In a week when Twitter finally announces it will be releasing an analytics package (GigaOm: Twitter offers analytics to try and prove its value) that will allow website owners to track activity around links that are shared to their site on Twitter (or more sprecifically, via t.co links?), I notice that @briankelly is banging on again about social media metrics (Bath is the University of the Year! But What if Online Metrics Were Included?).
To try and clarify some of my own thinking on this, here are a handful of things I think media metrics (online and offline) are typically used for:
- selling ads: publishers delivers advertisers audiences with a particular demographic. Publishers create publications that pull together audiences in particular demographic groups so that they can sell access to those groups to advertisers. So things like ABC figures… (although maybe they are falling out of favour? UBM’s ABC exit shows how publishers are moving from measuring users to building relationships /via, err, @paulbradshaw, I think…);
- measuring returns on investment: if you put a call to action out to an audience (for example, by advertising something, or putting it into a catalogue that you expect people to buy from), it helps if you know whether that call to action got a response, or generated some sort of return on the cost of making the call to action. Google reinvented everything when they worked out how to find a way of pricing ads and charging for them when someone actually clicked through… (and Google analytics then helps folk track whether these click-thrus actually result in things like online sales).
- ranking: if you’re measuring things like ‘reputation’ (whatever that is…?) it makes sense to ask why. One reason might be to as a signal to help organise the ranking or ordering of search results in a large set of results.
- recommendation: looking for clusters in data so that when someone picks one item in a cluster, you can recommend the rest;
- discovering new segments: another application of clustering, trying to find new audience groupings as part of a product development exercise, perhaps?
My own interest in things like hashtag community graphs has more to do with finding collections and understanding the structure and makeup of a system, but for no other reason than collection building and personal curiosity about how it may be structured…;-)
So – what else have I missed?
PS this is interesting, and bits are maybe related – Battelle on The Future of Twitter Ads. In particular, his thoughts on how Twitter might execute ad targeting:
– Interest targeting. Twitter will expose a dashboard that allows advertisers to target users based on a set of interests. … There are plenty of clear signals: What a user posts, of course. But also what he or she retweets, replies to, clicks on in someone else’s tweet, or who they follow (and who that followed person follows, and, and….).
– Audience targeting. I’d expect that at some point, Twitter will expose various audience “buckets” to the marketer for targeting based on unique signals that Twitter alone has views into. These might include “active retweeters,” “influencers,” or “tastemakers”
– Demographic targeting. This one I’m less certain of…
– Device/location targeting.
The question is, when it comes to social media metrics in Higher Education, what are we trying to do with them?
Related: Forget the online traffic tricks and start measuring value [h/t @paulbradshaw]