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To The Future And Beyond: What GRIT Does—And Doesn’t—Tell Us About The Future Of The Insights Industry

While the GRIT report remains the most forward-focused perspective on the forces shaping our industry, more profound change will occur as we release ourselves from pre-defined notions of what research is.

By Greg Heist

The semi-annual release of the GRIT report is always a must-read item. This temperature check of the insights space illustrates a collective view of our unfolding future.When reading GRIT, I tend to look for divergences and gaps. How has time shifted perceptions?  Where are the disconnects between client-side and agency-side researchers?  What do the results say about what we should pay attention to as we forge this brave new world?

After digesting this issue of GRIT, three things are on the forefront of my mind:


Of all the things that mobile promises, the mobile survey is arguably the least compelling. While GRIT cites 64% adoption of mobile surveys, mobile qualitative is really where the game will change. It’s obvious from these results that a whole new tranche of emerging applications is poised to drive a sea change – shifting our discussions from mobile-as-a-collection-device to mobile-as-a-window-of-measurement.

In one sense this shouldn’t be surprising, since the smartphone revolution was really the impetus for this. But, at this point in time, it’s merely an alternative medium for completing a traditional survey. Though, this “first phase of adoption” is rapidly approaching maturity.

When looking at future consideration of mobile qualitative by clients, it’s only a matter of time before this “second phase of adoption” will bring us a much more vivid representation of consumers and their worlds.


“Sleepers” often lie where the crowd is most bearish. In GRIT, several techniques scored as very low future consideration, including facial analysis, biometric response, wearables, and the Internet of Things.  While this may be true today, there is heavy investment in these areas outside of our industry.  Therefore, we simply cannot lose sight of these fringe applications. As technologies like Nest, Hue, Sense, and Quirky gain market traction, they are sure to permeate both the insights and foresight industries.


It’s increasingly clear that the insights industry can no longer be defined solely by the collection of primary data and its analysis.

In fact, I believe that the biggest future opportunities lie outside the data collection techniques measured in the GRIT report.

Case in point, while big data is briefly mentioned in the current incarnation of the report, it’s only a matter of time before its disruptive effects are felt broadly within the industry. As organizations learn to harness the fusion of primary and enterprise data, a new tipping point will be reached—one that is less focused on reporting the past and more focused on predicting the future.

Beyond this, organizations are awash in insights gathering “dust” on shared drives. There is incredible opportunity to help clients derive greater meaning by curating and socializing this latent wisdom. This discipline is in its nascent stages, but nonetheless offers great promise for better informing organizational decision-making at all levels.

So while the GRIT report remains the most forward-focused perspective on the forces shaping our industry, more profound change will occur as we release ourselves from pre-defined notions of what research is.

When that occurs, the conversation will shift from incremental change to visible transformation.


Conclusive Proof That Social Media Data Predict Sales…Now What?

Now that we know that social media data are quantitative and predictive, we must create research protocols to harness their full transformative power.



Editor’s Note: We’ve previously posted a few articles by Michael Wolfe on the progress in utilizing social media data to predict sales, and now thanks to an ambitious multi-sponsor study it appears that we have independent corroboration that when using the right tools and the right data that social data does indeed have a high correlation to predictive accuracy.  On a personal level I have seen this first hand via my work with Decooda, which has also demonstrated this consistently in non-public research with several major CPG clients, as well as a deeper level of insight available through combining other frameworks such as the Censydiam model by Ipsos.

Joel Rubinson is THE thought leader in market research on developing a new model for utilizing a variety of datastreams to produce game changing insights, and in today’s post he highlights recent public studies that pretty conclusively demonstrate that the long awaited new reality is indeed upon us. he laos gives some specific and very useful suggestions on how market researchers need to adapt.

Before we go into a lull for the Thanksgiving holiday here in the U.S. that kickoffs the busiest shopping period opf the year, there is no more important message we can deliver to the industry than this one.


By Joel Rubinson

Last Tuesday, the results of a landmark study were made public proving that the quantity of social media conversations about a brand has a statistically significant relationship to changes in its sales.


“Researchers today announced the results of a landmark study that measured the impact of “consumer word of mouth” in six diverse categories, finding that online and offline consumer conversations and recommendations account for 13% of consumer sales, on average…About one-third of the sales impact is attributable to word of mouth acting as an “amplifier” to paid media, such as television, with consumers spreading advertised messages. The study was based on sophisticated econometric modeling of sales and marketing data.”

–Word of Mouth Marketing Association (WOMMA), sponsors include AT&T, Discovery Communications, Intuit, PepsiCo, and Weight Watchers.


This industry learning comes on top of an academic paper by Prof. Wendy Moe at the University of Maryland that showed a correlation of .8 between social media listening data and brand equity metrics derived from survey questions.

So now that we know that social media data are truly DATA…with predictive value, how do we act on this?

First, research needs to take social media listening seriously

As I said in an earlier post, “…finding the prediction question”, research needs to become an equal opportunity employer.  If the data has predictive value, it should be hired! Traditional survey researchers need to come to grips with the proven predictive value of social media data.  We need to stop treating social media listening as a hobby and find its mainstream roles alongside surveys and other important data streams such as clickstream and transaction data.

Second, we should create new brand metrics from social media data.

In my last post about the marketing ATOM, I demonstrated how building brand audiences is the key to success in a digital age. People become part of an audience for a brand that is significant and relevant to them and audiences talk about the brands they join.  Hence, it is no surprise to me that social media would provide an important set of brand metrics.  Once researchers enrich brand KPIs beyond the venerable survey tracker with social (and other digital) data, they will become an agent of change for the enterprise. Social KPIs will encourage marketers to focus on building their audiences, creating content that is worth sharing, and tracking advertising and promotional campaigns through peoples’ willingness to talk about them and share them. In fact, turning social media data into must have metrics has already been done via Social TV ratings that both Nielsen and Rentrak offer and it affects pricing of TV spots.

Third, we extend.

I plan to investigate if social media listening can replace continuous tracking of attributes.  I am optimistic that we can do dipstick studies with attributes but track brand perceptions throughout the year via social data, creating a leaner, more agile, and more effective tracker program.

I would like to see us begin to partition social media conversation by client segment or audience.  To illustrate, it is now possible to match social media profiles to customer lists using machine based logic that matches on name, e-mail, etc.  As such, for example, Verizon could create a segment of customers called “On the bubble” who are more likely to defect and, as an aggregated segment, their social media conversation could be monitored.  What are they saying about Verizon, competitors, life, TV programs, etc.?  Where are the conversations occurring?  This would be very powerful and the technology in fact does exist.

I’d like to see research turn report card trackers into predictive engines built from time series data that includes social media, digital, weather, survey tracking results, etc.  Our goal is to get ahead of future trends for a brand so we can influence these outcomes positively before they happen.

I urge research panel providers and brand websites to encourage social log-in so the power of Facebook and Twitter profiles can be harnessed.  In this way, interest profiling and ad targeting merge into one thing.

Yes, the genie is out of the bottle but as you head into this world of integrative measurement, please be mindful of rigorous practice for social media listening.  Different providers can actually produce very different data streams for the same brand, depending on whether they access the full Twitter firehose, include all social channels, how their semantic engine works etc.  To understand the complexity on the last point, consider social media listening for Target the retailer.  Extracting meaningful conversations on the retailer “Target” rather than Seeking Alpha talking about a company hitting its financial targets is not trivial. Also some conversations map to brand preferences while others map to a hot promotion or topic.

This is a significant stage in the journey the ARF started in 2008 when I was Chief Research Officer.  We began to explore how social media listening could become a valuable partner or even partial replacement for surveys.  The first meeting included Unilever, Procter, and General Mills…a highly unlikely event…but we all agreed that social media listening had tremendous potential for insights value creation.  This then became the big springboard into the ARF Research transformation super-council.

