January 23rd, 2013
By Shaili Bhatt, Senior Analyst
When writing a discussion guide, it’s wonderful to be able to tap into resources that already exist in order to craft a well-rounded discussion.
A treasure trove of creative activities to elicit people’s thoughts and feelings beyond a surface level already exist. They are readily available to moderators of all experience levels, so it’s a big research-geek thrill when inspiration sparks for a projective activity with a new angle!
Our online qual team here at C+R enjoys passing around new links, for information or sheer entertainment. Twitter searches, Pinterest, and social publishers like Mashable, BuzzFeed and Reddit are some of our current sources for inspiration. In fact, when I came across the “What I Really Do” storyboard meme in 2012, one of the Top Memes for 2012, with all of its visual glory and bite-size insights, I was very excited!
The sharing fad around this meme, “What I Really Do,” which you probably saw last year, surreptitiously inspired me to transfer the basic visual layout of the meme to adapt it for use in online qualitative research.
This meme consists of a stylish comic montage of people’s thoughts related to the author/participant and his or her occupation, and boils down to a self-aware confession of “what I really do.”
The visuals are compelling connotations of their perceptions when the author spends time to find just the right pictures. The honesty in that last frame is often insightful, and exudes just the right magic that we qualitative researchers like to capture. In short, this new projective gives us a multi-angle lens into consumers’ lives.
The “What I Really Do” meme works well for research in its multi-frame storyboard layout (usually six frames). For a lighter exercise, I like this twist for the meme-theme:
Participants individually select an image for each of the three buckets. As a flexible, thought-provoking format, it’s easy to change out the “What” to a “Where” or “How,” such as, “Where I Vacationed” or “How I Cooked”, even ask about group perceptions—just change the “I” to “We” (like “What We Watched” or “What We Played”) and refer to friend or family connections in the instructions.
The stories and depictions that people generate through these activities are almost always entertaining and insightful for all involved, and early results suggest these activities would float well across a variety of category discussions.
January 16th, 2013
By Jessica Benoit, Senior Qualitative Analyst
It’s no secret that we live in a fast-paced, multi-tasking, smartphone-reliant, Google-searching society. We’ve grown used to quickly finding the answer to any question by simply reaching for the closest source of the Internet and accessing the information we’re looking for. In fact, instant gratification has increasingly become the norm for society everywhere. So what does that mean for qualitative research? Fast-turnarounds and last-minute research questions are becoming a part of everyday reality; we need answers, and we want them now. So how can we find answers to our research questions as quickly and efficiently as we can find out where our favorite band member enjoys dining on the weekends? Well, maybe we can’t find all the answers quite as quickly as doing a Google search (yet). But as it turns out, with a new method C+R dubs “Flash Qual” we are able to gather meaningful qualitative consumer insight in as quickly as a few short hours.
C+R’s online qualitative department recently conducted a study using the Flash Qual method in order to understand the overall perceptions and attitudes toward the New Year, and to measure the significance of New Year’s resolutions. Using a variety of open-ended questions, photo uploads, and links leading to print and video stimuli, we were able to find out that the great majority of people feel positive and hopeful heading into 2013. Getting a fresh start and setting new goals create a refreshed feeling of having a “second chance.” Most people take the opportunity of a new year to set new goals, both for the year and as a part of their overall life’s journey. Beyond the typical resolutions of losing weight, eating healthy, and saving more money, many people see the new year as a chance to live more meaningfully, to spend more time with loved ones, and take a few more time-outs from technology each day. Furthermore, we were able to quickly garner detailed feedback and opinions on advertising that uses New Year’s resolutions messaging. Similar to how they are feeling about heading into 2013, we discovered that most people find advertising using New Year’s resolutions messaging to be motivational, personally relevant and a gentle reminder of their goals surrounding feeling healthy in the New Year.
So how were we able to find this out in a few short hours? Thanks to the GutCheck research platform that enabled us to recruit respondents and instantly send them into a brief but interactive online community, we were able to quickly interview over 30 respondents and find the answers to our inquiry that very same day. Using our Flash Qual method, we were able to:
- Quickly access and recruit respondents
- Instantly create an online community
- Directly immerse ourselves in a back-and-forth online discussion with respondents
- Inexpensively acquire valuable input that could potentially inform time-sensitive business decisions
Flash Qual thrives because it focuses on collecting rich, top-of-mind key findings; respondents are able to provide colorful feedback and creative, to-the-point responses without unnecessary distraction. We heavily screen for articulation and set high expectations upfront for what we are looking for from respondents. By limiting the number of questions we ask, Flash Qual doesn’t waste any time getting to the main point. Though the collection of responses is not quite as holistically involved or in-depth as a typical-length online research community, the results nonetheless yield purely qualitative insights and top-of-mind key takeaways needed for quick insight and last-minute decision-making time crunches.
