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Q&A: Big data and its meaning for marketing researchers

big_data

Editor’s note: Greg Mishkin is a vice president of research and consulting at Market Strategies International, Livonia, Mich. This is an edited version of a post that originally appeared here under the title “Answers to the four most popular big data questions.”

2013 was the year of big data, although not always in a good way. We saw some of the largest privacy breaches in history affect major brands like Target, Facebook and Adobe, as well as government related snafus (Edward Snowden, the NSA and the Federal Reserve Bank) impact hundreds of millions of people. The public now understands that we are leaving a data trace with every cell phone call we make, Web site we browse, debit card we swipe and security camera we pass. No matter where you stand on whether our data are being used responsibly, one thing is absolutely clear…

Big data is everywhere.

In addition to what we leave behind, we willingly offer our data in exchange for valuable benefits:

  • We pay up to $100 to give the government detailed personal information in exchange for faster access through TSA PreCheck lines at the nation’s airports.
  • We install connected thermostats that know when we are home, away and asleep, enabling companies to learn about our daily routines.
  • We wear devices like Google Glass and Fitbit to track and share our activity with friends.
  • And don’t even get me started on Facebook, Twitter, LinkedIn, Pinterest, Flickr, etc.

With this trend comes an insatiable demand for big data analytics. Marketing research used to focus on asking people how satisfied they are with a service or whether they prefer Product A or Product B. But businesses want to know more about their customers than what they are willing or able to reveal in a survey. Today, businesses want to predict the future and they are turning to big data to feed a new breed of predictive analytics. Here are a few of the questions I hear most frequently from clients:

Q: What is big data in simple terms?

A: In simple terms, big data is data so large and complex that it cannot be effectively analyzed using previously established systems, processes and resources. One of the easiest ways to understand it is by the three Vs: volume, variety and velocity.

Volume means that big data is big. Big data analyses do not typically look at hundreds or thousands of rows of data, but rather billions and trillions of rows, so forget desktop spreadsheets!

Variety means that big data analyses often bring together lots of different types of data from different sources in ways not done before. Sometimes the data are structured with clearly defined rules and logic, and other times they are unstructured (like streams of comments from Twitter or Facebook). As a result, the data are usually messy and require a fair amount of cleanup.

Velocity means that big data comes in fast and changes quickly. I have worked with large data warehouses that receive more than 25 billion rows of new information each day! This type of data analysis requires processes that can incorporate newly-generated data quickly and efficiently.

Big data can come from customer data, social media data or the “Internet of things,” which includes data resulting from Internet-connected products (e.g., Web browsers, cell phones, GPS navigation and connected cameras, cars and thermostats). Market Strategies is currently working with data as diverse as call detail records from mobile phones, smart energy meters, financial services transactions and gambling patterns, just to name a few.  The point is that every aspect of your customer’s life leaves behind a data trail and, when analyzed correctly, these trails can lead to immense knowledge.

To effectively analyze big data you need an infrastructure that has been specifically designed to handle it (e.g., SAS, Hadoop or Teradata) along with specially trained data scientists. Many people are surprised to learn that while big data looks at more data than most people can wrap their head around, it is almost always riddled with holes. As Nate Silver tells us in his book, The Signal and the Noise, “Data-driven predictions can succeed – and they can fail. It is when we deny our role in the process that the odds of failure rise.” To increase the chances of success people with industry and data domain expertise need to fill these holes – not machines.

Q: If my company is just starting to consider using big data in our marketing research, what would be most useful to include?

A: There is a trend to replace the term “big data” with “small data.” Most organizations are sitting on an enormous amount of untapped data and should look to their own networks for transactional data that can help build a more holistic view of their customers. The data are often siloed across disparate teams and have not been combined because the task was deemed too difficult, expensive or nebulous. But impressive ROI can be found when data are combined into a single warehouse and integrated with existing and new data points from traditional marketing research sources.

At our firm we take clients through a two-step process to identify and document the goals of big data integration and to review existing qualitative and quantitative research, as well as internal databases. The first step involves one-on-one interviews or a roundtable discussion with all key stakeholders to create a shared vision of success. The second step involves a series of information-sharing meetings to dig into all of the existing internal data and other research to determine what has worked, what has not and what prior work can be repurposed.

Often there is so much excitement to get started that clients want to skip these initial steps or complete them in a cursory manner. However, doing so can prove fatal to the overall project. Without taking the time to build a thorough analytical plan there will not be a solid road map, and the likelihood of success is greatly diminished.

Q: What about privacy? Are we even allowed to use the data we collect from our customers?

A: Based on the public debacles that unfolded last year, privacy is a hot topic. As each situation is unique, there can be no hard and fast rules; however, there are best practices to consider:

  1. Different industries have specific legal regulations for the collection and use of customer data. This is especially true in health care (HIPAA), financial services (RFPA) and telecommunications (CPNI). Most organizations in these industries have internal or external counsel they can turn to for industry-specific advice. Big data analytics can be accomplished safely and effectively within these environments as long as methodologies are created with the regulations in mind.
  2. It is always important to remember the court of public opinion. Before using your customers’ personally identifiable information, ask yourself if using it is in the best interest of the customer. When you are conducting research to improve the customer experience, it is clear that this use benefits the customer. I like to use The Wall Street Journal test. Simply put, ask yourself what would happen if the details of the proposed action became a front-page headline: Would the negative fallout outweigh the positive benefits?