Now that we know that social media data are quantitative and predictive, we must create research protocols to harness their full transformative power.

Note: For both studies, the social media data streams were provided by Converseon, to whom I am a strategic adviser.

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Text Analytics for 2015 – Are You Ready?  

OdinText SaaS Founder Tom H. C. Anderson is on a mission to educate market researchers about text analytics

140717_hamerman_18Judging from the growth of interest in text analytics tracked in GRIT each year, those not using text analytics in market research will soon be a minority.  But still, is text analytics for everyone?

Today on the blog I’m very pleased to be talking to text analytics pioneer Tom Anderson, the Founder and CEO of Anderson Analytics, which develops one of the leading Text Analytics software platforms designed specifically  for the market research field, OdinText.

Tom’s firm was one of the first to leverage text analytics in the consumer insights industry, and they have remained a leader in the space, presenting case studies at a variety events every year on how companies like Disney and Shell Oil are leveraging text analytics to produce remarkably impactful insights.

Lenny: Tom, thanks for taking the time to chat.  Let’s dive right in! I think that you, probably more so than anyone else in the MR space, has witnessed the tremendous growth of text analytics within the past few years.  It’s an area we post about often here on GreenBook Blog, and of course track via GRIT, but I wonder, is it really the panacea some would have us believe?

Tom: Depends on what you mean by panacea. If you think about it as a solution to dealing with one of the most important types of data we collect, then yes, it can and should be viewed exactly that way.  On the other hand, it can only be as meaningful and powerful as the data you have available to use it on.

Lenny: Interesting, so I think what you’re saying is that it depends on what kind of data you have. What kind of data then is most useful, and which is not at all useful?

Tom: It’s hard to give a one size fits all rule. I’m most often asked about size of data. We have clients who use OdinText to analyze millions of records across multiple languages, on the other hand we have other clients who use it on small concept tests. I think it is helpful though to keep in mind that Text Analytics = Text Mining = Data Mining, and that data mining is all about pattern recognition.  So if you are talking about interviews with five people, well since you don’t have a lot of data there’s not really going to be many patterns to discover.

Lenny: Good Point!  I’ve been really impressed with the case studies you’ve releases in the past year or two on how clients have been using your software. One in particular was the NPS study with Shell Oil. A lot of researchers (and more importantly CMOs) really believed in the Net Promoter Score before that case study. Are those kinds of insights possible with social media data as well?

Tom: Thanks Lenny. I like to say that “not all data are created equal”. Social media is just one type of data that our clients analyze, often there is far more interesting data to analyze. It seems that everyone thinks they should be using text analytics, and often they seem to think all it can be used for is social media data. I’ve made it an early 2015 new year’s resolution to try to help educate as many market researchers as I can about the value of other text data.

Lenny: Is the situation any different than it was last year?

Tom: Awareness of text analytics has grown tremendously, but knowledge about it has not kept up. We’re trying to offer free mini consultations with companies to help them understand exactly what (if any) data they have are good candidates for text analytics.

Lenny: What sources of data, if any, don’t you feel text analytics should be used on?

It seems the hype cycle has been focused on social media data, but our experience is that often these tools can be applied much more effectively to a variety of other sources of data.

However, we often get questions about IDI (In-Depth-Interviews) and focus group data. This smaller scale qualitative data, while theoretically text analytics could help you discover things like emotions etc. there aren’t really too many patterns in the data because it’s so small. So we usually counsel against using text analytics for qual, in part due to lower ROI.

Often it’s about helping our clients take an inventory around what data they have, and help them understand where if at all text analytics makes sense.

Many times we find that a client really doesn’t have enough text data to warrant text analytics.  However this is sad in cases where we also find out they do a considerable amount of ad-hoc surveys and/or even a longitudinal trackers that go out to tens of thousands of customers, and they’ve purposefully decided to exclude open ends because they don’t want to deal with looking at them later. Human coding is a real pain, takes a long time, is inaccurate and expensive; so I understand their sentiment.

But this is awful in my opinion. Even if you aren’t going to do anything with the data right now, an open ended question is really the only question every single customer who takes a survey is willing and able to answer. We usually convince them to start collecting them.

Lenny:  Do you have any other advice about how to best work with open ends?

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Tom: Well we find that our clients who start using OdinText end up completely changing how they leverage open ends. Usually they get far wiser about their real estate and end up asking both less closed ended questions AND open ended questions. It’s like a light bulb goes off, and everything they learned about survey research is questioned.

Lenny: Thanks Tom. Well I love what your firm is doing to help companies do some really interesting things that I don’t think could have been done with any other traditional research techniques.

Tom: Thanks for having me Lenny.  I know a lot of our clients find your blog useful and interesting.

If any of your readers want a free expert opinion on whether or not text analytics makes sense for them,  we’re happy to talk to them about it. Best way to do so is probably to hit the info request button on our site, but I always try my best to respond directly to anyone who reaches out to me personally on LinkedIn as well.

Lenny: Thanks Tom, always a pleasure to chat with you!

For readers interested in hearing more of Tom’s thoughts on Text Analytics in market research, here are two videos from IIeX Atlanta earlier this year that are chock full of good information:

Panel: The Great Methodology Debate: Which Approaches Really Deliver on Client Needs?

Discussing the Future of Text Analytics with Tom Anderson of Odin Text


What Do Clients Think About MR Impact?

As part of the last round of GRIT we asked 185 MR buyers about their views on the impact and effectiveness of market research studies. Contrasting the ideal characteristics of a market research study and our actual practice reveals a number of interesting gaps.
Payday Loans Now Company

business impact


By Niels Schillewaert & Katia Pallini

As a special addendum to the most recent wave of GRIT we wanted to get a deeper understanding of the impact and effectiveness of market research studies from the client side perspective of. We partnered with InSites Consulting and Gen2 Advisors on this special “MR Impact Study” addendum. 185 market research users (marketers and insights managers, excluding professional research providers) participated in our survey and reflected about their most recent market research study as well as their ideal study[1].

We share the results of this fascinating study around 3 uncovered facts linked to our profession.

niels1An adaptive system is a flexible organism that changes its behavior in response to its environment. Such change is often required to improve performance or increase chances of survival. Consumers (our context and most important resource) have changed their behavior significantly over the last years. The surge of social media and mobile has been a major driving force behind consumers gaining power over brands. Accompanying consumer behavior (as a cause and result) such as participation, information contribution and sharing, social networking, brand liking, product reviewing, user collaboration and co-creation… has become the new ‘normal’ when it comes to consumer behavior. Gradually, we see digital companies, marketers, software providers… move up to collaborate with consumers and achieve goals through them. Gone are the days when we sent out a message and waited for people to respond. Today, marketers need consumers to want to participate in brand activation and market through them, not to them. With 6 in 10 research users indicating they believe in proven and traditional methods, our study indicates that research and the use thereof may not have made that shift to the same extent and has not aligned with contemporary consumer behavior.

While survey research is mainly conducted online, there is a platform gap. Even though 19% of consumers fill out surveys on a mobile device (GRIT study 2014, Greenbook), only 5% of all surveys are actively programmed to be fit for mobile.



Qualitative research is mainly conducted offline. 1 in 2 research users still work with traditional focus groups or in-depth interviews. Online research communities are growing as a method, but only 19% of researchers actually uses research communities to learn from and collaborate with consumers.

