mi summer 2013

MarcoM EffEctivEnEssDetermine effective messaging in the modern media marketplace

ModErn dEMographicsWhere in the world is the middle class?

Brand trackingHow to ditch a bloated survey in favor of targeted, strategic questions

aMErican MarkEting association

summer 2013

mArKeTINGPOWer.COm

Despite increasingly fragmented media

channels, companies like ESPN, comScore

and Arbitron collaborate to find the

right data integration fit

Solving the Multiplatform

Puzzle

10 marketing insightssummer 2013

T oday’s marketing insights process of gathering, synthesizing and acting on information differs

from that of five years ago and things likely will be different five years from now. Why is that? Some changes come from “new ideas” about insight creation, some from the availability of data, some from new requirements of marketers, but it’s clear that developments in the insights process can be linked with external trends in the marketplace.

These trends are worth watching as lead indicators of future changes. They can highlight gaps and limitations in the process that need to be actively improved upon.

Digital Media ProliferationTen years ago, digital media accounted for less than 10% of advertising spending, relative to TV and print. Five years ago, its share was in the low teens. Now it exceeds 20%, according to Zenith Optimedia and TNS Media Intelligence. Recently, social media and mobile have fueled this digital growth, and neither has reached its potential (and the associated stock market expectations), so it’s conceivable that the

share of digital spend can go higher.The most direct implication for insights

is the need to measure effectiveness in an increasingly greater number of media than were relevant a decade ago. This has spawned measurement services devoted to individual media, such as the copy testing and tracking of executions on digital formats. And it has raised questions about the logic of measurement approaches that assume one-way communication, from marketer to consumer, when consumer-generated communication is growing. There are more contexts to measure, with new constraints on formats.

The increase of individual stimuli that require measurement breeds the complexity of cross-media measurement. How do the media work together to create a campaign effect and help sustain a positioning and brand equity?

Consumer behavior around new media is still changing, too. About half of adults in the U.S. have smartphones, according to eMarketer—a high number, yet still well below the saturation level. And recent research suggests that a small fraction of users account for most of the “likes” and

postings in social media. As these factors evolve over time, there will be more challenges for those looking to develop insights around media effectiveness.

Financial AccountabilityThe Marketing Science Institute’s poll of its approximately 70 member companies (leading marketing organizations from all industries) has consistently placed the need for the financial justification of marketing investment at the top of the priority list, ahead of issues like brand, communication, innovation and organizational improvement. The difficult economic environment continues to force marketers to become familiar with finance and its practitioners in the organization, and to stretch their own scope beyond their own areas of responsibility.

Many companies have adopted the discipline of measuring marketing effectiveness, and using the results to plan and allocate resources in a systematic way. Surprisingly, a large portion of companies still don’t use key financial metrics, like ROI or customer lifetime value. A 2010 study by Mark Jeffrey estimated that about half of companies don’t use these metrics.

The need for insight into how marketing actually works to create financial value likely will continue. The trend toward digital marketing and e-commerce continues, and carries with it the expectation that marketing activities can be tracked at a granular level and tied to business outcomes. Slow economic growth suggests that budgets for marketing resources will continue to be constrained and carefully evaluated.

Remaining challenges for the insights professional include the complexity factor, due to the proliferation of media and sales channels that make the attribution of marketing impact harder to do. Communication between marketers and finance people is not easy. Each side looks

Linked InDevelopments in the insights process map back to marketplace trends

The Big PicTure

GorDon Wyner

[email protected]

dAtA mArketPlAce

11marketing insightssummer 2013

at the world through different lenses, but must collaborate to assure funding and ongoing implementation of marketing plans. While continued analytical tool development is essential, some resources ought to be devoted to improving the process by which different organizational units coexist.

Data ExplosionThe digital revolution has enabled more and different types of data points than were previously available. It’s apparent that more consumer behavior is tracked via computer clicks, video and audio recording than ever before, which offers the potential for more consumer insight. For example, social network data allows analysis of social interactions that have been difficult to study via traditional questionnaire methods. This can reveal patterns of word-of-mouth communication that have been hypothesized for decades but measured with great difficulty.

A distinguishing feature of Big Data is that it may be unstructured. Consumer postings online may come at any time, which could be random or could be caused by a particular brand experience and then followed by long periods of inactivity. While it’s beneficial (from an insights perspective) that the behavior being observed is not framed or influenced by the researcher, it has disadvantages, too. A collection of such observations may not be a stable indicator of individual or market behavior over time.

The availability of data in a somewhat manageable form has stimulated insights professionals to look for ways to harness the information. There also is great interest in real-time applications with automation that might eliminate or reduce the need for ongoing marketing analysis. Predictive algorithms that recommend the next logical product for a customer based on past behavior could be updated periodically without much human

intervention. The result would be a set of predictions, followed by marketing treatments, response measurement and further updating.

To avoid being too data-supply-driven, insight professionals ought to:

1. Pose strategic business questions that can be addressed with Big Data.

2. Identify the relevant data elements that are needed to answer the questions, rather than start by demanding the real-time capture of all possible data points.

3. Define the analysis scope based on business objectives and questions.

4. Anticipate how the analysis output will translate into interpretations and programmatic applications.

The answers to these issues will determine the resources that marketers ought to devote to insight development or whether the application is better suited to other disciplines like IT.

Across these three major trends, there are clearly avenues to pursue for insight development. Understanding external influences should increase the odds that research is addressing important business issues. However, the impact of external trends shouldn’t be taken as the sole driver of new developments.

For example, understanding, developing and managing brands requires multiple inputs that may not be well-represented in these trend areas. The measurement of brand positioning and long-term equity is important to marketers even if no trend tells us so. Insights professionals should understand the trends and also look beyond them to identify other pressing issues that require their attention and resources. Mi

✒ gordoN WyNer is vice president of client

solutions at Millward Brown and contributing editor of

the Marketing Management section of Marketing News.

connect! Join the AMA’s MR community on AMAConnect at MarketingPower.com/community.

m arketers and their agencies have split personalities—not in the psychological sense,

but in the marketing communication sense—and that’s one of the major problems in the new, interactive communication world.

When marketers start to talk about customers, they go to great lengths and generate voluminous detail to describe them: their needs, wants, aspirations, desires, nuances and on and on. They create detailed views of what are believed to be buying motivations, “hot buttons,” the things that will turn folks from non-believers into truly loyal, shout-it-from-the-rooftops brand advocates.

Models of the consumer’s shopping trips are built, identifying key inter-sections. Massive amounts of data, gathered online, or through in-person focus groups or face-to-face interviews, or secretly or openly gathered by cookies and other surreptitious tools, are sifted and sorted. In short, marketers and their agencies spend days and weeks trying to figure out what they want to say and how they want to say it.

It’s all about improving the effectiv-eness of the marketing communication programs. But when it comes time to deliver those artfully crafted, persuasive messages, marketers throw all of the stuff

that they’ve learned out the proverbial window. Actual living, breathing, active and reactive people suddenly become nothing more than “target markets,” demographic groups with a few common similarities (e.g., women 18 to 49, or those with $35,000-plus household incomes, or stay-at-home moms with 2.6 children). And all of the people in those groupings are assumed to be the same—at least in terms of their media usage and patterns, and the Nielsen group in which they fit. So in spite of the expensive, detailed, insightful data that has been so carefully prepared for the messaging, instantly, these consumers become simply part of a rating point, or they become a member of a cost-per-thousand measure.

The critical ingredient in message distribution is efficiency, lowest cost for greatest message distribution—a continuing holdover from the days of Leave It to Beaver and Walter Cronkite.

These two views of marketing communication have existed for years, certainly in agencies and in the minds of brand managers. Initially, separate agency departments were created to focus on one or the other—creatives to the left, media to the right—and it’s little different today. Seemingly, any learning or sharing occurs through osmosis. But

that isn’t efficient enough, so agencies have spun off specialist media agencies to get even more efficient delivery of the hoped-to-be effective messages.

But it never quite worked that way. For message delivery, efficiency always ruled. Some would call it “optimal message distribution,” but, in reality, the “media specialists” didn’t (and still don’t) know if any or all of the targeted audience actually saw, heard or experienced the carefully crafted “efficient” messages, so the agencies and their media friends invented “exposures,” or messages thrown out by the medium. The audience was supposed to be there to catch it. If they didn’t, it wasn’t the medium’s or the agency’s fault.

That was yesterday. Today, the “mass media marketplace” supposedly is being rapidly replaced by more efficient methodologies stemming from Big Data and other statistical wizardry. Today’s media choices are driven by theoretical modeling, behavioral targeting, real-time messaging and the like.

The sophistication of today’s media planning and allocation models and tools are praised and tweeted to the skies, despite the fact that, except for a few directly delivered digital applications, marketers, agencies and media forms still can’t identify, explain or illustrate cross-media comparisons (e.g., Is a visit to a Web page the same as an exposure to a 30-second TV commercial? How does one compare either of them to a re-tweet of your latest nifty YouTube creation?).

