Voices: the CMB Blog

Anyone who’s ever managed a service-oriented team knows that success or failure is often contingent on the successes or failures of your employees. Their professional successes—and sometimes even their personal successes—have a positive and lasting impact on your organization. Then, of course, there’s the flip side of that coin that can result in collateral damage across an organization.At CMB, we take talent management seriously because it’s serious stuff. We literally live and breathe in a team-oriented environment to deliver against our client commitments. To do this day-in-and-day-out takes both the hard skills required of market researchers coupled with softer skills required for teams to thrive.  From the interview process to active team management to individual development plans, we are building and supporting teams and individuals that align with our core CMBehaviors:

• Accountability

• Attention to Detail

• Autonomy

• CMB Citizenship

• Collaboration

• Communication

• Flexibility

• Initiative

• Problem Resolution

• Strategic Thinking

The CMBehaviors aren’t just a bulleted list of the latest organizational buzz-words; these concepts have both meaning and impact, in addition to being closely related and producing significant interaction effects. For instance, Strategic Thinkingis a hallmark of CMB and one of the reasons we have a history of successfully helping clients solve their unique business problems. It’s not good enough to have only the senior-most team members engaged in Strategic Thinking—it’s expected of the entire project team. This, of course, can only be realized through effective Collaboration. Project teams are often greater than the sum of their parts, but to fully realize the full potential of the team requires clear and concise Communication.

It’s not easy to hire and manage to these standards, but we believe it’s part what our clients recognize as unique (and dare I say better?) about working with CMB. Our commitment to our colleagues (we like to call ourselves CMBers) is a direct reflection of our commitment to exceptional client service.

Heather is VP of Client Services and is awestruck with the potential and commitment of the current Client Services team members at CMB. She’s proud to be a member of the awesome team who get to call themselves CMBers.

Would you like to work with some of our top clients like eBay, Facebook, Hilton, Bank of America, Starbucks, Avis-Budget, Neiman Marcus, and Electronic Arts?

Join our team! We are a Honomichl Top 50 company offering the flexible collaborative environment of a small company with the big world expertise that comes from working with leading brands across a wide array of industries.

Check out our open positions here.

I know what you are thinking...“What the heck is she TALKING about? How can Tinder possibly relate to mobile ethnography?”  You can call me crazy, but hear me out first.For those of you who may be unfamiliar, Tinder is a well-known “hook up” app that’s taken the smartphone wielding, hyper-social Millennial world by storm. With a simple swipe of the index finger, one can either approve or reject someone from a massive list of prospects. At the end of the day, it comes down to anonymously passing judgment on looks alone—yet if both users “like” each other, they are connected. Shallow? You bet. Effective? Clearly it must be because thousands of people are downloading the app daily.

So what’s the connection with mobile ethnography? While Tinder appears to be an effective tool for anonymously communicating attraction (anonymous in that the only thing you really know about the other person is what they look like), mobile ethnography is an effective tool for anonymously communicating daily experiences that we generally aren’t as privy to as researchers. Mobile ethnography gives us better insight into consumer behavior by bringing us places we’ve never gone before but are worthy of knowing nonetheless (Cialis, anyone?). Tapping into these experiences—from the benign to the very private—are the nuts and bolts behind any good product or brand.

So how might one tap into these experiences using mobile ethnography? It’s actually quite easy—we create and assign “activities” that are not only engaging for participants, but are also designed to dig deep and (hopefully) capture the "Aha!" moments we aim for as researchers. Imagine being able to see how consumers interact with your brand on a day-to-day basis—how they use your product, where their needs are being fulfilled, and where they experience frustrations. Imagine “being there” when your customer experiences your brand—offering insight into what delights and disappoints them right then and there (i.e., not several weeks later in a focus group facility). The possibilities for mobile ethnography are endless...let’s just hope the possibilities for Tinder come to a screeching halt sooner rather than later.

Anne Hooper is the Director of Qualitative Services at CMB. She has a 12 year old daughter who has no idea what Tinder is, and she hopes it stays that way for a very long time.

