Predicting the future, part two

Verdict

With the benefit of hindsight, this seems a very quaint prediction. Unlike the paperless office, which has been touted for years but hasn’t come to fruition mainly because it simply isn’t practical, many other forms of communication – research included – have migrated almost entirely on to digital devices.

Mobile technology available at the moment has limitations – small screens, outdated SMS technology and poor connectivity all limit current research capabilities to very short questionnaires and time-use surveys. But as these technologies improve, we anticipate a fast-paced, large-scale shift to mobile research. And the benefits of contextual research are tangible – the use of images; the access to instant reactions as well as considered responses; the ubiquity of mobile devices; the advantage of automation, which means that willing participants can participate in some tasks passively – particularly important for tracking low-interest/repeated activities such as shopping habits.

The move to mobile has the added benefit of allowing opinion research to expand its borders into developing countries, where the ‘technology leapfrogging’ effect is strong – researchers are able to access respondents via mobile/SMS who have historically had no internet connectivity or access to the wider world. It has also enabled the widening of demographic spread – slowly researchers will be able to expand to non-urban/less affluent populations.

Verdict

This prediction provoked a fair amount of controversy at Future Foundation. Our initial reaction was that it will never happen as there is simply no such thing as the ‘average’ consumer. Big data is all about the noise but the signal will always be vital for researchers. We strongly believe that market researchers will always need real people interpreting the data gathered. And there is the Semantic Web debate to consider – can an AI ‘bot’ ever really capture the nuances of human language?

But there is something in this; something in its infancy which could play an important role in the future of research. Some sectors are already gaining huge ground in this field – eye-scanning technology is of great interest for retailers, for example. New practices, from neuromarketing to hypnosis, are being touted as feasible routes to understand the cognitive processes behind consumption. The Human Brain Project is on the way to mapping every neuron in the brain, which will be of significant interest to those aiming to understand the inner workings of human behaviour. And although decades away, intelligent algorithms which can understand and unpick the nuances of language will exist one day.

The question will remain though – where will this all lead, and what benefits will they really offer researchers?
And the ongoing danger is that brands and researchers will get it very wrong. A famous example is Target’s predictive advertising model, which is so powerfully accurate that it informed a pregnant teen’s father of her happy news before she had told her family. Until these technologies become a tried, tested and trusted part of the research toolbox, they will remain on the periphery of feasibility.

Verdict

To all extents and purposes, this already exists – Sensum (www.sensum.co), for example, claims to “visualise real-time emotions so you can understand the true value of your audience’s response”.

It is worth considering the increasing role regulation will play in this field, though. The European Commission’s General Data Protection Regulation law is set to come into force in 2016 and will regulate, among other things, who is responsible for data collected, how consent should be given and whether/how consumers activate their right to be forgotten. All this will significantly impact the ways in which data collected via biometric sources, among others, can be used.

Verdict

On the surface, this seems unrealistic. There are digital wars being waged already and none have done enough damage to the framework to bring down an industry.

However, we did think about Twitterbots and how, with enough money, quantifiable results can be influenced. There is a sort of implicit trust in big data, an assumption that unified data can’t be skewed – which could well break down public resistance to data collection. This could then feasibly leave the door open for hacker groups to break into huge data banks.