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Black Friday Is Dead…Long Live Black Friday

Posted by Megan McManaman

Tue, Dec 22, 2015

retail2.pngIf you noticed the annual coverage of Black Friday shoppers seemed somewhat muted this year, you weren’t imagining things. While Cyber Monday sales were the highest since its debut in 2005, Black Friday sales were at their lowest since 2011. We all know how many elves flew (or didn’t) off the shelves, but to learn more about consumer holiday shopping behaviors, we partnered with Research Now for a quick survey of smartphone owners, ages 18 and up. 

Does 2015 mark the end of Black Friday—retail’s highest and holiest holiday? One retailer, REI, even opted out of this year’s Black Friday altogether, though their website did allow shoppers to make purchases online. The 87% of respondents who reported shopping on Black Friday might suggest that its imminent death is exaggerated. But the 81% of those Black Friday shoppers who did at least some of their shopping online suggest the explosion of ecommerce may have circumscribed the usual Black Friday frenzy.   

And then we have mobile—2015 marked the introduction of app-only deals from retail giants Amazon, Walmart, and Target. Of respondents who did shop from their smartphone or tablet, on either Black Friday or Cyber Monday, a full 27% purchased through an app. Still, a Cyber Monday dominated by in-app sales may be a few years away—61% of the Black Friday and Cyber Monday online shoppers used a PC to make their purchases. 

Need further evidence that online shopping and mobile technology are disrupting the traditional holiday shopper customer journey? “Just” 67% of Black Friday deal-seekers said they actually braved a brick and mortar store—this on a day once defined by the in-store experience. Is nothing sacred? 

Megan is CMB’s Senior Product Marketing Manager. She can’t stand Christmas music and was once visited by 3 ghosts. 

Topics: technology research, mobile, retail research, customer journey

Say Goodbye to Your Mother’s Market Research

Posted by Matt Skobe

Wed, Dec 02, 2015

evolving market researchIs 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.    

Topics: data collection, mobile, consumer insights, marketing science, internet of things, data integration, passive data

When Only a #Selfie Stands Between You and Those New Shoes

Posted by Stephanie Kimball

Thu, Aug 13, 2015

mobile, shopping, mobile walletThe next time you opt to skip the lines at the mall and do some online shopping from your couch, you may still have to show your face. . .sort of. MasterCard is experimenting with a new program that will require you to hold up your phone and snap a selfie to confirm a purchase.  MasterCard will be piloting the new app with 500 customers who will pay for items simply by looking at their phones and blinking once to take a selfie. The blink is another feature that ensures security by preventing someone from simply showing the app a picture of your face in an attempt to make a purchase.

As we all know, passwords are easily forgotten or even stolen. So, MasterCard is capitalizing on technology like biometrics and fingerprints to help their customers be more secure and efficient. While security remains a top barrier to mobile wallet usage, concern about security is diminishing among non-users. In addition to snapping a selfie, the MasterCard app also gives users the option to use a fingerprint scan. Worried that your fingerprints and glamour shots will be spread across the web? MasterCard doesn't actually get a picture of your face or finger. All fingerprint scans create a code that stays on your phone, and the facial scan maps out your face, converts it to 0s and 1s, and securely transmits it to MasterCard.

According to our recent Consumer Pulse Report, The Mobile Wallet – Today and Tomorrow, 2015 marks the year when mobile payments will take off. Familiarity and usage have doubled since 2013—15% have used a mobile wallet in the past 6 months and an additional 22% are likely to adopt in the coming 6 months. Familiarity and comfort with online payments has translated into high awareness and satisfaction for a number of providers, and MasterCard wants a slice of that pie. Among mobile wallet users, over a quarter would switch merchants based on mobile payment capabilities.

mobile wallet, wearables

Clearly the mobile wallet revolution is well underway, but the winning providers are far from decided, and MasterCard is taking huge leaps to see how far they can take the technology available. If MasterCard can successfully test and rollout these new features and deliver a product that their customers are comfortable using, they can capture some of the mobile wallet share from other brands like Apple Pay and PayPal.

So what’s next? Ajay Bhalla, President of Enterprise Safety and Security at MasterCard, is also experimenting with voice recognition, so you would only need to speak to approve a purchase. And don’t forget about wearables! While still in the early stages of adoption, wearables have the potential to drive mobile wallet use—particularly at the point of sale—which is why MasterCard is working with a Canadian firm, Nymi, to develop technology that will approve transactions by recognizing your heartbeat.

Since technology is constantly adapting and evolving, the options for mobile payments are limitless. We've heard the drumbeat of the mobile wallet revolution for years, but will 2015 be the turning point? All signs point to yes.

Want to learn more about our recent Consumer Pulse Report, The Mobile Wallet – Today and Tomorrow? Watch our webinar!

Watch Here!

Stephanie is CMB’s Senior Marketing Manager. She owns a selfie stick and isn’t afraid to use it. Follow her on Twitter: @SKBalls

Topics: technology research, financial services research, mobile, Consumer Pulse, retail research

Embracing Mobile Market Research

Posted by Brian Jones

Thu, Jul 23, 2015

Who are the mobile consumers?

mobile research, cmbLet’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. 

mobile research, cmb

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. 

Watch Here!

Topics: methodology, qualitative research, mobile, research design

Mobile Passive Behavioral Data: Opportunities and Pitfalls

Posted by Chris Neal

Tue, Jul 21, 2015

By Chris Neal and Dr. Jay Weiner

Hands with phonesAs 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 individualsThere’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).
  1. 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.
  1. 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.
  1. 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.
  1. 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.
  1. 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.

Watch Now!

Topics: advanced analytics, methodology, data collection, mobile, Dear Dr. Jay, webinar, passive data