FEATURE1 January 2009
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FEATURE1 January 2009
Tom Ewing, social media knowledge leader at Kantar Operations, lays his expertise on the line… and confesses to not knowing everything.
Anyone who’s been to a research conference in the last few years will have seen at least a few presentations dazzling us with tales of the remarkable possibilities just around the Research 2.0 corner. It’s not that those presentations were wrong – quite the opposite: most researchers are now well aware of collaboration, conversation, crowdsourcing and the chance to transform research participation into something genuinely, well, participatory. But as social media starts to slip down the hype curve, and sceptics get to have their say, it’s time to focus more on the practical side of making social media research work.
Politicians and economists have groped for comparisons to the current recession. The 90s? The 80s? The 30s? In one way at least, it will be unlike any other: this is the first wired recession, the first downturn in which the majority of consumers are part of digital networks as well as real-world ones. Beyond the economic effects on social media as an industry – advertising looks set to be hit, but usage less so – we simply don’t know about how people will use the networks they’ve created. Businesses who find out quickly will be at a distinct advantage. And that’s where we come in.
Research in the pre-digital world offered respondents a rare opportunity to offer their opinion, which meant the experience of giving it didn’t have to be especially enjoyable. But not only is opinion now the commonest online coin of all, there’s already a thriving research culture on the web. It involves short surveys designed for viral transmission, instant results and outputs – like personality tests – that are themselves social objects. We don’t offer participants anything unique, which means we should probably think about changing our methods to meet their expectations.
In a brilliant white paper on youth new media use, the MacArthur Foundation has described “genres of participation” – like “hanging out” and “geeking out” – among the kids who have grown up with the rise of social media: ‘digital natives’, to use the pop-sociology buzz phrase. It talks about genres to emphasise how different individuals switch modes depending on topic and social environment. It would be incredibly valuable to understand how these genres work for ‘digital immigrants’ – adults who’ve adapted social media to their existing lives. Is the digital divide really that huge?
A lot of conversation around social media talks about how to set up communities, tools and blogs – or how to use existing ones. Done even slightly right this can generate staggering amounts of information. The streets of the web are paved with data. So the real issue is analysis – how do you get to the important stuff quickly, how do you make sure you’ve got a grip on the conversational context from which insights emerge, and how do you filter and present so much info? The injection of planning skills into the research community will prove crucial here, but so will the continued development of more sophisticated quantitative text and conversational analysis tools.
Large-scale social media research projects will give us terrific opportunities to look at how interactions in a network affect the spread of ideas and opinions – an understanding of influence which could revolutionise tracking studies. For example, we would learn whether influencers are born or made – whether they share certain attributes which make them key marketing targets, or whether the process of interaction magnifies tiny differences, meaning their emergence can only really be modelled after the fact. Network science will be at the heart of the technical development of social media research.
When I started in the industry I enjoyed some superb training, based around some standard distinctions: between qual and quant research, between fieldwork, analysis and reporting; between operational and client-facing staff. I’m not going to claim that social media erases these binaries, but what I’ve found is that you have to shift between different skillsets quite rapidly over the course of a project, so it makes sense to specialise as little as possible. If I had to characterise the shift in mentality needed to do good social media or online community work I’d say it was a move from composition to improvisation.
Social media isn’t a new phenomenon: it’s essentially the transformation of the interactive elements of the web into a mass medium. As such any pre-existing online community is likely to have gone through forced radical change in the last few years, so we don’t know enough about how web communities and networks evolve in the medium to long term. They may, for instance, be inherently unstable, since the physical difficulty of leaving a virtual community is extremely low. Understanding this will be crucial for brands looking to build and maintain customer communities.
If we’re going to accept the metaphor of the conversation as the model for both client-consumer and researcher-participant interactions, we have to realise that there’s an etiquette involved too. For instance, you don’t just break off and walk away from a conversation – or not if you want to have another one. Similarly, social media researchers need exit strategies for their communities as much as they need recruitment ones – how do you wind down a project, how do you keep interest levels high (or gradually lower them) and what happens to the ties of community your work may have created? These are questions that industry bodies, as well as individual researchers, need to start thinking about.
With every iteration of social media the overlay between the virtual and real worlds becomes closer. In ten years we’ve moved from abstract, anonymised virtual presences to social networks that map closely on to people’s real lives. The next step, hastened by mobile internet and GPS uptake, is the ‘end of cyberspace’ – digital information becoming an extra layer on top of real space. For our industry, this is potentially another big transfer in power from researcher to respondent. But it’s also a huge opportunity in terms of the richness and relevance of the data people might provide.
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