Less Followers? | Fewer Comments? | Sales Down?

 

Don't leave it to your brand manager or your social media gal to see that you fail. If you don't understand how algorithms, autobots and data science works at least on the most basic level, your brand will fail.

 

Did you know Facebook’s algorithm dictates who sees your content and who doesn’t. This is now true for all social media but you don't have to stand by and watch.

 

Before I forget (and I will), I'm offering a brand evaluation for the first 25 companies who book. You'll receive an assessment of your brand's current social status, brand identity and overall marketing evaluation. This includes a one on one session with me. The consultation and brand assessment are normally $100. Click here for details.

 

In this article, you will learn how to optimize posts for maximum news feed visibility.

 

How Facebook's Algorithms Rank Your Content

 

Recently, Adam Mosseri (VP, News Feed at Facebook) broke down the four steps Facebook uses to rank your content in news feeds during his talk at this year's (2017) F8 Summit.

Mosseri explains algorithms as "A formula or set of steps for solving a particular problem.”  He shares a story about ordering dinner for his wife at a restaurant.

 

To find a solution, Adam says:

  • First I check the menu to see what my options are.

  • Then I considers all of the information (e.g., what does my wife like to eat, is it lunch or dinner time, what’s good at the restaurant?).

  • From there, I make lightweight predictions (e.g., would she enjoy having salmon for lunch today, would it be weird if I ordered her a chocolate soufflé for breakfast?).

  • I then consider all of the information and place an order for my wife.

Our brains execute these simple algorithms every day. Facebook’s algorithms use four similar steps to decide content ranking for your news feed.

 

Here are the steps are the 4 steps

 

When you first open your news feed, Facebook’s algorithm measures and inventories each story posted by your friends and the pages you follow.

 

Signals (Is it lunch or dinner time?)

Facebook then considers all available data and tries to make an informed decision about how interested you may be in a certain story. The algorithm looks at hundreds of thousands of "signals" in order to rank your content, such as who posted a story, what phone a user is on, what time it is, how much bandwidth you have, etc.

 

Predictions (Would she enjoy the salmon?)

Facebook then uses these signals to help make predictions and calculate the probability of certain outcomes; for example, how likely you are to comment on a story, share a story, spend time reading a story, and so forth.

Facebook looks at a variety of signals to rank content and determine what to show in the news feed.

 

Score (Place an order)

Facebook consolidates the information to calculate a “relevance score,” in order to decide if it a good match. According to Adam, Facebook doesn’t really know how interested you are in a certain story; so it's still a guess.

Facebook tries to predict what content users will want to see in the news feed.

 

This ranking process occurs each and every time you open Facebook, and ultimately determines what your feed looks like. There are some settings a user can control to help Facebook better determine what you like.

Facebook calculates a relevance score based on a variety of factors, which ultimately determines what users see in the Facebook news feed.

 

 

Signals Facebook Considers When Ranking Content

 

Adam says one of the most critical factors in determining what you see in your news feed is who you decide to friend and what [pages] you decide to follow in the first place.

 

Some of the specific signals that will affect your ranking on Facebook include:

 

Who posted a story

  • Frequency of posts from that person / publisher
  • Previous negative feedback on an author

Engagement

  • Average time spent on content
  • Overall engagement a post already has
These signals affect how your content is ranked in the Facebook news feed.
 
 
Special thanks to Social Media Examiner. Partial data for this blog is based on the original story written by Paul Ramondo and data from Facebook's F8 summit.
 
 
 
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