Data-Driven Social Media Strategy (Part 4): Evaluation

It’s been some time since I’ve shared my progress as I work toward finalizing my university’s social media strategy. This is no accident. Some time was spent researching measurement tools. Then some time was spent playing around with said tools. At the moment, the testing of measurement tools is on hold. This sounds cryptic and official, yes? Not really. The tool I spent a bit of time with is called Radian 6, which offers an expansive suite of listening and measurement instruments. I think there is some great potential in working with Radian 6 and it may be something I explore further down the road.

So now, back on my path, it’s time to evaluate all that’s been put in place thus far. How can I best determine whether we’re successfully growing our communities and building relationships within them? As we’ve resolved to focus on Facebook, Twitter, and YouTube initially, the analysis will begin with these channels. My goal is to identify trends for what works (or doesn’t) over time.  Comparative reports will be compiled on a quarterly basis.


  • Percentage of growth per quarter in: total likes, people talking about this, and total reach
  • Most and least successful content per quarter using post-level data categorized by content type


  • Percentage of growth per quarter in: total followers, interactions (mentions and retweets)
  • Most consumed content per quarter using click-through and share rates


  • Performance (number of views) per quarter, and percentage increase or decrease
  • Engagement per quarter, including comments, likes, favorites added, and subscribers net change

Mixed in with these numbers, I will share new efforts, notable events, and success stories. And if my measurement is on-track, the analysis will allow us to truly evaluate our social media strategy. If our growth slows or interaction level decreases, I’ll need to investigate. Are we not sharing content of interest to our communities? Are we being active participants in our spaces? Should we be sharing more  photos? Fewer news stories? More event updates? More or less about undergraduate research? What about international perspectives? If all goes according to plan, these answers and many more will lie within the numbers.

This post originally appeared on the UM-Flint University Relations blog.

Related posts:

Data-Driven Social Media Strategy (Part 3): Content & Messaging
Data-Driven Social Media Strategy (Part 2): Choosing Channels
Data-Driven Social Media Strategy (Part 1):  Goal-Setting
A Social Media Strategy in Five Steps 

2 thoughts on “Data-Driven Social Media Strategy (Part 4): Evaluation

  1. These are great goals for social media. You may have already thought this through and not explained it in this post. But it seems as though this data also needs to be connected further to the marketing departments overall goals. It is great to have increasing numbers of YouTube video watchers, but does it help increase the number of inquiries, applications, and/or enrollments or does it even increase the number of views of a specific webpage. Take the next step and tie the social media goals to the overall marketing department goals. Finding data that helps reach overall marketing goals will help provide a ton more ammo for explaining why social media is worth the time and effort.

  2. Thanks for your feedback, Travis! Your thoughts are always appreciated. I did spend a little time talking about the goals my department set in another post (it’s the link in this post for “goal-setting”) but I may not have addressed all of your points. In our very specific case, the team decided to set social media goals that were related only to engagement and growth of community as an extension of the brand experience. This resulted from a number of factors. We felt that cultivating the online community to enrich the experience of our audiences was our immediate goal, and that once we solidify this strategy we can better focus our efforts toward a direct impact on admission. Another thing worth noting is that we’re not currently in a position of easily accessing the data that would allow us to effectively measure those kind of application/enrollment goals. It’s a place we aspire to reach, but right now we’re working with what we’ve got.

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