For some reason, creative directors are paid a pirate’s ransom to develop persuasive, impactful, engaging messages—messages designed to be effective. Then that is all turned over to a pre-programmed bunch of machines that don’t know squat about the people for whom the messages were intended. Every member of the supposed targeted


Customer Knows BestDetermining effective messaging in the modern media marketplace

inSighTS Machine mArcom effectiveness

Don e. Schultz

[email protected]


audience are, in truth, just slots in a digital punch card. (Remember those?)

So what is the industry doing to remedy this situation? Simple. Take those well-designed and -developed messages created for effectiveness with real, live, breathing people and put them into the most spectacular online, interactive communication systems ever developed: the new digital, mobile, interactive social media. Then try to force-fit all of those into the existing TV planning model based on efficiency. Develop some type of rating points system for Facebook. Build a CPM model for Twitter. Why? Because that’s what marketers and agencies, and, yes, even the media know how to do: Estimate and compare

everything against television, even when consumer usage of the medium has been trending downward for some time.

It makes absolutely no sense. When you have an effectiveness program, why focus on efficiency? Yet that’s what too many marketers are trying to do: Find something that can be used to plan and measure the “new stuff” by using tools designed for the “old stuff.”

In terms of research, insights and success, all that marketers need to start with are customers, consumers and prospects. Identify the media forms that they use—and how often, under what conditions and in what sequence. Which media forms do they prefer? Which ones do they go to for information,

for guidance, for ways to live their lives? In short, we need to start making use of new concepts, approaches and methodologies that are customer- and effectiveness-focused, rather than just trying to stuff the new stuff into the “old stuff” bag.

What makes effective media delivery? Only the receiver (i.e., the consumer) knows. Maybe we should ask him, rather than just creating a new algorithm. It might actually be more efficient than what we’re doing now. Mi

✒ doN e. schulTz is a professor

(emeritus-in-service) of integrated marketing

communications at Northwestern University

in Evanston, Ill.

To read more of Schultz’s work, go to MarketingPower.com/ marketingnews and click on “featured contributors.” .com

T he developed economies of the West have been the primary home of the urban middle

class for more than 100 years. This has shaped our market economy and consumer lifestyle, along with our economic and political institutions and confidence. But times are changing and the middle class is shifting from West to East, driven by the rapid urbanization and industrialization of dynamically growing economies. Three million Chinese move from rural areas to cities each month for higher incomes, while the U.S. median income falls. U.S. marketers must adjust to this reality. After a career of marketing to the U.S middle class, they have to aim their skills eastward to a new and larger middle class.

Middle-class Americans are acutely aware of their declining assets, jobs and disposable income. They fear, quite correctly, that their children will not live a middle-class life (nor will they, if they are 25 years old or older). They think that the middle class is shrinking. But in fact it’s growing—just not in the U.S. and Europe.

How can these new middle classes in China, India and elsewhere in the so-called developing world, without American political, moral, economic and social institutions, accomplish

this? This question confounds the American visitor, who dissolves this perplexity with either a false hope of U.S. economic recovery, or the idea that this foreign middle class is a false illusion that cannot be real. The very term “developing” blinds Westerners to this economic reality, but there is a larger urban middle class outside of the West than inside the West. I have chosen to illustrate this reality by a comparative view of two great cities, one new and one old: Shenzhen, China,

and Chicago. American marketers must shift their attention to consumer and business marketing in Asia, and align their practice with Chinese and other Asian partners.

Shenzhen, ChinaChina did not have a market economy until 1980, when the People’s Republic established “special economic zones” in Shenzhen and several other cities in South China. Special policies were applied to these zones to enable the establishment of private business, grant special development privileges and loans to state-owned enterprises (SOEs) for business expansion, and attract foreign investment. State banks gave loans to the SOEs and foreign investors, and on-shore private wealth supplied credit to the private sector, as well. Manufacture, commerce and property development grew rapidly. Hundreds of thousands of countryside people came to Shenzhen for employment and enterprise.

In two decades, Shenzhen grew from a small fishing village of 30,000 people on three square kilometers of land to a population of 10 million people. By 2012, Shenzhen reached a nominal per capita GDP of $13,581, with a purchasing power parity of $23,897,

according to the CIA World Factbook, nearly equal to the 2010 per capita GDP of $29,535 of Chicago. Shenzhen residents’ purchasing power will surpass Chicagoans’ by 2015.


A Tale of Two CitiesWhere is the middle class?

Milton Kotler

[email protected]

gloBal MarkeT modern demogrAPhics

American marketers must shift their

attention to consumer and business

marketing in Asia, and align their

practice with Chinese and other

Asian partners.

According to Forbes’ Helen Wang, China’s urban population has reached 691 million, with a middle-class population of 474 million: “The middle class accounts for 68% of the urban population. … Assuming 2% are super rich, still about 30% of the urban areas are poor.” By this denominator of 68% and a population in 2012 of 10 million, there are roughly 6.8 million middle-class people in Shenzhen.

“Middle class” in China is defined as home ownership, family vehicle ownership, dining out, and money for entertainment, travel and education of the family child—not much different from the American definition.

Chicago The 2010 population of Cook County, Ill., which includes Chicago’s population of 2.7 million and surrounding municipalities, is 5,194,675, according to the U.S. Census Bureau. Applying the Forbes ratio of 68% to Cook County, there were 3,116,805 middle-class residents. This means that there are 60% more middle-class residents of Shenzhen than Chicago and surrounding Cook County.

If we use the official Chicago standard metropolitan area (SMA) population of 9.8 million and assume a similar spread of per-capita GDP for

the entire Chicago metropolitan area, then we have roughly the same number of middle-class people in Shenzhen as in the Chicago SMA. But note an interesting fact about land productivity: Shenzhen is 1,991.64 square kilometers. Chicago is 2,272 square kilometers. Cook County is 4,234.6 square kilometers. The Chicago SMA is 5,498 square kilometers. Shenzhen has roughly twice the urban intensity as Cook County, with 60% more middle-class people. This has important implications for marketing efficiency.

Boom TownsChicago was a boom town between


log on! Learn more about the AMA’s expanding global reach at MarketingPower.com.


1871 and 1900, during which its population grew 5.86 times from 289,977 in 1870 to 1,698,575 in 1900. Following the great Chicago fire in 1871, the city was rapidly rebuilt. Immigrants poured in and manufacturing flourished. There are similarities between Chicago’s 19th-century three-decade boom period and Shenzhen’s three decades of boom a century later. After the Chicago fire and from Shenzhen’s original fishing village, both cities grew rapidly from almost nothing. Both became manufacturing centers for domestic and external trade. Both were principally immigrant cities.

Shenzhen has reached a GDP level in 30 years that took Chicago 173 years to reach, since its incorporation in 1837. Further, Chicago’s rate of GDP growth in 2010 was -0.4%, while Shenzhen’s growth was 10%.

The point of this tale of two cities is fourfold. First, these numbers belie the observations of Americans who visit China’s major cities. They are awed by the economic vitality, though

few understand it. If they come from Chicago, they are astounded by what they see in Shenzhen or any other major Chinese city. They cannot square what they see with their fixed beliefs that economic growth is tied to the superior democratic institutions of their home city and country. While they are looking at market economics, they’re thinking politics.

The second purpose is to encourage American marketers who serve middle-class needs and wants to adjust their business models so that they can focus on larger middle classes in BRIC and other developing countries in Asia, Africa, the Middle East and Latin America. The middle class is growing at a faster pace in the developing economies than in developed markets. American marketers must move with this current.

The third purpose is to show that ideologies bury an understanding of the fundamental dynamism of market economics and the different forms it takes. The U.S. or EU models of market economies do not

define market economics in the developing world. Private enterprise in China accounts for 60% of the country’s 2012 GDP, according to the China State Administration for Industry and Commerce. Forty percent of China’s capital is outside of the state banking system and financing the private sector. This “shadow banking” market provides credit to entrepreneurial SMEs and profits to investors to support innovation, create jobs and accumulate capital for overseas direct investment. If we’re open to the variety of market economic models and not dogmatic about free enterprise, we not only will be able to understand the economics that we see in China, but also may learn a few things to correct the malaise in the U.S. and start

growing in new ways. Our final purpose is to review

the current doldrums of Western economies to see if we might learn a few things from the growing economies of the developing and emerging economy world; as well as invent new adaptations to re-invigorate Western market economies.

The story of Shenzhen and Chicago can be extended to 150 global urban centers that collectively comprise 46% of current global GDP, and compete among each other for growth and share. They comprise only 12% of the global population. The trend of global urbanization in all market economies, whatever their variety, continues on the march. Mi

✒ milToN koTler is chairman of Kotler Marketing

Group USA, headquartered in Washington, D.C., and

chairman of Kotler Marketing Group China, with offices

in Beijing, Shanghai and Shenzhen, China. He is an eco-

nomic advisor to the mayors of Xian, Dalian and Harbin

and Zhengzhou. He is author of A Clear-Sighted View of

Chinese Business Strategy and co-author of Market Your

Way to Growth: 8 Ways to Win with Philip Kotler.


knOWLeDge BaSe

A classically trained market researcher, Kyle Nel calls himself a heretic.

