Voices: the CMB Blog

Is it time for the “traditional” market researcher to join the ranks of the milkman and switchboard operator? The pressure to provide more actionable insights, more quickly, has never been so high. Add new competitors into the mix, and you have an industry feeling the pinch. At the same time, primary data collection has become substantially more difficult:

• Response rates are decreasing as people become more and more inundated with email requests
• Many among the younger crowd don’t check their email frequently, favoring social media and texting
• Spam filters have become more effective, so potential respondents may not receive email invitations
• The cell-phone-only population is becoming the norm—calls are easily avoided using voicemail, caller ID, call-blocking, and privacy managers
• Traditional questionnaire methodologies don’t translate well to the mobile platform—it’s time to ditch large batteries of questions

It’s just harder to contact people and collect their opinions. The good news? There’s no shortage of researchable data. Quite the contrary, there’s more than ever. It’s just that market researchers are no longer the exclusive collectors—there’s a wealth of data collected internally by companies as well as an increase in new secondary passive data generated by mobile use and social media. We’ll also soon be awash in the Internet of Things, which means that everything with an on/off switch will increasingly be connected to one another (e.g., a wearable device can unlock your door and turn on the lights as you enter). The possibilities are endless, and all this activity will generate enormous amounts of behavioral data.

Yet, as tantalizing as these new forms of data are, they’re not without their own challenges. One such challenge? Barriers to access. Businesses may share data they collect with researchers, and social media is generally public domain, but what about data generated by mobile use and the Internet of Things? How can researchers get their hands on this aggregated information? And once acquired, how do you align dissimilar data for analysis? You can read about some of our cutting-edge research on mobile passive behavioral data here.

We also face challenges in striking the proper balance between sharing information and protecting personal privacy. However, people routinely trade personal information online when seeking product discounts and for the benefit of personalizing applications. So, how and what’s shared, in part, depends on what consumers gain. It’s reasonable to give up some privacy for meaningful rewards, right? There are now health insurance discounts based on shopping habits and information collected by health monitoring wearables. Auto insurance companies are already doing something similar in offering discounts based on devices that monitor driving behavior.

We are entering an era of real-time analysis capabilities. The kicker is that with real-time analysis comes the potential for real-time actionable insights to better serve our clients’ needs.

So, what’s today’s market researcher to do? Evolve. To avoid marginalization, market researchers need to continue to understand client issues and cultivate insights in regard to consumer behavior. To do so effectively in this new world, they need to embrace new and emerging analytical tools and effectively mine data from multiple disparate sources, bringing together the best of data science and knowledge curation to consult and partner with clients.

So, we can say goodbye to “traditional” market research? Yes, indeed. The market research landscape is constantly evolving, and the insights industry needs to evolve with it.

Matt Skobe is a Data Manager at CMB with keen interests in marketing research and mobile technology. When Matt reaches his screen time quota for the day he heads to Lynn Woods for gnarcore mountain biking.    

Hello Dr. Jay, 

What is the internet of things, and how will it change market research?

-Hugo 

Hi Hugo,

The internet of things is all of the connected devices that exist. Traditionally, it was limited to PCs, tablets, and smartphones. Now, we’re seeing wearables, connected buildings and homes. . .and even connected cows. (Just when I thought I’d seen it all.) Connected cows, surfing the internet looking for the next greenest pasture. Actually, a number of companies offer connected cow solutions for farmers. Some are geared toward beef cattle, others toward dairy cows. Some devices are worn on the leg or around the neck, others are swallowed (I don’t want to know how you change the battery). You can track the location of the herd, monitor milk production, and model the best field for grass to increase milk output. The solutions offer alerts to the farmer when the cow is sick or in heat, which means that the farmer can get by with fewer hands and doesn’t need to be with each cow 24/7. Not only can the device predict when a cow is in heat, it can also bias the gender of the calf based on the window of opportunity. Early artificial insemination increases the probability of getting a female calf. So, not only can the farmer increase his number of successful inseminations, he/she can also decide if more bulls or milk cows are needed in the herd. 

How did this happen? A bunch of farmers put the devices on the herd and began collecting data. Then, the additional data is appended to the data set (e.g., the time the cow was inseminated, whether it resulted in pregnancy, and the gender of the calf). If enough farmers do this, we can begin to build a robust data set for analysis.