Big data can be scary and intimidating to the public and it is critical to consider the impact to your customers and stakeholders. By applying The Wall Street Journal test, companies can tweak their big data projects to minimize risks. It is important for companies to have an experienced partner who appreciates the inherent hazards associated with big data analytics and is able to keep them safe while gaining the most value from the data.

It is equally important to ensure that your efforts comply with your company’s established privacy policies. If they do not, change the methodology to comply with the policy or update the policy if it is outdated. The bottom line? When big data research is conducted appropriately and ethically, there will be no need to hide it from customers or regulators.

Q. Will big data replace surveys?

A: No. Here’s why:

Big data analytics does a really good job of telling us what our customers are doing, who they are doing it with, where they do it and when they do it. However, it does a pretty awful job of telling us why our customers do what they do. Understanding the why behind the actions is critical to marketers since they are tasked with finding ways to change customer behavior. A marketer is focused on how to get people to buy more of their stuff and less of their competitors’ stuff. Without fully understanding the why behind their actions, marketers are left to guess which strategies and tactics will actually motivate customers.

There is no doubt in my mind that there is a place for both big data analytics and traditional marketing research. By skillfully integrating the two, researchers can understand what their customers are doing, why they are doing it and, most importantly, how to change their behavior. Read our complimentary white paper on this topic to learn more.

Important takeaways

  • Big data is everywhere and here to stay.
  • Big data will continue to fundamentally change the way companies look at their customers and their businesses.
  • Big data analytics can be intimidating to the public so we must be mindful of how customers and stakeholders might perceive this work.
  • While big data analytics is real, it is not the panacea that many “experts” portray it to be.
  • Integrating big data analytics with traditional marketing research allows marketers to understand what their customers are doing, why they are doing it and most importantly what can be done to influence or change their behaviors.

Posted in Big Data, Data Privacy, Research Industry Trends, Social Media and Marketing Research, Text Analytics, The Business of Research | Comment

Researchers, don’t be afraid to change

Editor’s note: William C. Pink is senior partner, creative analytics at research company Millward Brown. This is an edited version of a post that originally appeared here under the title “Liberating research: a manifesto for change.”

172587879I have previously argued that big data is not replacing research, it is liberating it. Researchers are liberated from generating a new survey for each new learning occasion; instead, ongoing big-data assets can be leveraged for many topics, allowing subsequent primary research to go deeper and fill in the gaps. Researchers are liberated from needing to rely upon bloated surveys and instead can keep surveys short and focused on those variables that they are ideally suited for, resulting in better data quality.

I stand by that argument, and we have many examples of forward-looking brands adopting this approach for their research programs. However, we still see far too many brands clinging to research practices that are out-of-date. For example, the default approach remains to ask each survey respondent all possible questions. Building research in this manner is convenient and comfortable but it does not encourage consideration of alternative sources of insight.

The challenge of the new

This hesitation should not be surprising; well-established behaviors and practices are hard to change. As any observer of human behavior will tell you, the best predictors of an individual’s future decisions are his or her past decisions. In other words, you can’t teach old dogs new tricks. When we examine our actions and decisions as researchers who study consumers, brands and marketing effectiveness, we see that far too often we are still acting like the proverbial old dogs. I say this not to offend but rather to ignite a movement throughout the research community to revisit our first principles of design and data quality. Liberated research can only deliver to its highest potential and promise if we actually liberate ourselves from practices that are not working. Otherwise, we risk bringing about our own obsolescence.

Let’s talk about specifics. What do we need to do in order to liberate research?

Shorter surveys. First, we need to stop burdening consumers with long surveys. The evidence is overwhelming that shorter surveys yield better data quality and better consumer engagement. In a recent example, Kantar, TNS and Millward Brown collaborated on parallel studies for the same consumer target. The first study matched the historical design and took over 25 minutes to complete. It was built to ensure a completed data set of respondent-level information for each consumer. The second study was purposefully designed to be much shorter, taking around 12 to 13 minutes to complete. Each consumer was asked only those questions deemed core to understanding the category and meeting the primary analytic objectives.

The results were startling. For one product category, 3.5 times more attributes were identified as important in the shorter survey than in the longer survey and the average level of endorsement for brands in this product category was 31 percent in the shorter survey, compared to 17 percent in the longer survey. In effect, the contextual differences of the survey environment generated very different results and consumers were willing to share more information in the shorter survey.

Shorter is better. Yet, we are very slow to reduce the length of our questionnaires for fear of giving up information that we are used to having. How many competitive brand sets are lingering to ensure consistency with the past, even though we know the past is not a reflection of the current market? Why do we cling to information generated by a long survey that is familiar and comfortable but potentially inaccurate?

Elimination of redundancy. Second, we need to stop asking consumers redundant questions. What makes questions redundant? When consumers give the same patterns of response to multiple questions. Data reduction techniques have existed for years to detect this but how often are we implementing those findings by removing redundant content? Taken further, data reduction techniques provide a line of sight into the themes that consumers perceive. Given the maturity of many markets and categories, we expect to see very stable themes emerge from our analysis – themes of product quality, corporate reputation, consumer motivation, etc. These themes are typically few and rarely change. However, we often see 20 questions reduced to only two themes in consumers’ minds. In that case, why are we continuing to ask all 20 questions? If, year after year, we see so few themes emerge from so many questions, then we are missing key opportunities to optimize our questionnaire designs.

We know redundancy only increases consumers’ frustration levels and reduces the quality of their responses. The bottom line is that we are running suboptimal research designs by keeping the status quo. We should remove redundant questions without hesitation.