It is not only the channels or platforms that are lagging but also the techniques and tools. Only 9% of quantitative projects apply creative research techniques – at best, surveys use graphical scales (36%). Despite the fact that gamification has been in vogue for quite a few years, leaderboards, badges, challenges and tasks, feedback systems or social interaction are hardly used in surveys. Still, gameplay, audio-visual or creative techniques allow getting a better and deeper understanding of consumer behavior. Such tactics allow for better engagement with participants which leads to a richer consumer understanding. The latter might explain why the picture is different in qualitative research: 81% of research users feels that qualitative research helps them engage with how consumers really live, while only 1 in 3 believe surveys are capable of bringing consumers to life.

niels34 in 5 research users stated that the research output was actionable and readily usable for their marketing teams. An overwhelming 92% reported their research projects generate insights worth sharing with their colleagues. Great job, right? Yet only 65% actually share the results of their research internally. So it seems there is a lot of unused potential when it comes to leveraging research internally. In fact, the research we conduct does not seem conducive to telling a good story and it is not the start of a conversation. The majority of researchers use PowerPoint reporting to present the research results: 86%. A mere 22% have an interactive workshop to discuss the research findings and less than 10% use creative reporting formats such as interactive videos or infographics.

Related to our first fact, it would be better if research relied on content-rich methodologies and used creative communication channels to convey research results. All too often, we rely on numbers and text as well as single media. We need to combine video, photos, physical spaces (e.g. exhibitions), (private) social media, quizzes, infographics and apps. It would be so much more enriching to have consumers upload pictures and complete a mini-ethnographic self-description in a survey. Make sure you have the ingredients to tell a good story: use consumers as characters, describe their ‘who, what, when, where’ and also explain the ‘why (not)’ of their behavior.



It seems research users are satisfied yet not delighted or overly proud to share the results throughout their organization. So, the time is now to step up our game and create reporting formats that help research users share consumer stories with all internal stakeholders more easily.

niels5The first two facts about the status of market research are linked to that fact that our profession is far from adaptive and lacks creativity in the way research projects are conducted; furthermore the (presentation) output is far from inspirational. Nonetheless, our data indicate that research users are quite proud of what they do and consider what they do as being great. Researchers even seem somewhat tenacious: if we had to run a similar project again, only less than 1 in 10 would advise a different approach. 86% of researchers believes their research leads to actionable results and 3 out 4 declare using the information of their study to steer very concrete actions. This is surprising, considering the fact that we admit that our research does not entirely allow us to engage with how consumers really live. Even stronger: we found that 60% (even 71% for surveys) of all research just confirms executives’ thinking and less than half of all research studies is perceived to generate surprising results (and for quantitative surveys we only generate surprise about 30% of the time). Only 1 in 2 projects lead to change within an organization.

It is our interpretation that these number are way too low if research wants a seat at the boardroom table. It is about time that we as researchers start to think and self-reflect on that. What service are we providing if we do not make a difference? If we are repeating ourselves continuously, then in the end, what is our value proposition?

Conclusion: we do not deliver on our own expectations

Based on a MaxDiff analysis we assessed what research users want the most. Choosing from 20 characteristics, research users composed their ideal study. By far the most important element was the research’s ability to ‘change the attitude and decisions of marketing executives’, followed by establishing a ‘good connection between researchers and marketers’. Next, ‘rigorous analysis’ and a ‘clear storyline’ shared a tied 3rd place in importance. Research as a positive touch-point experience for consumers which provides a ‘good consumer connection’ and results based on ‘a representative sample’ completed the top five of a study’s most desirable characteristics.

Interestingly, ‘low price’” research and the ‘use of proven traditional methods’ were the least important features of the ideal market research study. The agency’s ‘reputation’ or ‘collaboration with third parties’ were classified as less important overall – while ‘experience with the client’ and its ‘flexibility’ were more important.

But it is apparent there is a gap between what we ‘want’ and what we ‘do’. Contrasting the ideal characteristics of a market research study and our actual practice reveals a number of interesting gaps. First of all we underachieve in making the change happen in executives’ minds and actions, we do not provide systematic rigorous analyses, clearly underperform in creatively reporting research results and could do better at using innovative methods.



These findings are in line with previously discussed facts and provide clear guidance to researchers as to what to focus on to make a difference. However, we can learn quite a bit from our ultimate clients – the marketers. It is our firm belief that market research results should be managed along the lines of content marketing (based on “Insight as Content”, presented by Niels Schillewaert and Mark Uttley at IIeX 2014 in Atlanta). While research findings are our core product, we do not manage it as a ‘product’ or ‘service’. We are actually bad at marketing it – we do not think about its promotion, distribution and delivery, let alone about the ‘experience’ marketers go through when utilizing it. At best, we are good at delivering findings based on solid methods and representative samples. We should make the presentation of results to be more ‘experiential’. If executives feel consumer realities, experience the findings and co-create the implications, they will feel ownership and we can extend the shelf-life as well as the impact of our work.



There are systematic steps a researcher should take in order to treat insight as content. These include:

  1. PLAN – define the goals, develop a strategy and create a calendar.
  2. DO – install research methodologies that allow for a structural collaboration with your consumers, but make sure you produce content-rich observations.
  3. FEEL – market your findings, as if you launch a product. Because of the very end goal of research, it is best to promote your findings experientially. If executives experience the data, it will amplify the usage and impact of research.
  4. REVIEW – analyze and measure the impact of what you are doing.

Installing a virtuous circle of treating insight as content will make your insights go viral in your company and enter the consciousness of your executives.

[1] The study was global with 46% of its participants based in the United Stated, 17% of the sample from Europe and 11% from Asia. The majority of our participants work in a consumer environment and 37% are focusing on only B2B clients. 4 out of 5 participants are active in market research or have a consumer intelligence role for a brand or company, while 19% have a more marketing-oriented function. As for sector spread: 31% were active in professional services; 1 in 4 of the participating professionals came from the financial industry; 22% from CPG / FMCG and 21% in technology.




Is Data Science Friend or Foe of Marketing Research?

The term data science has entered business vernacular with a bang...but what exactly is it?

 Toward Digital Encryption


By Kevin Gray

The term data science has entered business vernacular with a bang…but what exactly is it?   Despite all the media buzz, one story that has gone largely untold is that statisticians are asking themselves the very same question: “The exact meaning of this term is a matter of some debate; it seems like a hybrid of a computer scientist and a statistician.”  I have quoted from Statistics and Science: A Report of the London Workshop on the Future of the Statistical Sciences, a product of a meeting in London in November, 2013 that was attended by more than 100 prominent statisticians from around the world.

If such a distinguished body doesn’t have the answer, for me to declare that I do would strain credibility.  In place of suggesting my own definition of data science I will offer some thoughts about it and what I feel is its place in marketing research, based on my experience as a marketing science person as well as interaction with contacts and business associates who describe themselves as data scientists.

The first dimension

As noted in Statistics and Science, “data science” is loosely used to refer to lines of work that make extensive use of computer science and statistics.  Most of these occupations are not directly related to marketing, genomic research and seismology being two examples, and now play a role in many fields.  Data science is often coupled with the term big data, and I should note that there doesn’t appear to be much agreement about what big data means either (see, for example  http://datascience.berkeley.edu/what-is-big-data/?utm_source=linkedin&utm_medium=social&utm_campaign=blog. )

Many working in these areas are computer scientists and mainly concerned with IT matters.  However, I perceive a rough continuum, on the opposite side of which there is greater emphasis on analysis and interpretation of data.  Statisticians and marketing scientists (with assorted job titles) are mostly located on that side of the continuum.  Of course, it’s not quite as simple as IT people on one side and statisticians on the other and there are other attributes, such as industry or subject matter expertise, that distinguish the various kinds of data scientists. There is more than one dimension to data science.  Here is another, psychographic, perspective on data scientists that may also be of interest: http://www.information-age.com/industry/uk-industry/123458536/uks-data-scientists-face-burnout-due-work-related-stress .