As the phrase “fast-paced society” continues to mean an exponentially quicker pace and greater satisfaction with instant access to information sought, Flash Qual is likely here to stay for the long run.
January 9th, 2013
By Bob Relihan, Senior Vice President
Walt Dickie had done a very nice job of knitting together the trends in the adoption of various electronic devices. Certainly PCs are flattening out and will eventually decline. And, I agree there will be a time when virtually every cell phone is a SmartPhone. Walt also plots a curve that predicts exponential growth in the tablet/e-reader market, but backs off from the implications. “I’ve gone with a growth curve that can’t be right in the long term – it has to flatten out – but might be okay in the short term.”
I am not so sure.
Now, it is likely true that all but a few high flyers and the tech obsessed (as well as those involved in illicit activities) will ever have more than one smart phone. The device, after all, is tied to one’s personal phone number. But, the same constraining logic does not apply to tablets and e-readers, particularly when they merge into one category with vaguely similar features and price points ranging from $79 to over $800.
So, in the next few years, as more and more users acquire new tablets with better features and still have serviceable old ones on hand, it is easy to imagine a home with a first generation Kindle by the beside, an older iPad on the kitchen table for reading the news and checking the weather in the morning, and the latest tablet sitting on the coffee table in front of the television. Will there be a television? Why carry a tablet with you? You can have one wherever you turn.
There was a time when the household was dominated by one large console television in the living room. Over the years conducting focus groups, I have asked in passing, “So, how many TVs do you have?” Seven is no longer an uncommon answer…in a household of two. A future with a tablet in every room is not that far fetched.
By Walt Dickie, Executive Vice President
I love the Pew Research Center, especially their Internet & American Life Project. I can always find something interesting to think about on their website, and I admire their invariably solid methods. We use Pew data to make strategic decisions, but we also go to Pew for inspiration when imagining future scenarios.
A recent Pew post, including data through September 2012, based on a long-running tracking study and a more recent update on smartphone ownership brings together information about consumer ownership of desktop computers, laptops, cell phones, smartphones, and tablets among U.S. adults.
Some of the most important parts of the dataset are still a bit sketchy. Not because Pew didn’t do their usual excellent job collecting it, but because the number of data points is still pretty limited. In the spirit of fooling around with numbers and the informality of blogging, I decided to analyze this data to generate some hypotheses about smartphone and tablet adoption. The analysis that follows plays somewhat fast and loose–extrapolating trends beyond the range of the data and basing these trends on an inadequate number of data points. This sort of thing is fun and may be stimulating; it is not conclusive, nor is it meant to be.
Here’s a selection from Pew’s device ownership data plotted together on a single graph:
Desktops + Laptops (“PCs”)
I aggregated Pew’s data on desktop and laptop ownership to create this curve for traditional “personal computers,” which goes above 100% because it’s quite possible for someone to own one or more of each species. If you look at the original Pew data for laptops and desktops separately (not shown here), you’ll see that laptop sales are still rising while desktop sales are dropping precipitously. The curve shown represents a leveling-off of PC ownership at somewhere between 1.1 and 1.2 PC’s per household, which the Pew data suggests will be mostly laptops as desktops die and are not replaced.
I feel reasonably okay with fitting this flattening curve to the cellphone data, and although projecting curves forward in time like this always gives me the willies, the result looks at least somewhat plausible. Cell phone ownership is clearly flattening out with something like 15% of the population being reported as doing without. Cell ownership will probably never hit 100%–landline phones never did–but it will certainly get further into the 80s, and maybe even into the 90s, as landlines pretty much fade away and all the people who grew up in the pre-cell phone era disappear. Of all the curves on this graph, I think this one may be the most realistic.
Fitting a curve to the four data points on smartphone ownership is clearly beyond the pale, but having decided to work with what we’ve got why not do it anyway? An exponential growth curve may capture what, by all accounts, has been a startling adoption rate, but of course no trend, even a really powerful one, will continue into the future without slowing-probably more noticeably than is predicted here. Smartphone ownership seems to have paused in the middle of this year, but with the phenomenal sales figures being reported for the iPhone 5 maybe predicting penetration to continue its strong growth isn’t so bad, at least in the short term. In any case, I wanted an aggressive scenario to explore the impact of smart phone growth, and that’s what this curve represents.
Not many data points (5) here either but tablet adoption sure sounds like it’s accelerating according to the news reports, and an exponential curve fits the existing data almost perfectly. I’m writing this immediately in the wake of “Cyber Monday,” and the news outlets and blogosphere are reporting tales of tablet frenzy following the debut of the iPad mini and Amazon Fire. Again, whether exponential growth will be sustained is questionable, but it doesn’t seem wildly off to estimate that we’ll experience that kind of growth in the short term.