Meaningful measurement. Third, we must ensure that we have the most consumer-friendly and accurate mechanisms for capturing relevant insights about what matters, even if this means changing the historical measurement system and implementing a better, more appropriate measurement system for today.

Every day we work with brands to improve their measurement programs. This ranges from linking different data assets for new perspectives on old phenomena to utilizing the latest protocols for survey design and measuring brand equity. We bring our data and new learning to the table and our clients share their category-, brand- and market-specific experiences. Our conversations typically revolve around how to bring our collective expertise together for improved market measurement. However, at some point, clients often worry that implementing changes to their measurement programs will result in changes to historical trending – and this is usually when the air deflates from the progressive tires.

* * *

Of course, giving up historical trends from a long-built and well-invested data asset is a tough pill to swallow. It is difficult to explain to executives that we are making changes to a research instrument and losing comparability to history, even though we are quite good at devising statistical protocols to preserve trends and can make the old data act like the new or calibrate new results to match the old. The fundamental point is that we must opt to change. Trend maintenance should not be our first principle of design; we should abandon instruments and approaches that may have been suboptimal in the first place.

Liberation in practice

Millward Brown’s framework for understanding brand equity is an example of the benefits of this kind of thinking. First, we designed the framework so it can be asked quickly, averaging about three minutes per consumer, and we only ask the questions that consistently link to behavioral success across categories and markets. Second, we designed the question format to mirror the competitive context that consumers experience in their daily lives. This is done through a design called “associative scale and rank.” For the key dimensions in the model, the consumer positions each brand on a 0-10 scale and overlays all the brands on that scale to give a relative ranking. This provides the sensitivity of leveraging a full scale for each dimension while being much more consumer friendly and engaging than a separate assessment of each brand, so we get the information we need quickly and accurately. Most importantly, our brand measurement framework serves as connective tissue across research solutions and data assets. The new model is designed in a short, engaging, repeatable and standardized manner that can be implemented across a range of research scenarios.

Time to change

This brings us back to why we want researchers to embrace the opportunity the modern data landscape provides and liberate themselves from lengthy, single-source surveys. To suggest that this is a necessary step to effectively utilize mobile devices for surveys is valid but misses the essential point: Shorter, focused surveys are better surveys. If we properly frame business problems and think about them from a research-program mind-set, then we will be empowered to enjoy the benefits of liberated research. What business problems can only be answered by having all the information from the same individual in one survey? What learning objectives can be better addressed across a suite of connected research solutions? Which questions are redundant and repetitive?

Of course, we raise the stakes when we remove the security blanket of asking each consumer about all the pieces of the business puzzle. This requires clarity of planning and purpose but it is a challenge that we should embrace. The evidence shows that we are kidding ourselves if we think that analyses of long surveys with poor-quality data can provide accurate stories and actionable recommendations.

Stand and deliver

That is why I see this as a manifesto for change for the research community. We know what works: shorter surveys that respect consumers’ limited availability in a time-pressed world; research tools that engage consumers in a dialogue using everyday language; and research solutions that encourage participation, not frustration.

We can no longer reasonably claim, “I don’t want to rock the boat,” as an excuse for not rethinking research designs that don’t meet these criteria. The boat has already been rocked.

As an industry, we talk a lot about moving from backward-looking insights to research with foresight and forward-looking actionability. I endorse and hope to amplify those goals in this post. With shorter surveys run as part of a larger research program that includes both big and small data assets, we will be well-positioned to deliver on those goals. But if we don’t actually speed up our implementation of shorter surveys and move away from bloated, historical survey designs, our talk will simply be hot air.

Posted in Market Research Best Practices, State of the Research Industry, Survey Development, The Business of Research | Comment

Taking the leap into the future of surveys

Editor’s note: Katrina Lerman is senior researcher and corporate videographer with Boston research firm Communispace.

I have seen the future of surveys. And it’s glorious.

But man, do we have a way to go.

470933185When a recent project for one of Communispace’s hospitality clients involved a request for a slightly younger sample to complement their community audience, our consultants immediately thought of Google Consumer Surveys. The low cost and fast turnaround meant we could provide added value without missing a beat. Given my familiarity with the tool, I was asked to advise the team on the modifications needed to run the existing survey through Google’s “survey lite” platform.

At first glance, the survey didn’t look so bad: 15 questions, mostly single-select voting. (It’s worth noting that, while 15 questions might sound like a lot to someone outside the industry, Communispace, like most partners, routinely receives surveys twice this length from clients. Knowing that the future of customer engagement involves moving away from this model, we encourage shorter, more engaging questionnaires. But we also must operate under the realities of our clients’ needs, which, for the most part, are still tied to legacy models.)

From my past experience, I had to recommend against including detailed open-text questions, as Google’s respondents are not generally willing to put the time or effort into thoughtful answers (and certainly nothing as detailed as we have come to expect from community members). But I told them that the voting questions would be no problem, we agreed on screening criteria and sample size, and I went off to program the survey.

An hour later, I was eating my words. Or, more precisely, I was deleting them. A lot of them. To my astonishment, not a single question could be programmed into Google Surveys as originally written. The flowery text soared past Google’s character limits on questions (175 max; 125 “recommended”) and answer options (44 max; 36 “recommended”). Even knowing there were fairly strict limits, I was still shocked; the survey had failed my initial “eye test” – badly.

Gone was the conversational introduction that kicks off most of our surveys. Gone were the long “e.g.” lists and flourishing adjectives. By the end, my survey was a linguistic shell of its former self. But it fit into the software’s restrictions – and without compromising the fundamental purpose of each question.

