Voices: the CMB Blog

Who are the mobile consumers?

Let’s get this straight: I am not addicted to my smartphone. Unlike so many of my fellow train commuters who stare zombie-eyed into their small screens, I am not immersed in a personal relationship with pixels. I have an e-Reader for that. But, my smartphone IS my lifeline.I’ve come to depend exclusively on my phone to keep me on-time and on-schedule, to entertain me (when not using my e-Reader), to stay in touch with family and friends, and to keep up-to-date with my work email. It’s my primary source for directions, weather, news, photography, messaging, banking, and a regular source for payment, shopping, and ticketing/reservations. I haven’t purchased a PC in nearly a decade, and I don’t have a landline. I also use my smartphone to take market research questionnaires, and I am far from alone. 

Data around smartphone usage aligns with my personal experience. In a recent CMB online study of U.S. consumers, optimized for mobile devices, 1 in 6 Millennials completed the questionnaire on a smartphone. Other studies report similar results. This example illustrates the issue with representativeness. Major panel vendors are seeing over half of Millennials joining their panels via a mobile device. 

How do we adapt?

Much has been hypothesized about the future of market research under the new paradigm of mobile commerce, big data, and cloud services. New technologies and industry convergence (not just mobile) have brought sweeping changes in consumer behaviors, and market researchers must adapt.

A key component of successful adaptation will be greater integration of primary market research with other data streams. The promise of passive or observational data is captivating, but it is largely still in the formative stages. (For more on passive data, check out our recent webinar.) We still need and will likely always need active “please tell me” research. The shift from phone to online data collection has quickly been replaced with the urgency of a shift to mobile data collection (or at least device agnostic interviewing). Our industry has lagged behind because the consumer experience has become so personalized and the trust/value equation for tapping into their experiences is challenging. Tackling mobile market research with tactical solutions is a necessary step in this transition.

What should we do about it?  

1. Understand your current audience. Researchers need to determine how important mobile data collection is to the business decision and decide how to treat mobile respondents. You can have all respondents use a mobile device, have some use a mobile device, or have mobile device respondents excluded. There are criteria and considerations for each of these, and there are also considerations for the expected mix of feature phones, smartphones, tablets, and PCs. The audience will determine the source of sample and representation that must be factored into the study design. Ultimately, this has a huge impact on the validity and reliability of the data. Respondent invitations need to include any limitations for devices not suitable for a particular survey.
2. Design for mobile. If mobile participation is important, researchers should use a mobile first questionnaire design. Mobile optimized or mobile friendly surveys typically need to be shorter in length, use concise language, avoid complex grids and answering mechanisms, and have fewer answer options, so they can be supported on a small screen and keep respondents focused on the activity. In some cases,questionnaire modularization or data stitching can be used to help adhere to mobile design standards.
3. Test for mobile. All questions, images, etc. need to display on a variety of screen sizes and within the bandwidth capacity of the devices that are being used. Android and iOS device accommodation covers most users. If app based surveys are being used, researchers need to ensure that the latest versions can be downloaded and are bug-free. 
4. Apply data protection and privacy standards. Mobile market research comes with a unique set of conditions and challenges that impact how information is collected, protected, and secured. Research quality and ethical guidelines specific to mobile market research have been published by CASRO, ESOMAR, the MMRA (Mobile Marketing Research Association), and others.
5. Implement Mobile Qualitative. The barriers are lower, and researchers can leverage the unique capabilities of mobile devices quite effectively with qualitative research. Most importantly, willing participants are mobile, which makes in-the-moment research possible. Mobile qualitative is also a great gateway to explore what’s possible for mobile quantitative studies. See my colleague Anne Hooper’s blog for more on the future of qualitative methodologies.
6. Promote Research-on-Research. Experts need to conduct and publish additional research-on-research studies that advance understanding of how to treat mobile respondents and utilize passive data, location tracking, and other capabilities that mobile devices provide. We also need stronger evidence of what works and what doesn’t work in execution of multi-mode and mobile-only studies across different demographics, in B2B studies, and within different countries.

