Deconstructing and Reengineering #HIMSS17 Twitter Statistics: Symplur vs. Twitter Analytics

This is just a short post about #HIMSS17 Twitter statistics. Since I tweeted tweets containing the #HIMSS17 hashtag the fifth most number of times during #HIMSS17, let’s take a look at how the top five got there.

I just tweeted the tweet above. It shows another tweet from another Twitter account containing a single #HIMSS17, which was immediately retweeted! You can’t get more content-free (but still be included in #HIMSS17 Twitter stats) than that! Of course, my tweet was automatically retweeted by yet another of the high ranked accounts.

These three accounts, @ACharlesPlatt, @sorena997, and @smartmedrt, one tweeting a tweet with just a single #HIMSS17 hashtag, and the other two simply retweeting a tweet with the #HIMSS17 hashtag, ranked first, second, and fourth in #HIMSS17 Twitter stats.

Let’s look at @CI4CC, the fourth of the Twitter accounts ahead of me in the stats. I looked through a couple hundred tweets, but could not find a single tweet that was not simply a retweet.

Why do I care? I think statistics about tweeting during conferences are potentially useful measures of digital audience engagement. However, there are a wide variety of ways to “game” these stats. This is also true for Impressions and Mentions, though the specific mechanisms of gaming are different. Impressions does not take into account either ratios of followers to followees, or numbers of pairs of eyeballs who actually saw or interacted with the tweets (such as is documented by Twitter Analytics). Similarly, Mentions can be gamed by repeatedly tweeting images and tagging up to a dozen-and-a-half-tweeps, or using multiple Twitter accounts.

A Grain of Salt

I’m not complaining. (OK, maybe just a little bit.) I also occasionally RT or tag tweeps. However, this is not my predominate modus operandi. Most of my tweets are of original material I created (such as long-form blog posts about healthcare workflow, or, this year, lots of #HIMSSmakers photos) or links to substantial news, posts, or videos about same or related subjects. In general, I try to not repeat links to the same material. I make an exception during HIMSS conferences. For example, I was interviewed by workflow by HIMSS17 TV. I tweeted that a half-dozen times. I gave an invited state-of-the-healthcare-workflow-industry presentation at a vendor-sponsored Lunch & Learn. I uploaded that to Youtube and tweeted that out multiple times.

My basic calculation and rule-of-thumb is this. If I put a great deal of work into creating a piece of content about healthcare workflow, and the result is particularly compelling, re raising awareness about healthcare workflow and workflow technology, then I’ll tweet that content more than once. As long as I am also tweeting a wide variety of high-quality, unique content about healthcare workflow and workflow technology. I think this is consistent with my brand:

“Everything in moderating — except workflow!”

Why bother to write this post about HIMSS17 Twitter stats? I just think we need to drill down a bit into tweet statistic leaderboards, to understand how and why they work the way they do, and how who is there gets there.

And I’d like to suggest a possible alternative. Twitter Analytics. They aren’t publicly available. Each Twitter user can only view their own Twitter Analytics Dashboard (see its help document) and here is a screenshot of my Twitter Analytics Dashboard. Feel free to share yours! The workflow is this: just log into Twitter Analytics dashboard, navigate to tweets, adjust date range to from 2/20 to 2/23, capture screen as an image, and then tweet image (don’t forget the #HIMSS17 hashtag). I suspect many Twitter accounts that do not even appear in the top ten of the Symplur leaderboard, are way more impressive than my Twitter Analytics dashboard stats.

Using the Twitter Analytics Dashboard, you’ll get a much better sense of how many people actually “see” and interact with your tweets than most publicly available Twitter statistics leaderboards. For example, Impressions on Symplur is how many people follow an account times how many time it tweets (I think; I’d be happy to be corrected!). In contrast, Impressions on the Twitter Analytics Dashboard is based on Twitter’s estimate of how many Twitter users actually see and engage with your tweets (again, I think; I’d be happy to be corrected!). Using Symplur’s definition of Impressions, @wareFLO had almost seven million impressions w/r/t the #HIMSS17 hashtag. However, according to Twitter Analytics definition of Impressions, 237,900 tweeps actually saw my tweets. In other words, the number of folks who actually saw my tweets is about 3.5 percent of the number of folks who follow me on Twitter. Lots of people who follow me probably didn’t even see a tweet from @wareFLO, while lots of folks who don’t follow me, but monitored the #HIMSS17 hashtag, did see some of my tweets containing the #HIMSS17 hashtag.