The entire experience was eye-opening. While we typically think of strong, descriptive writing as a way to make surveys more engaging, this paradigm does not necessarily hold up in a mobile world. Twitter didn’t evolve alongside the mobile Web by accident. To a writer like me, the idea that, in the future, brevity will be valued over facility is both terrifying and disheartening.

But as my evening spent editing showed me, working within character limits requires its own set of skills. I found myself replacing commas with slashes, throwing around slang with indiscretion and eschewing prepositions altogether. Though I began my task annoyed and exasperated, by the end I felt exhilarated and liberated.

In a sense, language and length have become a luxury – dare I say, a crutch? – in the survey business. Not sure if you captured everything? Add another question! Does that wording make sense? Add a longer description! What if they forgot the previous question? Show it again!

It’s become clear that surveys need to evolve for a mobile world. Soon enough, if you’re not reaching respondents on mobile devices, you won’t be reaching them (a representative sample, at least) at all. And in the world of mobile, speed and substance trump style. That means shorter surveys filled with shorter questions – the right questions. Based on my experience, and that of others, we have a lot of work to do to get there.

Probably best to start now.

Posted in Innovation in Market Research, Market Research Best Practices, Mobile Interviewing, Quantitative Research | Comment

Why ad campaign testing is so elusive

Editor’s note: Ben Proctor and Kerri Norton are insights strategists at New York research firm Miner & Co. Studio.

A combined 15 years of ad campaign testing. Work on ads that have launched some of the biggest entertainment properties of the past decade … ads that have redefined some of the nation’s leading providers of media and technology. So, do we call ourselves experts? Not even close.

481331431The reason is that campaign testing remains one of the most elusive aspects of market research. Let’s be honest – if anyone had figured it out, they would be the only research shop in existence and their clients would have no competitors after putting everyone else out of business.

But, we have learned a thing or two in our years behind the curtain. Here are some of our favorites and how they’ve led to our approach to campaign testing.

• Most important is understanding the additive quality of campaigns. Many times, clients want to find and use the TV commercial that scores best in perhaps dozens of rounds of ad testing. And that’s understandable. But the problems arise when the digital team is testing their banner ads in a separate vacuum. And the outdoor team is testing their billboards without knowing that TV commercials are even in development. It’s a new era of new technologies and channels for getting the message out there. In reality, people experience a campaign that spans all of these channels, so it’s important to get a read on a campaign that spans all of these channels.

While we understand the importance of testing individual ads and do it often, a key component of our testing is a campaign walkthrough. Whether a rich online survey, a qualitative assessment where we literally walk people through each phase of a campaign as they would experience it in reality, or a geo-targeted mobile survey that pings people as they move past various campaign elements after the initial launch, we want to measure not only impact but also cohesion. An amazing poster can be little more than artwork if it doesn’t connect back to the rest of the campaign. A groundbreaking TV ad can become a hindrance when all of the outdoor spreads a completely different message. We don’t want to see this happen and that’s why we use various methodologies to understand the whole.

• Norms – some clients live by them, some clients never want to hear about them. We try to think about something a bit different – benchmarking. If we’re able to create a rich enough set of norms that’s on-target with the exact type of content we’re looking at, we’re happy to use them. But things have changed. Even a TV ad is no longer just a TV ad – it’s an experience in and of itself. In a lot of ways it’s nothing like the TV ads from five or 10 years ago, so using data from five or 10 years ago to inform current norms may not be the best approach.

With benchmarking, we look for the best comparative tool that’s going to give us the strongest sense of how well our client’s ads are driving consideration and ultimately action. Sometimes this means norms. Sometimes it means a social media analysis to look at volume, sentiment and engagement (there’s a big difference between a post that’s been retweeted a million times and one that has created a million sparks of unique, thoughtful discussion). Sometimes it means comparison alongside ads from competitors to see whether the message is overshadowing the competition or getting lost in its wake. By thinking about each campaign individually and assessing each client’s particular goal, we use unique benchmarks to get a truer sense of how the campaign will perform in reality.

Furthermore, while norms make us feel “safe” about what we’re putting out there, they many times keep us from taking risks to create truly innovative campaigns that breakthrough. And while “innovation” is sometimes hard to measure in a quantitative survey, as consumers don’t always know how to rate something they haven’t seen before, it makes a unique and mixed approach to campaign research all the more important.

• Mixing monadic and sequential. Whoever came up with monadic testing is a genius. In most cases, people see a single piece of advertising for a single product at one time, so testing a single piece of advertising on its own makes a lot of sense. Of course, however, we can hit a roadblock.

Let’s say we show an ad to 400 people on a given weekend and get crystal-clear feedback that couldn’t be a better road map for moving forward. The next weekend, we test our new ad with 400 people. But they are different people. And now it’s snowing and everyone is angry. And in the middle of the week, some competitor released an ad that looks an awful lot like what we’ve come up with. The comparison is far from apples-to-apples.

Our way to combat this is to pair monadic studies with sequential studies. By testing single ads in isolation in one survey, but also testing a variety of ads against each other in another, we help to minimize the pitfalls of each approach and come up with an answer that has a bit more clout (and a lot more reality) behind it.

All of this is to say that there is no single answer when it comes to campaign testing. With clients rolling out marketing efforts that include TV ads, YouTube mini-movies, digital banners, mobile games, static posters, lenticular posters, animated posters and much, much more, we can’t rely on a single methodology or technology to give us all of the answers. Campaign testing works best when there’s not a set template. Mixing quant, qual, social media and desktop research is key to our approach. And, moving forward, our next project will likely require us to find a completely different way to mix them than we have in the past.

