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Jeff McKenna

Recent Posts

Stop, Collaborate, and Listen: Market Research in the Information Economy

Posted by Jeff McKenna

Tue, Jul 09, 2013

Vanilla IceYou 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.” 

women looking transA 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.


CMB is proud to be named to the Honomichl list of the Top 50 U.S. Market Research Organizations. Check out our case studies to learn more about our business decision focused approach.

Topics: big data, consumer insights, marketing science, conference recap, growth and innovation

More Cowbell? What Market Research Needs Right Now

Posted by Jeff McKenna

Mon, Jul 01, 2013

morecowbellWelcome 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.


Topics: big data, consumer insights, marketing science, conference recap, growth and innovation

When Customer Experience Surveys Attack (or Just Go out of Scope)

Posted by Jeff McKenna

Wed, Jan 30, 2013

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?” 

asleep at deskHere’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.

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






Topics: travel and hospitality research, research design

Big Data: For Disney, It's All in the Wrist

Posted by Jeff McKenna

Thu, Jan 10, 2013

Disney MagicBandYou 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.

Topics: technology research, big data, travel and hospitality research, digital media and entertainment research, retail research

Data vs. Confusion

Posted by Jeff McKenna

Thu, Nov 08, 2012

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

data versus confusionBecause, 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:

The Little Orange Kitten 753345

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.



LionData 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.



jeffs simple chart


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.


Topics: big data

Marketers: Don't Despair!

Posted by Jeff McKenna

Wed, Oct 24, 2012

The recent research study showing that marketers rank lower than politicians on the “respectability scale” might feel like a kick in the gut for most of us in this role. 

From the research: only 13% of consumers agree that marketing benefits society.  It’s no surprise that teachers, scientists and engineers are the top of the list, but marketing even falls below bankers (32%), lawyers (34%), and politicians (18%). One point of solace, marketers are tied with actors and dancers; so, we’re not alone.


If we deconstruct the research, we can find plenty to take issue with.  What research study isn’t immune to that?  For instance, the focus of the research is about online advertising, while the questions about professional respectability come after questions about the effectiveness of different marketing methods.  To what extent has this approach primed respondents in a certain direction?

Additionally, when you look more deeply at the results, you find that people still “respect” the need for marketing within business.  Most, in fact, consider it “strategic” and necessary for sales.

Adobe marketing research

So, the research findings shouldn’t be taken too personally.  As noted earlier, marketers are in the same boat as actors and dancers.  It makes me think of Ode by Arthur O’Shaughnessy:

We are the music makers,
And we are the dreamers of dreams,
Wandering by lone sea-breakers
And sitting by desolate streams;—
World-losers and world-forsakers,
On whom the pale moon gleams:
Yet we are the movers and shakers
Of the world for ever, it seems.

Or, as the great Willy Wonka puts it:


Who but a marketer would ever create lickable wallpaper with snozzberry flavor???

So marketers, don’t give up on your role and profession. And remember that without you, the world would be a place with much less flavor and much less fun.

When Jeff's not busy contributing to society at large, he serves as a senior consultant and methodologist for CMB; making sense of big data, and speaking on topics like mobile and the future of market research.

Topics: consumer insights, marketing strategy

Data Oceans: You're Gonna Need a Bigger Boat

Posted by Jeff McKenna

Tue, Jul 17, 2012

big data cmbWe hear a lot about Big Data—from Target using predictive analytics to tell which of its customers are pregnant, to MIT and Intel putting millions behind their bigdata@CSAIL initiative. Yet, I’m struck by the fact that most of what I read, and hear at conferences, is about  the wealth of data technology can provide researchers, managers and analysts. There is very little about how these folks can avoid drowning in it, and most importantly make the decisions that address business challenges.

For the uninitiated, the Big Data revolution is characterized by three traits:
  • Volume - Technology has led to an exponential increase in data we have available

  • Diversity - We can aggregate data from a wide range of disparate sources, like customer relationship management (CRM) systems, social media, voice of the customer, and even neuro-scientific measurement.

  • Speed – We are able to field and compile quantitative studies within days; before online, IVR, and mobile data collection methods were available this took weeks

While there may be other definitions of Big Data, it is clear that technology is making data, larger, wider, and faster. What we need to think about is how technology can make our response to and analysis of data larger, wider and faster as well, and avoid drowning in it.