At the IIR #TotalCEL conference this week in Miami, behavioral motivations fueled the majority of presentations. In 2014, the economics of behaviors are getting quantified. Marketers and their peers in Operations are gently guiding their companies to a deeper understanding of the emotional drivers behind their problems. 

7 of the Emotional States Presented at #TotalCEL: 

1. Neutral: “People prefer to be at a neutral state emotionally,” says Daryl Travis, the CEO of BrandTrust and author of Emotional Branding – How Successful Brands Gain the Irrational Edge.  However, the customer journey is far from neutral. For example, customers who go to a department store might have emotional peaks (e.g. found a product on sale) and emotional valleys (e.g. had to wait in a long line). No matter the actual journey, Travis states that the customer’s end state and how problems are resolved are the two aspects of the journey that matter most. 

2. FOMO (Fear of Missing Out): Kassandra Barnes of CareerBuilder noted that Millennials are seeking promotion, advancement, training, and mentorship opportunities when searching for a job and are less concerned about benefits. Unlike their older counterparts, Millennials are casually, but continuously, looking for another job due to this primary emotional motivation: FOMO. In fact, 83% of full time Millennials are actively looking for new job opportunities, and 49% of Millennials search for new jobs while at work.

3. Let’s Bond: Moms today experience the lofty emotional attachment of bonding when they pick up the bottle with the orange ribbed cap, pristine curves, and the chalkboard image on the package of the glue that cements a relationship during play. Elmer’s Michelle Manning elaborated on parents, emotions, and the perfect packaging.

4. Forgiveness: According to Dr. Mark Ingwer, author of Empathetic Marketing, leveraging empathetic emotions is “the buried treasure of customer service.” The question he says is where to drop anchor? After working with Allstate, Ingwer conducted the fuzzy front end discovery research he calls “psych ethnographies” and this research yielded the insight about creating empathy in the product, hence Allstate’s Accident Forgiveness and Your Choice Auto. Both products have appealed to the audience and have dramatically ramped up revenues, increased customer satisfaction and staved the churn rate.

5. Eliminate Worry: Emotional and behavioral goals vary widely, depending upon the degree of trust each consumer has. One critical consumer obsession is to eliminate worry. You can see companies responding to that obsession in the mobile payment space, where we found security is a primary concern. While it’s still unclear which companies will win over the consumer mobile payment market, it will be interesting to see how and when these competitors adequately address the primary emotional and functional needs of the mobile payment user—worry free transactions. CMB’s Brian Jones presented the Future of the Mobile Wallet where he shared which industry (and which companies) may be best positioned to eliminate worry. Will it be a bank, a credit card company, an internet service provider, a technology company, or a retail store like Starbucks?

6. Belonging: Every time I hear Keith Ferrazzi speak, he’s written a new book, and I learn something new. He says that people don’t want to change so we should focus on which of the fewest people can change which narrowest set of practices and behaviors that can accelerate our results.  The key is to dig deep into the willingness to change.  Change is an emotional journey, and the highest order of the emotional food chain is “belonging.”  From childhood (“I said so”) to reason (“that makes sense”) to being "mission driven" and then focusing on “your stuff” we end up with belonging—our  basic need to relate to other people.

7. Look at me: What does your brand offer that your customers need?  The key thing that a lifestyle brand like Starwood gives is experiential currency for their social life.  The affiliation—you’re in their physical world gaining experiences—creates that sharable moment that is currency for us all. In contrast, other hotel properties don’t make it easy for you to take that perfect picture of yourself and your loved ones from a design standpoint.  When I go to an Aloft or a Westin, I have that very cool picture in their design inspired context. Starwood’s Stephen Gates, VP and Creative Director for Global Brand Design, talked about all of their brands, about the rain room at the MoMa, Starwood’s presence on stage at 3 of the last 5 Apple keynotes and their being featured in Apple’s advertising as well as “on the phone.” He says the work is king. The work dictates everything. The work runs his department. The work sells the brands and hotels. The work is what matters. His design thinking begins with some basic tenets—keep it simple, sweat the details, build a lifestyle or a visual personality that reflects the consumer, be relevant and authentic, break new ground, push innovation, think globally and go with swagger.

