As kids sitting in math class we all uttered the words “we will never need to understand this!” in regards to equations like the one above. Unfortunately, the joke’s on us now that machine learning is at the heart of LinkedIn’s Community and Professional standards.

In this post, we’ll work through what these LinkedIn algorithm changes mean, in layman’s terms, so you can understand what you should be doing differently to ensure your content is continually seen and engaged with by your target audience.


What Is the LinkedIn Algorithm and Why Do We Care?

Simply put, an algorithm is used to turn large amounts of data into a model. We care about this, because what each individual algorithm is “taught” affects us every day, and most of the time you don’t even know it. With LinkedIn doubling down on the way users interact with their platform, they’re constantly updating their algorithms to ensure the most engaging users get the most out of the platform.

Every time you’re sipping on your morning cup-o-joe and open up LinkedIn, you make a subconscious, split-second decision to either a) yes, read this extremely relevant article that is at the top of my feed, or b) the opposite, and scroll right past.

That split second decision was you playing a role in teaching the new LinkedIn Algorithm how to better serve you, and every other professional like you.


Breaking It Down

If you decided to leave a comment after you read the post you have just given Creator Love. It’s actually called that.

Maybe you liked the post so much you reshared it creating a downstream Viral Effect.

What’s important to remember, is even if you didn’t take an action at all, the time you spent to decide whether or not you were going to, is what the LinkedIn Engineers are fondly referring to as Dwell Time.

LinkedIn is heavily taking into account these three pieces of information in order to present users with the most relevant content, for their specific needs.


The Previous Algorithm

Alright, so we understand what an algorithm is, and that we are constantly helping it learn. Plus, we’ve covered important LinkedIn Engineering Lingo. Let’s piece it back together.Prior to the algorithm change, what you saw on your news feed was strictly based on viral actions, creator love and the probability of upstream and downstream effects. It was geared towards the idea of “getting small content creators to create more content.”

This model was slightly flawed because it focused on predicting the virality and/or clickability of a post vs. if the post was worthy of reading.


Adding Dwell Time

With the newly added dwell time, or amount of time spent looking at an update after half the post is visible on the screen you are pursuing LinkedIn on, it also stops the previous algorithm from rewarding those who may receive viral actions (such as mindless likes.)

Take this recent post by Daymond John, the CEO of FUBU and Shark from Shark Tank, for example. Great person, don’t get me wrong,  but not an extremely valuable post. I would most likely click like and keep scrolling, only doing so because 4,302 other people already had.

The Dwell Time variable inherently stops this from being at the top of your feed every time you sit down for coffee, and makes it more likely that you see a relevant post rather than a viral one. There’s not much to read here, and since you’re just barreling through it, it will not get rewarded.


So What Do I Do Now?

The best advice we have for you is to continue to create great, meaningful content that people spend time reading. Avoid creating clickbait style posts as you’re now being penalized. Other than that, there’s not much else you should change about your workflow if you’ve been following those two notions!

At first glance, this graph looks like job postings, videos, and articles are going to be the death of your company’s content on LinkedIn. It also looks like images steadily perform the best. This isn’t totally true!

What this graph actually shows is an introduction of a new metric called Tskip. Tskip is simply a threshold marker for posts that are viewed for less than a certain amount of time, and therefore not particularly engaging. What happens after Tskip, or the green vertical dashed line on the graph actually has nothing to do with the new LinkedIn Algorithm.

As you can see in the infographic below, the magnifying glass is highlighting the three colored lines which are almost identical in all 6 graphs above. This indicates that the time for skipping an update, regardless of the time, are almost equivalent.


LinkedIn’s Next Focus: Messaging

Messaging has been an integral part of  the LinkedIn ecosystem since its creation in 2002, yet has architecturally remained unchanged until recently. 

When looking at the messaging redesign there were requirements laid out by the engineers at LinkedIn that can be summed into two categories: Product requirements and Engineering Requirements. 



On the product side, great things are coming for LinkedIn Messaging. This includes a faster user experience, possible third-party plug-ins that can enhance your experience and easier to use.

Let’s be honest, we all know the pain of trying to filter through the current messaging inbox – these updates will be a welcome change.



Architecturally speaking, there is an increased focus on security, up-time and availability to change architecture very quickly. What that means is the engineering team is going to be able to keep up quickly with changing times, be able to quickly detect spam, and adjust on a dime. 

At the end of the day, LinkedIn’s Engineering team has a high-focus on LinkedIn Professional Community Policies – and every change they make is to ensure an enhanced respect to these policies.

These graphs provided by LinkedIn’s engineering team.


We love creating LinkedIn friendly content that withstands even the craziest of algorithm changes. If you need help becoming a thought leader in your space, contact us today to get the conversation started.