The Problem with “Global” Social Media Statistics

twitter infographic best practices maximizing your tweets infographicYou have heard them, you have seen them – the data that tells the “right day and time to Tweet,” best days for Facebook engagement, how many times a week to blog…

Just look at this one (well, I shrunk it because like too many infographics it’s too big to make sense visually in this blog format – if you want to see it full size, click here: A Twitter infographic by Fusework Studios). It’s easy to make fun of them because they are simple facts based on limited data samples. However, they also represent things we do want to know. The intent of studies like this is noble: they are trying to give us trends on how people use social networks, in hopes that we will get insights in to how to use them better (oh, and of course inquire as to the services provided by the companies behind the “research.” Please download our white paper and sign up for our newsletter).

Fair enough. Noble enough. But the data is useless.

For data about social media that is practical, you must look at relevant data.

General data makes for some pretty infographics (and a ton of butt-ugly ones), but they are general – that’s not relevant.

Where to look for the relevant versions of this data? Your own data.

When is the best time to Tweet? Overall, this infographic says weekends. But whom are you trying to reach? Are those people engaging on weekends? What does your Twitter data say? Perhaps you get more retweets, mentions, and clicks on your Twitter links on Mondays. Maybe your Facebook page gets more action on a Tuesday afternoon. Are you a beer company? Maybe “beer o’clock” on Friday is the time to post – 0n any social network. I don’t know that, but if you represent a beer company I trust you are checking it out.

“Global” social media statistics are fun conversation starters, and are best when recognized as superficial examples of . But they are not practical. Enjoy the pretty pictures, but follow the muse in front of your nose (or in your analytics programs).


  1. Great post, Doug. All analytics must be viewed using the relevant filters and open to following where the numbers lead. Oh yes, then there is that correlation vs. causation discussion…

  2. Doug Haslam

    There’s no one culprit. Just trying to make sure we put these “fun facts” in their place, and put the work in to get data that is actually useful

  3. Couldn’t agree more!
    Things like this are great conversation starters and things to discuss, but there’s no such thing as a “universal best time to post on Twitter.” Could you imagine if there was and every company/brand tweeted at the exact same time? Oh man.
    But where you go with your advice is absolutely correct: look at your own data and determine when is YOUR best time to tweet. Every audience is different and every company/brand will have an optimum time to post that is uniquely theirs.
    Not to be all selly-sell (because I hate doing this in other people’s blogs), but our software actually looks at a Twitter handles followers and determines when their followers are being most active on Twitter. We then suggest to you that this would be an optimum time for you to post that is unique to you and your online community. Not someone else’s average.

    Sheldon, community manager for Marketwired (formerly known as Marketwire & Sysomos)

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