In case you weren’t able to attend – or if you just want a fresh perspective – here’s the first entry in an occasional series of posts that will provide you with our “snapshot view” of conferences, seminars, and other programs that might be of interest to followers of the Consensus Point blog and devotees of Huunu.  We brave airport security so you don’t have to!


Consensus Point gets Out of the Office 

2014 MRA Insights and Strategy Conference – June 4 – 6 – Chicago


Sponsored by the Market Research Association (MRA) – a premier organization that provides education/certification, career advancement, promotion, governmental advocacy, and networking opportunities for the market research profession – the annual Insights and Strategy (ISC) Conference drew hundreds of attendees to Chicago’s Hyatt Regency McCormick Place.  Attendees from virtually every “spoke” of the market research wheel (e.g., corporate researchers, independent supplier-side researchers, research analysts, research executives, and innovators) were well-represented.


In addition to providing participants with the opportunity to learn about industry innovations and trends, the three-day conference offered extensive opportunities to focus on:


  • Both basic and advanced MR technical skills,
  • Analytics and data synthesis, and
  • Developing or enhancing business integration and storytelling skills …


… the building blocks that help ensure that the research we request, design, conduct, or analyze provides optimal value to its end-users.


This year’s conference offered more than 30 hours of thought-provoking, engaging speakers who were selected through a process of competitive juried evaluation.  Thought Leaders comprising the diverse panel of keynote speakers at this year’s MRA ISC conference included Consensus Point’s own CEO, Linda Rebrovick.


Linda, along with Julie Wittes Schlack (SVP of Innovation at the global “consumer collaboration” enterprise, Communispace) hosted a joint presentation under the conference’s “Innovation” umbrella, entitled Unleashing the Power and Dynamics of Prediction Markets.  During their session, Linda and Julie explored the value of predictive market research within the context of the broader trend towards engaging gamification techniques for consumer collaboration.  In addition to providing a thumbnail history of the science and art of predictive market research, the presentation offered attendees a sneak peek at the critical role that predictive market research methodology technology will play globally over the next three years and beyond, within the larger scope of our industry’s future.



The audience seemed to find the discussion of specific case histories to be one of the most compelling parts of this well-attended presentation.  Linda and Julie used these case histories to illustrate how several of the largest global companies have used prediction market research in support of business objectives as diverse as prioritizing product features, honing new initiatives, and optimizing concepts, messaging, and promotions.  Julie provided fascinating in-depth detail about the ways that her own company Communispace, leverages Huunu – the research platform on which prediction market research is built – to yield “research on research” and client successes.


In addition to the Consensus Point’s presence among the keynote sessions, Consensus Point’s Henry Pile (Vice President of Sales and Marketing) and John Barrett (Vice President of Business Development) spent a productive and energetic several days engaging the crowd from the vantage point of their booth in the Vista Lounge.  The two offered onsite demos of Huunu technology, and discussed areas of interest that included employee markets, consumer studies, and ongoing prediction market projects with consumer enterprise companies and research companies alike.  After spending three days interacting with an enthusiastic crowd, Henry described participants’ general reaction to the predictive markets paradigm in very positive terms, stating, “MRA ISC is perfect. The crowd is not overwhelming in size or skewed to one type of research field, rather, a wide variety of researchers and supporting groups are easily accessible for multiple conversations that, while casual, are also meaningful. We walked away with new relationships and a list of potential clients. We couldn’t ask for anything more.”


For a copy of Linda’s MRA ISC presentation or for answers to any questions you may have about prediction markets research or the Huunu research platform, please email us at

“I Like It” … But How Much?

 Those of us of a “certain age” probably remember hunkering down in front of the TV to watch eternally-young Dick Clark host his American Bandstand television series.  Along with serving as a primer for learning the “must-know” dances of the day, a much-awaited feature on each week’s broadcast was the Rate a Record segment.  In that segment, a pair of teens was asked to use a rating scale (ranging from 35 to 98 points, because Clark truly believed that no song was either completely terrible or utterly perfect) to describe how much they did or did not enjoy a featured record.  The teens rated two songs per segment, and their ratings were averaged to produce a single score for each record played.

This feature – which was the genesis of the saying “I like it … it’s got a good beat and you can dance to it” – served as a launch pad for countless hit records.  Viewers put a lot of stock in the ratings and comments provided by their peers, so if a song received a high rating and exceptionally positive comments, it was likely on its way to frequent rotation on your favorite radio station and a successful ride up the Billboard charts.

Great.  But what does any of that have to do with market research?

It may seem like a leap of Grand Canyon-sized proportions to go from discussing an American Bandstand segment to thinking about current marketing sciences methods.  However, Rate a Record is, in actuality, an excellent example of “primitive” predictive market research:

  • It uses a self-selected sample (the participants presumably agreed to appear on the segment) of “non-experts”;
  • Rating scores are aggregated to yield a single rating per record; and,
  • Participants are asked to rate the intensity of the appeal of each song and explain why they provided the rating they did.

Of course, predictive market research á la Huunu is several magnitudes of certainty above Rate a Record in terms of its credibility!  The Huunu platform includes a complex algorithm behind the façade of a deceptively-simple approach.  This platform has been tested by myriad marketing sciences professionals and is a proven tool for assessing the likelihood of virtually any behavior or market scenario coming to fruition.   It also takes the exercise a step further by asking participants to consider how they believe the market will behave or react overall, as opposed to thinking just in terms of their own response to a situation. In other words, will other people like it… does it have a good beat everyone can dance to?