So, what does this mean for humans? Well, many of you already own some sort of fitness band or watch, right? What if a company began to collect all of the data generated by these devices? Think of all the things the company could do with those data! It could predict the locations of more active people. If it appended some key health measures (BMI, diabetes, stroke, death, etc.) to the dataset, the company could try to build a model that predicts a person’s probability of getting diabetes, having a stroke, or even dying. Granted, that’s probably not a message you want from your smart watch: “Good afternoon, Jay. You will be dead in 3 hours 27 minutes and 41 seconds.” Here’s another possible (and less grim) message: “Good afternoon, Jay. You can increase your time on this planet if you walk just another 1,500 steps per day.” Healthcare providers would also be interested in this information. If healthcare providers had enough fitness tracking data, they might be able to compute new lifetime age expectations and offer discounts to customers who maintain a healthy lifestyle (which is tracked on the fitness band/watch).  

Based on connected cows, the possibility of this seems all too real. The question is: will we be willing to share the personal information needed to make this happen? Remember: nobody asked the cow if it wanted to share its rumination information with the boss.

Dr. Jay Weiner is CMB’s senior methodologist and VP of Advanced Analytics. He is completely fascinated and paranoid about the internet of things. Big brother may be watching, and that may not be a good thing.

This year has been rife with corporate scandals. For example, FIFA’s corruption case and Volkswagen’s emissions cheating admission may have irreparably damaged public trust for these organizations. These are just two of the major corporations caught this year, and if history tells us anything, we’re likely to see at least another giant fall in 2015. 

What can managers learn about their brands from watching the aftermath of corporate scandal? Let’s start with the importance of trust—something we can all revisit. We take it for granted when our companies or brands are in good standing, but when trust falters, it recovers slowly and impacts all parts of the organization. To prove the latter point, we used data from our recent self-funded Consumer Pulse research to understand the relationship between Likelihood to Recommend (LTR), a Key Performance Indicator, and Trustworthiness amongst a host of other brand attributes. 

Before we dive into the models, let’s talk a little bit about the data. We leveraged data we collected some months ago—not at the height of any corporate scandal. In a perfect world, we would have pre-scandal and post-scandal observations of trust to understand any erosion due to awareness of the deception. This data also doesn’t measure the auto industry or professional sports. It focuses on brands in the hotel, e-commerce, wireless, airline, and credit card industries. Given the breadth of the industries, the data should provide a good look at how trust impacts LTR across different types of organizations. Finally, we used Bayes Net (which we’ve blogged about quite a bit recently) to factor and map the relationships between LTR and brand attributes. After factoring, we used TreeNet to get a more direct measure of explanatory power for each of the factors.

First, let’s take a look at the TreeNet results. Overall, our 31 brand attributes explain about 71% of the variance in LTR—not too shabby. Below are each factors’ individual contribution to the model (summing to 71%). Each factor is labeled by the top loading attribute, although they are each comprised of 3-5 such variables. For a complete list of which attributes goes with which factor, see the Bayes Net map below. That said, this list (labeled by the top attributes) should give you an idea of what’s directly driving LTR:

Looking at these factor scores in isolation, they make inherent sense—love for a brand (which factors with “I am proud to use” and “I recommend, like, or share with friends”) is the top driver of LTR. In fact, this factor is responsible for a third of the variance we can explain. Other factors, including those with trust and “I am proud to wear/display the logo of Brand X” have more modest (and not all that dissimilar) explanatory power. 

You might be wondering: if Trustworthiness doesn’t register at the top of the list for TreeNet, then why is it so important? This is where Bayes Nets come in to play. TreeNet, like regression, looks to measure the direct relationships between independent and dependent variables, holding everything else constant. Bayes Nets, in contrast, looks for the relationships between all the attributes and helps map direct as well as indirect relationships.

Below is the Bayes Net map for this same data (and you can click on the map to see a larger image). You need three important pieces of information to interpret this data:

1. The size of the nodes (circles/orbs) represents how important a factor is to the model. The bigger the circle, the more important the factor.
2. Similarly, the thicker the lines, the stronger a relationship is between two factors/variables. The boldest lines have the strongest relationships.
3. Finally, we can’t talk about causality, but rather correlations. This means we can’t say Trustworthiness causes LTR to move in a certain direction, but rather that they’re related. And, as anyone who has sat through an introduction to statistics course knows, correlation does not equal causation.

