Data Science blog post series
At Wiselytics, we have been gathering a huge amount of social media data for over a year and thought it was time to share some insights we are getting from it. This post is part of a Data Science short blog posts series we hope you will find interesting.
We know itâ€™s short
Facebook postsâ€™ lifetime is known to be very short. Several studies have found that most engagement with a post happens within the first few hours (see for instance nice studies done byÂ Edgerank Checkerâ€™s, Optim.al, Sotrender).
Having our hands on the right data, we’ve decided to take a peek at this lifetime thing. And yes, we found, just like others, that 75% of engagement occurs within the first 5 hours. But we’ve also looked at Impressions, and Reach.
How much shorter can it get?
On the graph below, we present median Engagement, Impression and Reach, over time with a confidence interval of +/-5% (to show variance among posts)
Impressions are even shorter than Engagement, with only 2 Hrs 30 min to reach 75% of its max, and Reach is even worse: 75% of your audience sees your message in less than 2 Hrs!
Can it get worse? Well if you care most about your fans, it takes only 1 Hr 50 mins for a post Reach to get to 75% of itâ€™s potential (not shown on this graph).
There is quite a bit of variance between posts, but for short period of time all posts show the same behavior, and it takes a mere 30 minutes for a post to get 50% of its global Reach.
The idea behind looking at posts’ progress over time is to be able to predict, as soon as possible, if a post will fail or beat all expectations to adjust community management efforts (e.g.: rushing in order to publish a new post or waiting a bit more).
In terms of modeling, the log-log shape of posts progress over time is a nice discovery. However even though all posts do seem to share a common shape, variance is very large (as we could see with the large tubes from our initial graph). Using a linear log-log fitting, we thus couldn’t map each post into a â€œas-usualâ€ or â€œkiller-postâ€ category just by looking at performance from the very first few minutes of a post.
Going a step further, through a machine-learning approach based on derivatives of the curve (speed of increase) to predict end-point, weâ€™re finally getting OK results, but it’s still not quite convincing. It needs to be coupled with models predicting a post performance even before it is published. But thatâ€™s another story.
Many brands care for fansâ€™ Reach, sometimes more than for Engagement. Optimizing the timing of your post is thus mandatory.
Facebookâ€™s new InsightsÂ allows one to see when his fans are on Facebook. Thatâ€™s a start, but as pointed here, it wonâ€™t help you compete with other posts.
The best thing to do is to thoroughly analyse your posts history, as well as your peers & competitors, looking at dozens factors at a time, and predict the optimal timing, just for you (good news, it can be done through machine learning).
Next Data Science blog post coming in a few weeks
â€œPaid posts: will the thousands of likes & comments you get boost your future unpaid posts?â€
bitly link for this post:Â http://bit.ly/15Alvdi