Interestingly, the curve of e-Reader ownership shows an almost identical rate and pattern of growth, which raises the question of whether these are one species or two. Although tablets and readers started out as separate species, it’s hard to imagine how they continue to evolve without merging into a functional/price continuum competing in a single market, even if some of them continue to be distinguished by very different screen technologies adapting them for use under different lighting levels. If the two combine into a single product line the case for exponential growth may be strengthened.
So, again, with some basis in statistics and observation, but mostly because I want to create a best-case scenario for new device adoption, I’ve gone with a growth curve that can’t be right in the long term – it has to flatten out – but might be okay in the short term.
Using some obviously bogus methods but maintaining at least a nodding relationship to “reality,” I hereby predict that smartphones will essentially eliminate “feature phones” from the cell phone marketplace in about 3 years; at which point, everyone who owns a cell phone—something on the order of 85% of US adults—may own a smartphone. I further predict that in about 1 year ownership of tablets in the US will equal ownership of traditional PCs, with most households owning one or more mini/maxi/reader “tablets.”
All of which means that MR has a very short window for adapting its data collection methods from a PC-centric paradigm to one centered on smartphones and tablets.
This will be one of the biggest challenges in our immediate future for both C+R and in the MR industry as a whole. We simply have to get “mobile” right. As long as our clients demand data-driven insights, we’re going to depend on consumers being willing to share their perceptions and opinions with us, and we depend on technological means to collect that data.
Looking back, the conversion from phone/mall/mail survey data collection to online methods at the end of the 90s seemed like a major revolution that upended almost everything and rang in a new era. But, in hindsight, although the mechanisms of research changed a lot during that period, what now seems to stand out is that the basic paradigm of question-and-answer surveys changed very little. Other than porting surveys from “CRT terminals” in the phone room to PC screens in the nation’s family rooms, dens, bedrooms, and kitchens, the underlying form of the survey hardly changed at all. Surveys grew images, videos, and Flash widgets but the great majority of MR surveys created today for online administration could be ported (back) to the phone room quickly and with ease.
But mobile is going to be different. Cellphone use is dominated by short interactions while few online surveys take less than 20 minutes (and many take more). Today’s surveys, though they can be taken using a mobile device, are a miserable experience because the industry mindset is still fixated on the PC. (My friend and colleague, Bob Relihan, says that the most miserable experience on a cell phone is trying to fill out a web form, and online surveys are composed of one web form after another.) A whole new survey paradigm will have to be invented to model a virtual “long survey” from a series of very short interactions on a mobile device or something resembling a Google Survey. And the sampling industry is going to have to reinvent itself once again.
The “PC revolution” (the 80s) and the “Web revolution” (the 90s) are going to give way to the “Mobile revolution.” The question is “When?” and the answer could easily be, “Very soon!”
December 19th, 2012
By Walt Dickie, Senior Vice President
(If you haven’t already, be sure to read part 1 to this blog post.)
I’m assuming that if you’re reading this you’re probably a marketing researcher. If that’s the case, you probably have access to a bunch of survey questionnaire. Look on your hard drive or reach into your file drawer if you’re old school and take out three or four. Look them over and back up far enough so you’re looking at their general structure rather than the details. What do you see? I’ll bet they all share a similar approach:
- First, you almost certainly see a bunch of screening questions designed to eliminate all but a pre-defined target group from taking the main survey. This makes sense because you pay for sample, pay incentives, and pay for resources to administer, manage, and analyze the survey and you don’t want to waste resources.
- Next your surveys probably have a section or two devoted to category-level questions, so you can profile results based on pre-established dimensions of opinions about and usage within whatever product/service category the survey focuses on. Again, this makes sense. It establishes context and in many cases may allow you to compare your findings to previous work.
- Then your surveys probably show or reveal something in order to get respondents’ reactions to it. Purchase interest. Likes and dislikes. Batteries of questions dissecting emotional reactions. Maybe some open-ended questions to lay bare the underpinnings of those emotions. Maybe you’ll go around a couple of times on the reveal/react carousel. Or dissect the combinatorial possibilities with some conjoint or discrete choice questions. All sensible choices. This is the meat of the survey and you want to be able to mine it for key findings and “insights,” which are, after all, what the client is paying for.
- Finally, you’ll almost surely find a bunch of classification questions – again, quite a sensible thing to include for establishing context and tying your work back to the client’s tracking data and historical survey archive.
Everything in your surveys is sensible, serious, and workmanlike.
