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Chris Neal

Recent Posts

[New Webinar] Winning the Virtual Assistant War

Posted by Chris Neal

Tue, Jul 10, 2018

Have you ever asked Siri for the weather? Or maybe you've had Alexa look up a dinner recipe. Siri, Alexa, Google Assistant, and others have become household names as more people adopt virtual assistant technology. But most people are still only using their virtual assistants for basic search functions.

In this latest 20-minute webinar, I explore:

  • The barriers keeping people from using this tech
  • How emotional and identity benefits can drive mainstream consumer adoption and deeper engagement
  • What brands should do to drive adoption and win the VA war

Watch Now

While this webinar looks at the virtual assistant category, there are valuable learnings for anyone  experiencing disruption within their industry.

If you have any questions about the research, please reach out to to me directly at cneal@cmbinfo.com.

Chris Neal is CMB's VP of Tech and Telecom and has over 20 years of experience in the high tech, telecom, and media space.

Topics: technology research, webinar, Artificial Intelligence

How to Win Virtual Assistant Rejecters Over

Posted by Chris Neal

Wed, Jun 20, 2018

It seems like every week, tech giants are adding new features to their virtual assistant (VA) tech arsenal. See Google’s new Duplex technology—an AI system for accomplishing real-world tasks by phone. 

While companies are pouring millions into making their virtual assistants smarter and more integrated, most users don’t stray beyond its basic functions like asking for the weather.

Learn about the emotional and social identity dimensions keeping people from adopting and using this tech to its full potential, and what brands need to do to win the VA war.

CMB01_VA_Infographic_07_AW

Topics: technology research, Consumer Pulse, emotional measurement, AffinID, Artificial Intelligence

Emotions Run High with Virtual Assistants

Posted by Chris Neal

Wed, May 09, 2018

woman with VA

The pace of innovation and disruption is accelerating. Just 10 years ago Uber and Airbnb didn’t exist and the iPhone was still a novelty shown off at parties by overenthusiastic tech lovers. Now, we have a hair salon receptionist convinced she's speaking to a real person when in fact it was Google Assistant that was scheduling an appointment. While it might be hard for many of us to remember the last time we took a cab or used a flip phone, change is hardly straightforward and tech adoption raises critically important questions for brands.

Why do some people resist change while others embrace it? What emotions trigger true acceptance of a new technology and a new way of doing things?  What is that “a-ha” moment that gets someone hooked on a new habit that will be enduring? 

To help understand consumers’ journey with evolving technology, we applied our BrandFx framework to the broad virtual assistant category—measuring the functional, social identity, and emotional benefits that people seek from Siri, Alexa, Google Assistant, Cortana, etc. I shared our findings from the identity aspect here.

And while each of these three benefit types play a role in adoption and use—the role of emotion is profound.

We asked a lot of people about how they use virtual assistants—from information seeking to listening to music to planning and booking a trip. Then we ran analytics on the overall emotional activation, valence, and specific emotions that were activated during these different use cases. 

Our findings have broad implications for anyone in the virtual assistant category creating marketing campaigns to drive adoption, or product UX teams looking to design customer experiences that will deepen engagement.

Currently, virtual assistants are primarily used as information-seeking tools, basically like hands-free web queries. (See Exhibit 1):TOP VA USES CASES

Even though virtual assistants are evolving to do some pretty amazing things as voice-based developer communities mature, most people are only scratching the surface with the basic Q&A function. Asking Siri or Alexa for the weather forecast is a fine experience when they’re cooperating, but it can be extremely frustrating when you don’t get the right answer—like getting the current temperature in Cupertino when you live in Boston.

Meanwhile, watching TV or shopping through your virtual assistant turns out to be a much more emotionally rewarding experience, based on the analytics we ran. The problem for the industry as a whole is that these more emotionally rewarding use cases are among the least used VA functionalities today. Teams that market these experiences must motivate more consumers to try the more emotionally rewarding VA use cases that will deepen engagement and help form a lasting habit (see Exhibit 2):

Use, emotional activation, and emotions activated by use case v2

Listening to music and watching TV/movies yields high emotional activation in general—specifically “delight.” Our driver modeling shows that feeling “delighted” is one of the top predictors of future usage intent for a virtual assistant product (see Exhibit 3):

emotions that drive VA usage-2

As Exhibit 2 above indicates, using virtual assistants for scheduling and calendaring has overall moderate emotional activation, but is particularly good at activating feelings of efficiency and productivitythe single strongest predictor of use in this category.

emotions that drive VA usage-1

 

Tellingly, however, the scheduling and calendaring function also over-indexes on feelings of frustration because this task can be more complex—currently AI and natural-language processing (NLP) technologies are more apt to get these kinds of requests wrong. 