The water metaphor is often used to describe Big Data, and the folks at the GreenBook Consulting Group use the term “oceans of data.”  They describe three business models driven by data: The Traditional (based on Data Ponds), Transitional (based on Data Rivers), and Future (based on Data Oceans).

Traditional market research based on small discrete amounts of data has its place – but as the folks at GreenBook point out, market researchers must face the fact that progression from these Data Ponds to Data Oceans is inevitable.  The Traditional mindset is faced with inertia and will decline in relevance in the next five to ten years.

Those who are in the Transitional phase are moving forward, folks here are in a “reactive” position.  They are seeing these changes around them and applying some big data solutions in their word.  They might have tried one or two tools or are even using them now on a regular basis. But when faced with large amounts of data, they think about technology only in terms of how to collect more data – not in how to manage and apply it quickly and in big ways.  In contrast, the Future mindset takes a proactive approach; these are the people who think about how technology will be the fundamental basis for applying the ideas and solutions to lead companies.

In the coming weeks I’ll be discussing specific examples of technologies that are helping push market researchers towards this future. I’d love to hear from you about things you’re doing to respond to Big Data and the challenges and opportunities you are facing as we confront these Data Oceans.

Watch our webinar Using Technology to Help your Entire Company CMB techUnderstand and Act on Customer Needs here

Posted by Jeff McKenna, Jeff is a senior consultant at CMB and team leader for Pinpoint Suite-our innovative Customer Experience Management software. Want to learn more about how Pinpoint Suite can help you make sense of your "Big Data," schedule a demo here.

Topics: data collection, big data, data integration

"Big Data," Expert Systems, and the Future of the Market Researcher

Posted by Jeff McKenna

Wed, May 16, 2012

Big Data future of market researchEarlier this month I had the chance to present at the Market Research Technology Event in Las Vegas. Beyond the fact I just could not get accustomed to watching people walk by conference rooms swigging beer and wearing in flip flops; for me the event raised more questions than provided answers.

During the conference, one of the most quoted reports was McKinsey’s: Big data: The next frontier for innovation, competition, and productivity.  For me, one of the most striking takeaways from the report was a prediction that by 2018, the US will have a shortage of talent necessary for organizations to take advantage of big data—the US alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis to make effective decisions.

After we market researchers take a moment to celebrate our job security, we should consider that skilled market researchers will be asked to fill the space by taking on more tasks and working longer hours.  As the gap widens between the influx of data and the analysts we need to make sense of it, are 80 hour weeks inevitable? Certainly workforce globalization will be a key to filling “big data” needs, but I was very surprised to hear little discussion of how technology will help us deal with this shortage.

I left the conference with the theory that the “new technology” we need is the yet-to-be-realized application of a tool to change a process to yield a quicker, lower cost, or better quality outcome.  I think market researchers have yet to focus on how technology can act as a surrogate for the role they play within their organizations

So what might the future hold? I expect technology will allow market researchers to develop “analytical bots” to make sense of the vast ocean of data to answer specific business questions raised by internal clients. Watching Watson and Siri answer questions of fact with extremely high accuracy makes me wonder what our role will be.  If these machines “have all the answers” then what purpose do we have?  I don’t believe technology will replace market researchers; their skillset and output are still critical for companies to be competitive.  The purpose is to create the rules and algorithms that convert the facts into relevant information.  This is where market research skills will combine with technology to fill the resource gap.

We’ve heard a lot about expert systems—computer systems that emulate human decision-making. It’s my view that the market researchers who will lead in the next 5 to 7 years will be those who are setting up and managing expert systems, that take all of the facts and computations within large sets of data and apply what is relevant, to make decisions quickly, anywhere, and at any time.

Did you miss us at TDMRE? We'll be at the Audience Measurement Event in Chicago from May 21st to the 23rd. Register for a 25% discount by entering CMB2012 here.

Posted by Jeff McKenna, Jeff is a senior consultant at CMB and team leader for Pinpoint Suite-our innovative Customer Experience Management software. Want to learn more about how Pinpoint Suite can help you make sense of your "Big Data," schedule a demo here.