But perhaps the most important thing to remember is that this is just a start. Market researchers and other insight professionals must evolve from data providers to become integrated strategic partners—harnessing technology (not just mobile) to industry expertise to focus on decision-making, risk reduction, and growth.

Brian is a Senior Project Manager for Chadwick Martin Bailey, the photographer of the image in this post, and an 82 percenter—he is one of the 82% of mobile phone owners whose phone is with them always or most of the time. 

Watch our recent webinar that discusses the results of our self-funded Consumer Pulse study on the future of the mobile wallet. 

By Chris Neal and Dr. Jay Weiner

As I wrote in last week’s post, we recently conducted an analysis of mobile wallet use in the U.S. To make it interesting, we used unlinked passive mobile behavioral data alongside survey-based data.In this post, I’ve teamed up with Jay Weiner—our VP of Analytics who helped me torture analyze the mobile passive behavioral data for this Mobile Wallet study—to share some of the typical challenges you may face when working with passive mobile behavioral data (or any type of passive behavioral data for that matter) along with some best practices for dealing with these challenges:

1. Not being able to link mobile usage to individuals. There’s a lot of online passive data out there (mobile app usage ratings, web usage ratings by device type, social media monitoring, etc.) that is at the aggregate level and cannot be reliably attributed to individuals. These data have value, to be sure, but aggregate traffic data can sometimes be very misleading. This is why—for the Mobile Wallet project CMB did—we sourced mobile app and mobile web usage from the Research Now mobile panel where it is possible to attribute mobile usage data to individuals (and have additional profiling information on these individuals). 
   
   When you’re faced with aggregate level data that isn’t linked to individuals, we recommend either getting some sample from a mobile usage panel in order to understand and calibrate your results better and/or doing a parallel survey-sampling so you can make more informed assumptions (this holds true for aggregate search trend data, website clickstream data, and social media listening tools).

2. Unstacking the passive mobile behavioral data. Mobile behavioral data that is linked to individuals typically comes in “stacked” form, i.e., every consumer tracked has many different records: one for each active mobile app or mobile website session. Analyzing this data in its raw form is very useful for understanding overall mobile usage trends. What these stacked behavioral data files do not tell you, however, is the reach or incidence (e.g., how many people or the percentage of an addressable market) of any given mobile app/website. It also doesn’t tell you the mobile session frequency or duration characteristics of different consumer types nor does it allow you to profile types of people with different mobile behaviors. 
   
   Unstacking a mobile behavioral data file can sometimes end up being a pretty big programming task, so we recommend deciding upfront exactly which apps/websites you want to “unstack.” A typical behavioral data file that tracks all smartphone usage during a given period of time can involve thousands of different apps and websites. . .and the resulting unstacked data file covering all of these could quickly become unwieldy.

3. Beware the outlier! Unstacking a mobile behavioral data file will reveal some pretty extreme outliers. We all know about outliers, right? In survey research, we scrub (or impute) open-ended quant responses that are three standard deviations higher than the mean response, we take out some records altogether if they claim to be planning to spend $6 billion on their next smartphone purchase, and so on. But outliers in passive data can be quite extreme. In reviewing the passive data for this particular project, I couldn’t help but recall that delightful Adobe Marketing ad in which a baby playing with his parents’ tablet repeatedly clicks the “buy” button for an encyclopedia company’s e-commerce site, setting off a global stock bubble. 
   
   Here is a real-world example from our mobile wallet study that illustrates just how wide the range is of mobile behaviors across even a limited group of consumers: the overall “average” time spent using a mobile wallet app was 162 minutes, but the median time was only 23 minutes. A very small (<1% of total) portion of high-usage individuals created an average that grossly inflated the true usage snapshot of the majority of users. One individual spent over 3,000 minutes using a mobile wallet app.