In general, “frustration” indexes high on more complex use cases (e.g., arranging travel, coordinating schedules, information seeking). This is a warning to the tech industry not to get too caught up in the hype cycle of releasing half-baked code quickly to drum up excitement among consumers. It also helps explain why younger demographics in our analysis actually experienced more frustration with VAs than older cohorts (contrary to my initial hypotheses). 

Younger consumers are attempting to do more complex tasks with virtual assistants, and therefore bumping up against the current limits of NLP and AI more frequently. This is dangerous, because they are the key “early adopter” segments that must embrace the expanding capabilities of virtual assistants in order for the category to become pervasive among mainstream consumers.

Consumers will quickly abandon a new way of doing things if they get frustrated. Understanding and activating the right positive emotions and minimizing the negative ones will be critical as brands continue to vie for the top virtual assistant spot.

Interested in learning more about the emotional dimensions of Virtual Assistant users? Reach out to Chris Neal, CMB's VP of Technology & Telecom.

Topics: technology research, growth and innovation, AffinID, Artificial Intelligence, BrandFx

AI's Image Problem: Who's the "Typical" Virtual Assistant User?

Posted by Chris Neal

Tue, Jan 09, 2018

siri2-1.png

Every nascent technology and every tech start-up faces the same marketing challenge of “crossing the chasm” into mainstream adoption.  Geoffrey Moore framed this very well in his 1991 classic, “Crossing the Chasm”:

adoption curve.pngWord of mouth can play a huge role in motivating certain segments to sip the Kool-Aid and make the leap.

With CES 2018—the world's largest gadget tradeshow—happening in Vegas this week, I can't help but wonder if mainstream consumers don’t relate to the early adopters of a new technology? What if they think it’s used by people who aren’t part of “their tribe”? Will it prevent them even considering the new tech? There are countless technology categories that have faced this challenge, for example:

  • certain gaming categories trying to expand beyond 15-24-year-old males
  • consumer robot products to this day
  • social media when it was first introduced
  • Second Life and other virtual worlds

I hypothesized that the virtual assistant (VA) category—and specific brands within it—faces this challenge. Yes, many people have tried and used Siri, but few mainstream consumers are truly using virtual assistants for anything beyond basic hands-free web-queries. To further complicate things, an increasing number of “smart home” products that connect to intelligent wireless speakers in the home (e.g., Amazon Alexa, Google Home, Apple’s forthcoming HomePod) are proving divisive. Some people love the experience or the idea of commanding a smart device while others categorically reject the concept. 

My team and I had the chance to test out a few hypothesis through our Consumer Pulse program and —voila!—we’ve got some tasty (and useful) morsels to share with you about how social identity is influencing consumer adoption in the virtual assistant space using our proprietary AffinIDSM solution.

Here’s what we found:

Social identity matters in the virtual assistant space. We studied US consumers (18+)—covering usage, adoption, and perceptions of the virtual assistant category and a deep-dive on four major brands within it: Apple’s Siri, Amazon Alexa, Google Assistant, and Cortana by Microsoft. We covered rational perceptions of the category, emotional reactions to experiences using virtual assistants, and perceptions of the “typical” user of Siri, Alexa, Google Assistant, and Cortana.

We then ran fancy math™ on our data to create a model to predict the likelihood of a virtual assistant “category rejecter” (i.e., someone who has never tried a VA before) to try any one of those assistants in the future. Our analysis indicates that how much a current VA category rejecter relates to their image of the type of person who uses a virtual assistant is the number one predictor of whether they are likely to try the technology in the future:

Blog_Chris.png

Unfortunately for the industry, category rejecters do not find the typical VA user very relatable. 
AffinID metric by brand.png

As the chart indicates, relatability (biggest predictor of likelihood to try as shown previously) scores the lowest of the three components of AffinID: relatability, clarity, and desirability. You may ask yourself: “are scores of 12 to 14 ‘good’ or ‘bad’?  They’re bad: trust me. We’ve now run AffinID on hundreds of brands across dozens of industries, so we have a formidable normative database against which to compare brands. The VA category does not fare well on “relatability,” and it matters.