But back up and consider your surveys from the perspective of someone who, somehow, had never seen anything like them before. There’s really quite a lot about your respondents and what they do and think that your surveys make to mention of. Your surveys contain such a limited set of choices!
There is, for instance, no information at all about what their friends and family members think, or what they would say about either your product or new idea. Nothing about what your respondents search for on Google or read online. Nothing about where they go everyday as they travel around the places they live, or what they see or what they do there. Nothing about their inner lives: what they love, long for, dream about, pledge allegiance to, dislike, despise, or denigrate. Nothing about their pasts, really, or about their futures. And there’s nothing about them that they are not or cannot become conscious of and respond to in answer to your question. Not to mention the problem that all of your questions come in prefab form, so the issues they address are constrained to the issues the client already recognizes. And let’s not forget that there’s no information from anyone who fell outside of your carefully limited screening quotas. How can you be sure that there are no discoveries to be made among them?
You’ll object that you didn’t omit any of that because you aren’t interested in experimenting with it or would reject the possibility of finding value out of hand, but you have a limited budget and only so much time to spend. The client won’t pay for experimentation and, besides, there’s a decision to be made. The client has a lot riding on the research and they’ve given a good deal of thought to the question of what they need to know to make that decision. You need and want to give them good value for their money.
I’d like to suggest that this is a powerful example of what drives bubbles and echo chambers. Marketing research has evolved to serve structured corporate decision making and that process has evolved to demand the inputs that marketing research provides.
Corporate decision making and marketing research have coevolved the MR survey with a closed set of constructs and almost ritualistic format. Unfortunately, the evolution of surveys has been driven by the imperatives of the Red Queen – running faster and faster to stay in the same place, becoming an ever-closer fit with the needs of corporate decision making, budgets, and timelines. Each step has not been determined by the possibilities of utilizing new data sources, analytic approaches, or even accurately predicting future behavior, but by the ever-closer interlocking of the decision process and the information machine that feeds it.
Like Republican pundits talking to Republican audiences and eventually creating a closed worldview that, among other things, mis-called the election, we’ve created a mutually evolved worldview that has satisfied us and our clients for many years, but, as the CEOs keep telling us, often seems to fail to predict the real world and doesn’t seem to be getting any better.
Will the appearance of data nerds bearing data on things like clickstreams, search histories, location, social influence, sentiment analysis, and facial expressions – complex real-word behavior and data that isn’t based on the ability to frame a conscious answer to a prefabricated question – be the beginning of the opening-up of that closed co-evolved world? Will MR break off the echoing conversation before our clients do? Will it take the equivalent of a public defeat on a national election stage to turn things around, or will MR put the genuine technical expertise, hard work, and ingenuity to work before we have to cobble together a last minute concession speech?
December 17th, 2012
By Walt Dickie, Executive Vice President
I’ve been working in the MR industry since 1978, and the one unchanging theme of that long period has been the constant complaint by the senior corporate executives – who fund the industry by their demand for research-based decision making – that MR just isn’t very good at identifying opportunities or pointing to the new products, services, and ventures with the best chances of successfully capitalizing on them.
I remember being surprised when I first encountered a CEO denouncing the entire MR enterprise at a major conference – which probably happened sometime within my first or second year. Was my new, desperately needed first job after grad school, and maybe the whole industry, going to go down in flames before I even got started? It hasn’t but the drumbeat of C-suite dissatisfaction has never lessened.
This all came to me as I was reading one of the many articles from the “nerdiest election” in which Romney had not prepared a concession speech because – by all the accounts I’ve seen – he didn’t think he’d need one. It sounds like Romney really got blindsided. Not only did he not have a concession speech held in reserve, but he also planned to celebrate the election with a fireworks display over Boston Harbor and his campaign even had a “Romney Wins” website up and ready. (Someone posted screen grabs, of course.)
As Slate’s John Dickerson said, “He got the numbers wrong … in the end Romney and Ryan had to watch CNN to find out how their campaign was doing.”
The blog posts and news stories lay blame on what David Frum labeled the ”conservative entertainment complex.” And it looks like the Romney’s “marketing research” group followed the media’s lead in asking the questions they wanted asked, hearing the answers they wanted to hear, and reinforcing an internal viewpoint that, in the end, failed as MR and left the CEO “gobsmacked” and angry.
I read all of this with the shock of recognition. My mind snapped back to 1985 and the introduction of New Coke. All the research that had been done! And, the magnitude of the disaster that followed! The astonishment of everyone inside Coca-Cola that their carefully constructed edifice had been built on sand. I had seen all of it and had enjoyed having the inside scoop thanks to two colleagues who had come from Coke’s MR department and understood the backstory, which had included a huge research effort.
