Voices: the CMB Blog

You know you’ve been to a great conference when the ideas and insights are still percolating and expanding weeks later; the Insights Innovation Exchange Conference in Philadelphia definitely fit that bill.  In part one of my take on the conference I talked about the change we’re seeing in the market research industry.  In this post, I’ll discuss the implications and manifestations for the change.Technology is driving the change, but people will lead it 

Technological changes are a primary piece of the “revolution,” but does this mean we will do more with less? The short answer is no. Technology will not reduce our need for people.  In fact, the big changes introduced by technology and new tools & techniques will require most market research firms to aggressively hire more people, not fewer. The challenge, however, is defining and finding the new talent and skills that will apply to the market research of the future.  Data management skills will be critical, as will business systems knowledge. Most importantly, strong logic and an understanding of decision theory will be big differentiators for the professionals of tomorrow. 

A wider view of consumer behavior
Besides the change in how we conduct our work, technology is changing the way we view behavior.  IIeX focused an entire track on neuroscience and emotional measurement, with a variety of emotional measurement techniques like fMRI, EEG, eye tracking, and facial recognition becoming more mainstream (see Mediapost’s: The State Of Neuroscience In Market Research)

If some in our industry see these new technologies as just measurement techniques, they’re not seeing the forest for the trees.  In fact, the trends and changes in the industry reflect new consumer behavioral models that reflect multiple aspects of decision making processes. During the conference, I even noted the fact that we seem to have reached “critical mass” with regard to behavioral economics.

Gone are the days of the rational economic decision maker.  Instead, advances in neuroscience and behavioral economics reveal the strong emotional components of all decisions.  If you don’t have an understanding of the core value and applications of behavioral economics and the new research in neuroscience, you may as well go back to using MS Office ’98, collecting data on 80-column punch cards, and worrying about how to conduct interviews via that new-fangled Internet.  Cognitive models developed within the past couple decades have gained acceptance and are frequently being applied in market research. The growing regard for intrinsic measurement gives me hope that we will achieve a more cohesive framework for addressing the emotional and subconscious layers of behavior. 

New innovators, new partners, new collaborators
The conference’s final day wrapped up with two presentations around a common theme: collaboration.  Gayle Fuguitt, CEO/President at Advertising Research Foundation (and former Vice President Global Consumer Insights at General Mills), presented “A Call For A New Collaborative Model,” highlighting ARF’s efforts to bring clients and competitors together to address the promises and challenges of biometric and neurological research methods .

Gayle’s central argument is built on well-regarded themes—organizations need to find new ideas and innovations by fostering the diversity of thought and value a broad team can provide.  Her advice:  “work with people who don’t laugh at your jokes” and “seek partners who are frenemies,” highlighting the fact that true collaboration doesn’t occur among the like-minded.  In a similar vein, Kyle Nel, head of International Consumer Research for Lowe’s Home Improvement, presented “Data Philanthropy: Unlocking The Power Of Adjacency Across Sectors.”  For Kyle, the focus for the future will be on “uncommon partnerships” to help companies gain a competitive advantage.    

These new relationships will take market researchers out of their comfort-zone, working with partners who might not bring the same rigor and methodological requirements. The hard work arises from more than accepting compromises; instead, the greatest effort (and reward) comes from working with new partners to find an optimal solution aligning the strengths of each participant with the desired objective.  When working with technology partners, market researchers must be aware of tradeoffs when using the technology; no technology solves all problems. (BTW, technology partners, you’re not off the hook either. You must be aware that you can't solve all problems and will need to partner with market researchers to create optimal solutions for the business objectives). The effort of collaboration is a matter of compromise and acknowledging that “perfection is often the enemy of progress.” 

A great opportunity
In spite of all of the posturing about the end of market research as we know it—the irrelevance of the “long-form survey” and the un-engaging nature of many online interview formats, I came away from the conference with a positive outlook on the industry.  We‘re in a unique position, intimately involved in the largest trends that are shaping business and the economy: mobile, social and big data. The Information Economy is fully upon us, and market research has the opportunity to seize the value that new technologies are bringing to businesses and the economy.  It’s a matter of hard work, collaboration, and courage to accept new ideas and change that will allow us to take advantage of these opportunities.  

Jeff is VP of Market Science Solutions at CMB. This marks the first, and probably last, post accompanied by a picture of Vanilla Ice. Find Jeff tweeting @McKennaJeff.

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Welcome to Part One of my coverage of the Insights Innovation Exchange Conference (#IIEX) that recently wrapped up in Philadelphia. The event was three solid days of presentations and panel discussion on the changes and innovations that are shaping the future of market research and the business insights industry. The event targeted insights practitioners and anyone who wants to deliver evidence-based business insights to their clients. The event focused on the future of the industry, and the usual suspects were there: mobile, social, gamification, Big Data, neuro-measurement tools (like eye tracking and facial coding), and communities. The vendor space was filled with companies offering technological solutions, and the lion-share of presentations focused on at least one of these tech aspects. I was surprised, and pleased, to discover that this collection of innovation agents focused less on the tools and technology (partly because speakers were limited to just 20 minutes) and more on fundamental elements of change in our industry. In Part One, I’ll briefly summarize our current state. In Part Two, I’ll describe the manifestation of that change for future growth.