The extremes of my (real or imagined) continuum have become increasingly mindful of one other and in LinkedIn discussion groups and other public forums there are often heated exchanges between them.  The former often characterize the statto types as stuck in the past and out of touch, while the latter frequently see the IT focused as lacking in basic analytical skills and scientific thinking.  Both score points in these debates but what I think is more important is that these two groups differ in educational background and skills, and also seem to be different sorts of people.  Statisticians, for instance, are notoriously comfortable with uncertainty; probability, after all, lies at the heart of their discipline and if you want a quick yes-or-no answer, don’t ask a statistician.  (I confess…)

Heavily IT focused data scientists are often not well-versed in statistics and some are actually distrustful of statistical models.  Data management and related tasks are their main concerns.  Conjoint, structural equation modeling, time series analysis and many other statistical tools widely-used in marketing research are a foreign world for some, and statisticians often criticize current data science practice as mechanical and algorithm driven or as focusing too much on the What and not enough on the Why.

To flesh out these criticisms, let’s consider an example from marketing.  While we may be able to predict future purchase patterns of consumers from their demographics and past purchases fairly accurately, by integrating data from various sources, such as consumer surveys, and by using advanced statistical modeling, we can gain insights into why certain types of consumers behave the way they do in certain situations.  Marketing is also about changing behavior, not just predicting it, and these insights can help us develop more effective and profitable marketing, as well as improving our predictions.  Generally speaking, I believe these criticisms have substantial merit but will concede that causal modeling is not feasible or necessary in every data science project.

Quite a few universities now offer Data Science or Analytics programs that blend statistics and computer science but, with swift advances and increasing specialization within each discipline, these programs may be difficult to sustain.  Needless to say, it will always be hard to develop individuals who are highly competent in statistics and computer science, to say nothing of subject matter expertise or the political savvy needed to survive in today’s rough work environments.  Admittedly, I am greatly simplifying here and quite a few job descriptions for data science positions I’ve seen are not that dissimilar to what I do for a living, and I now include data science in my LinkedIn headline and company website.  More importantly, though, data science teams can include computer scientists, statisticians, economists, psychologists and specialists from many other backgrounds and there is no mandate for such teams to be comprised of only one type of data scientist.

Not quite plus ça change, plus c’est la même chose

So, what is the role of data science in marketing research?  Many aspects of data science are actually already part of marketing research, even if the term data science is fairly new.   Beyond doubt, in the last few years there has been an explosion in the amount of data we are able to capture, store and retrieve, accompanied by rapid developments in computer hardware and software.  Nevertheless, over the past several decades many organizations have increasingly been using data and analytics in decision-making, including marketing.  Since the 1990’s, much of this activity has been referred to as data mining or predictive analytics, though data science is now commonly used in their place.

I can recall a senior colleague who had spent much of his career with multinational manufacturers commenting, in this context, that the strongest competitors MR agencies faced were their own clients.  That was back in the last century!  The popular data mining software was developed by a company called Integral Solutions Limited (ISL) and originally known as Clementine and released in 1994.  SPSS acquired ISL four years later and SPSS Clementine was launched with much flourish – the rollout event I attended drew a crowd of more than 1,000 people.  So, while many things have changed, many things have remained more or less the same.

That said, I wouldn’t agree with those who believe data science and big data can be dismissed as mere semantic fiddling.  I also disagree with MR colleagues who fear them as tsunamis racing towards us and, instead, I see data science and big data more as opportunities for marketing research than as threats.  Though gut feel will always be a part of most decisions, I concur with those who predict that data and analytics will play a much larger role in management than is now normally the case, and this dovetails very neatly with the essential purpose of marketing research.

Back to the present

We shouldn’t let ourselves get carried away, though.  In A Practitioner’s Guide to Business Analytics, Randy Bartlett devotes considerable space to organizational cultural challenges and more than he does to technical matters.  We should note that the author is not a journalist or software vendor but an analytics veteran of more than 20 years who holds degrees in both computer science and statistics.  I share his view that the old ways still dominate true science in most decisions: “Corporations are not as sophisticated or as successful as we might grasp from the sound bytes appearing in conferences, books, and journals.  Instead opinion-based decision making, statistical malfeasance, and counterfeit analysis are pandemic.  We are swimming in make-believe analytics.”  That is the real world as I see it too.

Big data and data science are Big Business and in my opinion have been overhyped.  We humans do not appear to be hard-wired to use data to make decisions and for years, if anything, managers have complained about information overload.  Our schooling by and large has not prepared us fully exploit new data sources and advanced information technology.  Even if there were radical changes in the way we are educated, as long as there are human managers and human consumers, data and analytics will never entirely replace gut feel in decisions.  We are emotional and not easily persuaded by logic or evidence and the often rancorous debates about data science are ironic reminders of that part of our nature.

Besides, many important decisions cannot simply be calculated; after all, even thermostats are regularly overruled by humans!  Something else we should be alert to is that more data, particularly when the numbers aren’t trending in the same direction, will be more fuel for organizational politics in some companies and only make decision-making more unwieldy.  Add to these our natural inclination to stay the course and the very bureaucratic character of many organizations, and an abrupt and radical transformation in the way we make decisions would seem unlikely.

The future?

Decision-making will gradually evolve and become more, if never wholly, evidence-based.  Over the next few years I foresee decreasing emphasis on data infrastructure and more emphasis on what data tell us and how they can be leveraged.  With bigger and frequently messier data, understanding people will become more critical, not less, and demand will rise for marketing scientists able to see beyond math and programming who truly understand marketing and consumers.  Incompletely observed behavior or conversations only tell us part of the story and have the potential to mislead.  More analytic options also mean more risk and increase the need for well-trained and experienced researchers.  The resurgence of Bayesian statistics is further evidence that human judgment cannot be purged from analytics; as Noel Cressie and Chris Wikle point out in their heavily mathematical textbook, Statistics for Spatio-Temporal Data, “Science cannot be done by the numbers.”

An unfortunate corollary of rapid technological change is increasing specialization and even more silos and misunderstandings.  Buyers may not really know what they’ve bought and sellers may not really know what they’ve sold.  Closer to home, in marketing research, the well-rounded generalist is already becoming hard to find and I think over-specialization is hurting our profession.  We have lots of shiny new tools that many of us don’t know how to use properly, and MR educational and training programs will need to provide more cross-training to counteract this flip side of progress.

Though there will always be things outside our control, there is much we marketing researchers can do to shape the destiny of our profession.  Besides embracing new technologies and methodologies, less exotic activities such as educating clients about how to use marketing research to make better decisions will not lose their importance.  Just the opposite.  Changing habits of thinking will be crucial and improving our own decision-making skills would do us little harm.   We must also be on guard against dubious claims and pseudo-science, which I see as threats to genuine innovation.  After all, not everything that is far-fetched actually works!

We must also learn to be more effective at marketing marketing research; paradoxical though it may be, I think many us will admit that our industry has historically been pretty lousy at marketing itself.  We must compellingly respond to contentions that data science has made marketing research irrelevant, and one way is to demonstrate that “data science” has, in fact, been part of marketing research for quite some time.

Data science is not entirely new and not entirely old.  It can do amazing things but cannot work miracles.  Despite the hype and hogwash, I see it much more as friend than foe.


CASRO Transformation Series: Research Now – Necessity is the Mother of Transformation

In this edition of the Transform blog we interview Kurt Knapton, President and CEO of Research Now. Kurt identifies the principles for company transformation that continue to drive Research Now to success.



By Jeff Resnick of Stakeholder Advisory Services

Kurt Knapton   JLD_4284Most of us think of Research Now as one of the hugely successful companies in our industry. It hosts online survey panels worldwide with 24 offices in 15 countries. Ten years ago, however, the firm’s revenues were around $10 million (more than 30 times lower than today), and roughly twelve years ago it almost went out of business when the dot-com bubble collapsed around them. A bold decision to transform the company not only saved it, but created the foundation for its tremendous growth over the next decade. In this edition of the Transform blog we interview Kurt Knapton, President and CEO of Research Now. Kurt identifies the principles for company transformation that continue to drive Research Now to success.