What are you doing with respect to emotional or behavioral economics? Continue the dialogue on Twitter with @julie1research using hashtag #MRX.

Julie is an Account Executive, she loves to connect with innovative big thinkers on topics ranging from emotion to complex choice modelling.

Not too long ago, after hosting a gathering of some of the most talented, innovative researchers on the west coast (or really anywhere) I heard a story about another gathering of talented elites—The Mavericks Invitational—the greatest surfing event in the world. Despite the fact that people often request I wear a wetsuit, and I once appeared in local stage production of the Keanu Reeves’ classic Point Break, this was the first time I’d heard of this event. The Cliffs Note version: the event is only held when the waves will be at their most challenging and the 24 invitees are given just 24 hours to make it to Half Moon Bay, CA to have a chance to compete.Basically, a group of the most talented people in their field, heavily invested in a single purpose, makes a beeline to a single place to make the most of a precious moment. The parallels with customer insights are obvious…no? As I see it, we in the customer insights world also have incredible waves of opportunity—for innovation, for serving new segment or entering markets, basically for helping our business partners make critical decisions with confidence. And just like our Mavericks, the best among us need to be nimble and driven enough to bring our partners in analytics, marketing and operations together to capitalize on these precious opportunities as quickly as possible.

Why customer insights in particular? For the same reason they don’t invite belly boarders to the Mavericks. The Customer Insights function (or if you prefer “Analytics Artists”) are in the best position to strangle the data, build coalitions, synthesize results from prior work and multiple data sources and seize the most impactful moments. I mean, who else can confidently talk about robust predictive models with Analytics folks over breakfast, then pivot to a discussion of the results of brand positioning work with in-house ad agency folks over lunch, and finally finish the evening with a nightcap of profitability projections from a conjoint study that will be shared with a CFO?  Insights folks, that’s who!

So I say to you, Customer Insights Professionals, when you hear the call to of a business critical insight that you work has produced, sound the cavalry charge yourselves and bring key members of your organization.  And if you’re feeling at all squeamish, then take inspiration from these famous Mavericks below:

Brant is CMB's Segmentation guru and VP of CMB's eCommerce and Retail Practice; he awaits his invitation to next year's Mavericks Invitational.

In Miami for Total Customer Experience Leaders? So are we. Stop by our booth and say hello to Julie Kurd @julie1research, and make sure you catch our presentation on the Future of the Mobile Wallet at 2:30 on Thursday.

Guess what? It’s 2014! The year of Super Bowl XLVIII©, the 100th anniversary of the start of World War I, the 70th anniversary of D-Day, and a whole host of other, generally not-that-impactful events, anniversaries, and changes. One event that will happen in 2014, though, is something which happens every two years: U.S. national elections.This seems like an odd way to start a blog, but bear with me for a moment.  Show of hands out there (ed. note: you’re welcome to actually raise your hand if you want, but I wouldn’t): how many of you readers have, at some point, become tired of the relentless political horse-race, always talking about who’s ahead and who’s behind for months and years on end?  I know I have, and chances are it’s happened to you too, but I’m going to ask that we all take a deep breath and dive once more into the fray.

The question of “who’s ahead” and “who’s behind” brings us to our discussion of statistical significance.  I’m going to talk today about how it works, how it can be used, and why it might not be quite as beneficial as you might think.

First, a quick refresher: when we take survey responses, test results, etc. from a sample of people that we think represents some broader population, there is always the risk that whatever results we see might be due to random chance instead of some other factor (like actual differences of opinion between two groups). To control for this, we can conduct significance testing, which tells us the likelihood that the result we have obtained is due to random chance, instead of some other real, underlying factor. I won’t bore you with the details of terms like p, α, one- vs. two-tailed tests and the like, but know that the methodology is sound and can be looked up in any AP-level statistics textbook.