Some brands’ VA ads, while amusing, are not very relatable to “normal” mainstream consumers. For example as my colleague Erica Carranza points out in her recent blog, Siri’s ad featuring Dwayne “The Rock” Johnson doing impossibly awesome things in one day (including taking a selfie from outer-space) with the help of Siri isn’t exactly a “normal” person’s day. A-grade for amusement on this one, but it is playing into an existing perception problem.

Stereotypes about users’ age and income are currently keeping “rejecters” away from the virtual assistant category.

The age gap between rejecters and “typical” virtual assistant users is a social identity construct keeping rejecters out of the category. Current rejecters, not surprisingly, skew older while current heavy VA users, also not surprisingly, skew young.

We uncovered this disconnect with a big predictive model using “match analysis” on a variety of demographic, personality, and interest attributes. For every attribute, we examined whether there was a “match” or a “disconnect” between how a rejecter described themselves vs. how they perceived the typical user of a virtual assistant brand.

The two specific perceptions that had the greatest ability to predict a rejecter’s likelihood to consider using a brand in the future was an age-range match and an income-range match. For example, if I’m over 35 years old (hypothetically!), and I perceive the “typical” user to be under 35 years old and higher-income than me…so what? Well, it does matter. For new technologies to achieve mainstream adoption, they must debunk the widespread perceptions that the early adopter is “young” and highly affluent, and that their product can be used by everyone (think: Facebook). SNL pokes fun at this generational discrepancy.

But in all seriousness, if a virtual assistant brand wants to achieve more mainstream adoption among older demographics, the brand gurus and creative teams working on campaigns need to tackle this head on.

And they must try to do this—ideally—without alienating the original early adopter group that made them their first million (think: Facebook, again…how many Gen Zers do you know who actually use it actively?). I—prototypical 45-year-old suburban dad—can’t imagine using Snapchat, for instance. If Snapchat wanted to get me and my tribe to buy in as avid users*, it needs to convince me that Snapchat isn’t just for teens and early twenty-somethings. Or it needs to launch a different brand/product targeted specifically at my tribe, and market it appropriately.

It’s worth noting there are other social identity constructs that help predict whether a non-user of a virtual assistant is likely to try a product in the future. For instance, the few VA category rejecters who perceive the typical (young, affluent) user as being as “responsible/reliable” as themselves are more open to trying a VA in future than those who do not perceive VA users this way. So, we’re seeing this stereotype that virtual assistant products are for young, affluent professionals living in a major coastal city with no kids to contend with yet, and this is turning some consumer segments off from trying out the category in earnest.  

Stay tuned to this channel for more on our study of the virtual assistant category. I’ll be covering some key insights we got by applying our emotional impact analysis—EMPACT℠to the same issue of what virtual assistant brands should be doing to achieve further adoption and more mainstream usage of their products. 

*I am more than 95% confident that the Snapchat brand gurus do not want me as an avid user…and my ‘tween daughter would definitely die of embarrassment if I ever joined that particular platform and tried to communicate with her that way.

 

Topics: technology research, EMPACT, Consumer Pulse, AffinID, Artificial Intelligence

Flying the Friendly Skies?

Posted by Chris Neal

Thu, May 18, 2017

pexels-photo (1).jpgI don’t envy the United Airlines (UAL) management team these days. Last month’s removal of passenger Dr. David Dao from an overcrowded plane in Chicago sparked a major PR nightmare for the airline carrier.  This debacle comes to a brand that was already struggling from image problems in an industry that has long been comedic fodder for bad customer experiences.

Overbooking, heightened security procedures, skyrocketing baggage fees, and shrinking legroom have made domestic air travel a very stressful experience. With emotions running high on the tarmac, in the air, and on Twitter, what’s an airline to do?

Emotions matter... and we've proved it.

Emotional analytics are a critical tool to help create a truly consumer-centric brand. Emotions are a key driver in consumer brand adoption/loyalty and will undoubtedly play a major role in how United performs going forward. In our self-funded study of the impact of emotions across 90 brands in 5 industries, CMB found that a brand’s overall emotional impact score can heavily influence future likelihood to purchase along with other key KPIs (advocacy, engagement, etc.).