Do not fear the ‘pivot’. Research Now (originally named e-Rewards) was originally in the advertising space – offering a ‘by invitation only’ program that rewarded individuals for time spent viewing advertisements. While this business model ultimately proved unsuccessful at the time, it demonstrated that online technology could efficiently incentivize targeted groups of people – whether consumers or business executive populations – to open and respond to email invitations. While this doesn’t sound revolutionary today, Research Now was at the forefront of this technology in the early 2000s. The ‘big pivot’ was the movement from the advertising space to the market research industry. The willingness of the early team to step back and objectively analyze its initial failures and to refocus as the market changed were core elements of its ultimate emergence as a highly successful company.

Reinvention is a necessity. One of Kurt’s favorite quotes is from Winston Churchill, “to improve is to change; to be perfect is to change often.” This is the essence of the strategy Research Now follows to ensure its sustainability and success; the firm reinvents itself on a regular basis. According to Kurt, the very assumptions on which your company is based must periodically be challenged. A management team must always be able to answer the question “where do we need to move faster and what do we need to do better?” For example, with people spending enormous amounts of time on their mobile devices, the conversion from a tethered to an untethered world is in full swing. Understanding this behavioral change has huge implications for the market research industry in terms of skills of the people it employs and the technology it must develop and harness. As Churchill implied, perfection is no more than the willingness to identify what needs to change and having the fortitude to do it.

Celebrate continuous improvement.  This is one of Research Now’s core values. The firm empowers and encourages employees to challenge the company status quo in order to affect positive change. Research Now’s leadership believes collaboration and cross-pollination of ideas lead to business improvement and innovation from the lowest level of the organization to the top. Kurt highlighted one example, a ‘dumb rules contest’. This contest asked employees to identify the dumbest company rules that Research Now had implemented (for whatever reason) over the years. The official killing of those ‘dumb rules’ was celebrated at a large employee meeting, with the result being an ever transforming company.

Embrace new imperatives.  Research Now is evolving from a technology-enabled business to a technology-driven business. While this change may appear subtle, the impact is not. Client demand is moving toward a requirement for real-time data. The implication of this ranges from exploring new methods of data collection and assembly, to the increasing demand for sophisticated visual data display. The Research Now team knows that if they do not provide these tools to their clients, others will.

Give employees a reason to be proud beyond business success. Research Now leadership fundamentally believes that successful companies should give back. Among other charitable causes, Research Now is an active supporter of Kiva, an organization that provides microfinance solutions to fight poverty in developing countries. Through its employees and panel members, Research Now has funded more than $1 million of microloans through Kiva since announcing its partnership in September 2012. Getting involved with non-profit organizations can provide employees with a new perspective – again helping to fuel ongoing transformation.

Research Now is a story of a great idea transformed out of necessity, where the willingness to step out of one’s comfort zone, the tenacious pursuit of a vision by its founders, coupled with a culture of continuous improvement and exploration result in business success. It is something the entire Research Now team can be proud of.


Research Now, the global leader in permission-based digital data collection, powers business insights for its clients worldwide. Founded in 1999 and headquartered in Dallas, Texas, Research Now’s integrated data collection capabilities enable fast and accurate business decision-making.



Move Beyond The Insight To Find The Prediction Question

In the age of data driven marketing, we need to find the prediction question in every study and address it.

business woman - growth and success graph


By Joel Rubinson

Yes, insights are powerful but there is a downside. Focusing on “insights” as the endgame might be a barrier to what we must do…embrace big data.

So if “insights” aren’t the end game, then what is? In the age of data driven marketing, we need to find the prediction question in every study and address it.

When you are in the prediction business, you are trying to predict unknown values of importance to the enterprise.  It might be a prediction about the future share of a brand, identifying which users are most likely to be in play from their cookies to deliver advertising selectively to the right user, at the right time on the right screen, or modeling what is the most relevant content possible to serve up a personalized experience. Or it might be how to most accurately predict who will control the senate, as Nate Silver just did using his data science-based approaches.

To be good at prediction, you will need to integrate as many data sources as possible to determine empirically which ones demonstrate predictive value. That is why prediction questions encourage big data approaches. No NIH, no statistical snootiness about whether or not the data came from a random sample (as if that really exists anymore…). A focus on prediction leads you to integrate data from different sources and score the usefulness of information based on its incremental prediction value.   Data science is an equal opportunity employer.  If the data make sense to use AND they have predictive value, they’re hired!

The prediction business is critical for marketers because it drives up marketing ROI in a repeatable way.  Consider the world of programmatic digital advertising.  For every million page view requests, algorithms are PREDICTING which one thousand should be targeted with your ad because they are most likely to respond.  Such targeting can be based on models that use surveys, clickstream patterns, social media profiles, demos, time of day, weather, etc. and has been proven to drive up marketing ROI.

A great example of moving to prediction-based thinking comes from Nate Silver, creator of the fivethirtyeight blog and author of the book, “The signal and the noise”. Also, acknowledged to be the most accurate source of election results predictions and he nailed it again in the U.S. mid-term elections earlier this month.

Before Nate, political polling was centered on the single proprietary study. Each pollster ran their own poll, trumpeting its superiority, and implied who will win an election as if no other pollsters or predictive factors existed. Nate takes an unprejudiced view.  ALL polls have value and need to be weighted together but the weights are not equal…they depend on prior track record, sample size, “house effects” leaning towards one party vs. the other, etc.  Also, he doesn’t only use polls.  He finds that other factors add predictive value such as fundraising, candidate ideology vs. voter views, economic index, job approval ratings, etc.  In other words, each poll, for all its sampling purity is INADEQUATE on its own at maximizing prediction accuracy.  However, insights ARE important to framing his model.  He would not use some data stream that made no sense, regardless of statistical correlation, like which league won the World Series this year. What he does is essentially use big data principles.  He has Moneyballed political polling and is paid millions because he is the most accurate political forecaster on the planet.  I think marketing research practice needs to follow Nate’s footprints in the snow and go beyond the survey.

In marketing research, we can find the prediction question by thinking about the future, differentiating one user from another in terms of how they would respond differently to a marketing stimulus, or sales response to a marketing activity.

For example, when we make a trial forecast from a concept test for a health-oriented new product we do so without reference to prior studies. Purchase intent results are just accepted without adjustment or enhancement.  Is there really no Bayesian prior that we can extract from the hundreds of other concepts that were tested based on similar health claims? Also, we drop out at launch.  Using prediction approaches we could provide guidance to algorithmic media approaches to predict and target the likely users. Couldn’t we harness other predictive factors like frequent shopper data patterns for that user or possibly that those visiting retailer websites are more likely to try new things?

Another example is brand tracking.  Stop focusing on the report card and start thinking about brand health…predicting the FUTURE trajectory. To do this, certainly we need to include digital and social signals about the health and positioning of the brand. (Note: I am currently working with a leading supplier and have begun bringing this out to the marketplace.)

To become like Nate Silver, the Moneyballers, and the data scientists, Marketing Insight teams need to challenge themselves to find the prediction question in every study and commit to bringing together the data streams or conducting the experiments that are needed for prediction and then marketing action.


Michelle Adams 2 Minute Review of NIMF

Michelle Adams of Marketing Brainology gives a great summary of the recent Nonconscious Impact Measurement Forum, with the key learnings and outcomes from the event.




The business of market research is changing rapidly, and nonconscious measurement techniques are growing ever more important as a class of consumer insight techniques. Nonconscious methods are an area exhibiting rapid cycles of innovation and growth, especially with the advent of consumer wearables and other devices embedded with a variety of biometric and neurological sensors, and the ubiquity of facial scanning technology in mobile devices, PCs, billboards, entertainment systems and anywhere else a camera can be embedded.