Even though 238K impressions is way less than 7M impressions, I’m quite happy with that statistic. First of all, it is more accurate and valid. Second, I aim at a relatively narrow, but none-the-less growing, niche: healthcare workflow and workflow technology. If even half of 238K are interested in healthcare workflow and workflow technology, to me that’s a home run. Certainly, qualitatively, this is consistent with my subjective gestalt impression of a whirlwind hurricane of RTs and replies and DMs concerning my favorite topic of conversation of all time: workflow!

Here are some of the other statistics available on the Twitter Analytics dashboard:

  • Detail expands: Clicks on the Tweet to view more details
  • Embedded media clicks: Clicks to view a photo or video in the Tweet
  • Engagements: Total number of times a user interacted with a Tweet. Clicks anywhere on the Tweet, including Retweets, replies, follows, likes, links, cards, hashtags, embedded media, username, profile photo, or Tweet expansion
  • Engagement rate: Number of engagements divided by impressions
  • Follows: Times a user followed you directly from the Tweet
  • Hashtag clicks: Clicks on hashtag(s) in the Tweet
  • Impressions: Times a user is served a Tweet in timeline or search results
  • Leads submitted: Times a user submitted his/her info via Lead Generation Card in the Tweet
  • Likes: Times a user liked the Tweet
  • Link clicks: Clicks on a URL or Card in the Tweet
  • Permalink clicks: Clicks on the Tweet permalink (desktop only)
  • Replies: Times a user replied to the Tweet
  • Retweets: Times a user retweeted the Tweet
  • Shared via email: Times a user emailed the Tweet to someone
  • User profile clicks: Clicks on the name, @handle, or profile photo of the Tweet author

It is tempting to re-imagine Twitter conference statistics using Twitter Analytics. Instead of Impressions based on tweets containing #HIMSS17 times followers, Impressions would be based on Twitter’s estimates of how many tweeps actually saw and engaged. I’d argue the latter is a much more valuable statistic. Of course, Twitter Analytics apply to a Twitter account as a whole, not only tweets mentioning #HIMSS17. One could export tweets, then slice-and-dice and come up with specific hashtag filtered statistics. But I don’t think that extra work is necessary. Most of my tweets during #HIMSS17 are about #HIMSS17, anyway.

If I am a potential #HIMSS18 exhibitor, intending to leverage Twitter during #HIMSS18, and working with “Twitter Famous” health IT social media thought movers and shakers, take Twitter stats with a grain of salt. Ask to see Twitter Analytics statistics (screenshots, live screenshares, etc.) and compare how many tweeps actually see and engage, not just how many might theoretically possibly do so on an extremely good day.

I’ll see you next year, online before and during #HIMSS18, and inline at #HIMSS18, in Las Vegas. Where, from a purely health IT social media perspective, I guarantee that “What happens in Vegas stays in Vegas” will not apply.


@wareFLO On Periscope!

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2 thoughts on “Deconstructing and Reengineering #HIMSS17 Twitter Statistics: Symplur vs. Twitter Analytics”

  1. Excellent analysis Dr. Webster 😀
    True engagement is more than just blasting out tweets and getting a bunch of retweets from accounts that are not even active in the industry.
    One thing I would add is that users who add you to a list, but do not follow your account would have seen your tweets when viewing their list. With Twitter lists one could monitor a lot of activity without actually following anyone.

    1. Chuck! Please!

      Thanks Brian. Why I like Twitter Analytics is that it compensates for lots of things, including the example you’ve cited (non-followers seeing, or not seeing, your tweet due being on their list).

      Downside is that Twitter Analytics reports are not public. Only the folks who own the account can see their Twitter Analytics stats, which rely on a definition of Impressions (actual estimated eyeballs) I like better than that of Symplur (simply # of followers, most of who see very few tweets).

      So, a modest proposal for #HIMSS18! Let’s start tweeting screenshots of Twitter Analytics dashboards. Personally, I think we should standardize on the four days of the HIMSS18 conference (M-Th), but I’m fine on standardizing on a different period of time, just as long as it become a standard, so we can more easily compare apples to apples.

      Keep up the great work!

      Chuck

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