We hope this gets others talking about how they tackle campaigns because, face it, the research community is a small one. If we didn’t have each other to bounce ideas off of, we’d be spending our days staring at the wall.

Posted in Advertising Research, Market Research Best Practices, The Business of Research | Comment

Qualitative recruitment in a digital world

Editor’s note: Lisa Boughton is director and co-founder of U.K. fieldwork agency Angelfish.

A quick glance at the hot topics setting tongues wagging on market research forums suggests that while researchers are discussing how to harness the power of big data or maximize the utility of online communities, few are talking about the benefits of using digital and social media to recruit respondents for qualitative research.

187458884Robust recruitment is the bedrock of market research, so it may come as no surprise that there has been significant resistance to the use of digital technologies in recruitment. Clients are concerned about quality control, about the importance of the personal touch and about the need to comply with rules on good practice. Despite these issues, the potential for digital recruitment in qualitative fieldwork has advanced in leaps and bounds over the past few years, with innovations that are starting to challenge conventional thinking that could directly improve the quality of the market research we carry out.

At Angelfish, we specialize in digital recruitment so we know what a powerful tool it can be but we also draw on a heritage of conventional market research practice to create a combination of the old and the new. For example, a “killer argument” for digital recruitment is the ability to reach and recruit fresh respondents – the lifeblood of good-quality market research – through channels such as Google, Facebook and Twitter. Used effectively, digital recruitment can create a steady stream of new respondents that would not be financially feasible through proactive street recruitment or a print campaign. Digital channels can also be highly targeted, allowing the quick recruitment of respondents in any location or among those with an interest in a specific topic area which may be otherwise inaccessible via street or telephone recruitment.

Critics of digital recruitment rightly point out that the use of digital channels alone removes the personal touch. Without the ability to engage and motivate potential respondents, there is always the danger of a high dropout rate.

One answer is a hybrid of digital and traditional approaches. This involves using digital channels, such as prerecruited panels, social media and approaching forums to reach out to potential respondents and providing an online screener to register interest, thereby removing any recruiter bias and boosting quality. The next step is to follow up with a phone call to validate a respondent’s answers and ensure accuracy, countering the danger that respondents may be more likely to provide false answers online. Crucially, this call also helps to build a personal rapport that helps to ensure levels of over-recruitment are no different to traditional methods. By contacting every respondent personally, this approach enables deeper attitudinal profiling and also adds a new level of robustness to the validation of recruited respondents.

A key benefit of digital recruitment is the ability to use the power of social media in a highly targeted way, to focus only on the target demographic. With the right in-house knowledge and infrastructure, social media can be targeted at those in a specific age range, gender, geographical location and interest level of the topic in question with excellent response rates. Facebook and Twitter have massive potential, with many posts reaching up to 20,000 eyeballs overnight and bringing in excellent response rates across all manner of topics.

Speed is often cited as a key benefit of using digital technologies, and recruitment is no different. In establishing the feasibility of a study, panels and social communities can be polled very quickly to find out vital information. In live projects too, it is possible to identify issues with a screener within as little as 24 hours, allowing time for changes to the screening criteria before it’s too late to recruit new respondents. Volume here is vital too as larger screened sample sizes deliver greater confidence in the data. Where responses from street recruitment may be quite small and can be slow to materialize, digital recruitment can deliver 300+ people applying for a project overnight with a detailed analysis of screened out respondents by the following day.

Social media also has the potential to become a live recruitment and interviewing platform itself. Twitter is being used in increasingly sophisticated ways for live research. For example, in a recent project conducted by Angelfish, live complaints to businesses via Twitter were intercepted and the customer contacted at the moment of discontent to better understand their feelings and reaction to their poor customer experience. This research would not normally have been possible without access to customer service call centers to access the live complaints.

While digital methods offer many advantages for successful recruitment of market research respondents, the key to success is to use a combination of old and new approaches depending on the target group required. Not everyone resides in this purely digital world and there is major scope for using technology to support existing recruitment techniques to make them more effective. For example, using online screeners during street recruitment can increase the speed of data return. Another method is using LinkedIn to identify business professionals in a particular industry and then posting a good old-fashioned letter to their desks, avoiding the e-mail fatigue and social media overload that can hamper a busy professional life.

In conclusion, it’s critical to build a recruitment campaign that suits the required target and gives the best chance of reaching them quickly and effectively. There is, however, no doubt that the additional use of digital recruitment methods, correctly combined with traditional techniques, can hugely increase the range of available weapons in the qualitative recruiter’s arsenal. In the ongoing fight against shrinking timelines and budgets in the fast-paced world of MR, this can only be a good thing for researchers and field managers.

Posted in Market Research Best Practices, Qualitative Research, Research Recruiting, The Business of Research | Comment

10 new skills that marketers must learn

Editor’s note: Larry Weber and Lisa Leslie Henderson are co-authors of The Digital Marketer: 10 New Skills You Must Learn to Stay Relevant and Customer-Centric. For more information click here.

467133313Marketers have been gulping down change for the last decade. Low-cost and ubiquitous communications technology has rapidly changed human behavior, causing seismic shifts in marketing philosophy, practices and careers. At its core, marketing is still about creating and keeping customers, but the “how-to” questions for accomplishing this have changed considerably.

