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A Facebook Post's Lifetime Is Even Shorter Than You Thought

Data Science blog post series

At Wisemetrics, 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 it’s short

Facebook posts’ 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 by Edgerank Checker’s,, 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 it’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 “as-usual” or “killer-post” 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, we’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 that’s another story.


Many brands care for fans’ Reach, sometimes more than for Engagement. Optimizing the timing of your post is thus mandatory.

Facebook’s new Insights allows one to see when his fans are on Facebook. That’s a start, but as pointed here, it won’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).


Join The Conversation

  • Randy Milanovic's picture
    Aug 25 Posted 3 years ago Randy Milanovic

    Reach is the least useful metric. 

  • Aug 7 Posted 3 years ago PaulaCappa1

    Very helpful post. Question: do FB "Groups" perform differently? I post on a number of Facebook Groups to promote my novels since the audience is more targeted with readers and writers. Do you have any stats on FB groups (ex. Amazon Book Club) and how they perform?

  • bbmcKinney's picture
    Aug 7 Posted 3 years ago bbmcKinney

    For a Facebook post, it takes certain level of quality that catches the eye of a user enough to interact with such post. It takes three—Like, Comment and Share. An interaction between a user and a post channels to more potential interested users, thus, lengthening the lifespan. The shorter the interval of user contact, the quicker the post will die. Facebook’s Analytics only shows vague graphs but I agree that timing helps. What helps more, though, is how you can make a user interact with your posts.Thanks for the insight!

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