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You Ask, I Answer_ Ways to Optimize for Social Media Algorithms

Judi asks, “Why am I not seeing the content of people who matter most to me on LinkedIn?”

We know from an interview with LinkedIn’s chief data scientist that the algorithm is doing both a combination of tradeoff analytics and boosting (which in itself is amazingly sophisticated) in its news feed. In this video, you’ll learn a little about how the algorithm works and what you need to do to work with it. Then, you’ll learn the one proven way to get the content you want.

You Ask, I Answer: Ways to Optimize for Social Media Algorithms

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode, Judy asks, Why am I not seeing the content of people who matter most to me on LinkedIn? Well, the short answer is algorithms. The long answer is a couple of different algorithms a few months ago,

the podcast this weekend, machine learning and AI, which is an

excellent technical podcast, if you want to learn the details, like dive super deep into what’s happening in AI, and and hear all about the different algorithms that people are using and stuff, that’s a great podcasts to subscribe to. We know from their interview with LinkedIn, LinkedIn, chief data scientist, that what they’ve got, they’ve got two different algorithms going on in the background, plus some human qualities checking along line one is an algorithm is called a multi objective optimization also knows trade off analytics, where instead of having one outcome that they optimize for their optimizing for sounds like based on the interview of five or six, obviously, user engagement was one of them quality of another complaints, they do actually measure complaints to see if complaints of trending up or down, they obviously measure for things like engagement with sponsored posts, getting people to do things like sponsored posts, list, jobs, etc. So these two, that’s the first algorithm is this trade off analytics. A second

algorithm that they use, if I remember correctly from was it was using gradient boosting, which is how they do a lot of the, I guess summarization, to get to the trade offs. And what boosting does is, it takes a whole bunch of individual variables that can be dozens, hundreds, thousands, even millions,

and it starts rolling them up until so that would be one indicator, like number of posts you’ve liked, may not carry much weight by itself. But that combined with Taiwan page, Taiwan site time on individual authors combined with comments, engagements, shares,

recommendations, endorsements, all the possible variables, think of everything that you could possibly do on LinkedIn, you know, do you interact with an author in their feed? Do I interact with an author in a group, and so on, and so forth. And by rolling up all these variables together, you can create stronger predictors of the outcomes you want.

And so what LinkedIn is doing is combining these two techniques. And I think there’s actually more than two, but these are the two that we’re going to publicly talk about

into a master algorithm that dictates what you see in the news feed. And

the answer to Judy’s question is,

in order to get these algorithms to work for you, you have to do a lot of things engagement with the individual people you want, if you want to see their content on LinkedIn. So every time that somebody whose content, you want to see posts, you need to like it, and you probably need to comment on it, and it wouldn’t hurt to share it.

And that may mean for a time when you’re not seeing someone’s content, you may need to bookmark their profile or their activity page on LinkedIn, and manually check

it, you know, once a week, and if they posted some stuff, like a comment and stuff like that

for the marketer, that means that if you want your content to be seen, you have to share content that you know, is going to get engaged

that you know, others will engage with. So if you’re just sharing random stuff, and you’re not focusing on what actually gets clicked on what actually gets shared the most things like that, then you’re going to be publishing content that doesn’t get engagement and the less engagement your content gets the worst you do in these competing algorithms. Because the boosting algorithm will not get enough signal from you to roll you up into stronger predictors. And then the predictors that go into trade off analytics will say, Gosh, this, this profile is really not doing well. And we want to optimize for quality. So you’re out. So focus, make sure you’re focusing on stuff that gets shared the most. And the catch with that, of course, is that LinkedIn turned off their public sharing feed. So you have to use other indicators of sharing quality, and it’s typically other social shares. So if your post is getting great traction on Twitter, or Facebook, which are pretty much the two surviving networks, you can get sharing data on now,

you can use as a proxy to say, Okay, this might get good sharing on on LinkedIn as well. And it’s not guarantee it’s not perfect, but it is better than nothing. So that’s how the algorithm works and why you’re not seeing certain posts. If you are not, as the user are not actively engaging with your favorite people, whoever they are, you will see less and less of them, because the algorithm is also doing sampling, whether it’s tossing other stuff into your feed that you maybe you don’t even subscribe to feel stuff from influencers, LinkedIn influencers, for example.

And if you’re engaging more that stuff, obviously, it’s going to crowd out other people. Now, here’s the way around this

as a user,

the easiest way to guarantee get the stuff you want is to subscribe to people’s newsletters, to email newsletters. It’s old school is old fashioned, but it is the easiest way to make sure that you’re getting what you want. And that’s important. So there’s a link at the end of this video, of course, to subscribe to the newsletter, the for the marketer,

you need to have an email newsletter, and it needs to be frequent, it needs to be available, you know, in a timely manner. It’s one of the reasons I call my newsletter almost timely, because the timely publication that wraps up all the stuff that I know people missed, because the algorithm that powers all this stuff,

the algorithms on the social networks isn’t showing it to everybody, it may be showing, you may be seeing five or 10% of what I publish it and given a week. And so

as a marketer, I’m going to publish this email newsletter that summarizes what’s happened that are what is of interest that I think you should pay attention to.

And that way you get the benefits of without having to do a lot of active work on social networks, like wonderful if you do, I’m happy if you do, but at the same time, that’s probably not the best use of your time. So

subscribe to newsletters if the user and if you’re the marketer, make sure you’re publishing a newsletter, heck, call your newsletter, in case you missed it, right? We publish social posts literally with that, is that

the hashtag I see why am I in case you missed it? So

why wouldn’t you make your newsletter that and and if someone really wants to hear from you and and catch everything and publish

make that make it easy for it, make it as easy as possible for them to get caught up. So

that’s how Lindsay algorithm works. And that’s how we get around it as both users and marketers. We

sidestepped email and Handley said

at a recent talk your content marketing email is the guaranteed way to beat the social algorithm because nobody is controlling what content appears in your newsletter except for you as a super important point. So as always, please subscribe to the newsletter so you don’t miss stuff and the YouTube channel if you want a notification when these videos come out as soon as they do, and if you have additional questions, please leave them in the comments. Thanks for watching and I’ll talk to you soon. Take care want help solving your company’s data analytics and digital marketing problems.

This is trust insights.ai today

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