Most organizations assume an “error range” of 5%, meaning that a data finding is statistically significant if the odds are 5% (or less) that the results are due to random chance. So, if we run significance testing on Millennials vs. Gen X’ers in a survey, and we find that the two are significantly different, we are saying there is a 5% (or less) chance that those differences are just random, and not due to actual underlying opinions, or price-sensitivity, or political beliefs, or receptiveness to that new hair-growth prescription, or whatever else you might be testing.

Now, if you have a huge data set and a fairly advanced statistical program, calculating significance is easy. But since most people don’t have access to these tools, there is another, much simpler way to think about significance: the margin of error. The margin of error is a simple way of determining how much higher or lower a result can be before it is considered significantly different. For instance, if your margin of error was ± 5%, and your data points were 60% and 49%, your data is (likely) significantly different; if your data points are 55% and 51%, they are not.

This brings us back to the political analogy; calculating the margin of error is how we determine whether Politician X is ahead of Politician Y, or vice-versa.

Let’s say, for example, a poll of 1,000 registered voters was conducted, with a sound methodology, and asks which of two candidates respondents support (assume no other options are presented in this circumstance, a small but notable difference for a future blog). We find that 48% support Politician X and 52% Politician Y. Because the sample size is 1,000, the margin of error is ± 3.1%. Since the difference between the two politicians is less than twice the margin of error (i.e., if Politician X’s share might be as high as 51.1% and Politician Y’s share as low as 48.9%), you would hear this reported as a “statistical tie” in the news. This would be because news organizations won’t report one candidate as ahead of the other, as long as the two are within that acceptable margin of error.

So that’s the political world, and there are many reasons networks and polling organizations choose to behave this way (aversion to being wrong, fear of being seen as taking sides, and fear of phone calls from angry academics, among others).  But in the research world, we don’t usually have nice, round sample sizes and two-person comparisons – and that’s why relying on statistical significance and margin of error when making decisions can be dangerous.

Let’s go back to that political poll.  The original sample size was N=1,000 and produced a margin of error of ± 3.1%.  Let’s see what happens when we start changing the sample size:

·        N=100: ± 9.8%

·        N=200: ± 6.9%

·        N=500: ± 4.4%

·        N=750: ± 3.6%

·        N=1,000: ± 3.1%

·        N=1,500: ± 2.5%

·        N=2,000: ± 2.2%

·        N=4,000: ± 1.6%

Notice the clear downward trend: as sample sizes grow, margins of error shrink, but with diminishing returns.

Now, we at CMB would advocate for larger sample sizes, since they allow more freedom within the data (looking at multiple audiences, generally smaller error ranges, etc.).  It’s no secret that larger sample sizes are better.  But I’ve had a few experiences recently that led me to want to reinforce a broader point: just because a difference is significant doesn’t make it meaningful, and vice versa.

With a sample size of N=5,000, a difference of 3% between Millennials and Gen X’ers would be significant, but is a 3% difference ever really meaningful in survey research?  From my perspective, the answer is a resounding no.  But if your sample size is N=150, a difference of 8% wouldn’t be significant…but eight percentage points is a fairly substantial difference.  Sure, it’s possible that your sample is slightly skewed, and with more data that difference would shrink.  But it’s more likely that this difference is meaningful, and by looking at only statistical significance, we would miss it. And that’s the mistake every researcher needs to avoid.

If I can leave you with one abiding maxim from today, it’s this: assuming some minimum sample size (75, 100, whatever makes you comfortable), big differences usually are meaningful, small differences usually are not.  Significance is a nice way to be certain in your results, but we as researchers need to support business decisions with meaningful findings, not (just) significant ones.

Nick Pangallo is a Project Manager in CMB’s Financial Services, Healthcare, and Insurance practice.  He has a meaningful-but-not-significant man-crush on Nate Silver.