An Overview of Ranking Signals on LinkedIn’s Algorithm

As mentioned earlier, there are three ranking signals the LinkedIn algorithm uses to rank posts in a user’s feed:

?

1. Personal connections
2. Interest relevance
3. Engagement probability

?

And here’s how each signal impacts a post’s ranking:

?

Personal Connections

In 2019, LinkedIn began deprioritizing content from?mega influencers?(think Oprah and Richard Brandon) and instead began highlighting content from users’ personal connections.?To determine a user’s connections, LinkedIn considers these two things:

?

1. Who a user works with or has previously worked with
2. Who a user has interacted with before on the platform

?

At the top of the feed, users now see posts by people they engage with often and by anyone who posts consistently. Users also see more posts from connections with whom they share interests and skills (according to their LinkedIn profiles).?

That said,?as of 2022, LinkedIn?is also “creating more ways to follow people throughout the feed experience,” including thought leaders, industry experts, and creators that may be outside of a user’s network. So it’s important to remember that personal connection is just one factor influencing post ranking.

?

Interest relevance

?

Relevance is another of the three ranking signals – and in many ways, the most important one.?LinkedIn explains on its engineering blog:?“We already have a strong set of explicit and implicit signals that provide context on what content a member may find interesting based on their social connections and the Knowledge Graph (e.g., a company that they follow, or news widely shared within their company).”

LinkedIn also uses what they call an “interest graph” that represents the relationships between users and a variety of topics. This lets the LinkedIn algorithm measure the following:

?

• How interested users are in certain topics
• How related are different topics to one another
• Which connections share a user’s interests

?

The algorithm also considers the companies, people, hashtags, and topics mentioned in a post to predict interest. To maximize the interest relevance ranking, you have to understand your target audience and craft content that they’ll find relevant.

?

Engagement Probability

?

Interaction plays a significant role in a post’s ranking on LinkedIn. The platform uses machine learning to rank interaction in two ways:

1. How likely a user is to comment on, share, or react to a post based on the content and people they have interacted with
2. How quickly a post starts receiving engagement after it’s published. The faster users interact with a post, the more likely it will appear at the top of others’ feeds

?

Users who regularly interact with others’ posts in their LinkedIn feed are more likely to see interactions on their content, which in turn means that they’ll be more likely to show up on other people’s feeds.

?

Elevate Your Brand’s LinkedIn Presence

?

The LinkedIn algorithm can seem intimidating, but it really isn’t. It relies on a series of rules and ranking measures that can be understood and mastered to present users with content they find helpful in their professional lives.

Knowing that the algorithm prioritizes engagement, relevance and connection will help get your posts in front of more LinkedIn users and improve your overall performance on the platform. And by following the eight best practices outlined in this article, you’ll be able to keep your audience’s interest and create plenty of opportunities for them to engage with your content.?

Tinuiti helps brands strengthen relationships with new and current customers through expert social media strategy and brilliant creativity.?Reach out to our?Paid Social services?team to learn how to start advancing your LinkedIn strategy today.