MARKETING
How the LinkedIn Algorithm Works in 2023 [Updated]
LinkedIn bills itself as “the world’s largest professional network” — and they have the numbers to prove it. With over 875 million members in more than 200 countries and regions, LinkedIn is immensely popular and well-used. On top of the sheer size of the platform, nearly 25% of users are senior-level influencers; about 10 million are categorized as C-level executives, and LinkedIn classifies 63 million as “decision makers.”
If you’re a B2B marketer or brand, you probably already know this social media platform offers you an excellent opportunity to reach your target demographic. However, seizing that opportunity is easier said than done since LinkedIn uses a unique algorithm to serve content to users.
In this article, we will walk through how the LinkedIn algorithm works in 2023, best practices for beating the algorithm with organic content, and how brands can elevate their presence on the platform.
What is the LinkedIn Algorithm?
The LinkedIn algorithm is a formula that determines which content gets seen by certain users on the platform. It’s designed to make each user’s newsfeed as relevant and interesting to them as possible to increase engagement and time spent on the platform. In this way, the LinkedIn algorithm is similar to the Facebook or TikTok algorithm, though LinkedIn’s is slightly more transparent (which is good news!).
In fact, LinkedIn itself is a good source for demystifying the algorithm and understanding what content is prioritized for members. But the general function of the LinkedIn algorithm is to review and assess billions of posts every day and position those that are most authentic, substantive and relevant to each user at the top of their feeds.
How the algorithm achieves that function is a little more complex.
How the LinkedIn Algorithm Works in 2023
LinkedIn users’ feeds don’t show posts in chronological order. Instead, the LinkedIn algorithm determines which posts show up at the top of users’ feeds, meaning that sometimes users see older or more popular posts before they see more recent ones.
Several factors influence the LinkedIn algorithm, and the factors change relatively often. Let’s take a closer look.
1. Assess and Filter Content by Quality
When someone posts on LinkedIn, the algorithm determines whether it’s spam, low-quality, or high-quality content. High-quality content is cleared, low-quality content undergoes additional screening, and spam content is eliminated.
- Spam – Content flagged as spam can have poor grammar, contain multiple links within the post, tag more than five people, use more than ten hashtags (or use expressly prescriptive hashtags like #follow, #like, and #comment) or be one of multiple postings from the same user within three hours.
- Low-quality – Content categorized as low quality isn’t spam but is judged as not particularly relevant to the audience. These posts can be hard to read, tag people who are unlikely to respond or interact, or deal with topics too broad to be interesting to users.
- High-quality – “Clear” content is easy to read, encourages engagement, incorporates strong keywords, uses three or fewer hashtags, and reserves outbound links to the comments. In other words, it’s something your audience will want to read or see and react to in a substantive way.
2. Test Post Engagement with a Small Follower Group
Once a post has made it through the spam filter, the algorithm distributes it to a small subset of your followers for a short time (about an hour) to test its ability to generate engagement. If this group of followers likes, comments or shares the post within this “golden hour,” the LinkedIn algorithm will push it to more people.
If, on the other hand, the post is ignored, or your followers choose to hide it from their feeds (or, worst of all, mark it as spam), the algorithm will not share it further.
3. Expand the Audience Based on Ranking Signals
If the algorithm decides your post is worthy of being sent to a broader audience, it will use a series of three ranking signals to determine exactly who sees it: personal connection, interest relevance and engagement probability.
These signals boil down to the level of connection between you and the user who potentially sees the post, that user’s interest in the content’s topic and the likelihood of that user interacting with the content. We’ll break down exactly what these ranking signals are further in the post.
4. Additional Spam Checks and Continued Engagement Monitoring
Even after a post is pushed to a broader audience, the LinkedIn algorithm continues monitoring how users perceive it in terms of quality. If your content is marked as spam or entirely ignored by the new audience group, LinkedIn will stop showing it to those audiences. On the other hand, if your post resonates with new audiences, LinkedIn will keep the post in rotation. So long as the post gets a steady stream of engagement, posts can stay in circulation for months.
