When’s the best time to post on social to reach the most people? When are people most likely to respond on social? At Klout, we thought we’d take a deep dive into this and find some answers. Now granted, numerous studies look at the best times to post on social, but the study we just completed takes that one step further and looks at the importance of personalized posting schedules to maximize engagement. We also worked with a huge sample size, analyzing 144 million posts and more than 1.1 billion reactions (a reaction being likes/comments/retweets, etc. on posts).
Here’s what we found: User schedules need to be personalized for maximum engagement, and using a generalized schedule based on regional averages is limited in effectiveness. Why? Because any user’s audience is typically spread across various locations. So, when you tweet from Dallas, Texas it reaches your audience that is in the same city/time zone as you, but it doesn’t arrive at the prime time for reactions for any of your followers who are in different locations around the country or globe. Thus, the likelihood of them reacting is much lower. By doing so, your sharing times will be personalized for each user, resulting in maximum engagement. In fact, when those we studied followed the recommended personalized posting schedules, they saw an increase in reactions of 17% on Facebook and 4% on Twitter.
Other interesting facts the study revealed:
If your user accounts are connected to Klout you can create your own personalized schedule by sharing content from the “Explore” tab. Clicking on the “Schedule” tab on the pop-up shows you the personalized top 3 times to post for the user.
Further clicking on “see the full timeline” shows the optimal times to post for the entire week. Clicking on the Twitter and Facebook icons in the pop-up shows the respective schedules for that network.
This study analyzed 144 million posts, over 1.1 billion reactions, and includes in-depth analysis of post-to-reaction times, cross-city dynamics and cross-network dynamics.
For those interested in the data, it is publicly available on GitHub. Making this large, anonymized data publicly available will hopefully enable other researchers to perform studies in this area. This study has been accepted for publication at the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, taking place in Sydney, Australia, this August.
To download the full study, click here.
Study authors are Nemanja Spasojevic, Zhisheng Li, Adithya Rao and Prantik Bhattacharyya.
Lead Research Engineer
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