After all, anyone who challenges colleagues to take a one-month fast from surveys and who questions the use of traditional brand trackers is pushing industry boundaries. Cur-rently head of Lowe’s international insights, Nel leads the consumer research efforts across the home improvement store’s locations in the u.s., mexico and Canada.

Kyle Nel is head of inter-national insights at lowe’s CompaNies iNC. He and his team are responsible for all things consumer research for the Mooresville, N.C.-based retailer’s international business units, including trackers, segmenta-tions, experiential research and ad testing.

Nel previously was manager of customer insights for Wal-Mart’s global insights group. His research background extends to the media and advertising industries, where he served as research director for Clear Channel Radio, a division of San Antonio, Texas-based Clear Channel Communications Inc. Nel holds an M.B.A. from the A.C. Nielsen Center for Marketing Research at the University of Wisconsin and a B.A. in business management from Brigham Young University-Idaho.

A recipient of the 2013 Ginny Valentine Badge of Courage Award from the research Liberation Front, which recogniz-es bravery to pursue new market research approaches, Nel was honored for taking action through his collaboration with Google to transform Lowe’s brand-tracking strategy in its Canadian market. Nel is optimistic about new techniques such as Big Data modeling and predictive analytics, which he says will change the pillars of the marketing research industry. marketing Insights recently caught up with Nel to learn about his research experience in international mar-kets, his willingness to adopt new measurement tools, and his philosophy on what researchers and practitioners can learn from each other.

10 minutes withKyle nelInterview by MARGUERITE MCNEAL / [email protected]

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Q: Lowe’s has retail stores in the U.S., Mexico and Canada. What challenges does international research present compared with studies that solely are focused on American consumers?

A: I think being an American company, going into an in-ternational market presents a whole set of challenges. What works in the home country does not necessarily work the same or as well in others. I make the joke all of the time that it’s almost like a different country when comparing Canada and the U.S., or Mexico and the U.S., even markets within Mexico or markets within Canada. It’s a challenge because they are so different and they’re evolving.

The other thing is the difference between a very highly and industrialized country like Canada versus one that they’re still developing like in Mexico—they have very different kinds of research issues. For instance, in Canada there’s a ton of data available. The government collects data quite well and even the marketing research system, as far as suppliers and just going out to talk to people, is more established. In Mex-ico, the basic census data and those kinds of things are hit or miss. It’s hard to judge until you really get into it. It just pro-vides a lot of difficulties when you’re trying to scale research and you’re trying to really understand the market.

Q: The global landscape is becoming so competitive in all industries, including yours. You’re competing with not only the home Depots of the world, but also local suppliers. What do you do in research to get the leg up?

A: It’s just like any other research project, right? You have the competitive landscape and you’ve got your strategic ob-jectives. I look at the work that I used to do when I was at Wal-Mart and it’s really no different than what we’re doing in international, which is to understand each individual mar-ket, who the players are, who the competitors might be—and they might not even be direct competitors. You’re competing for spend and for time across a variety of different catego-ries, not just in home improvement, but also the way people spend their time and their money. It’s no different from any other research strategy or research question.

Q: As a buyer of market research, what criteria do you have for suppliers? What makes a success-ful relationship between buyers and sellers?

A: The agencies or the folks that I like to buy research from or work with, it sounds kind of cliché, but they’re my part-ners. I don’t want somebody who’s just going to be an order taker and just give me whatever I ask for. I always like it when I get pushback—in the right way, of course. When I get pushback, I know that they are in it to do the right job instead of just in it to try to get a sale. You can always tell the difference in the work that comes out of those kinds of sup-pliers versus those that just want to get their numbers up.

Q: how are you guys using Big Data and com-municating those insights across departments within Lowe’s?

A: Big Data is something that’s ever-evolving and is all over the place. Understanding data sets that aren’t traditional is something that we’re looking at. Then trying to find data sets that we can compare in a timely manner is also something that we’re looking at, much like everybody else. It’s a classic insights organization problem: It’s one thing for the insights organization to have it, and even for the senior leaders to have it, but it’s quite another to share that insight all the way down through the chain. That’s really where the good stuff happens, when people all across the organization understand where you’re going and what the insights are, and those in-sights are constantly evolving. So I think that’s a challenge for us like for many others.

Q: In April you presented at the Insight Innovation Exchange LatAm in Brazil: ‘how Google & Lowe’s Transformed Brand Tracking.’ Talk more about this collaboration and what you learned in the brand-tracking space.

A: Pretty much every organization has a brand tracker, and I have yet to meet, or have met very few, who like or even love their brand tracker. They do these massive studies that take up so much time and resources that people are just afraid to change or touch them. In Lowe’s Canada, we have one of those. We had a big, giant brand tracker that was tak-ing up a lot of time and money, quite frankly, and no one was really using it because it was so steady.

A brand tracker should be steady over time. It should be, in my view, a canary in the coal mine. If something dips one way or goes up another way, then that gives you a reason to go and do more research. Now you shouldn’t go and do a

giant, massive study every quarter, every month or whatever the time horizon is. That just seems like a waste of money to me, but that’s how it’s typically done.

I heard about Google Customer Surveys. It had just come online and I had a couple of relationships with people at Google, so I reached out to them and said: ‘OK, I know you guys aren’t in Canada yet, but would you be willing to launch the platform in Canada? I have this idea for a brand tracker.’ What I did was work with them to build the platform in Canada and we went from asking a lot of questions, like with a traditional brand track-er, to down to five questions that really drive the business. We went from a big, bloated survey down to something that was very quick, very to-the-point, and they’re questions that will cause action in the business. If something were to dip or change significantly, that would get us to do more work, more insights work or just to make changes to the business. I don’t really know if the old brand tracker would have led to that.

Q: You once proposed that researchers take a one-month fast from using surveys. Where did this idea come from?

A: It’s a little heretical, but I’m not a big fan of surveys. I’m a classically trained researcher, but I feel like in this day and age, there have got to be better ways of understanding how people make decisions and also what influences that decision. The things that I need to know I can’t ask.

It’s the behavioral economics principles that really make a big difference. Dan Ariely’s book Predictably Irratio-nal goes through very systematically and shows, without

question, that our behavior is directly influenced

by small things that we don’t even really perceive that we’re being influenced by, so how do I get to understand those things? I can’t do that in a survey.

This whole idea, this question-and-answer culture that market research has developed is, I think, wrong given where we are technology-wise and Big Data-wise, and the compet-itive pressures. I can’t go out—and I shouldn’t go out—and ask people questions every time. When you ask attribute

questions, like, ‘How much do you love Wal-Mart?’ ‘How much do you love Target?’ Well, those are somewhat helpful, but let’s say people don’t put that they love Wal-Mart very much. They’re still shopping at Wal-Mart. Well, why is that? It’s obviously something other than a top-level attribute ques-

tion, so how do you get to those things? You don’t get to it through a survey. Let’s try to find other ways to get to what’s really driving their behavior.

Q: What tools or techniques of the future will give truer insight and understanding?

A: I think Big Data modeling has gotten so much better in the last couple of years. I’ve seen some things lately that are just off-the-charts amazing as far as looking at data and having predictive analysis attached to it. I think that is huge, especially for researchers who have a lot of data and can cut it and splice it different ways.

There’s also a ton of free stuff out there. Google has a ton of tools that you can use for free. There are other things that you can do to measure traffic over time. Social media research is not quite there yet, I don’t think, but as it develops, I think it will get better and better, and that will lead to more insights. All of those things are not asking people questions in that traditional sense.

Q: Despite the massive amounts of Big Data available today, some say we lack the human skills and processing capability to fully take advantage of all of this information. how big of a problem is this and how are we going to solve it?


knOWLeDge BaSe

.comCheck out the 2013 Honomichl Top 50 report on MarketingPower.com/MarketingNews for an analysis of the market research industry.

“reSearcherS can learn a lot from marketers when it comes to two things: One is confidence to tell your story; two, wrapping strategy into everything you do.”

A: That’s a huge, huge problem. You have a select amount of really skilled people who have not just the technical skills, which are critical, but also the curiosity, problem-solving, insights skills. You see those people are in finance. They’re in consulting. They’re in economics. They’re all over the place and I don’t think they look at research, or they may not even think that it’s an option. Even if they do, they may not see research as the place for them to build their careers. I think that’s a huge structural problem.

No. 2 is finding people who have the curiosity skillset, or the soft skills, and training them in more of the harder skills. It seems like there are a lot of people who have the hard skills and are trying to turn them into soft skills. I haven’t seen that work too often, but what I have seen work is that you take people who have that natural curiosity and internal selling skills and all of that good stuff, and then you can train them in how to do more hardcore analytics. I don’t see enough of that and I think that that’s the path that we should be taking: Try to find those people and train them internally.

