We have written it before, but it never becomes old news: the EdgeRank on Facebook’s content ranking is part of the key to understanding how to create successful engagement between your brand and your fans and their friends.
Naturally, the actual algorithm is top secret – it would be too easy otherwise. However, the algorithm EdgeRank seems to be a very good starting point for understanding the secret deciding what gets the highest ranking in your fans’ and their friends’ news feed.
The EdgeRank model
At Mindjumpers, we have visualised the EdgeRank algorithm to make it a bit more comprehensible. Let’s be honest, who doesn’t feel a bit uneasy when the word “algorithm” is thrown into the conversation?
To sum up for those of you who don’t breath EdgeRank through the office air everyday:
Time: The newer the post, the better the visibility in the news feed.
Weight: Actions are weighted according to the amount of engagement they demand, meaning that creating content on a page will rank higher than throwing a quick “like”. Likewise, posting a photo takes more effort than sharing a link.
Affinity: This is about connectedness. Your posts will be ranked higher if your fans’ friends are fans as well, if your fans interact regularly with your page or if your fans’ friends interact regularly with your page.
Putting the model to the test
This is all very good – but can we trust the algorithm to guide us in the right direction? Track Social conducted a study throughout May 2012, examining the Facebook rankings and thereby level of engagement of 100 well-known and highly active brands on the social platform. Brands including Coca-Cola, New York Times and Red Bull.
They monitored the engagement on the pages for one month, meaning a total of 20,180 pieces of brand posted content. They categorised the posted brand content into seven groups:
Status: The most traditional form of post, containing text.
Photo: Post contains an image (a photo uploaded to a Facebook album) and text.
Link: A URL is provided to Facebook which creates a post based on the content at that URL.
Video: Video is uploaded and can be played inline.
Flash: A flash file is uploaded which can contain video, motion graphics and any content that can be included in a flash file.
Question: A Facebook Poll contains a question and options to select from (which optionally can be also added by users).
They then rewarded the different fan response categories according to the EdgeRank weighting: a “like” or vote was rewarded 1 point, a comment 3 points.
So, what did they discover?
Looking at the graph, the EdgeRank model still seems to be more or less accurate. Photo posts create the highest engagement in both “likes” and comments. More interestingly, Questions also attract a high level of engagement and thereby a high ranking in the news feed, despite only acquiring a simple cast of vote and occasionally writing an alternative option. If you have a good copywriter and understanding for your fans, a simple status with nothing else to accompany it will also have a potential of attracting plenty of “likes” and comments.
On the lowest end of the scale, links and videos seem to have a hard time creating engagement and thereby being shared in the news feed of your fans’ network.
Point being that EdgeRank still seems to be the go-to guideline for understanding how Facebook ranks content and thereby user engagement.