The post-election stories seem to be revealing a distinctive, internal worldview shared by the campaign and its media supporters. Republican pollster, Whit Ayres, described the research being drive by “rosy assumptions on a likely electorate…at…substantial variance with recent history.”
An “echo chamber” bubble developed when the campaign research staff and its clients elaborated a shared narrative about polling and sampling methods and how to interpret results. Outside that bubble, the academic social scientists and media stat nerds – with Nate Silver as their symbolic leader – were using different methods, asking different questions, and interpreting their findings differently.
They were right in the end, and the people in the bubble were wrong.
A case can be made that MR and our clients have created a similar bubble, in which we talk to each other in an echo chamber. And, a case can be made that the academic social scientists and techie stat nerds are “threatening” traditional MR with everything – including Big Data, social media and web based behavioral analytics, location data, remote facial analysis, and eye tracking – living in an entirely different world.
The GRIT survey as well as reports coming out of consultancies like Cambiar (site registration required), say clearly that corporate research departments are eagerly welcoming all the new “non-MR” vendors knocking on their doors. What’s worrisome is what would happen if the CEOs start sending more work to the guys outside the MR world, and they started having some successes calling the game. That’s what the voices that expect the leading “MR” vendors of the end of the decade to be companies like Google, Facebook, and Twitter.
Why didn’t all of these new approaches arise out of marketing research? At the very least, why weren’t traditional marketing research companies their earliest and most eager adopters? Why didn’t clients hear about all of these developments from their MR vendors? Is it because the conversation was “closed” and things like that simply had no place?
I’m not arguing that MR is operating in bad faith – only that they and their client audiences may have constructed a particular shared view of commissioned and conducted research that has become closed, limited, and overly rigid.
Marketing researchers are almost universally serious, sober people who see themselves as technical experts in a field that demands hard work, clear thinking, and ingenuity to provide vital information to decision makers while often suffering little respect, diminished budgets, and constricting timelines. But make no mistake about it, marketing research has been working with the same basic approaches for at least a couple of generations. I made a stab at some of the qualitative issues in a previous post, so in part 2 to this post, I will examine a few things about quantitative research.
December 6th, 2012
By Walt Dickie, Executive Vice President
The torrent of shopping data from Black Friday and Cyber Monday is coming in. Although their names suggest a f first person shooter game involving Robocop in some futuristic battle, these two days are the now-traditional kickoff to the U.S. Christmas shopping orgy, and a clarion call to the armies of the commentariat and blogosphere.
And, once again, in what appears to be as much a part of the new American Christmas tradition as the shopping experience itself, the big headlines are all about the massive growth of online shopping.
If you’ve somehow missed all the frenzied scribbling, you can turn to IBM which published the key data that almost everyone seems to have relied on in the IBM 2012 Holiday Benchmark Reports. There, in just a few pages of data, you’ll find both Friday and Monday’s online sales data broken down by retail category and compared to last year’s results.
The topline news story is, of course, the huge increase in online shopping: up 17.4% compared to last year’s Thanksgiving Day, up20.7% compared to Black Friday 2011, and a whopping 30.3% up on Monday compared to a year ago.
Close behind is the news about mobile: IBM estimates that 24% of retail site traffic came from mobile devices on Black Friday this year, up from 14.3% last year – a monster increase of 67.8%. On Cyber Monday – traditionally understood as the day people went back to work and shopped their brains out on their employers’ broadband connections – mobile users were responsible for 18.4% of retail site traffic, which was up from 10.8% a year ago – an incredible 71.4% increase.
The oddity of the week was the unexpected difference between Android and iOS (Apple) users that emerged after correcting for Android’s dominance in the smartphone race. On Black Friday iOS devices accounted for 77% of mobile shopping traffic while Android accounted for only 23%. This is an oddity because currently Android phones and tablets outnumber iOS phones and tablets by about 60/40. Work it all out and “iPhone (and iPad) users are about three times more engaged in shopping with their devices than Android users.”
Horace Dediu does an excellent job of unpacking the “Android engagement paradox,” which he attributes mostly to “later adopters” buying Android phones in numbers sufficient to have overcome Apple’s early lead in smartphones. But, in the end, he finds this answer unsatisfactory. I wonder if the Android/iOS “paradox” contains a message for marketing research about mobile sampling – should we be thinking about weighting our mobile samples or imposing Android/iOS quotas?
But the main message for MR comes from some much more basic observations about mobile usage.
What caught my attention in IBM’s data wasn’t the comparison between this year’s Black Cyber Days to last year’s, but the column comparing Black Friday 2011 – last year’s Big Gun – with Friday, November 16, 2012 – seven days before this year’s opening round, a “normal” pre-holiday Friday, which I will henceforth refer to as, “Normal Friday 2012.”
