A major game-changer in market research (as for much else) over the last few years has been the explosion of powerful computational analytics and the enormous expansion of available data, fueled by the internet. A great and diverse horde of new ideas, techniques, and systems have been deployed, such as text analytics, sentiment analysis, social network analysis, web analytics, data visualization, and on and on.
So what has staying power and what is destined to fade away?
To make things a little more interesting (at least to me), let’s ignore, for the most part, existing ideas and methods, and look at some interesting things emerging from the ether, not necessarily directly related to market research but with definite relevance. And as I’m a confirmed interdisciplinarian, I’ll emphasize ideas coming from the collision of disparate disciplines, as these are among the most interesting and likely most impactful. Will they really be hot? Well, who the heck knows? But it would be very surprising indeed if something very like each of these were not in a top-ten list of key NGMR developments in the next few years.
The big theme: Bridging the gap between sweeping qualitative analyses and highly granular and quantitative analyses by using new techniques from computational intelligence on big data to contextualize patterns and identify niches and segments.
- Using big data and analytics to find more specific niche markets in the long tail of the distribution of consumer preferences. Most preferences in many markets are niche preferences, so analyses that only find overall preferences and trends will inevitably leave a large part of the market on the table. This is where segmentation must come in, but it is only recently that tools for demographic and sociographic analysis for online data are becoming available (since we only observe online, and can’t directly ask questions). These tools, when they are applied to the immense numbers of virtual respondents that can be examined in automated online studies, will enable more detailed understanding and exploitation of the myriad of niche segments hiding in the long tail.
- Using text mining as an experimental methodology, not just for data exploration and classification as is currently done. This focuses attention on the important role of interpretation and modeling at the textual level, when using automatic text processing to gauge thought and opinion. When dealing with the complex mathematical and linguistic models that are used in text analytics, it is hard to know exactly what results mean without a bit more investigation. This will amount, in some sense, to a fusion between qualitative and quantitative approaches to understanding online data, or, in other words, will bring the a priori models and intuitions of qualitative analysis into the automated and scalable quantitative techniques, for far greater insight and understanding. This sort of bidirectional method (explore/classify, model/test) has been applied to interesting work in digital humanities both to help find novel and interesting scholarly hypotheses in masses of data and also to experimentally test them. These methods will be applied and expanded to market research, enabling more accurate and deeper understandings to emerge from online data.
- Deeper and more meaningful visualization techniques for seeing real patterns in enormous data (text, social networks, and more). Word clouds are already clichéd and never really gave much more than a kind of insightiness. Developing greater understanding and sophistication about visualization methods will enable us to move beyond bar-charts and word clouds to more informative visualizations that represent real insights.
- The best market research has always relied on using multiple methodologies in conjunction to substantiate results. As the variety and size of data sources only increases, a sort of massive triangulation is becoming crucial, and thus current less-formal “meta-research methods” will develop into more rigorous methodologies that integrate both qualitative and quantitative analysis of diverse kinds of online and offline data.
- One of the most exciting ideas I’ve seen is that of Research Through Gaming, where people play fun interactive games devised by researchers to provide useful information based on how they play the games. This notion bridges the gap between high-contact interactive methods (such as focus groups and in-person surveys) and high-volume non-interactive methods (such as online sentiment analysis), and also promises more precise and new kinds of understanding, since subjects can be observed minutely as they act in highly controllable environments. One trick will be figuring out how best to devise such games to yield useful information efficiently. The other will be ensuring that the games are fun so that people play them and share them (hopefully going viral). If these problems can be solved, market research games will utterly transform the field over the next decade if not much sooner.
- Online research using text, web, and social analytics that just gives overall trends or comparisons without detailed segmentation or identifying niche segments.
- Use of computational analytic tools and listening/monitoring systems as black boxes that provide useful information. Researchers will become as sophisticated in understanding text and network analytics as they are in understanding statistics.
- Research where one methodology or data type predominates, such as work that eschews sentiment analysis, or doesn’t account for social network effects, or ignores the possibility of in-depth qualitative analysis. Research will become highly multimethodological.
- Examining data only at a limited range of scales, whether small (as in focus groups), medium (as in telephone or online surveys), or huge (as in social media analytics). Multi-scale triangulation is the key, and larger scale analysis, even if more shallow, are needed to contextualize small-scale deeper results.
- Market research online communities (MROCs). I’ll go out on a limb and assert that while they will still exist, they will not be very important in a few years, as methods for gathering high-volume, high-quality data naturally from the internet improve. MROCs are expensive to create and to maintain, and user interaction in less artificial settings (such as blogs, twitter, and interactive games) will largely take their place, as we get better at extracting and interpreting information from them.
So, what do you think?