Q: What can marketers learn from researchers? And what can researchers learn from marketers?

A: Researchers can learn a lot from marketers when it comes to two things: One is confidence to tell your story; two, wrapping strategy into everything you do. I don’t think that happens enough with researchers. We think that the data will set us free and that the truth is in there, and as long as we’re right, that somehow it will all work out. That’s not always the truth and marketers are really good at fram-ing the story, selling it and then making it happen.

On the other side, I think marketers can learn a ton from researchers about understanding people and customers, [and] understanding the data that drives behavior. From that, you can take those insights and create stories, and create strategic plans that really and truly do work because they’re based on deep understandings and insights. We can learn a lot from each other and those who do are super successful. Mi



farewell,spaghetti

Rethinking charting for tracking

Written by Tim Bock • [email protected]

presenting tracking data is one of the great challenges of commercial market research. Tracking presentations need to communicate the latest results to an audience, show what has changed since the previous wave and

demonstrate trends—a difficult trifecta of goals that can result in what the research industry often refers to as “spaghetti,” a tangle of lines representing valuable but hard-to-decipher data.


‘SpagheTTi’Consider the line chart shown above. It clearly communicates the performance of Brand A, but the rest of the chart is the aforementioned spaghetti. With this chart, conclusions can only be obtained by careful examination. As a result, when such charts are used in tracking presen-tations they often need to be supplemented by tables showing the numbers and the results of significance tests.

It’s tempting to think that when a chart is hard to read, it’s because the underlying data is inherently difficult. This is not always the case. Our brains are skilled at interpreting complex patterns. Consider a photo of a family member. It’s substantially more complicated than the chart above, yet we can quickly look at it and obtain key conclusions, such as who is in the photo, when it was taken, whether the subject is happy or sad and, perhaps, what he has been doing.

What makes the “Spaghetti line chart” difficult is that the key information on the chart—the lines—essentially appears to our brains as near-flat lines of different colors, but our brains have little experience in analyzing such patterns, so we struggle to interpret the chart.

‘Small mulTipleS’The first chart on page 25 shows the same data but with the performance of each brand represented as a separate graphic (i.e., small multiples). It’s much easier for our brains to decode this chart, as the line representing each brand is not flat, allowing our brains to see the pattern.

The general principle is that our brains do better when lines on charts have an average slope of around 45 degrees. Also, one of the chart’s key fea-tures—the difference between the brands—is communicated by the location of the lines, rather than their color. This helps us because our brains are much better attuned to interpreting patterns in space than in color. (If you doubt this conclusion, think back to the example of the family photo: You can still make key conclusions when the photo is black and white.) Moreover, the individual graphics have been ordered to make it easy to see the average difference between the brands, which helps us appreciate the data

we use charts in the hope that they help the viewer quickly see patterns, but standard chart types often fail to communicate tracking data clearly. That’s why researchers should consider presenting such data with a chart that offers more well-defined visual cues.

“Spaghetti” line chart


in two different ways: in terms of the average performance of each brand and also in terms of each brand’s trend.

Color enhanCemenTSThe middle chart on the right improves upon the one above it by using color to highlight interest-ing lines. In this case, the red dashed line indi-cates a significant decline in performance. That is, time is negatively correlated with Brand C’s performance. This middle chart is also a dramat-ic improvement over the chart on the opposite page. Its enrichment relates to the third of the three objectives of a tracking chart: revealing trends in the data. A better chart would empha-size the most recent results and indicate how they had changed from the previous period.

Clear and Compelling The bottom chart shows a different way of chart-ing the same information. Each column represents the most recent wave of the tracker, with the num-ber shown at the top of the chart, so that the view-er can quickly see the current state of play.

Arrows are used to communicate which of the new results are significantly different from those of the previous wave. In this example, Brand C has improved since the previous month. The chart also can be augmented to show additional results. For example, it can show the results from the previous wave or the differences between the waves.

Sparklines are shown at the base of the col-umns and they plot the data for whatever length of time is strategically relevant. In this example, six months of data are shown. The sparklines have been color-coded, as shown in the middle chart on this page, with the red dashed line indi-cating long-term declines. Thus, we can see that Brand C’s recent improvement goes against a long-term trend of decline.

The bottom chart on this page allows the view-er to easily answer the three questions that need to be answered from tracking data: Where are we today? What has changed since the previous wave? And what’s in store regarding long-term trends? Mi

✒ Tim bock is managing director of Numbers, a market

research consultancy, and adjunct research fellow of the

Australian School of Business, University of New South Wales,

Australia.

Column chart with trend sparklines

“Small multiples” line chart

“Small multiples” line chart with color showing statistical significance


From the Crowd to the

CloudFive Ways That Big Data Will

Make Us Smarter Marketers

As far back as 2006, loyalty marketing

expert Clive humby declared data the

“new oil,” a resource with the same

transformative, wealth-creating power that

19th-century industrialists and 21st-century

magnates alike have associated with the

fossil fuel. It’s an analogy that has only grown

more appropriate over the intervening years,

although today we know that data differs from

oil in one important way: The world’s supply

of information is anything but scarce.

By greg verdino / [email protected]


a deluge of information of such unprecedented volume, variety and velocity that it strains even the largest enterprise’s capacity to extract action-able understanding that will inform its leaders’ decisions about everything from strategy to sales. Consider that many large corporations already sit on vast, fragmented, often underutilized store-houses of customer data gleaned from past ad-vertising campaigns, retail POS systems, product registrations, customer service call logs and the like. Now add the exabytes (one quintillion bytes) of real-time data generated as billions of connected consumers and customers engage with our brands and, more importantly, each other across countless digital, social and mobile touch points, along with

the long digital shadow cast by the rapidly expanding “Inter-net of Things” that distributes bits of processing power into everything from our cars to our clothing, and you start to get a true sense of the volume of unfiltered, unstructured and, some would argue, unmanage-able information that threatens to overwhelm us, as it promises to empower us.

Statistician Nate Silver, au-thor of The Signal and the Noise, shines a spotlight on just how big Big Data can be when he writes, “Every day, three times per second, we produce the

equivalent of the amount of data that the Library of Congress has in its entire print collection.” But he tempers his enthusiasm for scale with a sobering caveat: “Most of it is … irrelevant noise. So unless you have good techniques for filtering and process-ing the information, you’re going to get into trou-ble.” In other words, Big Data is certainly plentiful and may be valuable, but it’s not especially useful in its crude form. But like oil, which, in its mined and refined form, makes its way into everything from adhesives to aspirin, lipstick to linoleum, plastic to parachutes and, of course, into the gasoline that fills our tanks, insights that we glean from Big Data will have an unprecedented influence on both con-sumer culture and the corporate growth agenda.

Where Humby spoke

simply of Data,

today’s marketers

must consider Big

Data to be


It’s no secret that marketing leaders are being held to higher and higher standards of accountabil-ity, and many recognize the role that data can play

not only in improving results, but also in fostering deep customer intimacy. It’s easy to understand why technology research firm Gartner projects that by 2017, chief market-ing officers will wield bigger technology budgets than their IT counterparts do. In other words, it is fast becoming the marketer’s work to put Big Data analytics to work for marketing.

While bold claims like this point to a significant shift in how technology decisions get made and have major implications for what it means to be a “D-suite CMO”—a next-generation marketing leader who is digital to the core—they also distract from the fundamental truth that Big Data rep-resents a business advantage more than it does a technological choice. For all of the breathless buzz about Hadoop clusters on the one hand and all of the worrisome hand-wringing over processing power on the other, the real question for marketers isn’t, “Where will we house it?” or even, “How will we crunch it?” It’s, “How will Big Data empower us to serve our customers—and our companies—bet-ter than we could without it?”

Just like oil, Big Data is not valuable for what it is, but for what it makes possible. It matters not as a crude commodity, but as a prized currency for value creation in an age of hyper-connectivity. Let’s explore just five key ways that Big Data can be harnessed to create value as it makes us smarter D-suite marketers.

Big Data tears down media silos. Many market-ers continue to evaluate the performance of their television, radio, print, search, social and display tactics as if each functions independently when, in fact, they interact with one another dynamically—and in a nearly limitless number of combinations—to produce the outcomes by which you measure success. Simply put, the customer doesn’t engage with your brand on a single channel. He engages with it across many channels, sometimes sequen-tially but often, as in the case of second-screen behaviors, simultaneously.

In adopting a Big Data approach to advertising analytics, one that joins already large and previ-ously disparate data sets that report on everything from clicks to conversations, commerce to custom-er support inquiries, and even environmental fac-tors such as changes in weather and shifts in senti-ment, then processes them all together in near real time to find previously hidden patterns, marketers gain a newfound ability to attribute, allocate and optimize their media investments with a greater level of confidence. This, in itself, can be quite pow-erful, but clearly, the benefits of Big Data analytics extend well beyond mass media advertising.

