Throwing an event is hard work. Ask anyone who’s done it before. There are seventeen different layers of logistics (at least) that somehow never come to mind when you get swept up in the excitement of the initial idea.
Since pulling off even one event is a huge effort, stands to reason you should do everything you can to make sure you learn as much as possible from it so you can build on its success.
Which is why this recent article from Ryan Bersani, Director of Engagement Analytics at Massachusetts Institute of Technology, caught our eye.
How can you know if your event played out exactly as you intended or if something weird happened? Now, if something weird did happen, that’s not a bad thing. You can build from it and use that weirdness to your advantage in the future.
But you can’t know one way or the other unless you have some contextual analytics to compare your even to. Which is why Ryan says in order to gauge the success of an event (or appeal or, really, any engagement initiative), you first need to know how your population breaks down.
So what exactly contextual metrics anyway? Let’s put it this way. When you throw an event, you’ll collect all sorts of data about people who attended (or even just engaged with it but didn’t show up). But there’s only one way for those numbers to make even a lick of sense. You need to know how they stack up against your population as a whole. And those right there are your contextual metrics.
So if 40% of your attendees graduated in the last 10 years AND 40% of all your alumni graduated in the last 10 years, then those attendance numbers could be expected. But if only 20% of your alumni graduated in the last 10 years, then you might be onto something that resonates with your young alums.
That’s something you can build off. (And be sure to check out Ryan’s article for a bunch more examples.)
Which brings us to the thing about Ryan’s approach that we really liked: Anyone can adopt it! If you’ve got all the resources in the world, you can go super deep and take an incredibly nuanced approach to your next event.
On the other hand, plenty of us have, well, the opposite of all the resources in the world. In this case, that’s no big deal. As Ryan points out, even a baseline set of contextual metrics to work off will let you make some pretty smart guesses. Will the correlations you notice always equal causation? Nope. However, even without in-depth analysis, you can take those macro-level observations into your next event with a hypothesis and see if it works out. If so, great; you can continue to sharpen that strategy. And if not, well, that’s still another data point to help you take a smarter approach moving forward.
With contextual metrics in hand, you can not only optimize your events, but you can optimize your marketing efforts around them to make sure you’re creating occasions that people are interested in and getting invitations in front of the right people. That results in better engagement and, in turn, better prospects.
See how Oklahoma State University sifted through their enormous database to rethink their approach to alumni engagement.