We identified which specific emotions drive business outcomes in the airline industry, the top being “secure”, “efficient”, and “happy”. Of the negative emotions we tested, “anxious” proved to be the most damaging to a company:

drivers of airline use.jpg

We also found that of the five major airline brands tested, including United, UAL had the lowest Net Positive Emotion Score (NPES). NPES is the balance of positive emotions activated through experience with a company subtracted by the extent of negative emotions activated. It also accounts for overall emotional “activation” (high vs. low), and the general sentiment of that activation (positive or negative).

airline NPE net.png

Both United and American both share a special place at the bottom of the “Negative Emotion” spectrum (17 and 18, respectively), out-activating negativity by ~30% over the airline industry average of 13.

airline NPE neg.png

A Path Forward

Now let’s have a look-see at what specific emotions have the biggest impact on likelihood to consider flying United, specifically, and how that compares to the overall industry average of emotional drivers:

top emo drivers-airlines.png

The “Anxious” vs. “Relaxed” emotional spectrum is the biggest emotional driver of future United purchase intent. Lowering feelings of “Anxiety,” in particular, is much more important for United’s brand than it is for the industry average.

Unfortunately for United, their brand already generated 33% more “anxiety” than the industry average:

UAL anxiety.png

I can only imagine the anxiety Dr. Dao felt when he was removed from the seat he paid for to make room for a UAL employee. And I can also imagine the emotional connection felt by the millions of others who watched the video of him being dragged off the plane by airport security because they could relate to it in some way from their own travel experiences or common worries people have about flying:

  • “Will they arbitrarily change my flight times in a way that messes up the rest of my travel plans?”
  • “Will they cancel my flight altogether and put me on another (later) flight if it is under-booked?” (something that happens a lot on connector flights to smaller airports).
  • “Will I be forced to vacate my seat if they are over-booked?”
  • “Will there be delays that cause me to miss my connection, an important meeting, etc.?”
  • “Will I be sitting next to a 6’5” linebacker in a cramped coach class seat?”

No doubt this incident would have been a PR disaster for any airline, but the blowback was likely even more intense because it happened on a UAL flight—a brand that already activates more negative sentiments than most competing brands.

The bad news:

United Airlines was already in the hole before this incident, and now that hole is vastly deeper. Bad press and bad experiences linger longer in peoples’ memories than positive press or positive experiences, so it’s likely the image of Dao’s forced removal is here to stay (at least for a while).

Similarly, angry customers are much more likely to tell others about their bad experiences (typically with a bigger megaphone) than those with positive ones. Righteous indignation goes viral more readily than positivity. Furthermore, bad word-of-mouth has larger negative impact on a brand than good word-of-mouth has positive impact (by an order of magnitude). And some of the most prolific public haters will likely never be swayed otherwise, no matter what UAL does from this point forward.

The good news: 

In our analysis, we found that—across all industries tested—emotional reactions to the most recent experience have a much bigger impact on likelihood to buy in the future than the worst experience a customer has ever had with a brand (or the best). In other words, even brands that mess up big time can recover if they begin to deliver customer experiences and marketing communications strategies that foster the right emotions. With our “EMPACT” approach, we can identify very specific customer experiences, creative executions, and messaging that will deactivate the most damaging emotions like “anxiety” and activate key positive emotions like “relaxed.”

May the skies be friendlier.

If United wants to be a truly consumer-centric brand, they need to consider emotion measurements like NPES as a valid metric for tracking and analytics. United will need to profoundly understand which emotions matter, and how to proactively influence these emotions through specific customer experiences, promotional campaigns, and influencing what is (and isn’t) said about the brand on social media.

Emotional metrics deserve the same level of visibility and focus that traditional industry metrics like Revenue Per Available Seat Mile (RASM) and classic NPS receive. Until this happens, UAL may struggle to focus their customer experience strategies and creative campaigns in a way that helps them recover from this low point.

Chris Neal leads CMB’s Technology & Telecommunications practice. He gets emotional very easily. He is also a frequent flyer on United Airlines. While extremely angered and disgusted by the viral video of the UAL incident, he is curious to experience how UAL actually changes in future and will fly this airline again to find out.

Want to learn more about how we're revolutionizing  emotional measurement with our EMPACT solution? Watch our webinar:

 Learn More About EMPACT℠

Topics: EMPACT, emotional measurement, customer experience and loyalty

Passive Mobile Behavioral Data – Part Deux

Posted by Chris Neal

Wed, Aug 10, 2016

Over the past two years, we've  embarked on a quest to help the insights industry get better at harnessing passive mobile behavioral data. In 2015, we partnered with Research Now for an analysis37824990_thumbnail.jpg of mobile wallet usage, using unlinked passive and survey-based data. This year, we teamed up with Research Now once again for research-on-research directly linking actual mobile traffic and app data to consumers’ self-reported online shopper journey behavior.