As challenging as it is to be a marketer during one of the most rapidly changing business environments in history, both our customers and our businesses are benefiting from the disruption. The numerous changes that digital has ushered in are forcing us to move away from our traditional producer-based strategies and tactics, to focus on meeting our customers’ needs and desires. Organizations that have grasped the new reality and are redesigning the way they engage with their prospects and customers are discovering a new source of competitive advantage: remarkable customer experience. Successful companies are becoming customer-obsessed, creating highly relevant experiences that engage and delight their customers on a regular basis, across the entire customer journey. Think Amazon, Marketo, Warby Parker, IBM, USAA and L.L. Bean.

Myriad new tools and skills are making this new level of customer-centricity possible, including: combined big and little data and analytics; marketing automation; design thinking; customer journey analysis; converged media strategies; public and private social communities; software integration; location-based technology; content marketing; and near-field communications technology. And that is only the tip of the iceberg.

While much complexity remains, we are now entering an age of refinement. This is not to imply that innovation will stop, or that the pace of business will slow, or that those who are on top of the heap today will be there in another 20 years, or even two months from now. There is change ahead but the innovation on the horizon builds upon the seismic advances that have already transformed the business landscape, rather than disrupting it on the scale we have seen during the past decade. Shakeouts, consolidations, bankruptcies and initial public offerings will be plentiful as the market integrates and matures.

Marketers still have work to do to be successful in this next phase. Indeed there are 10 new skills that marketers must learn – now – to be able to compete on the basis of customer experience.

1. Marketers are becoming experience architects, able to design and deliver relevant experiences around products, services and places. To do so, marketers need to think holistically, designing integrated experiences across the entire customer journey, from upfront customer acquisition to customer service, retention and collaboration, and across multiple channels. Knowledge of design thinking and customer journey analysis help us build effective interactions as these disciplines help us more deeply understand our customers, the context surrounding their needs and desires and how they experience our brands at all stages of their journey with us. A basic understanding of the tenets of the emerging field of behavior science helps us orchestrate the conditions that are likely to induce a target behavior and create enduring habits around our brands.

2. Marketers are combining big and little data with powerful analytics to gain proprietary insight into customers and to enhance brand experience. Insight derived from new fields of information and combined data streams deepens our understanding of our targets, prospects and customers and builds a dynamic context and predictive component for our interactions. This ability is critical, as relevance is rapidly becoming essential to being found by potential prospects, and to sales, ongoing engagement, advocacy and collaboration.

3. Marketers are scaling creative, personalized communications across channels and throughout the customer journey using marketing technology platforms. Today’s marketing automation tools are evolving rapidly and are quite powerful. Embedded predictive analytics identify more qualified prospects and sophisticated lead-scoring techniques based on real-time behavior steward prospects through the customer journey in a customized fashion. Closed-loop analytics make it possible for companies to accurately trace pipeline, sales and after-sales customer activity back to the originating marketing and sales initiatives. Quantifying marketing’s impact on revenue validates our role and often facilitates greater alignment between marketing and sales.

4. Marketers are employing rich content to tell and catalyze stories. Content fuels engagement throughout today’s customer journey. A steady stream of relevant content is also essential to make effective use of marketing automation. Knowing how various types of content resonate with our prospects and customers and being able to contextualize our interactions across our customers’ preferred channels with the appropriate cadence helps us break through the noise.

5. Marketers are identifying relevant social networks, building authentic presences and often creating our own digital communities to better connect with our customers. Social media has multiplied the number of possible touchpoints with our prospects and customers and has transformed static one-way messaging into dialogue. Successful companies are becoming transparent and responsive, engaging with constituents publicly and in real-time to build brand awareness, generate leads, engage customers and foster advocacy. Many companies are also establishing their own private customer communities to foster co-creation of products and services.

6. Marketers are adopting converged media strategies to augment our brands’ reach and credibility. The delineations among paid, earned and owned media are blurring as marketers incorporate customers’ content into our own content, as professional bloggers are paid to create content for our blogs and as sponsored content populates social media news feeds and publishers’ sites. With organic reach waning on platforms like Facebook, marketers must be able to evaluate the variety of emerging paid media options for extending reach and determine their usefulness for our brands.

7. To build loyalty, marketers are proving a brand’s allegiance to customers, rather than asking for the reverse. Loyalty erosion is pervasive and despite their name, many existing loyalty programs do little to foster loyalty. The best way to create loyalty is by delivering a remarkable and consistent customer experience. Loyalty programs that augment the customer experience, by automating payments or loyalty points, have a much higher chance of being utilized. While we often measure loyalty program adoption, utilization is the name of the game, as it generates proprietary data that can be used to further enhance our customer experience.

8. Marketers must be agile, able to test ideas, read customer behavior and make adjustments in real time. Expectations for continual customer engagement, rapid proliferation of new platforms, the rise of behavior-based communication and shorter development cycles requires that marketers become more agile and responsive than we have been in the past. Answering the call, marketers are testing young ideas and potential improvements to programs in real markets and designing marketing programs in smaller components that can be tested, combined and recombined to for maximum flexibility.

9. Customer experience is a systems-level opportunity, requiring marketers to be able to lead a highly synchronized, ecosystem-wide effort. Building relationships with our prospects and customers involves integrating multiple – and often siloed – groups within our own organizations and across our broader ecosystems as multiple functions support each of our customer interactions. To realize the customer experience advantage, marketers must be able to inspire these multiple moving parts to work together toward customer-obsession.