Big Data lets you get closer to your customer. The vast amount of data that customers generate lets marketers understand them not just as broad target audiences, rough demographic segments or even elaborate albeit fuzzy personas, but as truly distinct individuals. Connecting the universe of transactional, service, social, search and mobile data to establish a greater understanding of your customers will provide you with the knowledge that you need to make more precise decisions about which individuals to engage and where. And it will allow you to do so much more than that.

Deep understanding of how your customer ac-tually behaves, from purchases to patterns in what they check into and share, “like” and tweet, trumps any notions that you may have of how customers might behave. It provides a fact-based foundation for the rich, individualized storytelling made nec-essary by a highly fragmented marketplace, the hy-per-relevant services and experiences that today’s connected customer demands, and even a mass customization of products that hold the potential to turn audiences of one into loyal advocates who attract new customers on your behalf.

Big Data lets marketers predict the future.

Famed management guru Peter Drucker once said, “The best way to predict the future is to create it.” For marketers, not only is it difficult to predict the future based on data that reflects nothing but the past but also it is foolhardy to base future-oriented decisions on historical data that is rendered unreli-able—if not outright irrelevant—by the accelerated pace of change.

Researchers have employed predictive analyt-ics for some time, but the exponential rise in the amount of up-to-the-minute data from Web logs,


RFID and sensor data, GPS location data, social networks, mobile apps, desktop and mobile search-es, and even traditional call center records allows us to sharpen our futures focus and anticipate consumer demand with greater confidence. While Big-Data-driven anticipatory thinking might shape your communications plan, its greater value lies in its influence over product and pricing, service and staffing, distribution and delivery models ready-made to better meet the needs of your current and next generations of customers. Where once mar-keters hoped to influence future customer desires and behaviors, now marketers gain a chance to predict them.

Big Data makes more powerful partnerships

possible. The social era forces businesses to think beyond competitive advantage and discover their collaborative advantage. Access to Big Data allows corporations to position themselves within vital business ecosystems, and shift their focus from the things that they can achieve toward the even great-er things that they and their partners can achieve together.

I recently had the opportunity to listen to Amer-ican Express CEO Ken Chenault talk about the role that data plays in the evolution of his organization’s growth strategy. AmEx is somewhat uniquely po-sitioned to own the data that comes from what he calls a “digital closed loop” because AmEx operates with both an extensive consumer network and an

extensive merchant network, and it therefore can derive powerful insights about attitudes and ac-tions throughout the entire buy/sell process. The resulting Big Data asset that American Express can bring to bear in discussions with everyone from global bricks-and-mortar retailers like Wal-Mart to digital platform partners like Google, Face-book and Twitter opens doors, strengthens busi-ness-to-business relationships and paves the way for game-changing opportunities to merge internal and external data to deliver clear-cut wins for both parties along with compelling value to their mutual customers.

Big Data is the guide that lets your gut decide. Smart marketing has always relied upon a delicate balance of the left brain and the right, a perfect balance of science and art. However, a recent study by the Corporate Executive Board Co. found that Fortune 1000 marketers depend on data for just 11% of all customer-related decisions. Clearly, any organization with a robust Big Data competency can improve upon that track record and, in doing so, bring front-end insights and back-end account-ability to its decision-making process.

That said, Big Data does more than reduce mar-keting to a formulaic numbers game. By serving up substantive and previously hidden insights, Big Data actually may allow marketers to become more creative in their roles, to embrace innovation more wholeheartedly, and to understand the consumer so fully that even right-brain-generated break-through ideas carry less risk and promise more reward than previously seemed possible.

Hopefully, by this point, one thing is clear: Big Data may, indeed, be the new oil, but its transfor-mative power isn’t limited to your personal fortune or your company’s bottom line. It holds the poten-tial to transform the way that you market, the way that you connect with customers and business part-ners, and the way that you create and capture value in the post-digital age. In the end, your ultimate payoff will come not from the fact that you’ve mas-tered Big Data, but, in reality, that you’ve achieved big understanding. Mi

✒ greg VerdiNo is a consultant, coach, author and speaker.

His most recent book, microMARKETING: Get Big Results by Thinking

and Acting Small, was published by McGraw-Hill. Find him online at

GregVerdino.com and @gregverdino.

Just like oil,

Big Data is not

valuable for

what it is, but

for what it

makes possible.


HittingY urTarget

Using the right data and metrics for each stage of the funnel

by dAvId [email protected]


he had hired a creative agency to build compelling ad units and spent $50,000 with an advertising network to run some campaigns. “every test ends the same way,” he said. “Things start out oK and we get a good click-through rate, but it’s tough to get those clicks to convert to leads. When I run the numbers, my cost per lead is a bit higher than I want to pay and the leads we receive from the campaign are not really resulting in enough enrollments. I just don’t know why we can’t get display to work.”

“display advertising just doesn’t work,”

grumbled a colleague of mine who heads up marketing for a major online university.

“Are you having success in any of your other on-line channels?” I asked.

“Yes. Search is doing well, but we are maxing out on keywords. We also get a nice bump to the Web traffic when we run TV commercial spots in our markets,” he replied.

“There are literally 13 billion impressions available for sale online every day. What kind of targeting were you using with the ad network?” I countered.

“We gave them some demographics parameters: age 24 to 45, income less than $75,000 and based in the markets in which we operate. They said they

are using a proprietary optimization algorithm that finds people with these characteristics and compares historical click patterns to maximize our traffic. We got a lot of clicks, for sure, but just not the right ones,” he said.

Can you relate to this ROI conundrum? Most likely, if you’re responsible for marketing planning, media buying or interactive strategies, you can.

As marketers invest more dollars into online channels, the need to find the right audience for a given campaign is imperative but continues to be a challenge. Technology is changing quickly with the


introduction of new platforms, channels, devices and acronyms. (“Do I need a DMP for my DSP to target RTB media?”) There will be many more whiz-bang technologies introduced in the next year, but under-neath it all, marketing 101 fundamentals still apply. With the help of Big Data and predictive analytics, you can successfully find the right audience for your message.

The traditional marketing funnel still applies on-line and a well-executed campaign must be aligned against the goals of each funnel stage. Whether it’s awareness building (branding), educating/qualifying (prospecting) or closing the deal (converting), differ-ent types of data signals and success measures should be used to find the right audience at each stage. Here are some key considerations for each stage of the funnel.

LOWer-FunneL COnVerSiOn CamPaign: CLOSing exiSting DemanD

Audience Targeting DataThe objective of conversion campaigns is to capital-ize on existing customer demand and turn it into a sale. How do you find this existing demand online and target the right users? The answer is behavioral data. Every day, consumers provide insights into

their immediate intentions (“in-market indicators”) through their search activities and Web navigation behavior. Examples of behavioral indicators and tar-

geting methods for conversion campaigns are given in the chart below.

The relative strength of these data signals in predicting buy behavior will vary by company, but the most valuable data signals tend to be the most scarce. To augment these behavioral signals, data scientists have harnessed Big Data through predic-tive analytics to create “look-alike” audiences. A behavioral look-alike audience is a group of people who have similar Web navigation patterns as those of your buying customers. For example, suppose that it turns out that some people who buy from your site visit a financial planning site within seven days be-fore they buy. This bit of information is an important clue in determining who may be in market for your product.

By analyzing massive amounts of online data, companies such as Quantcast can amplify and combine many different behavioral signals to extend the addressable audience for conversion campaigns. Similarly, media buying platforms, such as agency trading desks and demand side platforms (DSPs), can use response-optimization algorithms to mine campaign data and find specific media placements and times of day that maximize response.

Measuring the Success of Conversion CampaignsThe way that you ultimately assign performance

credit to conver-sion campaigns is a strategic marketing decision. There are no magic formulas that apply across the board, but make sure that your measure-ment methodology is congruent with the marketing funnel stage.

Since conversion campaigns are cap-italizing on existing demand, performance should be held to a high standard of responsiveness. Someone who is

presently in market should see the campaign and respond within a very short window (a few days or even hours). Performance should be measured on

BeHaViOraL inDiCatOrS targeting Strategy

Consumer visits your website directly Retargeting

Consumer does a branded or industry search term

Keyword buys, Website SEO

Consumer visits a website that does product comparisons

Third-party in-market audience

Consumer reads an article about your product/service category

Contextual targeting

behavioral indicators and Targeting methods (listed in order of “signal” strength)


a last-click basis, with some consideration given to view-throughs with a short attribution window. Even though it’s tempting to evaluate conversion cam-paigns on a strict last-click basis, comScore’s seminal study “Natural Born Clickers,” released in 2008 with Starcom and Tacoda, demonstrates that only 8% of Internet users account for 85% of all clicks.

miD-FunneL PrOSPeCting CamPaign: StimuLating neW DemanD

Audience Targeting DataThe objective of prospecting campaigns is to educate qualified consumers about your product and build demand. In order to justify incremental marketing dollars to educate new customers, they certainly should be in your target market and among the most valuable or profitable. As discussed, the target cus-tomers for conversion campaigns are those who are showing current demand by what they’re doing (i.e., behavioral signals). Behavioral signals are good for detecting in-market consumers, but to find strong indicators of your most desirable prospects, you need to look at who they are (i.e., profile data).