The Shift from Old to New Research: 

“We no longer live in a world where information is rare.  In contrast, we are overwhelmed with data, Big, Medium and Little. This represents the most fundamental challenge to the business model of market research since its inception.”

That’s Dr. Larry Friedman, Chief Research Officer at TNS, who packed a comprehensive synopsis of the market shift into his 20 minutes. The key points are nicely summarized here.

It’s true that because we are an industry that has established its value through collecting and managing data, market research faces a difficult future; its fundamental activity has become less valuable. For a hundred years, businesses and managers have turned to market researchers to design studies, collect data, and translate the data back to them. Some market researchers might find additional value in providing insights and recommendations, but it’s rare to be rewarded with full “consulting rates” for this work. 

Given that data can be collected at low cost, the management tasks of sample design are not as important today, and the science behind collecting the “right” data can be glossed over with more (and cheaper) data. Even the translation and application of research data to business decisions are becoming more common with easier-to-use software and training. Tableau, Good Data and (even) MS Excel are some of the analytical tools that now put data directly into the hands of business decision-makers. Heck, even kindergartners are learning the “basics” of market research.

But market researchers still have a head start. As the professionals who have experience with managing and translating data, we should be able to fill a vast need for curating the wide variety of data files and warehouses to support business analyses. Additionally, our knowledge of data types (e.g., categorical vs. scale, just to name one of the many ways we look at the multidimensionality of data) and structure is critical for laying the foundation for information users to access and translate data most efficiently and effectively.

We might not be able to design the right sampling methods, but who among us has not fixed a study where the sampling was done incorrectly? We might not be able to design the questions to get the best data for analysis, but who hasn't needed to come up with a method to fix data that had been coded incorrectly or had incorrect skip patterns applied? (Just to name a few of the complications that can occur). All of these new data streams bring many more opportunities to fix, translate, and apply the results to support the decisions our clients need to make.

The takeaway: there are major challenges but Market Research isn’t dying, and it’s not on life-support. It’s a reasonably secure business that has attracted other companies to its space because companies find great value in evidence-based decision making. Let’s be honest, Google wouldn’t be making a big investment in Google Consumer Surveys if it didn’t see an opportunity to make a lot of money.

But when Google enters your space, you better believe you need to put your helmet on, and get ready…

Jeff is VP, Market Science Solutions at CMB. He is just as comfortable explaining advanced analytical models as he is parsing the cultural significance of "Tommy Boy." Find him tweeting @McKennaJeff.

Last weekend, my family and I took a trip to Charlotte, North Carolina.  We rented a car and stayed at a hotel.  Within 12 hours of arriving home I received an online survey from each company.  In both cases, the experiences were excellent and I was happy to share the details.  In one case, the survey took me about 1 ½ minutes to complete.  The other one took me about 10 minutes. For the survey that took me 1 ½ minutes, when I reached the end, I thought “Well, they asked about the key aspects of the experience and got what they needed.”  In contrast, by the time I reached the midpoint of the 10 minute survey, I was exhausted and just wanted to end the damn thing – and then when I reached the end, they asked if I wanted to answer more(!?!) questions.

In the 1 ½ minute survey I could clearly see the questions focused solely on the experience and managing the key aspects of the service –they probably have more than enough data to get deep insights since they know who I am, my travel details, and have similar data for the thousands of other travelers who are also rating the experience.

In the 10 minute survey, I could see that the company was asking for details beyond the experience, they were seeking to understand competitive positioning and future intended travel behaviors—all things that are clearly outside the scope of the service experience.  They also asked questions about very detailed aspects of the experience e.g., the mechanical condition of the car and softness of the towels.  It led me to ask: “Really?  You want me to rate this aspect of the service?  Aren’t you guys smart enough to tell these things are up to standard?” 

Here’s an example from another industry: homebuilding.  I’ve seen surveys that ask buyers to rate the window quality in the home.  Why?!?  Shouldn’t the builder know if the windows they are putting into the home are high-grade or low-grade?  Remember, we’re assessing the home purchase experience, NOT homebuyer preferences.  If you’re trying to achieve both in the same research study, you’re going to be (as Mr. Miyagi says) “like the grasshopper in the middle of the road.” 

As researchers and companies asking our valued customers for feedback, we need to be very aware of the unstated agreement for what’s in scope and out of scope for these customer experience surveys.  I’m not opposed to having surveys do “double-duty,” but we should be clear with our customers that we are doing so, AND not kill them with gruelingly long surveys.