We asked over 1,000 shoppers, across a variety of Black Friday/Cyber Monday categories, a standard set of purchase journey survey questions immediately after the event, then again after 30 days, 60 days, and 90 days. We then compared their self-reported online and mobile behavior to the actual mobile app and website usage data from their smartphones. 

The results deepened our understanding of how best to use (and not use) each respective data source, and how combining both can help our clients get closer to the truth than they could using any single source of information.

Here are a few things to consider if you find yourself tasked with a purchase journey project that uses one or both of these data sources as fuel for insights and recommendations:

  1. Most people use multiple devices for a major purchase journey, and here’s why you should care:
    • Any device tracking platform (even one claiming a 3600 view) is likely missing some relevant online behavior to a given shopper journey. In our study, we were getting behavior from their primary smartphone, but many of these consumers reported visiting websites we had no record of from our tracking data. Although they reported visiting these websites on their smartphones, it is likely that some of these visits happened on their personal computer, a tablet, a computer at their work, etc.
  2. Not all mobile usage is related to the purchase journey you care about:
    • We saw cases of consumers whose behavioral data showed they’d visited big retail websites and mobile apps during the purchase journey but who did not report using these sites/apps as part of the journey we asked them about. This is a bigger problem with larger, more generalist mobile websites and apps (like Amazon, for this particular project, or like PayPal when we did the earlier Mobile Wallet study with a similar methodological exercise).
  3. Human recall ain’t perfect. We all know this, but it’s important to understand when and where it’s less perfect, and where it’s actually sufficient for our purposes. Using survey sampling to analyze behaviors can be enormously valuable in a lot of different situations, but understand the limitations and when you are expecting too much detail from somebody to give you accurate data to work with.  Here are a few situations to consider:
    • Asking whether a given retailer, brand, or major web property figured into the purchase journey at all will give you pretty good survey data to work with. Smaller retailers, websites, and apps will get more misses/lack of recall, but accurate recall is a proxy for influence, and if you’re ultimately trying to figure out how best to influence a consumer’s purchase journey, self-reported recall of visits is a good proxy, whereas relying on behavioral data alone may inflate the apparent impact of smaller properties on the final purchase journey.
    • Asking people to remember whether they used the mobile app vs. the mobile website introduces more error in your data. Most websites are now mobile optimized and look/ feel like mobile apps, or will switch users to the native mobile app on their phone automatically if possible.
      • In this particular project, we saw evidence of a 35-50% improvement in survey-behavior match rates if we did not require respondents to differentiate the mobile website from the mobile app for the same retailer.
  4. Does time-lapse matter? It depends.
    • For certain activities (e.g., making minor purchases in grocery store, a TV viewing occasion), capturing in-the-moment feedback from consumers is critical for accuracy.
    • In other situations where the process is bigger, involves more research, or is more memorable in general (e.g., buying a car, having a wedding, or making a planned-for purchase based on a Black Friday or Cyber Monday deal): you can get away with asking people about it further out from the actual event.
      • In this particular project, we actually found no systematic evidence of recall deterioration when we ran the survey immediately after Black Friday/Cyber Monday vs. running it 30 days, 60 days, and 90 days after.

Working with passive mobile behavioral data (or any digital passive data) is challenging, no doubt.  Trying to make hay by combining these data with primary research survey sampling, customer databases, transactional data, etc., can be even more challenging.  But, like it or not, that’s where Insights is headed. We’ll continue to push the envelope in terms of best practices for navigating these types of engagements as Analytics teams, Insights departments, Financial Planning and Strategy groups work together more seamlessly to provide senior executives with a “single version of the truth”— one which is more accurate than any previously siloed version.

Chris Neal leads CMB’s Tech Practice. He knows full well that data scientists and programmatic ad buying bots are analyzing his every click on every computing device and is perfectly OK with that as long as they serve up relevant ads. Nothing to hide!

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Topics: advanced analytics, mobile, passive data, integrated data

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

Upcoming Webinar: Passive Mobile Behavioral Data + Survey Data

Posted by Chris Neal

Mon, Jul 13, 2015

mobile research, mobile data collection, 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 16th 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.

Watch Now!