10. Marketers must proactively manage their careers, regularly nurturing their networks, skills and creativity. Resourcefulness is essential in today’s marketplace and responsibility for being on the cutting edge has by providing unprecedented access to people, events and discussions shifted to each of us as individuals. The Web has simplified this task, although it does come with the risk of being overwhelmed. Nurturing our creativity requires additional care, often requiring an intentional emptying, rather than a rush to take more in.

* * *

While marketers still have a lot of change to digest, in the near term, we will have the opportunity to chew a little more thoroughly. As we savor each new marketing morsel that the last decade has put on our plate, we will discover and enjoy new flavor combinations, spark our creative juices and build more productive relationships with customers whether they are already seated the table or just coming through the door.

 

Posted in Big Data, Brand and Image Research, Consumer Psychology, Customer Satisfaction, Marketing Best Practices, Shopper Insights | Comment

Employee engagement surveys: evolution or extinction?

Editor’s note: Ben Egan is a consultant at U.K.-based HR consultancy and bespoke technology firm ETS.

The world’s biggest companies spend big money on running employee engagement surveys each year. I’ll wager that many aren’t getting their money’s worth, though. What I mean is that, for some companies, running a survey is the employee engagement plan.

454206473In such cases the survey is probably delivering little or no business value. In fact, it could even be harmful to engagement if surveys are taking place with no follow-up or action taken on results.

Why evolution is needed: The approach to employee surveys must evolve to deliver real value. The survey should be a business improvement tool. It should be the starting point for addressing strategically important areas and themes. The springboard for more engaged employees and a more successful company.

In order for such an evolution to be realized, survey questionnaires must be aligned with a company’s strategy. And companies must give greater thought to the action-planning process and supporting managers.

Otherwise, for a large number of companies, employee surveys will remain an expensive check-box exercise.

How to evolve your employee survey approach:

Ease up on response rates. I’m not saying that encouraging a good level of response to your survey isn’t important. But driving up response rates can take over and lead to managers putting pressure on employees to participate. This is not to be recommended.

Of course you’d like as many employees to complete the survey as possible. And, they will. You just need to communicate with them, clearly explaining what the survey will measure, that their feedback will be valued and will remain confidential and how results will be used. This should lead to a naturally high response rate (which is recommended).

Focus on survey results. You should think at the very start of your planning process about what support line managers will need to act on employee survey results.

The reason for starting this thought process early is that you need to think about what data you want to get from the survey and how it’ll be used. Giving this thought from the outset will enable you to tailor the questionnaire design.

This will help managers to better understand what motivates their teams and be able to identify key themes in the results. In doing so, they’ll be able to create relevant action plans. This is the path that leads to business improvement.

Support your managers. Line managers are central figures in any employee survey. In many companies they hold the key to encouraging their teams to participate and will also have a big influence in taking effective action post-survey. With this in mind, make sure you get their buy-in from the start.

This isn’t always easy. We find it helps to make the survey relevant for managers – involve them in the questionnaire design and what the survey should measure. Explain to them how they’ll benefit from increased engagement.

Post-survey, it’s equally important to make it easy for managers to interpret reports and take action. Don’t swamp them with pages of data. You could use a statistical analysis to pick out a handful of questions which managers should focus their efforts on. You need to enable managers to identify the key actions that will improve engagement.

Keep it relevant. One of the main reasons for employee cynicism around employee surveys is that they don’t seem relevant to what people do or experience in a company every day. If surveys lack meaning in this way, the whole process will be undermined. Employees won’t complete the survey and managers won’t take action on results.

For it to be more meaningful, it must be designed with a company’s business strategy at its heart. The survey will thus provide insights and feedback from employees on real business issues.

So what does evolution look like? Evolving your approach in this way should ultimately benefit both employees and the business. Managers will see greater value in the survey process and acting on results. Employees will see that their feedback is listened to and things change as a result.

For the organization, it should lead to a more engaged workforce and business improvement. Of course, these outcomes have always been the point of employee surveys. Some companies have seemingly just forgotten this.

Posted in Employee Studies, Market Research Best Practices, Quantitative Research, Retailing | Comment

How retailers are missing the boat with their mobile engagement of shoppers

Editor’s note: Steve Rowen is an emerging markets specialist at Retail Systems Research. This is an edited version of post that originally appeared here under the title “What consumers say about mobile.”

If you read our research, you know that we typically don’t survey end consumers. Our core offering – benchmark reports – are conducted by a) picking a topic that is highly relevant to the current retail landscape and then b) asking retailers what they self-identify as the biggest challenges, opportunities and roadblocks within that topic. We also ask them about their implementation and plans for technology solutions.

However, a few months ago, we decided to try something a little different. Occasionally, when we’re conducting custom research, a client will ask us for a 360-degree view of a topic. It’s not something we get to do a lot of but when we do, we love it. The ability to get the “he said/she said” viewpoint by comparing retailers’ perceptions to consumers’ realities is fascinating; and the differences tend to be staggering. So when we were kicking the can around a few months back about new things to try, we thought “Why not do our own 360-degree view of mobile technologies?”

We recently polled over 1,000 adult consumers in the U.S. and asked them very straightforward questions about how they use their mobile devices in-home, at work/school or when out in the world shopping. Much of the resulting data will appear as point/counterpoint in our ongoing mobile benchmark reports (the most recent of which just published in February and the next retailer survey launches in October of this year). We’ve also provided much of the data to our friends over at Internet Retailer for an exclusive piece they published in March.