Jeff is VP, Market Science Solutions at CMB. He always takes time for a customer experience survey, but keep it short he's very busy, he needs time to blog and occasionally tweet @McKennaJeff.

See how CMB is helping Royal Caribbean measure guest experience and improve customer satisfaction and retention. Click here.

You may have heard the latest from Disney—they’re about to introduce a new “MagicBand” wristband letting wearers take advantage of perks like skipping to the front of the line for rides, as well as pay for meals, and purchase gifts.  It offers guests the ability to leave the wallet and paper tickets at home and focus on having fun.  The benefits to Disney can be huge, and a lot of people are seeing it that way; as one headline proclaimed: “Disney creates the happiest data mine on earth.”  Pretty clever, but of course there are those who aren’t quite as happy about the innovation; besides the thought of Big Brother entering our lives, won’t somebody think of the tan lines?But let's focus on the business aspect, the ability to track all activities and purchases on-park creates an immense opportunity for marketing, and much of the chatter concerns how Disney can use the data for direct marketing.  Did the guest ride all of the roller coasters?  Target promotional offers touting the latest thrill rides.  Did the guest get a picture with one of the cast members?  Send a doll to the guest’s suite to increase engagement.  Did the guest make a purchase at any of the retail stores?  Give them a coupon for a Disney store near their home.

Nearly everyone is coming up with ideas for how this might help Disney directly sell more of what it offers.  I’d like to think about how Disney can learn from this data in order to innovate and improve the experience.  In the direct marketing examples, the data remains data— it’s used solely to trigger marketing offers.  For market researchers, the data isn't useful until we find relationships that are relevant to decisions.

So, here is my challenge for you: what type of analysis do you think needs to be done?  What potential relationships might Disney find to innovate and change the experience?

I’ll get it started:

Disney could run on-property communication tests to improve messaging and information delivery.  By placing unique signs throughout the park, Disney can track all guests who pass each sign and capture behaviors after passing the sign.  Instead of waiting many weeks or months to gather feedback, Disney can get an “immediate” understanding of which signs work best – and potentially why.

Tell me your ideas in the comments:

Jeff is VP, Market Science Solutions at CMB. He'll have a pair of shiny new mouse ears for the most interesting idea. If he's not wearing his wristband you can still find him tweeting @McKennaJeff.

While thinking about the challenges of Big Data, I’m reminded of this simple chart from the neat site Indexed:

Because, as we get more and more data (and information) and move further to the right on the x-axis, we face more confusion throughout our work. We face questions like: how do we get a handle on all of the information?  How do we manage the volume to avoid information overload and confusion?  How do we find the right balance?

It’s an interesting challenge: initially, when I think about handling “big data” my eyes look to the right side of the chart and I think about how we can help clients to move the curve.  But I suspect there are many people and organizations who respond to the challenges of big data by simply staying on the left side.A colleague made the point that data quality and information relevance also play a big part in reducing confusion, and that’s very true.  Even when accounting for this, we still run into the challenge of having too much or too little of a good thing, so let’s just think about the volume in this discussion.

We’re always seeking to find that middle ground, but we choose to seek that middle ground from being on either the left or the right side.  It's tempting to think that one side is better than the other – for instance, it is better to err on the side of too much information and then reduce confusion by reducing the information (hence, my initial biased view). However, an equally strong argument can be made for erring on the side of too little information and then reducing confusion by seeking more information later.

I’m trying to figure out how Big Data plays in all of this.  Obviously, the information scale is rapidly increasing, leading to the potential for greater confusion. If you choose to err on the side of too much information, you will need to work harder to reduce information to find that optimal point; and if you err on the side of too little information, you will need to work harder to gather more information.

How might this play out in a project?  We find a lot of examples where managers have strong positions on the two sides of the chart:

Data Minimalists:These people like to “keep it simple” and desire just a few key measures and facts to make decisions.  If they don’t have enough information, they send the team back out to find more.

Data Maximizers: These folks need more data to make sure they haven't overlooked any important details or facts relevant to a decision.  If they don’t have the right information for a decision they send the team back for more analysis.

Neither side is “perfect,” and I suppose the optimal answer is to find the right balance of people who err on the right and who err on the left. Good managers know to balance the two sides and appreciate the benefits that each bring to the design and planning of a decision-support information system.

Jeff is a senior consultant, methodologist, and unabashed lover of charts. He's on a mission to make sense of Big Data and reduce confusion wherever it's found. He tweets occasionally at @McKennaJeff.

Have you ever experienced one of these data dilemmas? Tell us about it.

1. –You have so much data, it feels like you’re drinking from a fire hose.

2. –It’s too hard to “connect the dots” between your data sets.

3. –You’re paying for new studies to get data you already have…somewhere.

4. –It’s a struggle to get the data you need from your data warehouse.