You may remember that the headlines for Black Friday 2011 were more or less the same as they were this year: online as a whole and mobile shopping in particular were way up. But “Normal Friday 2012” blew away Black Friday 2011 on several measures. For instance, sales increased 10.8% on retail sites compared to last year’s Black Friday, and, on average, a sale in 2012 involved 3 items more than a sale in 2011. The headline-making shopping news of 2011 now trails the new normal.
Mobile is the major factor:
Mobile data is in blue; other data is in orange. Data involving sales is outlined in red.
Of the variables that increased on Normal Friday compared to Black Friday, most involve mobile and, overall that mobile site traffic increased 4.6%. Although mobile sales increased, sessions that involved viewing only a single page increased overall on mobile, as well as abandoned shopping carts. Moreover, with the exception of mobile sales, all the variables involving closing a sale fell, as well as sessions in which a visitor placed an item in a cart.
Normal Friday 2011 obviously involved more looking and comparing even though buying did manage an increase.
All of the other data collected over the past couple of years reinforces the obvious conclusion that mobile devices – smartphones and tablets – have become even more important components of shopping. Checking prices and features, sales, finding online coupons, and, for that matter, seeing advertising, are now normal parts of the shopping experience. As I said, we knew that.
But what seems to be fairly stunning is that in a single year the headline-making news of Black Friday is now the everyday expectation of Normal Friday.
The moral is that the retailers get it; they’re struggling with it but they get it. They all know that they have four screens to think about – TV, computer, tablet, and phone. They all know about showrooming: “when a customer visits a brick and mortar retail location to touch and feel a product and then goes online…to purchase the product.” And, the smartest of them have stopped complaining about it and are working on leveraging it with apps that provide extra services in-store – new items, sales notices, bar code scans – then let you “flip” to their website to take advantage of online deals. The same app lets you find their deals when showrooming in a competitor’s store, too. They’re adapting to the new normal.
Can this please be the year that marketing researchers – clients and suppliers – stop wondering whether to add a “cell phone segment” to a sample spec and accept the fact that we need to expand our data collection toolbox, to fit the the time, connectivity, and screen size constraints of mobile while also expanding our thinking? We should be focusing on leveraging the incredible capabilities that mobile presents for collecting new kinds of data in new kinds of situations. We need to walk away from the “online revolution” of the early 2000s and realize that we’re in a new, increasingly mobile-dominated age.
By the way, the average session on a mobile device on Black Friday 2011 took 4:03, and Normal Friday 2012 it had shrunk to 3:46. That session probably took place while the shopper was doing at least two other things at the same time, and everything that was going on was almost certainly of interest to MR. But, we probably missed it.
December 4th, 2012
By Patti Fernandez, Research Director
The Marketing Research Event was buzzing with excitement and anticipation. What tales would we hear, what knowledge would we uncover, what trends would take center stage? And, in the end, on what new paths would we, as researchers, venture?
Insight development via storytelling and storytelling through data visualization were very much in the air. Many a session encouraged us, like Dorothy, to follow the yellow brick road toward our own Emerald City where insights break the confines of numbers and quotes and live within visually compelling stories.
But, in today’s data-driven world, how can we tell a story visually while seamlessly satisfying the needs of the data literalists? And, how can we shake the compulsion to show everything we’ve uncovered because (in our minds) every nugget matters?
The key is not only to tell a story, but also to approach the insight development process in the same way as story-creation. Here are five key elements to a solid storytelling approach:
Relevance is Key
- There is usually a rhyme and reason for everything that is included in a story (foreshadowing, plot-building, etc.).
- In that same way, results and insights should serve as key puzzle pieces that help build and complete a bigger picture.
- Relevance, though, takes time. We must first go treasure-hunting through all of our findings in order to determine which ones truly are worthy of supporting the key insights that need to be communicated.
- Stories follow a natural, rational order that keeps us alert and engaged with the plot.
- Our insights and findings, then, should follow the same path. They should help take the audience on a journey that makes sense and keeps them on the edge of their seats.
Create Conflict and Resolution
- Without conflict there is no resolution – without resolution there is no end to a story.
- Always aim to keep the plot of your story anchored. Your role as a researcher is to tell a story that ultimately helps resolve some sort of conflict.
Define Your Characters and Their Roles
- Characters have set roles in the story – they exist for a reason.
- In order to approach research in an organized and rational manner, we must first define who the characters are and what role they play.
- We may be swayed to think that the brand or product is the hero, but it is the consumer who should wear this badge. Brands are simply the tools that help the hero resolve conflict.
Bring Your Story to Life
- A good story will keep us turning the pages if it’s told in an engaging manner. Overuse of descriptive or circular plots can deter engagement and leave us tossing the story aside without finishing.