But one of my favorite data points is worth sharing here, now. One of the things we asked consumers about was which devices they owned and what’s really surprising is that, according to our data, the more connected a consumer is, as of today, the more likely a retailer is to let them down (Figure 1).

rowen-1Furthermore, 43 percent of consumers surveyed agree that their mobile phone can tell them more about the products that they want to buy in-store than any store associate can. This is a direct result of the fact that consumers use their mobile devices differently when they shop in stores than when they shop from home – they’re more likely to use their phones in stores for late-stage purchase help (Figure 2).

rowen-2If retailers gear their mobile engagement towards the activities they see when consumers are shopping from home, they’re missing the boat on helping consumers in stores. That leaves consumers with only one real in-store activity – comparing prices – and that is hardly where retailers want/need to be headed.

Posted in Mobile Interviewing, Retailing, Shopper Insights | Comment

How to get the most from exploratory research

Editor’s note: Andrew Fu is a project manager with iModerate, a Denver research firm.

101697555There’s nothing more exciting for a market researcher than learning something unexpected and new from a respondent – and many times the initiatives that uncover the most surprising insights are exploratory in nature. Often thought of as a preliminary step within a larger research process, at our firm we consider exploratory research to be an important element of any qualitative project. A typical exploratory study occurs when there is no clearly-defined problem or hypothesis – this means the researcher has freedom and flexibility to adjust and adapt the study along the way. Good exploratory research has the ability to unearth new ideas, take surprising twists and shape your thinking in ways you would not have imagined – that’s why taking an exploratory mind-set can help you get the most out of any qualitative work, preliminary or not.

Here are our best practices for conducting exploratory research:

Remove all assumptions. If you approach your research thinking you already know who your customer is, then you may miss an opportunity to find out who they really are. Any assumptions about audience, demographics or what you expect to find in the data can keep you from discovering something you didn’t expect. Remember to keep an open mind, be curious and let the findings come to you.

Be willing to kill your darlings. Just as a writer must be willing to cut his or her favorite scene to improve a novel as a whole, researchers must accept the fact that their research may negate their favorite hypotheses. Exploratory research doesn’t have to be entirely without hypothesis but it does have to be flexible enough to allow for changes and improvements as you go. Be ready to accept new points of view throughout the course of your study and be prepared to probe further on findings as they arise.

Ask innovative questions. Exploratory research is often designed to tackle more nebulous, unformulated subject matter – without concepts to test or stimuli to present, exploratory research lets you get creative with the types of questions you ask. Instead of asking directly about a person’s thoughts on a particular topic, craft questions that will get at their experiences and personal identity.

Let your respondent be free. As with any qualitative work, make sure your methodology does not apply any influence or color to the respondent’s feedback. Anonymity, convenience and a moderator/interviewer who is free of judgment or bias can help a respondent feel comfortable and ready to share. Give your respondents a space to be open and honest and you will find out what’s truly on their minds.

Posted in Brainstorming Research, Concept Research, Focus Groups, Product Research, Qualitative Research | Comment

Is Amazon Fire TV a gateway streaming device?

Editor’s note: Ben Arnold is executive director, industry analyst with The NPD Group, a Port Washington, N.Y., research firm. This is an edited version of a post that originally appeared here under the title “The Amazon Fire TV shows streaming media devices are at a crossroads.”

72969324For nearly a year, the tech industry has been abuzz with rumors and speculation that Amazon would enter the rapidly-growing media streaming device market, challenging category incumbents Apple, Google and Roku, who accounted for 88 percent of category revenue during the 12 months ending February. Earlier this month, Amazon did just that, announcing its Amazon Fire TV to much fanfare.

There are plenty reasons for Amazon to make such a device. Unit sales of network content devices have grown 78 percent in the last 12 months according to NPD’s Retail Tracking Service. NPD’s Connected Intelligence’s Connected Home Forecast estimates 23 million Internet capable households will own one of these devices by 2015. A streaming video device also fits in nicely with the rest of Amazon’s product portfolio, which includes Amazon Instant Video but also the Kindle Fire Tablet which, it was mentioned during the launch event, will have some interesting second screen capabilities with the Fire TV. As far as network streaming devices go, the Amazon Fire TV appears to have most of the requisite features to make an impact on the market.

But just as the network streaming device market has grown, video streaming has become a commodity feature found on most connected devices with screens (and many that just connect to a screen). As such, Amazon spent time on the day of the Fire TV’s release talking about features that help differentiate it from the rest of the market. Voice search through the device’s remote helps users navigate the library of content. A feature called Advanced Streaming and Prediction cues up unwatched movies and shows based on a user’s prior viewing habits for faster load times. And, most notably, the Amazon Fire TV can act as a game console for mobile-style games using Amazon’s wireless game controller. We’ve seen a few of these features in other players (the Roku 3, for instance, can play games) but it’s clear Amazon wants the Fire TV to be more than a video streaming device.

The announcement also raised an important question: What does the Amazon Fire TV mean for the future of the network streaming device market? I fully expect manufacturers to pack in more features and improve the user experience with predictive search and alternative interfaces, just as Amazon has done, but the stage is set for network content devices to do more than stream video.

The Amazon Fire TV’s quad-core processor means it can take on some client-lite PC tasks – things connected TVs and Blu-ray players can’t do. Google, Apple and Amazon – the retailers – could conceivably use their devices to make the TV a new point-of-sale for things other than content. Opportunities also exist to tie these products in with home security and energy-monitoring devices, making the TV a dashboard for those products (Google did just buy Nest, after all). With the potential of these new features, network content devices could become the new disruptor in the living room. The question is, do consumers want more from them?

Posted in Media Research, Television Research | Comment