4. Understand what is (and what is not) captured by a tracking platform. Different tracking tools do different things and produce different data to analyze. In general, it’s very difficult to capture detailed on-device usage for iOS devices. . .most platforms set up a proxy that instead captures and categorizes the IP addresses that the device transmits data to/from. In our Mobile Wallet study, as one example, our mobile behavioral data did not pick up any Apple Pay usage because it leverages NFC to conduct the transaction between the smartphone and the NFC terminal at the cash register (without any signal ever being transmitted out to the mobile web or to any external mobile app, which is how the platform captured mobile usage).   There are a variety of tricks of the trade to account for these phenomenon and to adjust your analysis so you can get close to a real comparison, but you need to understand what things aren’t picked up by passive metering in order to apply them correctly.

5. Categorize apps and websites. Needless to say, there are many different mobile apps and websites that people use, and many of these do a variety of different things and are used for a variety of different purposes. Additionally, the distribution of usage across many niche apps and websites is often not useful for any meaningful insights work unless these are bundled up into broader categories. 
   
   Some panel sources—including Research Now’s mobile panel—have existing mobile website and app categories, which are quite useful. For many custom projects, however, you’ll need to do the background research ahead of time in order to have meaningful categories to work with. Fishing expeditions are typically not a great analysis plan in any scenario, but they are out of the question if you’re going to dive into a big mobile usage data file.
   
   As you work to create meaningful categories for analysis, be open to adjusting and iterating. A certain group of specific apps might not yield the insight you were looking for. . .learn from the data you see during this process then try new groupings of apps and websites accordingly.

6. Consider complementary survey sampling in parallel with behavioral analysis. During our iterative process of attempting to categorize mobile apps from reviewing passive mobile behavioral data, we were relieved to have a complementary survey sampling data set that helped us make some very educated guesses about how or why people were using different apps. For example, PayPal has a very successful mobile app that is widely used for a variety of reasons—peer-to-peer payments, ecommerce payments, and, increasingly, for “mobile wallet” payments at a physical point of sale. The passive behavioral data we had could not tell us what proportion of different users’ PayPal mobile app usage was for which purpose. That’s a problem because if we were relying on passive data alone to tell our clients what percent of smartphone users have used a mobile wallet to pay at a physical point of sale, we could come up with grossly inflated numbers. As an increasing number of mobile platforms add competing functionality (e.g., Facebook now has mobile payments functionality), this will remain a challenge.
   
   Passive tracking platforms will no doubt crack some of these challenges accurately, but some well-designed complementary survey sampling can go a long way towards helping you read the behavioral tea leaves with greater confidence. It can also reveal differences between actual vs. self-reported behavior that are valuable for businesses (e.g., a lot of people may say they really want a particular mobile functionality when asked directly, but if virtually no one is actually using existing apps that provide this functionality then perhaps your product roadmap can live without it for the next launch).

Want to learn more about the future of Mobile Wallet? Join us for a webinar on August 19, and we’ll share our insights with you!

Chris Neal leads CMB’s Tech Practice. He judges every survey he takes and every website he visits by how it looks on his 4” smartphone screen, and has sworn off buying a larger “phablet” screen size because it wouldn’t fit well in his Hipster-compliant skinny jeans.

Dr. Jay heads up the analytics group at CMB. He opted for the 6 inch “phablet” and baggy jeans.  He does look stupid talking to a brick. He’s busy trying to compute which event has the higher probability: his kids texting him back or his kids completing an online questionnaire. Every month, he answers your burning market research questions in his column: Dear Dr. Jay. Got a question? Ask it here!

Want to learn more about combining survey data with passive mobile behavioral data? Watch our recent webinar with Research Now that discusses these findings in depth.