But how can we rely on the predictions of a relatively small, self-selected sample – and a predominately “non-expert” one, at that – to accurately predict events and reactions that will occur within a given marketplace?  By and large those responding to the question have no inside information to inform their opinions when they predict the outcome of market research scenarios that are presented to them.   Since the “end game” in prediction market research is to correctly assess future behavior or opinions, the approach that was just described sounds counter-intuitive, and even somewhat preposterous.

What’s so important about “intensity of emotion”? 

This is where the metric of “intensity of emotion” – which some prefer to label as “confidence in the response” – comes into play.  In predictive market research, respondents are, in essence asked to “weight” their own data, based upon how confident they are that market behavior will respond as they’ve predicted.   So the more confident they are that market behavior will mirror their prediction, the more their response “counts” in the final aggregation of data.  If they believe that they have a great deal of knowledge and insight into the situation at hand, they will weight their data accordingly by providing a rating that reflects a large degree of confidence in their response; conversely, if they have less confidence in their prediction translating to reality, they will offer up a lower “confidence” rating.   In aggregate, the confidence ratings provided by respondents are remarkably accurate; predictions about which “the crowd” expresses a high level of confidence tends to be extremely likely to come to fruition.

Both empirical and theoretical research support this overall approach, and have shown us that a large number of diverse non-experts can make predictions that, when viewed in aggregate, are more accurate than those of a small number of experts1.  Why?  Because while each individual prediction that is provided by respondents may contain bits of truth mixed with various misconceptions, the bits of truth are correlated with each other so they add up to a “larger truth”. Conversely, biases, errors and misconceptions are not correlated with each other (because they are independent) across the entire respondent sample; these erroneous pieces of information, in essence, cancel one another out2.  The integrity of the predictive data is so exceptional in large part because aggregated responses reflect the highly-accurate confidence metrics described above.   This phenomenon even has a name:  It’s known as “The Wisdom of Crowds”3.

Data weighted by participants’ confidence in those responses = A more reliable foundation for critical decision-making

As no less of a visionary than Gandhi told us, “A ‘NO’ uttered from deepest conviction is better and greater than a ‘YES’ merely uttered to please.” Knowing the conviction – the confidence – behind the data that respondents have provided gives added credence to your predictive data.  And that, in turn, allows you to have more confidence that the decisions you supporting with this data are smart, solid ones.


1Sunstein, Cass R. 2006. Infotopia: How Many Minds Produce Knowledge. New York: Oxford University Press.


2Surowiecki, James. 2004. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. New York: Doubleday.


3Bingham, Alpheus and Nagar, Yiftach.  2013. Prediction Markets:  A Practitioner’s Guide.

The WHY Behind the BUY: 

Blending Closed- and Open-Ended Responses to Yield a Holistic View of Your Target Audience

From the lush Renaissance paintings of Sandro Botticelli to the iconic pop art soup cans of Andy Warhol and beyond, elements of shadow and light add a vital physical and emotional perspective across the creative spectrum of artistic styles.   These talented artists could certainly have rendered an accurate, recognizable reproduction of virtually any subject that we can imagine.  However, without gradations of shadow and light, it seems very unlikely that their work would have been nearly as compelling or nuanced.

Similarly, when you have questions that need to be answered to enable you to make critical decisions about the way you need to approach the direction, nature, or needs of your business, you can certainly start by asking how respondents believe your target market will behave given a specific set of parameters …

However, to really gain insight into the responses that you receive, you also need the “shadows and light” to understand why respondents answer as they do:

  • Why do respondents believe that a candidate’s position on creating jobs is more likely to drive election results than his stance on funding research into alternative sources of energy?
  • Why will most physicians continue to prescribe the current market leader in your product’s therapeutic class, even though they believe that it lacks sufficient efficacy?
  • Why do respondents believe that most consumers will be drawn to a blue car, mp3 player, or package of potato chips rather than a red one?


Why ask “Why?”:  Open-ended responses add a more granular, qualitative bent to your quantitative data.

The Huunu prediction market platform provides these nuances of shadows and light by going beyond asking respondents to provide “yes/no” closed-ended feedback. The inclusion of open-ended follow-up questions allows prediction markets research to offer a “deep dive” look into the dynamics of the issues you need to understand.   In addition to capturing the intensity with which respondents “buy in” to the closed-ended responses they provide, prediction markets research also captures a kaleidoscope of nuanced open-ended reasons that respondents answer as they do.

These open-ended follow-up responses are provided to you in two different ways – verbatim and coded/categorized – so you have the best of both worlds:

Viewing respondents’ verbatim responses in list format, you’ll be able to read exactly how each survey participant replied to the question, including the specific words and phrases that he or she actually used.

And, with the coded/categorized data, these open-ended responses are tallied within discrete “buckets” or groupings that capture the main theme of those responses; these groupings allow you to discern any patterns or trends that may emerge within different demographic or market-specific subgroups of interest.

So your depth of understanding and perspective based on a survey question that looks like this:

Screen Shot 2014-05-29 at 7.26.11 AM

Expands to look like this after processed through the Huunu prediction market algorithm:

Screen Shot 2014-05-29 at 7.26.34 AM

And this includes:

Screen Shot 2014-05-29 at 7.26.42 AM

 And is further enriched by this as well:

Screen Shot 2014-05-29 at 7.26.51 AM

 With the more nuanced and “full-color” perspective that the Huunu’s approach to research provides, you can have even more confidence that the decisions you need to make will be the best ones for your product or organization.  Why?  Because you know that your decisions are based upon a richer, deeper, and more comprehensive understanding of how your target market thinks and behaves.

Next week, we’ll be stripping away the marketing sciences jargon as we take a look at why intensity of feeling/confidence is included within the Huunu prediction markets platform, and why this metric is such a critical component of the Huunu algorithm.

In the meantime, for any questions you may have about Huunu, please email us at