for.
That is your area of expertise.
And if you’re changing areas of expertise, or you want to change areas of expertise, make sure that you are participating in things, you know, falling hashtags, etc, that are in the career or the subject that you want to be known for, rather than sticking solely to the stuff that you are known for.
If you are a creator, you need to make sure your upstream metrics are are just as important to you as downstream metrics.
So create, tag and get that time to first like down and engage like crazy with the with the content that you publish.
And then from a content perspective, make sure that you you increase dwell time, as much as possible, stop that thumb from continuing to scroll by you, you need to break that moment.
Otherwise, your post is going to get down ranked.
If it’s below whatever the arbitrary threshold is.
And these are machine learning models.
So I would imagine that number is a moving target day to day, as it’s constantly training.
Any other tips, John, for helping folks get more out of their LinkedIn feeds? No, that’s
John Wall 29:13
solid stuff.
Again, the big one is go clean up all your stuff.
I’ve got a lot of I’ve got a lot of delinquent invites, I gotta go deal with.
Christopher Penn 29:21
Exactly.
So if you’ve got comments or questions about stuff that we talked about today, you know, pop on over to the slack group over at analytics TrustInsights.ai dot AI slash analytics for marketers, happy to chat about the stuff there.
In fact, I may put together the links to the different papers and posts and push that into the group so that folks can can read in greater detail.
I will warn you that it’s very, someone’s very, very technical, so just be aware of that.
So with that, we will see you next week.
Thanks for watching today.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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2 thoughts on “So What? Understanding the LinkedIn Algorithm for Engagement”