The NGMR Top-5-Hot vs. Top-5-Not: Computational intelligence and contextualized data

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.


Long tail lemur

  1. 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.
  2. Red and Blue

  3. 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.

  4. 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.
  5. Sierpinsky triangle

  6. 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.
  7. 2010 Northwest Pinball and Game Room Show

  8. 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.


  1. Online research using text, web, and social analytics that just gives overall trends or comparisons without detailed segmentation or identifying niche segments.
  2. 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.
  3. 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.
  4. 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.
  5. 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?


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3 Responses to The NGMR Top-5-Hot vs. Top-5-Not: Computational intelligence and contextualized data

  1. After reading your predictions and comparing them to mine, the one thing that I’m really curious to see the development of is research through gaming. I think that if market researchers can create partnerships with existing game developers then there is a huge opportunity for deep insights to be drawn … otherwise this type of research is just not going to happen in the way people are expecting it to as I mentioned in my top 10 NGMR predictions.

    • Shlomo Argamon says:

      Indeed, although we seem to disagree on the surface, we are actually very much in agreement! I don’t think that anything here is guaranteed – the key to making any of these promising new developments stick will be stronger and deeper partnerships between disciplines. Researchers will have to partner with games developers to make good games, and with psychologists and ethnologists (as some already have done) to design the interactions and analytics so that real insights can be derived. Otherwise, as you rightly note, you just get an interactive version of WordClouds – all sizzle but no steak. This is also true, I believe, of text analytics – researchers need a deep understanding of what the tools really do, or at least deep partnerships with experts in the field (not just shrink-wrapped “text analytics listening solutions”). Developing depth in interdisciplinarity is hard, but if successful, very rewarding.

  2. Pete Mancini says:

    Wow, I’m blown away. These are very similar to the ideas I have been having. I felt alone in criticizing sentiment analysis. It was only recently that my criticism has become more constructive and along the lines that you suggest.

    I’ve not seen much on using game analysis for scientific benefit excepts for one study on protein folding. I like the idea, especially if you are doing research on game theory in general.

    I think a key hot topic for the future will be attention shaping. This is the use of discovery tools to bring attention to the emergence of important information or trends. Currently we are all use to alarms and disruptive attention getting services. These popups disrupt our thinking. The idea that our thinking must be roughly disrupted in order to get us to react is not well founded, I think. We exist in a highly distractive information environment currently. To prove this simply go out on a busy street and count the number of signs. Go to most web pages and count the ads, the links and button and compare the screen real estate given to things other than the data on that page. Interfaces with a discovery backend can be less disruptive and more natural in informing the person using them and thus not halt thinking but preserve the momentum as the thinking is channeled into a new and important dimension. For example, take a look at the interface of Google Analytics. The presentation is always the same and it is up to the person using it to build the right routine to discover and diagnose issues. Why can’t that type of task be something the system can aid in and thus alter the presentation to highlight what is interesting (the definition of “interesting” being something the person or standards define.)

    Apply this to unstructured data and you can limit the time spent guessing about what is interesting in it.

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