8 Best Practices to Make the LinkedIn Algorithm Work for You
Understanding how the LinkedIn algorithm works is the first step to reaching more people on LinkedIn and ensuring your content is well-received and engaging. The next step is optimizing your content based on the factors the algorithm prioritizes to maximize its effect. This is where mastering the ranking signals comes into play.
Here are eight tips for crafting high-performing LinkedIn content:
1. Know What’s Relevant to Your Audience
Relevance is what the algorithm prizes above all other content qualities. For LinkedIn, relevance translates to engagement, which leads to more time spent on the platform, which results in more ad revenue and continued growth. Following this tip will win you points in the “interest relevance” and “engagement probability” ranking categories.
The entire LinkedIn ecosystem is set up to prioritize highly relevant content. To ensure your posts are relevant, create content focused on your niche and your audience’s specific needs and interests. As LinkedIn’s then-Director of Product Management Linda Leung explained in 2022, “we are continuously investing in the teams, tools, and technology to ensure that the content that you see on your feed adds value to your professional journey.”
Use customer research and analytics from other social media platforms to learn more about what your audience wants to know. Focus on creating high-quality, valuable content that helps professionals succeed in formats they prefer (for example, videos, which get three times the average engagement of text-only posts). But above all, posting content that is personal and has industry relevance is vital.
2. Post at the Right Time
As with most things, timing is crucial for successful LinkedIn posts. It’s even more critical when considering the “golden hour” testing process integral to the algorithm’s rankings. Remember, how much interaction a post gets within the first hour after it’s published determines whether it gets pushed to a broader audience. That means posting at the optimal time when your followers are online and primed to respond is a central factor to success.
You are the best judge of when your top LinkedIn followers and people in your network are most likely to be on the platform and engaging with content. But for the general public, data suggests the best time to post is at 9:00 a.m. EST on Tuesdays and Wednesdays. Cross-reference these times with your own analytics and knowledge about your audience — like a common time zone, for example — to find the best time for your posts.
3. Encourage Engagement
Your post format can play a significant role in user engagement. The LinkedIn algorithm doesn’t explicitly prioritize videos over photo and text posts, but LinkedIn’s internal research has found video ads are five times more likely to start conversations compared to other types of promoted content.
Asking a question is another great way to encourage interaction with your post. If you’re sharing industry insights, open the conversation to commenters by asking them to share their opinions or experiences on the topic.
Additionally, tagging someone in your LinkedIn post can expand its reach, but only tag relevant users and people likely to engage with the post. You don’t automatically get in front of a celebrity’s entire following just because you tagged them. In fact, the algorithm’s spam filter can penalize your post for that. But when you tag someone relevant, the tagged person’s connections and followers will also see your post in their feeds.
4. … But don’t beg users to engage
The LinkedIn algorithm penalizes posts and hashtags that expressly ask for an engagement action like a follow or a comment. In an official blog post from May 2022, LinkedIn said that it “won’t be promoting” posts that “ask or encourage the community to engage with content via likes or reactions posted with the exclusive intent of boosting reach on the platform.” Essentially, content that begs for engagement is now considered low-quality and should be avoided.
5. Promote new posts on non-LinkedIn channels
LinkedIn doesn’t exist in a vacuum, and neither do its users. Content that gains traction in other channels can help boost LinkedIn posts and vice versa. Sharing posts on your website, other social media platforms, or with coworkers can spark the initial engagement required for a viral LinkedIn post. Promoting content on other channels can also encourage inactive LinkedIn users to re-engage with the platform, and that interaction will be interpreted as net new engagement for your post.
6. Keep Your Posts Professional
As the “professional social networking site,” LinkedIn has a well-honed identity that extends to the type of content it favors. Specifically, business-related content that users will find relevant and helpful to their careers or within their industry.
This might seem common sense, but it can be tempting to think that content that earns lots of clicks or likes on other social media platforms will perform similarly when cross-posted on LinkedIn. Unfortunately (or fortunately), hilarious memes, TikTok dance clips and personal videos don’t resonate with the LinkedIn algorithm.