There are many different kinds of profile data available for targeting. A great place to start is by understanding the characteristics of your existing, profitable customers. These raw data attributes can take the form of demographics, hobbies, interests, psychographics, asset ownership, lifestyles, finan-

cials and past purchase activity. You also can look at pre-built clustering schemes that factor in multiple attributes at the same time, such as those offered by Nielsen and Neustar.

For online targeting, the key is finding a set of char-acteristics that are predictive and actionable. Today there are a number of audience targeting companies that sell the ability to target display campaigns to users with specific attributes. Data exchanges such as BlueKai and eXelate, along with data providers such as Datalogix, Nielsen, Experian, Acxiom and even Mastercard all sell pre-built segments and audience data. The challenge now is in finding the right dataset for your prospective customers. How can you find the right data signals amongst all the noise? What if the most important data for predicting a valuable customer isn’t available for online targeting? One approach is to license different data sets and run test campaigns to see what works. But instead of picking and choosing different data sets, why not use them all simultaneously?

Predictive analytics lets you combine tens of thou-sands of data points into an accurate “look-alike” au-dience model. A look-alike audience model uses your existing best customers as a template and appends many different sources of third-party data. The mod-eling algorithm analyzes the appended data points and mathematically determines which ones are the best for predicting your target audience. Since some data elements are more important than others, a good algorithm also will assign different weights to each

PrOFiLe

PrOFiLe+ COntextuaL

SPOnSOrSHiPS

in-market

COntextuaL anD DemO targeting

emaiL CamPaignS

SearCH, BeHaViOraL anD retargeting

BranDingBuild Future Demand

PrOSPeCtingStimulate New Demand

COnVertingClose Existing Demand

WHOData

WHatData

The marketing Funnel


relevant data element. The completed audience model incorporates many attributes that define the detailed characteristics of your existing best customers.

Measuring the Success of Prospecting CampaignsGiven their mid-funnel focus of educating new customers, prospecting campaigns should be judged on the ability to influence “high-value” users and generate incremental demand. While some users exposed to prospecting campaigns do convert on a last-click basis, approximately 80 to 95% of conver-sions will occur on a view-through basis. The last touch point (click or view) for most conversions will be a conversion campaign. For example, a prospect-ing campaign will prompt a user to execute a search or navigate to a brand’s URL without recording a click. The campaign data that we’ve tracked illustrates that search is the largest beneficiary of prospecting campaigns. In other words, the prospecting display campaign sparks the demand and the search cam-paign closes the deal.

In the case of my colleague whose ROI conun-drum set the stage for this topic, “high-value” means

someone who enrolls in school and completes the program to graduation. While it’s nice to see a spike in the volume of inquiries, the value of those inqui-ries is paramount.

To measure the value of users who are being influenced by a prospecting campaign, it’s important to have transaction-level tracking on both view- and click-through conversions. With transaction-level tracking, it’s possible to assess the value of each con-version influenced by the prospecting campaign by tracking down to the ultimate financial transaction (e.g., enrollment in the education example).

“Incrementality” measures how much new de-mand is being created. In other words, how many new customers are influenced by the campaign who would not have just converted on their own? One way to measure incrementality is with a controlled A/B test. For the experimental group, execute a prospecting campaign and measure the number and quality of the new customers associated with the campaign on a view-through basis. For the control group, execute a prospecting campaign under the same conditions as the experimental campaign,


but use creative units that make no reference to your product or compa-ny. Everything else must be the same. When you compare the results between the control and experimental groups, differences in the magnitude and quality of sales can be attributed to the impact of the prospecting campaign. A well-ex-ecuted prospecting campaign should deliver high-value demand and incre-mental demand.

tHe BranDing CamPaign: generating aWareneSS FOr PrODuCtS Or BranDS

Audience Targeting DataThe objective of branding campaigns is to raise awareness for your product or service for consumers within the target market. A lot of expense goes into the de-velopment of creative assets and securing premium media placements. However, some marketers fail to use effective tech-niques for targeting the correct audience at this stage of the funnel.

This is a costly mistake. Online brand-ing campaigns can run into the $10- to $20-CPM range, so it’s paramount that you are actually targeting the right people. Otherwise, you run the risk of dramatically inflating the effective cost of a campaign. A $20-CPM branding campaign that only serves impressions to the right target audience 50% of the time has an effective CPM of $40! You may think that a 50% accuracy rate for a branding campaign is an exaggeration and you’d be right. It’s actually worse.

According to 2011 Nielsen research, only 44% of impressions for a CPG advertiser actually reached the target au-dience when buying media on the basis of a site index composition. In the same study, fewer than 30% of impressions reached the target market when buying through an ad network using age and gender as the targeting criteria.

For some brands, basic demographics may sufficiently define the correct target.

For other brands where there is a difference in potential lifetime customer value, relying on basic demographics alone leads to huge inefficiencies and many wasted dollars. Audience targeting tech-niques such as predictive mod-eling can dramatically improve the accuracy of campaigns so that nearly every impression connects with the right target.

Measuring the Success of Branding CampaignsBranding effectiveness metrics should measure the shift of consumer perception against one of the brand’s key performance indicators, such as awareness, at-titude, favorability, intent or pref-erence. As with any controlled study, it’s important to measure the difference between the ex-perimental population (those who saw the campaign) and the control (those who did not see the campaign). Companies such as Nielsen’s Vizu, comScore and Dynamic Logic can help you measure and quantify the brand lift created by a given campaign.

An effective brand campaign should create lift in one or more of these metrics for people with-in the target market. No brand lift studies or incredible creative units on premium websites will have any business value for your brand if the campaign is not correctly targeted. Mi

✒ daVid doWhaN has been the

president of TruSignal since January 2012,

when the company was spun off from its

sister company eBureau. He is a pioneer

in the online marketing industry and has

held several executive leadership positions,

successfully growing both private and public

venture-backed companies over the last 20

years.

Keys to success

1. Start with your existing customer data, and what you know and can learn about your customers. They are your ground source of truth to finding new, profitable customers.

2. Optimize from the bottom up. The upper-funnel strategies support the lower ones, so make sure that your conversion campaigns are dialed in before adding new prospecting or branding dollars. Otherwise, some of that new demand generated by prospecting or branding will go to waste—or to a competitor.

3. Make sure that you’re using the right data signals and success metrics. Behavioral data is useful for conversion campaigns, while profile data is best for branding and prospecting campaigns.

4. Partner with industry experts who can be your creative geniuses, campaign gurus and data scientists.


While business and media organizations across the marketplace try to piece together audience data over increasingly fragmented channels, companies like esPn, comscore and arbitron are collaborating to find the right data integration fitBy Marguerite McNeal, staff [email protected]

solving the Multiplatform

Puzzle


They listen to the radio and simultaneously shop online for electronics. Increases in mobility and interactivity create rich user experiences for media consumers, and they make audience measurement all the more complex for the marketers, content producers and researchers who want to track eyeballs and demonstrate ROI for advertising expenditures.

As companies throughout the marketplace grapple with the challenges of cross-platform measurement, stakeholders from across the business and media ecosystem are pushing for new techniques and initiatives to better understand media consump-tion and the contexts in which people access content. In an industry traditionally segmented by platform, marketers need new ways to compare assets across channels and evaluate the best media for their messages.

The multiplatform measurement puzzle is prompting the pairing of strange bedfellows, forcing competitors to share data; forming coalitions of researchers, marketers and broadcasters; and stan-dardizing metrics that once varied by platform—all in the hopes of assessing—and monetizing—modern media consumption.

People log into twitter while watching tV. they toggle between tablets and smartphones to read news headlines and play “words with friends.”


research is everyone’s BusinessThe cross-platform measurement challenge is breaking down barriers between media platforms and forging new alliances between once-disparate industry stakeholders who now share a common interest of tracking au-diences—and ad dollars—across the increas-ingly fragmented media landscape.

“For decades, we’ve lived with a measure-ment system that is heavily siloed,” says Mike Bloxham, executive director of marketing at the Media Behavior Institute (MBI), a New York City-based organization that research-es how people interact with digital media. Online advertisers consider cost-per-click (CPC) data and unique monthly visitors. Ra-dio advertisers focus on time spent listening (TSL) and average quarter hour (AQH), the average number of people listening in a given 15-minute period. Television set-top boxes and Nielsen panels provide advertisers with impression metrics, and consumer viewing behavior and demographic information.

“None of the currencies and these mea-surement techniques were ever designed to work together, and when you start to see the extent to which media, themselves, have

proliferated and that the functionality of many of those media has also developed radically, you begin to understand how the situation with measurement has become more complex,” Bloxham says.

