Using analytics to guide a multi-post strategy on Twitter

Over the summer I began experimenting with using Sprout Social to schedule repeat posts of tweets. In the past, Twitter for Bemidji State was a fire-and-forget type of operation; we’d have a story, we’d tweet about it when we released the story, and that’d be the end of it. 

That always intuitively felt like a mistake; after all, for that to be effective relies on a couple of things that simply cannot be true. We were assuming the entire audience that we hoped would see the tweet was:
• …on Twitter when we sent it
• …paying attention to the BSU tweets in their timeline when we sent it
• …would catch up on tweets they missed if they popped in a couple of hours later to catch up on Twitter

Twitter’s new analytics data, which is now available to the masses, not only takes the guesswork out of this, it helps prove that none of those assumptions are true and reinforces the necessity of multiple tweets for key messages.

Using data from Twitter, I put together a Google Sheets analysis of three tweets I sent yesterday — all three identical, about BSU’s position in this year’s U.S. News & World Report college rankings. The first was sent at 10 a.m., the second at 3 p.m. and the third at 10 p.m.

The 10 a.m. tweet had 751 impressions, with 603 (80 percent) in the first four hours. The 3 p.m. tweet had 1,442 impressions, with 1,087 (75 percent) in the first four hours. The final tweet at 10 p.m. had 861 impressions, with 763 (88 percent) in the first four hours.

Analyzing this data leads to some interesting observations:
• The 10 a.m. tweet  was the least-viewed of the three, but it took 13 hours for it to get to the point that it was getting less than five impressions an hour.
• The 3 p.m. tweet pulled much more traffic — it pulled 520 impressions in its first hour and had more impressions in its first four hours than the other two will get in total. It didn’t die as quickly as the other two, though — it pulled 244 impressions in the three-hour block from hours 4-7 after it was posted, while the first tweet had only 52 and the third had only 19. 
• The 10 p.m. tweet had huge initial traffic — 553 impressions in the first hour — and then tailed off quickly. However, unlike the first two tweets it picked back up again this morning, gaining 70 impressions between 6-9 a.m., or hours 9-11 after it was posted. The first tweet had 37 impressions in hours 9-11 and the second had 40.

I will have to do this more often with more tweets that are scheduled on a repeating basis to see if these patterns hold true. If they do, here are the adjustments I might make:

• Start the chain at 9 a.m. (when possible) to see if that will lead to a faster start for the first tweet
• Continue to schedule the second tweet five hours after the first tweet to see if there’s a similar mid-afternoon bump in traffic.
• Move the third tweet up an hour to 9 p.m. and see if that leads to either a bigger initial hour or a bigger number of impressions in the first four hours
• Add a 7 a.m. tweet the next morning to catch some of the rebound traffic that’s obviously coming in on the tail of the late-night tweet.
• Also, consider the possibility of adding a mid-evening tweet in between the 3 p.m./10 p.m. tweets and see what its impressions are like to take advantage of the fact that the 3 p.m. tweet did so well in hours 4-6 compared to the other two tweets. There’s clearly still an audience there.

I’m suddenly completely enthralled by all of this. I will share more as I learn more.

One Thought on “Using analytics to guide a multi-post strategy on Twitter

  1. Pingback: Replicating impression patterns in tweet schedules | andybartlett.com

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