- And, just like a poorly written story, research results that are loaded with data that makes the audience have to work too hard to decipher the true message can fall flat.
- Using visual depictions of information to surprise and make data easily digestible will not only make your research more engaging, but also make it easier to present the story in personal and animated manner.
In the end, it’s not simply how you present your insights with iconic figures, captivating prose, and visually stimulating graphics – it’s how you approach the insight-finding process. So, take a leap of faith and follow the rabbit down the hole through a journey of discovery.
November 6th, 2012
By Scott Hierbaum, CFO
Two colleagues and I recently attended the CASRO Annual Conference in Arizona. It got me out of the trees for a few days…out of the literal hardwood trees of Chicago and the figurative trees of my daily job.
The over-riding theme of this year’s event was the future of market research. Is it dead? Is it alive and thriving? One speaker emphatically referred to MR as sexy. Too bad my wife wasn’t listening.
Looking towards the future is important, but tricky. Especially when you’re simultaneously sticking your neck out and keeping your nose to the grindstone. Sometimes you even have to provide a shoulder to cry on. (Yes, this was an intentional abuse of body-part idioms).
The general consensus is that survey research is not dead. Not even on life support. However, it’s not a time to sit back because our industry is rapidly evolving. We will continue to conduct surveys, moderate focus groups, and facilitate online communities. But we’ll need to start integrating this information with data and findings from a variety of other sources. If you can do this, and do it well, you should find success in the marketplace.
Wharton professor, Eric Bradlow, kicked off the festivities with “The Golden Age of Marketing Research.” The name, in hindsight, was a clever bit of sarcasm because we’re entering our 4th or 5th Golden Age, and more will come. Past Golden Ages revolved around direct mail, store scanners, and the Internet. The next Golden Age will mesh customers’ behaviors with traditional marketing research. Non-survey data can tell us “what,” but our industry will have to continue asking the “why.”
Consultants, Timocin Pervane (OC&C) and Simon Chadwick (Cambiar), each presented research-based assessments of our industry. Pervane’s main point was that many new facets of MR (social media listening, neuroscience, mobile, and DIY) are not substitutes for high-value-added survey research. They are more complimentary than substitutive. This is good, I suppose, but doesn’t change the fact that we’ll probably see an allocation of budget dollars away from traditional survey work. Chadwick focused on the gap between what research providers think and research buyers want. This is a good perspective that we should all keep in mind. The message from both, though, was the same…our industry is evolving and so should we.
There was an interesting roundtable session titled “Transformative Times: The Corner Office View.” Three very different personalities offered three very contrasting visions. But they each represented very different organizations. Unlike politics (which can have only one right answer), there is room for more than one profitable business philosophy in market research.
The roundtable featured the notion that CASRO’s name is outdated. One person suggested simply swapping out the word “survey” with “social.” CASRO…The Council of American Social Research Organizations. Not perfect; but as a CFO, I respect the possibility of not having to get a new web site, logo, banners, etc.
As is the case with most conferences there was also time for catching up with old friends, making new ones, eating, and having a wee bit of fun. One dinner featured an improv troupe that displayed a spot-on grasp of professional respondents, attempting to navigate their way through a screener to the incentive at the end. Respondents don’t do that, do they?
So now it’s back to the office. I’m trying to catch up, do my regular job, disseminate what I learned, and help steer C+R towards the future. The conference and the participants did give me an outlook adjustment, providing some excellent advice on leading, facilitating change, hiring, and inspiring. The nature geek in me would be remiss if I didn’t acknowledge that Arizona has cactuses, and cactuses are technically trees. So I didn’t truly escape the literal trees. But I did hover over the forest of market research for a few days. All in all, I liked what I saw.
October 26th, 2012
By Walt Dickie, Senior Vice President
If you’ve ever read anything about SETI, the search for extraterrestrial intelligence, you’ve probably heard of Drake’s Equation—well, you might have heard about it on Star Trek or the Big Bang Theory instead! Formulated by the astronomer Frank Drake, it estimates the odds of intelligent life in our galaxy and is one of the foundations for the whole enterprise. SETI came into being when Drake, whose Project Ozma was the first systematic search for alien radio signals, addressed a meeting at the Green Bank National Radio Astronomy Observatory in 1960 and offered an estimate of the odds that the project could succeed. The Drake Equation is justly famous as one of the first attempts to put the possible existence of little green men into mathematical perspective.
I was in high school when I first heard about SETI and the Drake Equation. For no good reason, I’ve often found myself musing about it which is why it recently popped into my mind again when one of my partners was getting ready to moderate a discussion at an MR conference.