As media consumers seamlessly move from one screen to the next, industry players are considering more standard metrics. “There’s a lot of discussion in the industry now to come up with common metrics, particularly in the area of video, because you have online video and television, and many people are saying that you should be able to buy ads similarly using similar metrics,” says Jane Clarke, managing director of the Coalition for Innovative Media Measurement (CIMM), a New York City-based organization formed in 2009 by television content providers, media agencies and advertisers to promote innova-tion in audience measurement. CIMM works closely with the Media Ratings Council (MRC), the organization responsible for pushing industry standards in terms of buying and selling. Clarke says that the measurement standards likely will come from the MRC, but the effort will require participation from diverse online video and television measurement stakeholders.

CIMM is working to bridge the divide between media industries and create some marketplace-wide audience measurement standards. The coalition initially focused on two primary questions: What are the opportunities and challenges of using return path data from set-top boxes and smart TVs to measure television viewing? And what solu-tions can be developed to measure cross-platform media consump-tion? Through proof-of-concept pilot studies, CIMM has identified and evaluated new methodologies and approaches to audience mea-surement, such as its Trackable Asset Cross-Platform Identification (TAXI), an initiative calling for the industry-wide adoption of unique identifying codes to track ads across mediums. CIMM comprises 22 members today, including broadcast network CBS, advertising agency Publicis Groupe and consumer goods company Procter & Gamble.

Prompting collabora-tion in data sharing across disparate industries requires significant foundational changes to the way that they compile information, Clarke says. “We’re actually having to go in and change the

plumbing, and change the technology in the way that people trade and move around assets. There are many levels to getting these changes throughout the industry to enable cross-platform measurement.”

Multiplatform consumption could take the form of users who view content simultaneously, accessing information from two channels at once, or sequentially, following content from one platform to the next. These consumption patterns raise multiple considerations, such

.comAccess more information from ESPN’s Glenn Enoch at MarketingPower.com/marketingnews

R


as whether viewers are accessing related content, or if advertising effectiveness diminishes or increases when a consumer’s attention is divided across screens.

It also means that researchers need substantially larger sample sizes to assess unduplicated reach and frequency—understanding if it’s the same individual who accessed content across three or four platforms, or if it was three or four different people using the devices in question. ComScore’s digital panel of 1 million people is not large enough to answer this question, Clarke says.

One approach to measuring across platforms is to combine Big Data from Web analytics, for example, with smaller-calibration modeling data that come from panels, such as Arbitron’s Portable People Meter. Clarke says that the industry will adopt more hybrid approaches and data fusions in the future.

context considerationsWhile data that illustrate media consumption patterns are useful, researchers and advertis-ers also need to consider the context in which people view an ad or an entertainment program. “It’s a matter of relevance and receptivity,” Bloxham says. “It’s not just about reach. It’s the ability to be able to line this data up against not only a particular audience and a particular time of day, but also the context of what they’re actually doing.”

Bloxham is calling for a more consumer-centric definition of cross-platform consumption. “To get a coherent picture of


how people are using media across all of those platforms, you’ve got to start from ground zero and be able to follow con-sumers wherever they go. That is a very difficult thing to do, not least because many of these measurement techniques are based on passive measurement by device,” Bloxham says. Widely used set-top boxes and people meters passively track what content users consume, but they don’t account for people’s moods, social settings or other factors that influence their behavior. While the media context (types of programming, time of day) is important, Bloxham says that this is only half of the picture. Media stakeholders also should consider consumers’ locations, activity, social settings and emotions, in addition to the media consumed, to truly understand their audiences, he says.

To better understand media buying and ROI for advertisers, MBI developed USA TouchPoints, a smartphone-based consumer behavior measurement tool that is used by a nationally representa-tive, randomly selected sample of the population. MBI draws participants from the GfK MRI Survey of the American Consumer, an encyclopedic repository of data that provides a detailed view of the 226 million adult consumers in the U.S.—their media choices, demographics, lifestyles and attitudes, and their usage of almost 6,000 products in 550 categories—so that the organization has access to USA TouchPoints participants’ ready-made demographic and attitudinal profiles.

For 10 days, respondents log into the USA TouchPoints app in half-hour increments to answer questions about their activities, moods, locations, social company and media consumption. Each survey takes an average of 90 seconds to complete and participants receive

A new medium will creAte new strAtA (not new universes) of users. There is no “future” where “everybody” will do anything.

there Are no “new metrics.” What really matters is how many, how often and how long.

users And usAge. “how many” is not “how long.”

A heAvy user is A heAvy user. heavy users of one medium tend to be heavy users of many media.

cross-mediA usAge is not zero-sum. Doing one behavior more doesn’t necessarily mean that a consumer is using another medium less.

simultAneous usAge is widespreAd but limited. In brief: Large numbers of people, small number of minutes.

users defAult to the best-AvAilAble screen. People use different platforms at different times in different places for different purposes.

one

tWo

three

four

five

siX

seven

esPN’s seven Principles of cross-Media research

Source: Created by ESPN’s Glenn Enoch and originally published in the Journal of Advertising Research.


financial compensation for their time. As they answer questions in each category, the survey drills down deeper. Respondents who say that they’re watching TV, for example, will receive another question asking if it’s live or recorded via DVR.

While such day-in-the-life research isn’t new, it often relies on participant recall, asking respondents what they watched on TV last night, rather than asking them in the moment when recollections are more accurate. “If you’re asking that about a lot of different types of media, we know from research that will be great for telling you what their perception is, but there’s a lot of inaccuracy built in there,” Bloxham says. USA TouchPoints, meanwhile, is an at-tempt to compile information on audience behaviors in the moment.

MBI compiles its data from 2,000 of the 18- to 64-year-olds who participated in the GfK MRI survey. Researchers clean up the data and project the results from the subset onto the full survey sample, creat-ing nationally scalable insights. This type of data fusion is becoming more widely used in the American marketplace, Bloxham says. “Any client is going to look at the different data that they have available and it’s the creative triangulation of insights derived from multiple data sources that give a client competitive advantage. It’s not the data, itself.”

tracking sports fansIndustry coalitions and trade groups are hard at work on mining valu-able audience insights from multiple data sources, as are many corpo-rations. ESPN Inc., the Bristol, Conn.-based sports media company majority-owned by The Walt Disney Co.’s ABC Inc., has been work-ing internally for more than a decade to figure out how sports fans consume content across platforms, according to Glenn Enoch, vice president of integrated media research at ESPN. To track advertising campaigns during the 2010 FIFA World Cup, the sports coverage giant launched the ESPN XP cross-media research initiative, which mea-sured media usage and advertiser effects across five media platforms: TV, radio, PC, mobile and print. Further enriching the media matrix, ESPN offers multiple types of content within a single platform. For example, it provides PC users with both Web-based articles and vid-eos, while it offers mobile users online content and specialized apps.

In September 2012, comScore and Arbitron joined forces to develop a five-platform media measurement initiative, with ESPN as the

charter client. Called Project Blueprint, the initiative aims to provide audience insights by integrating comScore’s census- and

panel-based mobile and TV set-top box measurement capabilities with single-source data from Arbitron’s Portable People Meter technolo-gy. Arbitron and comScore rep-resentatives say that the ultimate goal of the study is to develop a measurement service that would be avail-able to all media companies and

agencies. “It’s being designed to be, potential-ly, a syndicated service so that any publish-er, advertiser or advertising agency could subscribe to it just like they would subscribe to any other comScore syndicated service for ongoing measurement of their assets on a cross-platform basis,” says Joan Fitzgerald, comScore’s vice president of television sales and business development. ComScore and Arbitron created the measurement service to be a joint marketplace offering, she says. The partnership took effect last fall and began rolling out insights for ESPN this spring.

For the partnership, Arbitron created a multiplatform calibration panel, a small but representative group of U.S. consumers whose behavior on all five platforms is mea-sured. The company also supplies radio and TV data from its 70,000-member Portable People Meter panel. ComScore measures the radio-television duplication, and captures digital media consumption data from both

iN March, the Project BluePriNt PartNers released oNe set of fiNdiNgs froM the research, showiNg that esPN is startiNg to deriVe More digital traffic froM MoBile deVices thaN froM Pcs or laPtoPs.


opt-in consumer panels and comScore- generated tags that clients incorporate into their websites and advertisements, according to Fitzgerald. “We’re designing the method-ology so that we can take in these complex sources of data, calculate the duplication, apply the duplications against the syndicated standalone media measures and output these reports very quickly,” she says.

Fitzgerald oversees a team dedicated to Project Blueprint, but she says that the initiative touches many different departments at various times. “When we’re at a point of doing some really heavy-duty analytics, we bring in our analytics team. When we’re at the point of figuring out how we’re going to weight the data and use Arbitron’s calibration panel, we bring in another team,” she says. ESPN receives data from Project Blueprint monthly, but Fitzgerald says that the mar-ketplace would want more frequent updates from a syndicated offering.