Topics: advanced analytics, methodology, data collection, mobile, webinar, passive data, integrated data

Tablet Purchase Journey Relies Heavily on Mobile Web

Posted by Chris Neal

Thu, Oct 16, 2014

consumer pulse, tabletsWe all know the consumer purchase journey has changed dramatically since the “mobile web” explosion and continues to evolve rapidly. In order to understand the current state of this evolving journey, CMB surveyed 2,000 recent buyers of tablets in the U.S. We confirmed several things that we expected to see, but we also busted a few myths along the way: 

1. TRUE: “Online media and advertising are now essential to influence consumers.”

  • Reading about tablets online and online advertisements are the top ways in which consumers learn about new brands or products. [Tweet this.]
  • Nearly everyone we surveyed does some type of research and evaluation online before buying—most commonly using online-only shopping sites (e.g., Amazon, eBay, etc.), general web searches, consumer electronics store websites, review websites (e.g., CNET, Engadget, etc.), or tablet manufacturer websites.

2. TRUE: “The mobile web is becoming more important in the consumer purchase journey.”

  • Over half of buyers use the mobile web during the research and evaluation phase, and nearly 40% of buyers do so as a part of the final purchase decision (although very few people actually purchase a tablet using a mobile device). [Tweet this.]

3. FALSE: Mobile applications are becoming very important in the consumer purchase journey.”

  • Although the mobile web is now highly influential, very little purchase journey activity actually happens from within a mobile application per se. This could be because tablet purchasing isn’t something that happens frequently for more individual consumers (high-frequency activities lend themselves better to a dedicated app to expedite and track them). [Tweet this.]

4. FALSE: “Social Media is becoming very important in the consumer purchase journey.”

  • The purchase journey for tablets is indeed very “social” (i.e., word-of-mouth and consumer reviews are hugely influential), but precious little of this socialization actually happens on social media platforms in the case of U.S. tablet buyers. [Tweet this.]

5. FALSE: “The Brick and Mortar Retail Store is Dead.”

  • The rise of all things online does not spell the death of brick and mortar retail in the consumer electronics category. In-store experiences (including speaking with retail sales associated and doing hands-on demos of tablets) were one of the top sources of influence during the research and evaluation phase, regardless of whether they ultimately bought their tablet in a physical store. 
  • Next to ads, in-store experiences were the top source of awareness for new tablet brands and models. 41% of those who learned about new makes/models during the process did so inside of a physical retail store. [Tweet this.]
  • Half of all buyers surveyed actually bought their tablet in a physical retail store. [Tweet this.]

6. TRUE: The line between “online” and “offline” purchase journeys is becoming blurred.

  • Most people use both online and offline sources during their purchase journey, and they typically influence one another. People doing research online may discover that a tablet model they are interested in is on sale at a particular retailer. At the same time, something a retail sales associate recommends to a shopper in a store may spur an online search in order to read other consumer reviews and see where they can get the recommended model the cheapest and fastest. Smartphone-based activities from within a retail store are just as common as interacting with an actual salesperson face-to-face at this point. 

The mobile web is undoubtedly here to stay, and how consumers go about making various different buying decisions will continue to evolve along with future changes in the mobile web. Here at CMB, we will continue to help companies and brands adapt to these shifts.

Download the full report. 

For more on our mobile stitching methodology, please see CMB's Chris Neal's webinar with Research Now: Watch the Webinar

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

Topics: technology research, mobile, path to purchase, advertising, Consumer Pulse, passive data, retail research, customer journey

CRE Research: Following the Path of Mobile Content

Posted by Chris Neal

Mon, Aug 26, 2013

It’s always exciting when we get the opportunity to conduct research that garners interest from everyone from the guy staring at his tablet on the train to the executives of the largest media companies in the world. We got that chance, when CMB partnered with the Council for Research Excellence to lead a study exploring how mobile media devices (tablets, phones, and laptops) impact overall television viewing behavior.

Highlights of the study include:

  • Mobile TV viewers tend to be younger (mean age 35), higher income professionals with graduate degrees, and reflect more ethnic diversity than non-mobile-TV users;

  • Mobile TV viewers are often heavy overall TV viewers and are more likely than non-mobile-TV viewers to be TV show opinion leaders and to use social media to talk about TV.

  • Viewers are more commonly engaged when watching TV on a mobile device than when watching on a television set: they are less commonly doing unrelated tasks on other devices, and more commonly doing activities related to the show they are watching (e.g., looking up info about the show, posting about the show on social networks, etc.) when on a mobile device.

You can download the report here: TV Untethered: Following the Path of Mobile Content

Watch the presentation here: 

 

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

Topics: technology research, mobile, digital media and entertainment research