7. Avoid Outbound Links
The urge to include an outbound link in a LinkedIn post is real, especially for B2B marketers using LinkedIn to generate leads and traffic to their websites. But this is universally regarded as a tactic to avoid. LinkedIn wants to keep users on the platform and engaging; link-outs defeat that purpose. Therefore, the algorithm tends to downgrade content that includes an outbound link.
Posts without outbound links enjoyed six times more reach than posts containing links. Does that mean there’s no room for a link to your brand’s website or blog with additional resources? No. But the best practice is creating content that encourages a conversation and letting the audience request an outbound link. If you feel compelled to link to something off-platform, include that link in the comments.
8. Keep an Eye on SSI
LinkedIn has a proprietary metric called the Social Selling Index, which measures “how effective you are at establishing your professional brand, finding the right people, engaging with insights, and building relationships.” Per LinkedIn, social selling leaders create 45% more opportunities than those users with lower SSI scores.
A higher SSI boosts users’ posts closer to the top of their audience’s feeds. While this impacts post visibility for individual posters rather than brands and companies, it remains a significant influence on LinkedIn’s algorithm and is worth noting.
Source: Business 2 Community
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:
- Personal connections
- Interest relevance
- 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:
- Who a user works with or has previously worked with
- 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:
- How likely a user is to comment on, share, or react to a post based on the content and people they have interacted with
- 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 creative. Reach out to our Paid Social services team to learn how to start advancing your LinkedIn strategy today.
Editor’s Note: This post was originally published in September 2021 and has been regularly updated for freshness, accuracy, and comprehensiveness.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
Introduction
With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.
Types of YouTube Ads
Video Ads
- Description: These play before, during, or after a YouTube video on computers or mobile devices.
- Types:
- In-stream ads: Can be skippable or non-skippable.
- Bumper ads: Non-skippable, short ads that play before, during, or after a video.
Display Ads
- Description: These appear in different spots on YouTube and usually use text or static images.
- Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).
Companion Banners
- Description: Appears to the right of the YouTube player on desktop.
- Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.
In-feed Ads
- Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.
Outstream Ads
- Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.
Masthead Ads
- Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.
YouTube Ad Specs by Type
Skippable In-stream Video Ads
- Placement: Before, during, or after a YouTube video.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Action: 15-20 seconds
Non-skippable In-stream Video Ads
- Description: Must be watched completely before the main video.
- Length: 15 seconds (or 20 seconds in certain markets).
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Vertical: 9:16
- Square: 1:1
Bumper Ads
- Length: Maximum 6 seconds.
- File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
- Resolution:
- Horizontal: 640 x 360px
- Vertical: 480 x 360px
In-feed Ads
- Description: Show alongside YouTube content, like search results or the Home feed.
- Resolution:
- Horizontal: 1920 x 1080px
- Vertical: 1080 x 1920px
- Square: 1080 x 1080px
- Aspect Ratio:
- Horizontal: 16:9
- Square: 1:1
- Length:
- Awareness: 15-20 seconds
- Consideration: 2-3 minutes
- Headline/Description:
- Headline: Up to 2 lines, 40 characters per line
- Description: Up to 2 lines, 35 characters per line
Display Ads
- Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
- Image Size: 300×60 pixels.
- File Type: GIF, JPG, PNG.
- File Size: Max 150KB.
- Max Animation Length: 30 seconds.
Outstream Ads
- Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
- Logo Specs:
- Square: 1:1 (200 x 200px).
- File Type: JPG, GIF, PNG.
- Max Size: 200KB.
Masthead Ads
- Description: High-visibility ads at the top of the YouTube homepage.
- Resolution: 1920 x 1080 or higher.
- File Type: JPG or PNG (without transparency).
Conclusion
YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
A deeper dive into data, personalization and Copilots
Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.
To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.
Dig deeper: Salesforce piles on the Einstein Copilots
Salesforce’s evolving architecture
It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?
“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”
Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”
That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.
“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.
Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”
Let’s learn more about Einstein Copilot
“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.
For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”
Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”
It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”
What’s new about Einstein Personalization
Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?
“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”
Finally, trust
One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.
“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”
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