Uncovering EdgeRank in 5 minutes

Are you...

?? an entrepreneur or business owner who uses social media marketing for their audience building and client attraction, yet your posts are being ghosted and your reach has decreased.

?? excited by tech-related things, but you don’t know much about Facebook’s algorithm.

?? interested in my writing – perhaps you’ve read some of my musings in the past, either at University, on my old blog or in some of my social media posts. 

*“What’s an algorithm?” – If you’re in this category, then let me know and I’ll write up a short piece about this, I wouldn’t want you to feel excluded. 

If this is the first time you’ve heard about EdgeRank, stick around for a few more minutes to read through my quick overview of what it likes and dislikes and how this would be relevant for you if you drive your audience reach and client attraction from your Facebook Business Page.

Why is this important? Because most of your Facebook fans never see your status updates.

If EdgeRank predicts a particular user will find your status update boring, then your status update will never even be shown to that particular user.

In 2007, a Facebook engineer said in an interview that only about 0.2% of eligible stories make it into a user's newsfeed. That means that your status update is competing with 499 other stories for a single slot in a user's newsfeed.

This sounds slightly terrifying, and perhaps you might think all of your social media marketing efforts on Facebook are in vain. 

But this doesn’t have to be the case if you understand a bit better what the Facebook algorithm ranks highly, and what will drop points; a bit like a credit rating. A.k.a it’s unique to each user.

The first thing someone sees when they log into Facebook is the newsfeed – a summary of what's been happening recently among their friends on Facebook.

Every action their friends take is a potential newsfeed story. Facebook calls these actions "Edges." That means whenever a friend posts a status update, comments on another status update, tags a photo, follows a page/joins a group, or RSVP's to an event it generates an "Edge," and a story about that Edge might show up in the user's personal newsfeed.

This would be very overwhelming if we’d actually see all the possible stories from our friends, so the algorithm ranks the edges by predicting how interesting each story will be to each user.

In short, our individual newsfeed show us the top-ranked stories for our particular interests after they’ve been filtered by EdgeRank.

So let’s get into the actionable tips on tackling EdgeRank:

EdgeRank loves …

• Posts with lots of Likes, comments, and shares
• Posts that receive a high volume of Likes, comments, or shares in a short time
• Posts that are Liked, commented on, or shared by one’s friends
• Link posts
• Post types that one interacts with often – e.g. opinion-driven questions or ‘fill in the blanks’ will bring in 90% more engagement than the average post
• Post types that users seem to prefer more than others (e.g., photo, video, or status update; photos and videos get 180% and 120% more engagement respectively)
• Videos uploaded to Facebook that receive a large number of views or extended viewing duration
• Posts that are timely or reference a trending topic
• Posts from Pages that one interacts with often (i.e. be relevant, but not pushy)
• Posts from Pages with complete profile information
• Posts from Pages where the fan base overlaps with the fan base of other known high-quality pages

According to comScore, 40% of the time spent by users on Facebook is in the News Feed -- and only 12% is spent on profile and brand pages. So why is that?

EdgeRank is not too keen on …

• Clickbait (when a publisher posts a link with a headline that encourages people to click to see more, without telling them much information about what they will see)

Clickbait intentionally omits crucial information or exaggerates the details of a story to make it seem like a bigger deal than it really is.

• Like-baiting (According to Facebook's business site, “like-baiting” is when a content poster explicitly users the words “like,” “comment,” and “share” to get users to take action)
• Posts that include spammy links
• Frequently circulated content and repeated posts
• Text-only status updates from Pages
• Posts that are frequently hidden or reported (a sign of low quality)
• Posts with unusual engagement patterns (a like-baiting signal)
• Overly promotional content from Pages—pushing people to buy an app or service, pushing people to enter a contest or sweepstakes, posts that reuse the same text from ads

All of these guidelines exist because one of Facebook’s News Feed values is authentic communication. 

As a platform, they want to ensure that their users enjoy spending time on Facebook and return for more content, as well as trying to fight the spread of misinformation/fake news that’s constantly populated the media outlets in the last few years, so they actively monitor and encourage posts that people consider genuine and not misleading, sensational or spammy.

Lastly, from the start of 2018, Facebook has announced that it will be prioritizing posts from family and friends over public content from Pages as they believe a person-to-person connection is more valuable than a person-to-page connection. An interesting point is that Mark Zuckerberg mentioned at the time that people will be seeing more posts from “friends, family, and groups“, which the latter have seen a big spike in growth and engagement since early 2020.

A consequence of this is that Pages will have likely seen a fall in their reach, video watch time, and referral traffic as less of their content will be shown to their Facebook fans. If your Page’s posts usually spark conversations between friends, you might see a smaller impact.

Overall, if you drive most of your audience reach and audience building from Facebook, now might be the best time to start investing time in a Facebook group for your brand.

Research sources used: Buffer, Hubspot Marketing, Facebook Business Help Center, empirical research, testing and analysis