The explosion of mobile web and mobile app usage presents enormous opportunities for consumer insights professionals to deepen their understanding of consumer behavior, particularly for “in the moment” findings and tracking consumers over time (when they aren’t actively participating in research. . .which is 99%+ of the time for most people). Insight nerds like us can’t ignore this burgeoning wealth of data—it is a potential goldmine. But, working with passive mobile behavioral data brings with it plenty of challenges, too. It looks, smells, and feels very different from self-reported survey data:

• It’s big. (I’m not gonna drop the “Big Data” buzzword in this blog post, but—yep—the typical consumer does indeed use their smartphone quite a bit.)
• It’s messy.
• We don’t have the luxury of carefully curating it in the same way we do with survey sampling. 

As we all find ourselves increasingly tasked with synthesizing insights and a cohesive “story” using multiple data sources, we’re finding that mobile usage and other data sources don’t always play nicely in the sandbox with survey data. Each of them have their strengths and weaknesses that we need to understand in order to use them most effectively. 

So, in our latest in a series of sadomasochistic self-funded thought leadership experiments, we decided to take on a challenge similar in nature to what more and more companies will ask insights departments to do: use passive mobile behavioral data alongside survey-based data for a single purpose. In this case, the topic was an analysis of the U.S. mobile wallet market opportunity. To make things extra fun, we ensured that the passive mobile behavioral data was completely unlinked to the survey data (i.e., we could not link the two data sources at the respondent level for deeper understanding or to do attitudinal + behavioral based modeling). There are situations where you’ll be given data that is linked, but currently—more often than not—you’ll be working with separate silos and asked to make hay.

During this experiment, a number of things became very clear to us, including:

• the actual value that mobile behavioral data can bring to business engagements
• how it could easily produce misleading results if you don’t properly analyze the data
• how survey data and passive mobile behavioral data can complement one another greatly

Interested? I’ll be diving deep into these findings (and more) along with Roddy Knowles of Research Now in a webinar this Thursday, July 16^th at 1pm ET (11am PT). Please join us by registering here. 

Chris leads CMB’s Tech Practice. He enjoys spending time with his two kids and rock climbing.

Watch our recent webinar with Research Now to hear the results of our recent self-funded Consumer Pulse study that leveraged passive mobile behavioral data and survey data simultaneously to reveal insights into the current Mobile Wallet industry in the US.

Back in 2005, Malcolm Gladwell told us that focus groups are dead. Just last November, Jim Bryson, CEO of 20/20 Research, questioned whether qualitative research was thriving or dying: “If we take a narrow, more traditional view that qualitative is defined by the methods of face-to-face focus groups or interviews, particularly those held in a qualitative facility, then the case can be easily made that qualitative is dying.”

To all of this, I say: wait, what?! Qualitative is dying? I refused to believe it, so I embarked on a journey to explore where qualitative has been, and more importantly, where it’s going. During my research, I found plenty of evidence to support the fact that qualitative is not, in fact, dying. Great news, right? (Especially for me, because if it were true, I just might be out of a job I love.)I took a look at the fall 2014 Greenbook Research Industry Trends (GRIT) Report and focused on the data from Q1-Q2 of 2013 and Q1-Q2 2014. In this data, I learned:

• The use of traditional in-person focus groups increased from 60% (Q1-Q2 2013) to 70% (Q1-Q2 2014).
• Within the same time period, the use of in-person, in-depth interviews increased from 45% to 53%.
• Interviews and groups using online communities increased from 21% to 24%.
• The use of mobile qual (e.g., diaries, image uploads) increased from 18% to 24%.

Yes, it’s important to note that not all qualitative methodologies saw an increase in usage within this timeframe. In fact, there was a decrease in the usage of telephone IDIs, in-store shopping/observations, bulletin board studies, both chat-based and webcam-based online focus groups, and telephone focus groups.  All this notwithstanding, I think it’s fair to say that qualitative is still very much alive and well.

So why do people keep talking about qualitative dying? We can’t deny that there are a number of factors that affect how and when we use qualitative methodologies today (technology, access to big data, and text analytics are a few). But, this doesn’t mean qualitative is disappearing as a discipline. Qualitative is evolving at a rapid pace and feels more relevant than ever. Sure, we need to keep up with client demands for faster and cheaper research, but there will always be a need for the human mind (i.e., a qualitative expert) to analyze and synthesize the data to provide meaning and context behind the way people think and behave—and that is where actionable insights are born.   

