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….).
– Geotargeting.
– 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]
OUseful.Info, the blog... 
Thank goodness you and Brian do “bang on” about these things! I think there is another reason for metrics. It is a step before looking at RoI or reputation, but i suspect it is where a lot of people are up to right now.
In the public sector we are needing to justify that the staff time spent crafting tweets, blog posts and in dialogue online is part of a wider system of communication that goes beyond a few geeks. Many decision makers are not (yet?) particularly users of social media: perhaps they only visit social media channels to check up on staff activity: they need reassurance that the stuff staff are doing is reaching people. The numbers are not even necessarily used to make comparisons: its just evidence that the number of hits is not zero, and that more than their best friend is listening.
So I’d suggest there’s something about “validating social media staff effort”?
@amber “The numbers are not even necessarily used to make comparisons: its just evidence that the number of hits is not zero, and that more than their best friend is listening” – I am suddenly reminded about ‘hits counters’ on web pages, and simple page view webstats. At best they say: ‘some people really are visiting my website’, though more realistically ‘my site is being crawled by a bot once every so often…’;-)
I also realised I’d missed an important usage model from the point of view of the social media metrics companies – egometrics, and ‘levelling’ yourself compared to other people you know (or maybe don’t…) on the same social networks. In many cases, the egometrician will also broadcast the availability of the service to their network, especially if they think they score more highly on the service than their network. I suspect that many people who are tempted to click through on a link to a social media metric site they received from one of the people they follow will also look up that person’s score as well as their own. (On the occasions I check such sites, I do of course compare myself to @mweller, just so I know what sort of basis level is set by a Professor in the area;-)
“Banging on”? I guess that’s short for “writing thoughtful blog posts” :-)
One of the reasons for the recent series of evidence-based posts about institutional use of social media was to help identify patterns of usage which can help institutions develop a better understanding of the ways in which such services can be used and perhaps identify good practices.
In addition, as I pointed out in my post, there are well-established processes for measuring the ‘value’/’worth’/’goodness’ of universities outside their use of online services which are provided by, for example, the Sunday Times and THE tables. Universities do take these findings seriously. So even if those with expertise in data journalist can point out flaws in the methodologies, there is, I feel, a need to recognise their importance within our sector.
So yesterday when I received an email from someone asking for advice on use of PeerIndex to benchmark her University’s use of Twitter against other 1994 Group Universities I don’t think she was engaged in ‘egometrics’ but simply doing her job. And I would applaud her for doing this (and for reading posts about the limitations of such services). I feel use of terms such as ‘egometrics’ are counter-productive and will lead to unnecessary divisions between academics and marketing people (especially from someone who has a far more blobbier Peerindex visualisation than @mweller :-)
@Brian ;-) I take the point that the metrics might also be of some interest when looking at evolution over time/deltas between measured values before and after a particular campaign or event etc. But if I’d written a balanced post, I suspect you wouldn’t have commented?!;-)
@brian ” I feel use of terms such as ‘egometrics’ are counter-productive and will lead to unnecessary divisions between academics and marketing people”.
Probably true but lets not forget there is over 200 years of social network analysis, and as I’m discovering, this area has a number of established terms. As part of my homework here’s what I learned:
If you are analysing a social network from an individual member (ie an institutional twitter account) this is a egocentric network. The account that is the focus of the analysis is the ‘ego’ and the connected accounts ‘alters’. The term for the number of connections between alters, egos and vice-versa is degree centrality. For directed networks connections from the ego to alters is out-degree (friends) and from alter to ego in-degree (followers). In other words “degree centrality is a simple count of the total number of connections … it can be thought as a kind of popularity measure, but a crude one that doesn’t recognise the difference between quantity and quality” (Hansen, Schneiderman & Smith)
Lots more to learn ;)
Martin
Well here’s my tuppence worth – I think it’s more about actually starting to surface connections than marketing – but again I’m just thinking from a funded service point of view. It’s increasingly important that we can present certain aspects of social metrics in an informed and meaningful ways to the not so web savvy decision makers Amber refers too.
@sheila So that fits in to community discovery for purpose of reaching into that community directly, maybe? One thing I noticed comparing friends vs follower networks is that you maybe see differences by how folk define themselves in terms of who they follow, vs how they are defined in terms of the the people who follow them?