Because of these changes GreenBook and The Burke Institute launched the Nonconscious Impact Measurement Forum (NIMF), a one day intensive event designed to advance the conversation and increase collaboration among corporate clients, market research consultants, technology providers and the academic community.

The inaugural NIMF was held last week in New York. Nearly two hundred professionals attended and from all accounts it was a great day of learning and networking. The attending companies reads as a “who’s who” of clients, suppliers, technology providers and academics leading the charge on utilizing nonconscious measurement approaches:

360i, AAAA, Accuen Media, Accuen Trading Desk, AIP Corporation, AXA Equitable, Benenson Strategy Group, Beyond Verbal, Brain Surgery Inc., Brain Surgery Worldwide, Inc., BrainJuicer, BrandNeuro, Brandtrust, Burke Institute, Burnham Marketing, C3Research, Campbell Soup Company, Capital One, CarbonSix, Checon Pesquisa, Clear Seas, Colgate-Palmolive, Conde Nast, Conquest, Control Group, Decooda, Discover Financial Services, Emotive Analytics, Envirosell Inc., ESPN, Estee Lauder, Etsy, EyeSee, Firmenich, Forbes Consulting Group, Fresh Squeezed Ideas, Fry Hammond Barr, G&R, Getulio Vargas Foundation – FGV, GFK, Givaudan, Glassic, Gongos Research, Greenhalgh Market Research & Consulting,  LLC, HawkPartners, HCD Research, Herbalife, Hershey, Hill Holliday, Icahn School of Medicine at Mount Sinai, IDC, iMotions,  Inc., InContext Solutions, Independence Blue Cross, Innerscope Research,  Inc., InsightExpress, Ipsos, Ipsos Healthcare, Janssen Pharmaceuticals, Johnson & Johnson Consumer, Kantar, Kantar Health, Keurig Green Mountain, Leibniz University of Hannover,  Institute of Marketing and Management, Lieberman Research Worldwide, Lowe’s, M.I.N.D Lab,  Newhouse school, Marketing Brainology, Mary Kay Inc., Mattel,  Inc., Merchant Mechanics,  Inc., Merck, MetLife, Millward Brown, MMR Research Worldwide,  Inc., Monigle Associates, Mosaic Group, MSW/ARS Research, NAXION, NBCUniversal, Nestlé Purina, Neuro-Insight US Inc., Neuromatters, Neurons Inc, NeuroSpire, Neurotrend, Nielsen, Nielsen Neuro, ORC International, PepsiCo, Perception Research Services, Procter & Gamble, Protobrand, Radius Global, Rosetta Stone, Saatchi & Saatchi, Sands Research Inc., Sawtooth Group, Seed Strategy,  Inc., Sensory Logic, Sentient Decision Science,  Inc., ShrinkWrap, SKIM Analytical, spear strategy, Sprout Research, Sticky, Takasago, The Ad Council, The ARF, The Dannon Company,  Inc., The Estee Lauder Companies, The Young Group, TNS, TNS Global, Triggerpoint, Turner, Villanova University, Volume Public Relations, Yahoo!, Young & Laramore, Youtail Retail


Michelle Adams, former head of Insights at Frito Lay and now CEO of Marketing Brainology recorded a two minute video with a high level summary of the event and her key takeaways. It’s well worth watching to get a quick read of why this topic is important to everyone engaged in the insights industry:

The message of the evolving role of nonconscious measurement across many different business issues was delivered loud and clear by 33 speakers throughout the day. They were world-class leaders from all along the value chain:

  • Mike Donahue, EVP: AAAA
  • Dan Marom, VP Products & GM for New Initiatives: Beyond Verbal
  • Caroline Winnett, Founder: BrandNeuro
  • Jim Berling, Managing Director: Burke Institute
  • Thania Farrar, Director Research Innovation: Burke, Inc.
  • Elita Baram, Manager, Consumer and Customer Insights: Campbell Soup Company
  • David Penn, Managing Director: Conquest
  • Amber Strain, Director of Cognitive Science: Decooda
  • Paul Conner, CEO: Emotive Analytics
  • Diane Mills, Executive Director, Global Shopper Insights : Estee Lauder
  • Amit Ghosh, Principal, Director of Research: Forbes Consulting Group
  • John McGarr, President:  Fresh Squeezed Ideas
  • David Zakariaie, CEO: Senseye
  • Michelle Niedziela, Ph.D., Scientific Director: HCD Research
  • Andy Smith, Director of Market Research: Hershey’s
  • Dr. Carl D. Marci, Founder/Chief Science Officer: Innerscope Research, Inc.
  • Sarah Snudden, Senior Consumer Insights Manager: Keurig Green Mountain
  • Jeremy Sack, Ph.D., Senior VP, General Manager / Director, Pragmatic Brain Science Institute: LRW
  • Michelle Adams, Founder & President: Marketing Brainology
  • Bill Bean, Vice President Shopper Insights and Competitive Intelligence: Mattel, Inc.
  • Matt Tullman, CEO: Merchant Mechanics
  • Marjorie Reedy, Director, Market Research Digital Innovation: Merck
  • Thomas Ramsoy, Ph.D., CEO & Founder: Neurons Inc
  • Jake Stauch, CEO: NeuroSpire
  • Michael Smith,  Director, Industry Relations: Nielsen Neuro
  • Nick Harrington, Principal Scientist: P&G
  • Dan Hill, President: Sensory Logic
  • Aaron Reid Ph.D., Chief Behavioral Scientist: Sentient Decision Science
  • Patty Goldman, Vice President, Research Director: The Ad Council
  • Horst Stipp, EVP, Global Business Strategy: The ARF
  • Will Leach, CEO: Triggerpoint
  • Jay Leon, VP/Turner Sports Research: Turner

We’re already planning NIMF 2015 and will be sure to keep everyone updated as we progress; my bet is that 2015 will see a big surge in these techniques in market research.

A special shout out to all the companies that helped support this event and bring it to life:

Title Sponsor

Merchant Mechanics
Merchant Mechanics has been delivering best-in-class market research utilizing consumer neuroscience and psychology for more than 13 years. We help our clients gain objective, actionable insights into critical moments along the Path to Purchase by untangling the intricacies of consumer behavior and decision making. We bring proven research methods, cutting-edge technologies and the latest academic knowledge to bear on the many challenges of engaging customers.


Gold Sponsors

Fresh Squeezed Ideas
Fresh Squeezed Ideas is an award-winning insight boutique delighting forward-thinking clients looking to uncover deep human needs to develop and grow their brands. Through both traditional and innovative approaches our strategists dig deeper to connect with individuals to truly discover their motivations, impulses and influences. We are strategists, synthesizers, methodologically agnostic and great storytellers.

Boasting clients from the Americas to Europe to Asia, Fresh Squeezed Ideas offers local and remote market research solutions that reveal the human truths that build our clients’ businesses through clear, actionable strategies.

HCD Research
HCD Research provides leading-edge marketing and communications research services that allow clients to make better informed decisions in a more timely fashion. HCD Research employs innovative research tools, comprehensive data collection techniques and well-honed interpretive skills to assist marketing professionals in making decisions that provide an optimal return on investment. For more information, visit www.hcdi.net.

Turning Insight into Impact® Lieberman Research Worldwide is the so what?® company, focused on using a unique understanding of how to use consumer feedback to drive business impact. their 3 P’s — People, Process and Perspective —drive their ability to provide a highly action-oriented, consultative approach to market research: They hire and develop marketing-oriented researchers and research-oriented marketers, they innovate processes that require intellectual contributions at key points in each project, and they integrate a so what?® lens into how they look at each client business issue.