Now that we know qualitative really isn’t dying, what does 2015 (and beyond) hold for us? The future is about truly integrated research—in which qualitative and quantitative are consistently, thoughtfully, and purposefully used together to provide well-rounded, actionable insights. We’re poised to do exactly that with our dedicated analytics team and network of expert industry qualitative partners. By using two equally important disciplines that are both alive and well, we can provide our clients critical insights they can really use. Far from killing off qualitative insights, technology and an evolving marketplace are helping make qualitative insights even stronger.

Anne Hooper is the Qualitative Research Director at CMB. After recently finding out that her 13 year old daughter did a quantitative assessment of her Jazz Band’s upcoming Disney trip itinerary, she’s determined that an intervention may be in order.

In market research, it can occasionally feel like the rivalry between qualitative and quantitative research is like the Red Sox vs. the Yankees.  You can’t root for both, and you can’t just “like” one.  You’re very passionate about your preference.  But in many cases, this can be problematic. For example, using a quantitative mindset or tactics in a qualitative study (or vice versa) can lead to inaccurate conclusions. Below are some examples of this challenge—one that can happen throughout all phases of the research process: 

Planning

Clients will occasionally request that market researchers use a particular methodology for an engagement. We always explore these requests further with our clients to ensure there isn’t a disconnect between the requested methodology and the problem the client is trying to solve.

For example, a bank* might say, “The latest results from our brand tracking study indicate that customers are extremely frustrated by our call center and we have no idea why. Let’s do a survey to find out.”

Because the bank has no hypotheses about the cause of the issue, moving forward with their survey request could lead to designing a tool with (a) too many open-ended questions and (b) questions/answer options that are no more than wild guesses at the root of the problem, which may or may not jibe with how consumers actually think and feel.

Instead, qualitative research could be used to provide a foundation of preliminary knowledge about a particular problem, population, and so forth. Ultimately, that knowledge can be used to help inform the design of a tool that would be useful.

Questionnaire Design

For a product development study, a software company* asks to add an open-ended question to a survey: “What would make you more likely to use this software?” or “What do you wish the software could do that it can’t do now?”

Since most of us are not engineers or product designers, this question might be difficult for most respondents to answer. Open-ended questions like these are likely to yield a lot of not-so-helpful “I don’t know”-type responses, rather than specific enhancement suggestions.

Instead of squandering valuable real estate on a question not likely to yield helpful data, a qualitative approach could allow respondents to react to ideas at a more conceptual level, bounce ideas off of each other or a moderator, or take some time to reflect on their responses. Even if the customer is not a R&D expert, they may have a great idea that just needs a bit of coaxing via input and engagement with others.

Analysis and Reporting

In reviewing the findings from an online discussion board, a client at a restaurant chain* reviews the transcripts and states, “85% of participants responded negatively to our new item, so we need to remove it from our menu.”

Since findings from qualitative studies are not necessarily statistically significant, using the same techniques (e.g., descriptive statistics and frequencies) is not ideal as it implies a level of precision in the findings that is not necessarily accurate. Further, it would not be cost-effective to recruit and conduct qualitative research with a group large enough to be projectable onto the general population.

Rather than attempting to quantify the findings in strictly numerical terms, qualitative data should be thought of as more directional in terms of overall themes and observable patterns.

At CMB, we root for both teams. We believe both produce impactful insights, and that often means using a hybrid approach. We believe the most meaningful insights come from choosing the approach or approaches best suited to the problem our client is trying to solve. However, being a Boston-based company, we can’t say that we’re nearly this unbiased when it comes to the Red Sox versus the Stankees Yankees.

*Example (not actual)

Ashley is a Project Manager at CMB. She loves both qualitative and quantitative equally and is not knowledgeable enough about sports to make any sports-related analogies more sophisticated than the Red Sox vs. the Yankees.

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