Here, Factor 7 (“I love Brand X”) is no longer a hands-down winner in terms of explanatory power. Instead, you’ll see that Factors 3, 5, 7 and 9 each wield a great deal of influence in this map in pretty similar quantities. Factor 7, which was responsible for over a third of the explanatory power before, is well-connected in this map. Not surprising—you don’t just love a brand out of nowhere. You love a brand because they value you (Factor 5), they’re innovative (Factor 9), they’re trustworthy (Factor 3), etc. Factor 7’s explanatory power in the TreeNet model was inflated because many attributes interact to produce the feeling of love or pride around a brand.

Similarly, Factor 3 (Trustworthiness) was deflated. The TreeNet model picked up the direct relationship between Trustworthiness and LTR, but it didn’t measure its cumulative impact (a combination of direct and indirect relationships). Note how well-connected Factor 3 is. It’s strongly related (one of the strongest relationships in the map) to Factor 5, which includes “Brand X makes me feel valued,” “Brand X appreciates my business,” and “Brand X provides excellent customer service.” This means these two variables are fairly inseparable. You can’t be trustworthy/have a good reputation without the essentials like excellent customer service and making customers feel valued. Although to a lesser degree, Trustworthiness is also related to love. Business is like dating—you can’t love someone if you don’t trust them first.

The data shows that sometimes relationships aren’t as cut and dry as they appear in classic multivariate techniques. Some things that look important are inflated, while other relationships are masked by indirect pathways. The data also shows that trust can influence a host of other brand attributes and may even be a prerequisite for some. 

So what does this mean for Volkswagen? Clearly, trust is damaged and will need to be repaired.  True to crisis management 101, VW has jettisoned a CEO and will likely make amends to those owners who have been hurt by their indiscretions. But how long will VW feel the damage done by this scandal? For existing customers, the road might be easier. One of us, James, is a current VW owner, and he is smitten with the brand. His particular model (GTI) wasn’t impacted, and while the cheating may damage the value of his car, he’s not selling it anytime soon. For prospects, love has yet to develop and a lack of trust may eliminate the brand from their consideration set.

The takeaway for brands? Don’t take trust for granted. It’s great while you’re in good favor, but trust’s reach is long, varied, and has the potential to impact all of your KPIs. Take a look at your company through the lens of trust. How can you improve? Take steps to better your customer service and to make customers feel valued. It may pay dividends in improving trust, other KPIs, and, ultimately, love.

Dr. Jay Weiner is CMB’s senior methodologist and VP of Advanced Analytics. He keeps buying new cars to try to make the noise on the right side go away.

James Kelley splits his time at CMB as a Project Manager for the Technology/eCommerce team and as a member of the analytics team. He is a self-described data nerd, political junkie, and board game geek. Outside of work, James works on his dissertation in political science which he hopes to complete in 2016.

We’ve come a long way, baby. . .

In the past three decades, the exponential growth in technology’s capabilities have given us the power to integrate multiple sources, predict behaviors, and deliver insights at a speed we only dreamt of when I was starting out. CMB Chairman and co-founder, Dr. John Martin, was an early cheerleader of the value of using multiple methods and multiple sources, so the promise of bringing disparate data sources into a unified view of customers and the marketplace is this researcher’s dream come true. 

While integrating data to help make smarter decisions has always been a best practice, it is the advances in technology that have allowed for an even greater and easier integration. Below are some recent examples we’ve implemented at CMB:

• In segmentation studies, we include needs/attitude-based survey data, internal CRM behaviors, and third-party appended data into the modeling to create more useful segments. Our clients have found that our perceptual data is a necessary complement to their internal data because it helps explain the “why’s” to the “what’s” that the internal behavioral/demographic data tell them.
• For our brand tracking clients, we often combine web analytics (e.g., Google search data, social media sentiment analysis, client’s web traffic statistics) and internal data (e.g., inquiries, loyalty applications) with our tracking results to help tell a much more nuanced story of the brand’s progress. Additionally, we use dashboards to tie that data together in one place, providing a real-time view of the brand.
• Our customer experience clients now provide us with internal data from call center reports (detailing the types of complaints received) and internal performance metrics to complement our satisfaction tracking. 

. . .but we’ve got a ways to go.