Neurons Inc.
Neurons Inc is a company that helps business to better understand customers’ conscious and unconscious thoughts and behaviours. By using state of the art technology, and inventing new solutions, we provide the best practice for gaining insight into consumers’ minds and behaviours. For more information, visit neuronsinc.com.

Sentient Decision Science
Sentient Decision Science is a behavioral science based research and consulting firm specializing in globally scaled implicit research technology and quantifying the impact of emotion on choice. For more information, visit www.sentientdecisionscience.com.


Bronze Sponsor

iMotions is a high tech software development company originally founded in 2005, headquartered in Copenhagen, Denmark with a US office at MIT in Cambridge, Mass., USA. iMotions develops and markets Attention Tool®, which is the most comprehensive, easy to use and scalable biometric research platform in the World. It integrates best-in-class biosensors and synchronizes eye tracking, facial coding, EEG, GSR, EMG, ECG and Surveys in one unified software platform. The platform which is targeted at Psychology, Neuroscience, Engineering, Health, Education and Human Computer Interaction research is used worldwide by leading universities such as Harvard, Stanford & Yale and corporations such as P&G and Nestle.



Forbes Consulting
Forbes Consulting Group LLC is a strategic and innovative market research company providing global clients with deeper levels of insight into their customers, recognizing that truly understanding a customer’s deepest needs and wants is the key to thrilling that customer. Forbes Consulting Group is a valued resource for Fortune 500 companies worldwide, with a focus on financial services, CPG, retail, advertising and pharmaceuticals. Our contribution to your business knowledge base can span the range from a broad understanding of marketplace dynamics and identification of business opportunities, to developing product concepts, positioning and communications for realizing those opportunities, to monitoring the success of these initiatives in the marketplace. With our patented applied neuroscience MindSight® Technology, we deliver authentic insights about the specific emotions that motivate consumers in the real world in realtime. MindSight® Motivational Profiling, MindSight® Mobile and MindSight® Experiential Discovery techniques are easy to implement – affordable, fast and globally scalable.

HCD Research
HCD Research provides leading-edge marketing and communications research services that allow clients to make better informed decisions in a more timely fashion. HCD Research employs innovative research tools, comprehensive data collection techniques and well-honed interpretive skills to assist marketing professionals in making decisions that provide an optimal return on investment. For more information, visit www.hcdi.net.

Neuro-Insight is a market research company that uses unique brain-imaging technology to measure how the brain responds to communications. We are the only company in the world licensed to use this patented technology, enabling us to measure second by second changes in brain activity. This allows us to deliver unique insights into how a piece of design or advertising is affecting people at both a rational and an emotional level. For more information, visit www.neuro-insight.com.

NeuroSpire is a world leader in EEG software, specializing in neuromarketing technology. Our software suite makes it easy to design experiments, collect synchronized EEG data, and analyze/visualize results. For more information, visit www.neurospire.com.

Protobrand is an independent research and branding consultancy that offers a unique perspective on emotional insight mining. Through Meta4 Insight – our online application for metaphor elicitation – we mine the human subconscious and uncover the rich, hidden motivations behind people’s behavior. With such insight as a foundation, we craft strategic and creative solutions that result in emotionally resonant brand relationships. Clients include major consumer brands such as Lee, Disney, Marriott, Bank of America, Target, Toyota, and Wendy’s.

Senseye is the sensory human interface technology company. Today, we’re unlocking the possibilities of wearable devices to leverage the true power of the enterprise cloud. Our solutions will greatly extend and redefine the use of wearables like Google Glass for medical professionals who must have patient vitals in line of sight, for aviation mechanics who seek to improve safety recording and workflow, and for market researchers who want to know what their consumers really think (without asking them). We’re working on new innovations that will shape how people interact with and harness the emerging potential of the quantum computing cloud over the next decade. For more information, visit www.senseye.co.


Media Partners

The Neuromarketing Science & Business Association is the global trade association for everybody with a professional interest in the field of neuromarketing.

The Neuromarketing group on LinkedIn is a social network of people involved in the fast growing field of neuromarketing / consumer neuroscience and its impact on the market research industry.

Market Research and Cognitive Neuroscience
The Market Research and Cognitive Neuroscience group on LinkedIn welcomes anyone interested in market research and cognitive neuroscience. The recent advances in fMRI and EEG, plus ideas from psychology have great relevance to market research. The group aims to foster discussion and advance the cause of the application of cognitive neuroscience to market research. All are welcome, come with an open mind!


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A Tale Of Two Events: What CASRO & TMRE Said About The State Of The MR Industry

I recently wrapped up the U.S. Conference season by attending the CASRO Annual Conference and The Market Research Event. Although each event was decidedly different in many ways, there were fundamental similarities in the overall view of where the industry is today and where it is likely headed in the future, as well as some pointers on how to get there.

tale of 2 cities

By Gregg Archibald


It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way–in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only.


I recently wrapped up the U.S. Conference season by attending the CASRO Annual Conference and The Market Research Event. Like The Charles Dickens quote from above, although each event was decidedly different in many ways, there were fundamental similarities in the overall view of where the industry is today and where it is likely headed in the future, as well as some pointers on how to get there.

Here is my take on both.




CASRO Annual Conference And The Tension Of Change

A few weeks ago in Denver, CASRO held their annual conference.  It was their 39th conference and my first attendance at one.  From those few days at the heart of the supplier side of the research industry – I took away a few key points that I will explain in more detail later.  Ron Burgundy has nothing on these people – CASRO is classy.  Change in the industry is affecting not only clients, but suppliers and associations.  And lastly, everybody wants to move forward together.

I’ve been to a lot of conferences in my life – good, bad, and, too often, ugly.  For pure appreciation of our time and knowing that life is not 100% about work – CASRO gets it.  From the facility (the Four Seasons) to the food (particularly good for conference food) to the entertainment.  My favorite of the entertainment options was the cigar bar by the pool – but anyone that knows me could have figured that one out.  Music, bike tours, brewery tours and more rounded out the options.  What I’m saying is “Thank you CASRO for realizing that life is about balance”.

So I’ve told you enough to make you want to go next year and still haven’t said anything about the content.  There was a noticeable tension at the CASRO Annual Conference that I still haven’t been able to completely define.  This tension is characterized by the need to move fast and adapt quickly vs. the need to move thoughtfully.  The debate moderated by Simon Chadwick was the most blatant example of the tension, and also the most optimistic sign of the transition that lays ahead for both suppliers and CASRO.  The debate focused on a key question – is the change happening now simply another iteration of change that has happened so many times before or is it fundamentally different change that will lead our industry to new places.  Both sides argued admirably, but the sway was to the team that argued the change is fundamental.  I believe this is a good representation of the tension that I mentioned.  Another example is the tension was exhibited by the winners of the CASRO awards – Ditto Labs and Morpace.  Ditto Labs is a cutting edge approach to analyzing visual content in social media and Morpace is a very traditional agency that is doing the right things incredibly well.  These companies could not be farther apart from each other in the spectrum of the research industry and CASRO could not have chosen two better companies to win these awards.

Let me say this loud and clear – I believe this tension is great for our industry and I believe that CASRO can navigate it.  The somewhat opposing views aren’t opposing views at all, but rather complementary with a bit of angst.  CASRO has recognized this and is working with suppliers and associations around the world to adapt from what has been the guiding principles of the industry to what will be the guiding principles.  Having seen the balance represented in the Annual Conference – I’m optimistic that CASRO’s influence within the industry will stay strong.