The conference, like most MR conferences these days, was heavy on presentations about New Methods and The Future of Marketing Research. Needless to say, not a few of the conference presenters agreed with almost every blogger in the industry that Major Change is Just Around the Corner and that the Future For Every Established MR Company Is Bleak.
My partner’s role at the conference was to moderate a discussion between the audience and one of the keynote speakers, and he was preparing by reviewing the speaker’s presentation, a copy of which he’d sent me. We were exchanging emails about the issues in the talk and the potential questions they raised when it occurred to me that MR needed its own version of the Drake Equation.
Just as the early SETI community needed some estimate of the likelihood of contacting alien life, the current MR community needs an estimate of the likelihood of being superseded by new research technology. It’s almost all we talk about these days, and, being mathematically inclined folks, we deserve a calculation of our odds.
So here, with apologies to Frank Drake, I propose the MR version of the Drake Equation as a contribution to SSRT, the Search for Superior Research Technology.
The Drake Equation
N = R** fp * ne * fℓ* fi * fc * L
SETI: N = the number of civilizations in our galaxy with which communication might be possible
SSRT: N = The current number of competitors capable of putting your company out of business
Drake’s proposed value
My proposed value
|The average annual rate of star formation per year in our galaxy||
|The average annual rate at which significant new approaches for understanding some aspect of human behavior or thought appear||
|The fraction of those stars that have planets||
|The fraction of those approaches that are applicable to the design/ delivery/ communication of consumer products or services||
|The average number of planets that can potentially support life per star that has planets||
|The fraction of the above that depend on data/inputs that can be collected using conceivable/ deliverable/ socially and ethically acceptable technology||
|The fraction of the above that actually go on to develop life at some point||
|The fraction of the above that are commercialized at some point||
|The fraction of the above that actually go on to develop intelligent life||
|The fraction of the above that are faster and/or less expensive than your company’s offerings||
|The fraction of civilizations that develop a technology that releases detectable signs of their existence into space||
|The fraction of the above that will produce results that are more actionable/ effective/ predictive than your company’s offerings||
|The length of time for which such civilizations release detectable signals into space||
|The length of time that those approaches will be seen as valid/interesting/relevant as the basis for commercial products/ services||
Some Comments on my Estimates:
R* I think my estimate of truly unique, significant new approaches coming along every two years may be generous. Not that new approaches or technologies don’t appear more often than that, but most of these are minor wrinkles on existing approaches – better ways to do something that’s already being done. If you’re a technological optimist you might go for more frequent discoveries. I think that one a year would be an optimistic estimate, but feel free to enter your own.
fp Almost anything – maybe not quite anything – that’s applicable to humans is applicable in some non-trivial way to consumer products and services. Optimistic estimate: .99
ne On the other hand, some of the new things that come along require approaches that either aren’t technically feasible, at least at scale, within any foreseeable future or would never pass a social/ethical or possibly legal challenge. I’ll allow that almost any technical obstacle can be overcome, but I’m not so sure about the social issues. I’d hesitate to make this a certainty under even the most optimistic scenario. Optimistic estimate: .9
fℓ With the rise of crowd sourcing augmenting venture capital and other ways to finance new business ventures, and entrepreneurial enthusiasm apparently being boundless, pretty much everything that can be commercialized will be, at some point. Optimistic estimate: 1
fi Not everything is quick and not everything can be made cheap. I scored this one a toss-up. If you believe that advances in technology will eventually bring down any conceivable cost, your optimistic estimate would be 1.
fc On the other hand, a lot of current technologies have already been pretty much optimized and aren’t advancing anymore, so something really new has a fair chance of bringing new insights to the party. That’s 2:1 in favor of the new stuff in my book. But it’s possible to reason that any legitimate theoretical advance will inevitably produce some significant new insight. Optimistic estimate: 1
L There is no useful data on the rate at which new basic approaches appear in the sciences or technology. (This question might be phrased in terms of the frequency of paradigm shifts, about which there is no consensus.) I went with my gut feeling that after about 25 years paradigms begin to be seen as played out. I think that an optimistic estimate might be 2-4 times longer than this.
Using my estimates, there are probably 2-3 technologies capable of putting your company out of business by undercutting the approaches your business is based on; using what I think are the most optimistic (pessimistic?) estimates for every variable in the SSRT Equation, there are somewhere between about 45 and 90.
By the way, although Drake’s original estimates yield an estimate of 10 advanced civilizations in the galaxy, the consensus estimates developed at the first SETI conference yielded something between 1,000 and 100,000,000. I wonder if a reasonable argument can be made using both the SETI and SSRT versions of the equation together that not only are there plenty of companies capable of putting yours out of business, some of them are or will be run by aliens.