While many organizations are leveraging technology to integrate data for specific decision areas, I see a number of stumbling blocks. Many companies are still failing to develop an enterprise-wide, unified view of the marketplace—and the barriers often have little to do with the data or tech themselves: 

• Organizational siloes make it very challenging for different functional areas to come together and create a common platform for this type of unified view. 
• Moreover, the politics of who owns what—and more importantly, who pays for what—oftentimes means efforts like this never get off the ground.  

So, while it seems like technology is helping make all sorts of different data “play together,” we as humans haven’t mastered the same challenge! 

How do organizations overcome these challenges to take advantage of this possibility? Like most challenges, the solution starts with senior leadership. If the C-suite makes it a priority for the organization to become customer-centric and stresses that data is a big part of getting there, that goes far to pave the way for the different personalities and siloes to come together. Starting small is another way to tackle this problem. Look for opportunities in which teams can collaborate, even if it’s something as simple as looking at subsequent purchase behaviors from customers six months after they complete a satisfaction questionnaire in order to develop/refine the predictive power of your customer experience tracking. Starting small can create a more positive beginning to the partnership, building the trust and communication necessary to attack the bigger challenges down the road.

Mark is a Vice President at CMB, and while he recognizes that technology has absolutely transformed all aspects of his professional and personal life, he sees meaning in the fact that he prefers his music playlists generated by humans, not algorithms. Long live the DJ!

Are you following us on Twitter? If not, join the party! 

Every year, Survey Magazine names 10 “Data Dominators,” who are conquering data in different ways at their companies. This year, our very own Talia Fein was chosen. She discusses her passion for data in Survey Magazine’s August issue, and we’ve reposted the article below.

When I first came to CMB, a research and strategy company in Boston, I was fresh out of undergrad and an SPSS virgin. In fact, I remember there being an SPSS test that all new hires were supposed to take, but I couldn’t take it because I didn’t even know how to open a data file. Fast forward a few months, and I had quickly been converted to an SPSS specialist, a numbers nerd, or—perhaps more appropriately—a data dominator.  I was a stickler for process and precision in all data matters, and I took great pride in ensuring that all data and analyses were perfect and pristine. To put it bluntly, I was a total nerd.

I recently returned to CMB after a four-year hiatus. When I left CMB, I quickly became the survey and data expert among my new colleagues and the point person for all SPSS and data questions. But it wasn’t just my data skills that were being put to use. To me, data management is also about the process and the organization of data. In my subsequent roles, I found myself looking to improve the data processes and streamline the systems used for survey data. I brought new software programs to my companies and taught my teams how to manage data effectively and efficiently.

When I think about the future of the research industry, I imagine survey research as being the foundation of a house.  Survey data and data management are the building blocks of what we do. When we do them excellently, we are a well-oiled machine. But a well-oiled machine doesn’t sell products or help our clients drive growth. We need to have the foundation in place in order to extend beyond it and to prepare ourselves for the next big thing that comes along. And that next big thing, in my mind, is big data technology. There is a lot of data out there, and a lot of ways of managing and analyzing it, and we need to be ready for that.  We need to expand our ideas about where our data is coming from and what we can do with it. It is our job to connect these data sources and to find greater meaning than we were previously able to. It is this non-traditional use of data and analytics that is the future of our industry, and we have to be nimble and creative in order to best serve our clients’ ever-evolving needs.

One recent example of this is CMB’s 2015 Mobile Wallet study, which leveraged multiple data sources and—in the process—revealed which were good for what types of questions. In the case of this research, we analyzed mobile behavioral data, including mobile app and mobile web usage, along with survey-based data to get a full picture of consumers’ behaviors, experiences, and attitudes toward mobile wallets. We also came away with new Best Practices for how best to manage passive mobile behavioral data, as it presents new challenges that are unique from managing survey data. Our clients are making big bets on new technology, and they need the comprehensive insights that come from integrating multiple sources. We specifically sampled different sources because we know that—in practice—many of our clients are being handed multiple data sets from multiple data sources. In order to best serve these clients, we need to be able to leverage all the data sources that are at our and their disposal so that we can glean the best insights and make the best recommendations.

Talia Fein is a Project & Data Manager at Chadwick Martin Bailey (CMB), a market research consulting firm in Boston. She’s responsible for the design and execution of market research studies for Fortune 500 companies as well as the data processing and analysis through all phases of the research. Her portfolio includes clients such as Dell, Intel, and Comcast, and her work includes customer segmentation, loyalty, brand tracking, new product development, and win-loss research.