Topics: advanced analytics, big data, conference recap, growth and innovation

How Target Knows You're Pregnant: A Predictive Analysis Perspective

Posted by Jeff McKenna

Tue, Feb 21, 2012

Shopping CMBOn Sunday, The New York Times Magazine published a piece: How Companies Learn Your Secrets, by Charles Duhigg, author of the forthcoming The Power of Habit: Why We Do What We Do in Life and Business.  It’s an interesting article, especially for market researchers, and I recommend everyone take the time to read it.

Consumer "habits” are a big focus of the work we (market researchers) do as we seek to understand consumer behavior. From the perspective of the article, a large part of what we do is identify behavioral habits to help marketers find ways to insert their product or service into people's habit processes. 

In this blog, I want to focus on the insights the story shared about predictive analytics. Much of Duhigg's article looks at how Target conducts advanced analytics to identify data within their CRM system to predict whether a shopper is expecting a baby.  From a business process POV, and how we think about using predictive analytics, it’s important to point out a few relevant facts for market researchers:

  1. It wasn’t a “fishing expedition”: The analysis started with a clear marketing benefit as the outcome – Target wanted to begin promoting itself to expectant mothers before the baby is born. As the article points out, by marketing to these families before the baby becomes public knowledge, Target can get beat the flood of marketers that begin pitching a range of products and services once the birth is entered into public record.  It was the marketing team that came to the analyst with a high-value opportunity.  The analyst did not create the winning marketing idea (“Hey! Let’s market to expectant mothers before the baby is born!”).  Instead, the analyst looked under every stone and in every corner of the data to find the key to unlock the opportunity.

  2. The research didn’t stop with finding the key: The application of these insights required a lot more research to determine the best method of implementing the campaign.  For instance, Target ran several test campaigns to identify the best offers to send to the expectant mothers, and cycled through several messages to find just the right one in order to avoid revealing that Target was prying into the data.  Although the predictive analytics found the key, Target still relied on a comprehensive plan to make sure the findings were used in the best possible manner.

  3. Don’t let this story increase your expectations: The Target approach has had a big impact on how the company markets to a highly valuable segment of shoppers.  It's a great success story, but it's also something that happened ten years ago.  While I’m sure the Guest Market Analytics team achieves many victories along the way, they also spent a lot of time reaching “dead-ends,” unable to find that magic key.  And most of the time, the predictive solution yields valuable but incremental gains, these high-profile stories are few and far between.

The article shares many interesting ideas and insights; the story about the re-positioning of Febreze highlights another great research success. I'm looking forward to reading Duhigg's book, and if it covers more of these thought provoking business cases, I expect we will be seeing Charles Duhigg’s name popping up in other discussions on market research.

Did you read the article? What do you think?

CMB Webinar tools and techniques

Did you miss our latest Webinar? Learn how Aflac Unleashed the Power of Discrete Choice, Positioning their Brand for the Future 


Posted by Jeff McKenna, Jeff is a Senior Consultant at CMB, and the creator and host of our Tools and Techniques Webinar Series.



Topics: advanced analytics, consumer insights, marketing science, customer experience and loyalty, retail research

Mode is the Most: Kids Get a Taste of Market Research

Posted by Jeff McKenna

Tue, Feb 14, 2012

A topic near and dear to my heart just became nearer and dearer. Take a look at one of the classroom assignments my kindergarten daughter recently brought home from school:

Market research kids

Isn’t this amazing? Kindergartners are learning the basics of market research! Ever since I got into this business of market research, whenever someone asked me about my job, the answer nearly always comes down to: “I write the survey questions that people ask when calling you during dinner time.”

But, not anymore! My answer can now be: “You know how you might want to learn what is the most popular animal at the carnival? I’m the one who writes the questions, counts the tally marks, and creates the bar charts to show you what is most popular.” How cool is that?

Webinar visual

Wish your research had more pictures of ponies, or just more visual interest? Check out our webinar: Appearance Counts: How to Tell a More Visually Compelling Story with your Data


Posted by Jeff McKenna, Jeff is a Senior Consultant at CMB, and the creator and host of our Tools and Techniques Webinar Series.

Topics: research with kids