Every conversation I had during this conference recognized the value of each of companies on either “side”.  “Side” is misleading, but forgive me, but it is the best that I have to describe it.  “Side” may be good as long as we think about adjacent instead of opposite.  During the panel on innovation, one question focused on how we can take advantage of the newer approaches and the more traditional companies.  It was noted that we simply need to be more open to the skills and abilities that each brings to the table – because the whole is greater than the sum of the parts.  Maybe thinking about the “Sides” as adjacent will give us all a new “angle” (I am so sorry about that pun – but I’ve spent the past 10 minutes trying to work it here).

I look forward to going to this conference next year to see the change that has been made.  I trust the CASRO team will be leaders in this effort.




Flipping The Fundamentals at TMRE

During a keynote speech at TMRE last month, Youngme Moon Senior Associate Dean for Strategy and Innovation at Harvard Business School, told us to “flip the fundamentals”.  By this, she meant that we should look for ways to transform our business by upsetting the status quo– a philosophy to which I personally subscribe.  As evidenced by the content of TMRE (please note that I am speaking in generalities for the moment), the market research industry has yet to truly embrace this philosophy.

TMRE was described by one client side participant as “dependable, but much the same as three years ago”.  Having worked in market research for many years and having been to TMRE several times – I have to agree.  But I think this is less a reflection on TMRE and more a reflection of the industry as a whole.

Throughout our industry’s history, almost all innovation has come from important advances at the margins.  Let’s take trade-off methods for example.  Conjoint was developed in the 70s and it was revolutionary.  The important innovations of discrete choice and max diff improved upon conjoint during the 80s and 90s – again, as a result of work at the margins.  The industry seems to be continuing in this fashion rather than truly upsetting the status quo – and this year’s TMRE mirrored that.

There were moments of “flipping the fundamentals” as evidenced by speakers from SocialGlimpz and RIWI.  Presentations on marketing mix by Heineken, data integration by Blueocean, predictive analytics, and real-time decision support were a few that stood out to me (and I did not go to all the presentations – there were a lot!).

TMRE covers a lot of topics which means that no matter what you need – there is something there for you.  From mobile to management, from insights to influence – TMRE covers it.  I heard a few concerns that there were simply too many topics covered and therefore too little focus – however, of all the problems to have, this is a pretty good one.

And one of the most important things that TMRE does is that it gets a LOT of us together in one place which, by definition, is healthy and valuable.  We meet new people and re-acquaint ourselves with old friends, we learn from each other and grow because of that  (and doing this in Boca Raton with days in the low 80s was not a bad thing!).

So thank you TMRE for another dependable year.

The Best of times & the Worst of times?

While the tension of change was a key thread throughout both events, by flipping the fundamentals and embracing the innovation showcased at both the insights space can choose to focus on the best of times and build a future that is bright for all stakeholders in the industry.


Is Qual Evolving to Extinction?

We know that qualitative research is evolving. But, is it thriving or dying?



Jim Bryson

Focus groups are dead.”  Malcolm Gladwell, Blink, 2005


How should we describe what qual is?”  Ray Poynter, LinkedIn Forum, 2014


We all know focus groups are not dead.  Such a statement is hyperbolic at best.  Even so, there is no doubt that the traditional method of conducting focus groups in research facilities is waning.  Today’s qualitative researcher must be so much more than a “focus group moderator.”

As a society and a profession, we are in the midst of a Technology Revolution that is being fueled by consumer access to digital communication tools.  This renaissance began to impact qualitative research in the mid-1990s as described in Jennifer Dale and Susan Abbott’s new book, Qual-Online, The Essential Guide.  The digital revolution in qual picked up steam in the U.S. in 2006 when consumer broadband access reached 60% and researchers realized that accessing consumers in their environment was possible, even desirable.

Even so, the rapid adoption of digital communication technology by consumers has shaken the qualitative world.  Weekly, research entrepreneurs introduce new technologies and applications promising to deliver more insights better, faster and cheaper than ever before.  Many researchers who thrived for decades utilizing their interpersonal skills and techniques in face-to-face settings now find themselves behind the technology curve and fearful that they will be shut out by young digital natives.  The neat, comfortable market of qualitative focus groups has become a chaotic menagerie of mushrooming methods that sometimes seem impossible to fully comprehend, much less execute.

Focus groups are not dead; they have simply been relegated to also-ran status.  No researcher can now blindly equate qualitative research and focus groups.  Today’s qualitative landscape is highly fragmented, chaotic and confusing.  Researchers must be well-versed in many methods and able to adapt different approaches to different scenarios.

This openness to new approaches and methods is the central theme of ESOMAR’s Qualitative Conference in Vienna in mid-November.  There, researchers will be exposed to new thinking that will help them navigate the rapidly changing qualitative landscape.

In this newly hectic world, Ray Poynter asks, “How do we define what qual is?”  It is no longer sufficient to say that qualitative research is simply “focus groups.”  Nor, is it adequate to define it as “the why behind the what.”

We know that qualitative research is evolving.  But, is it thriving or dying?

If we take a narrower, more traditional view that qualitative is defined largely by the methods of face-to-face focus groups or interviews, particularly those held in a qualitative facility, then the case can be easily made that qualitative is dying.

However, qualitative research is actually evolving to a more nuanced profession.  The blunt instrument of “focus group facility” research is being replaced by a methodological scalpel that cuts with precision and forethought, requiring significant multi-disciplinary expertise.  As research designs become more customized and present more contextual understanding, qualitative research will become even more insightful and useful to decision-makers.

From this influx of tools and capabilities is emerging a research profession with far greater capability to provide understanding and insights than ever before.  “Voice of the Consumer,” “Actions of the Consumer” and even “Emotions of the Consumer” are becoming more evident and better understood.  So, researchers have the opportunity to become more valued partners in the corporate decision-making hierarchy because they hold the keys to understanding and success.  Qualitative research will thrive in the near-to-intermediate term because it is providing an ever-increasing value to the marketer in terms of actionable insights.

However, qualitative research as a discipline is in a long-term decline.  In ten years, qualitative research, as an independent profession, will be much smaller than it is today.  There are two primary macro forces driving this trend.

First is the advance of technology to deliver more quickly information that meets a minimum threshold of quality and insight.  Qualitative research has traditionally been slow, difficult and expensive.  Technology is changing that.  Focused technology can deliver qualitative information for tactical decisions significantly faster than the typical qualitative researcher. Many qualitative researchers currently work on tactical research for which they will not be needed in a few more years.

Second, text analytics is blurring the line between qualitative and quantitative research.  As text analytics improve, the need for human interpretation decreases.  Today’s text analytics on research is generally inadequate to meet the need.  However, analytics improvement will eventually lead to a corresponding decline in the need for human analysts.

Both trends are propelling us toward truly Integrated Research where much qualitative research can be executed, not as a separate discipline, but as an expected skill set for any seasoned researcher. Much of today’s phased research featuring a qualitative phase followed by a quantitative phase will be fused into a single research phase with a single researcher/analyst.  That researcher/analyst will use the technology at his/her disposal to conduct the research and analysis.  The qualitative researcher as we define him/her today will no longer be needed for this work.  So, the size of the qualitative profession will contract.

While the above scenario will shrink the profession, the remaining qualitative professionals will be even more skilled and important than they are today.  These will be the qualitative experts who are proficient in deep, strategic customer studies that mine insights and apply them to strategic decisions that drive the business.  These “qualitative insights consultants” will be even more highly accomplished and highly valued than most qualitative researchers are today.

So, we return to our question, “Is qualitative research thriving or dying?”  The answer is an unequivocal, “Yes.”  The profession is more exciting than ever and is delivering more insights in more ways than before.  Technological capabilities will cause a decline in the number of qualitative researchers, but the profession’s value will increase.  Qualitative research is not evolving to extinction, just to a different place.  It will never look the same again.

If you’re interested in attending Global Qualitative 2014 from 16 November in Venice, Italy you can find more information and register at esomar.org/events-and-awards