A major issue with post-modern research methods, or ‘new MR’ as it is sometimes called – a recurrent theme at the Research 2010 conference – is the amount of data and consequent effort that goes into extracting any meaning from this data. This came home in the new technology session, chaired by Robert Bain and billed as ‘Research Unlimited’. Not that any of the technology being presented was essentially new – naming the session “incremental developments in technologies based around memory and newly applied to market research” may have added precision, but not made the message any clearer.
The pursuit of clarity should be at the heart of any new methods – and that is a challenge with two of the methods showcased based on neurometrics – from Nunwood’s head of R&D Ian Addie and Millward Brown’s new head of ‘Consumer Neuroscience’, Graham Page. Page is probably the first MR staffer to have the N-word in their job title.
Improvements in EEG measurement and analysis technology make the approach more affordable and slightly more applicable to surveys in the real world, but they still have a long way to go. The electrode caps and camera-rigged spectacles modelled on stage by Addie, and even the slimmed down version shown by Page, are still pretty clunky and intrusive. Addie also cautioned that ‘noise’ in the data collection meant that 30 per cent of the data they had collected had to be discarded.
Positivism with a big P
Both speakers showed that this kind of data can aid understanding, and can usefully cast a new light on some deeply held assumptions about consumer behaviour, which is no bad thing. Nunwood respondents who had been wired up with electrodes for supermarket visits had revealed that a significant amount of time in selecting products seemed to be spent in rejecting other projects – not something that is much questioned in conventional recall studies. As research was busy going po-mo in other sessions, this looked like a rallying call for Positivism with a big P.
Page cautioned: “Hype means it is very easy to get carried away with exaggerated claims [for neuroscience]. The results don’t stand on their own: you have to combine this with something else.”
Not only that, but you quickly accumulate a vast amount of data that takes time and effort to process. Furthermore, to give any meaning to it, you must be applying the qualitative judgements of the researcher or neuroscientist. This additional burden was also true of the other novel method in the session. Here, Bob Cook from Firefly presented an interesting extension to diary research – particularly those studies that lean towards the auto-ethnographic – with a methodology based on Lifelogging, or ‘glogging’ using a small fish-eye camera worn by the participant around their neck. This can take a shot and capture everything the respondent sees, paced out at minute intervals throughout the day. Cook reckons it can overcome the usual problems of incomplete recall that can arise over the more mundane and automatic activities respondents may be asked about.
Making sense of the data
The problem, in trying to move such techniques into the mainstream, comes at the analysis stage. To get meaning from these techniques takes extraordinary effort – and they are not amenable to the analytical methods conventionally applied to either qual or quant. We’re not usually short of data these days, but we are short of tools to make sense of these new streams of data. Without them, analysis is inordinately time-consuming. Technology makes it easy to add precision in volumes, but with all these new methods, it falls heavily on the researcher to bring out the message.