Last week I attended Londata: Using Data to Delight and Excite where the crowd heard insights from digital marketing agency, Bloom. CEO, Alex Craven and data expert Peter Laflin discussed how they handle big data, some challenges they face, and the way they determine who the major influencers are in social media.
Alex started off by introducing the idea of ‘little data footprints.’ Checking in on foursquare, tweeting a message, choosing a hashtag, swiping in and out of an office, using the tube – these are just a few of the hundreds of little data footprints left beind on a daily basis. Designers would probably want to make this into a beautiful poster, or installation. Marketers, on the other hand, want to use these little breadcrumbs of information to find out where their customers or potential customers are and how best to give them a little nudge when they’re more likely to buy their product at the exact right moment.
Platforms like Twitter and Foursquare have made it incredibly easy and fun to share even the smallest and most mundane things about our lives. I have no shame in tweeting photos of my sausage sandwiches on Friday mornings and if there was a way for Luxe (*hint hint) to tweet me a 10% discount on a coffee the moment I get in the queue, I’m pretty sure I would cave. It’s the timing of these brand “experiences” that poses the greatest challenge.
People are beginning to talk more and more about the challenge of Big Data. All these little data footprints add up to a collosal collection of tiny bits of information connected to people moving in real time in 3D space who are friends with a certain number of people who might or might not share similar interests or follow you back. There’s not only an insane amount of data being shared at any one time, but the speed in which people are able to analyse it and then decide what to do with it makes it even more complicated. Then mix in the fact that a lot of the data is qualitative and you’ve got a pretty complicated system to try and break into. Blows your mind a little bit, right?
Finding the right people at the right time is one thing data experts are looking at. Identifying the key influencers is another. Enter Bloom’s ‘Clarity’ index / measure which aims to identify key people on Twitter, for example, in a different way than Klout and PeerIndex. Not only does the measure look at the number of followers someone might have, but it takes into consideration the influence of the people that follow that person. For example, a person might only have 50 followers, but have a very high Clarity score because a RT from anyone of those 50 people might read a much wider audience. There’s a whole lot more thinking behind this which you can read about on Bloom’s blog, along with some visualisations of the key influencers tweeting the night of the talk, one of which I’ve included below.
Thanks again to the people at Londata for another inspiring evening, looking forward to the next one!