In March, the Project Blueprint partners released one set of findings from the research, showing that ESPN is starting to derive more digital traffic from mobile devices than from PCs or laptops. Among men 18 years old or older, 18 million accessed content only via mobile devices, compared with 16 million who only used a PC or laptop. Nine million used a combination of both options. Such data seem to be making an immediate impact on ESPN’s business strategy. In May, The Wall Street Journal reported that ESPN is consider-ing a plan to pay wireless carriers for mobile content that the sport channel’s subscribers consume so that users won’t run over their monthly data caps—thereby removing a major barrier that could limit consumers’ use of ESPN’s mobile content.

One of ESPN’s core objectives of the study is to measure the engagement that its projects generate on a larger scale, rather than in iso-lated sports programming cases, Enoch says. “What we can do, and we’ve done, is look at specific events—like during the

World Cup with our ESPN XP initiative, and NBC does this with the Olympics—and we can measure audiences, but we want this to be something that we can use 24/7, 365 days a year.”

There are two ways to approach cross-platform measurement, Enoch says. “You can do it through a single source, but that’s not necessarily scalable, and then you can do data integration, but that’s tricky because you run into issues with data quality and accuracy. We think a combination of the two is the best way to approach that, and that’s what we’ve done with comScore and Arbitron.”

Although Project Blueprint is in early stages, Enoch says that he expects to see many more insights generated from the effort this summer. And this measurement initiative is just one of many steps in understanding the ESPN audience’s modern media consumption, he says. Once cross-platform behaviors are under-stood, Enoch wants to learn more about simultaneous usage:

of course, understanding cross-platform media con-

sumption is paramount in this multimedia marketplace,

as most marketers are looking to get a better feel for

how their messages are being received across channels.

But tracking an ad’s impact from platform to platform is

just as challenging as tracking consumers’ use of those

platforms.

While the consumer packaged goods industry uses

UPC codes to identify products and track purchases,

the business and media ecosystem has yet to implement

a standard identification system for advertisements.

“This industry is one of the last to adopt the concept of

registration authorities,” says harold Gellar, chief growth

officer at Ad-ID, a Charlotte, N.C.-based advertising reg-

istration company. “In the advertising industry, we are

extremely focused and extremely good at the work. I

think sometimes process improvement takes a backseat

to good work and good creative.”

Each content platform identifies advertising assets

differently. On top of that, Gellar says that tracking ads

has become more complex than it needs to be. “I’ve

tracked the same ad across multiple platforms and

found, in the worst case, between 40 and 50 different

identities for the same ad between video on demand,

television, cable, network and local stations. That’s a

barrier to innovation,” he says.

To counteract that confusion, companies can register

impact factor:


“If someone’s watching a baseball game on television and they have an iPad out, are they looking up batting averages and RBIs, or are they uploading pictures of their grandkids?” he says.

Phase one of Project Blueprint concludes this fall, and Fitzgerald says that comScore and Arbitron will regroup with ESPN to plan the next steps. The initiative has gone smoothly because of the complementary nature of comScore and Arbitron data, and ESPN’s eagerness to address the challenges of cross-platform measure-ment, she says. “ESPN is a highly engaged customer. They like seeing all of the data. If you think about five platforms and all of the possible combinations of usage, they truly will get in there and think about each combination and say, ‘This really makes sense,’ or, ‘This is a surprise.’ They really like getting their fingernails dirty.”

CIMM, which has worked on individual projects with Arbitron and comScore in the past, is closely monitoring Project Blueprint to see how the results might affect its diverse media industry

constituents. “When ESPN finishes their tests, we’re hoping that CIMM will be able to take this whole new system for audience mea-surement to the next phase of industry-wide syndication,” Clarke says. “This approach of having hybrid measurement for cross-plat-form makes the most sense, where you have the machine data to get the right volume met-rics of the traffic across platforms, but also you have a panel to get the right inter-media relationships and the demographics to link behavior to the person.

“Some form of hybrid measurement, we believe, will be the future of cross-platform measurement and this is really the first test that’s putting it all together in a scalable way.” Mi

assessing the Value of a Multimedia ad Buy

their advertising assets in Ad-ID’s central database

and apply a unique code to each ad so that it can be

tracked across platforms. The information required for

registration is simple data—such as the year or a com-

pany prefix—that companies already share with their

vendors, Gellar says. “By registering with a central data-

base, we now have the ability to allow multiple people—

anybody in the ecosystem who needs it—to access our

system to get that information about the ad.”

In April, the Screen Actors Guild-American Feder-

ation of Television and Radio Artists (SAG-AFTRA), a

labor union representing 165,000 media professionals,

and the Joint Policy Committee (JPC), representing the

advertising industry, mandated a universal adoption

of Ad-ID. By March 31, 2014, all commercials produced

for television, radio and digital platforms that feature

SAG-AFTRA union members must use Ad-ID as the sole

standard commercial identifier. The adoption of the

coding system will provide the necessary identification

required by all parties to ensure fair talent compensa-

tion for the union members.

Ad-ID estimates that 85 to 90% of television com-

mercials and 25 to 35% of radio commercials use

SAG-AFTRA talent, and Gellar says that about 1,000

national advertisers will need to begin registering their

ad assets. While his primary focus is on national adver-

tisers, those who spend more than $5 million a year in

all media, he says that many of the 300,000 local and

regional advertisers who also use SAG-AFTRA talent will

follow suit.

In February, Nielsen and the Advanced Media Work-

flow Association, whose members include IBM, Turner

Broadcasting System and Adobe, endorsed Ad-ID. In

May, Digital Generation Inc., a multiscreen ad man-

agement company that handles the majority of video

advertising production, transcoding and distribution

for U.S. advertisers and agencies, announced that it will

adopt Ad-ID registration and tagging procedures.

With so many companies implementing universal cod-

ing standards, Ad-ID is actively involved in on-boarding

new customers. When Gellar started working at Ad-ID

six years ago, about 100 advertisers used the identifica-

tion. Today that number is 800 and growing, he says.

While Ad-ID has no direct stake in measurement, Gellar

says that the registration authority serves as a stepping

stone for innovation. he identifies four routes that

enable the long tail of content consumption: operations,

administration, measurement and residual management.

The engineers, broadcasters, advertising distributers

and content creators who work within these channels

will spend more time on innovation and less time on

low-value logistical work if they simplify the ad tracking

process, Gellar says. “Process improvement will enable a

much more robust long tail of content consumption.”

impact factor:


aNyoNe Who’s VisiTed aN ikea sTore kNoWs The drill: You’re obliged to follow a predesigned path through kitchen stools, shower curtains, bedding and other household items no matter what you plan to buy. This “forced walk” layout is an extreme example of what retail marketers have assumed for ages: Encouraging customers to travel more of the store will lead to increased purchases.

Sam Hui of New York University’s Stern School of Business and co-authors J. Jeffrey Inman, Yanliu Huang and Jacob Suher set out to prove whether or not that assumption rings true. They researched the causal relationship between in-store trip length and un-planned spending in grocery stores, publishing their study, “The Effect of In-store Travel Distance on Unplanned Spending: Applications to Mobile Promotion Strategies,” in the March 2013 issue of the Journal of Marketing. Given the prevalence of location-based smartphone apps like Foursquare and LocalResponse, Hui and his colleagues specifically studied the applications of mobile promotional strategies that encourage additional in-store travel. They found that customers who travel more of the store spend more money than those who don’t.

Using RFID tracking, Hui and his team followed customer paths throughout grocery stores and found that people who travel 55 feet more than the average trip (1,400 feet) generate around $1 in additional unplanned spending. The team also reports that send-ing a mobile coupon to shoppers, prompting them to travel farther from their planned path, results in a substantial increase in unplanned spending ($21.29) versus a coupon for a product near their intended path ($13.83).

unseen is unsold. Unplanned purchases can be impulse buys or items that shoppers need but forget until they see the products on the shelf. “Retailers have all of this traditional wisdom about putting the milk in the back of the store,” Hui says. “We found that, yes, it makes sense to put milk in the back because by making people travel longer, they actually have more unplanned spending.”

Leverage mobile. While rearranging store layouts may spur some unplanned purchases, sending customers on a wild goose chase might deter some additional sales. Balance your tactics with a mix of layout strategies and mobile promotions. “If you were to relocate the store, some people might get really annoyed because you’re making life harder for them,” Hui says. “For mobile promotions, it’s an opt-in. If you don’t like that coupon, you just don’t go over there.”

See the big picture. Marketing researchers have traditionally focused on what consumers purchase, according to Hui, but researchers should focus more on the overall process of how people shop, rather than just the end result. “[The study] highlights the utility for marketing researchers to actually look at the entire shopping trip, as opposed to just scanner data that only measures the outcomes.”

tailor research to other retail environments. Mobile promotion technology is in its nascent stage and future research can extend to different types of retail stores, using a range of technologies. “Using RFID and video cameras has really taken off in the last couple of years,” Hui says. He’s currently tracking consumer paths in fashion stores using hidden cameras. Mi

markeTers

researchers

conSuMer carTograPhy

sPlit screen

ss

to the right, hui breaks down his academic research to share insights for

retail marketers and research professionals.

by mArguerite mcneAl

[email protected]

mi summer 2013

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