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IBM presenta una nueva solución de Marketing Digital - Pida una cita hoy

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(English)

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Una nueva forma de hacer Marketing Digital - IBM.pdf

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Big Data: La Revolución Gerencial

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There's much wisdom in that saying, which has been attributed to both W. Edwards Deming and Peter Drucker, and it explains why the recent explosion of digital data is so important. Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.

Consider retailing. Booksellers in physical stores could always track which books sold and which did not. If they had a loyalty program, they could tie some of those purchases to individual customers. And that was about it. Once shopping moved online, though, the understanding of customers increased dramatically. Online retailers could track not only what customers bought, but also what else they looked at; how they navigated through the site; how much they were influenced by promotions, reviews, and page layouts; and similarities across individuals and groups. Before long, they developed algorithms to predict what books individual customers would like to read next-algorithms that performed better every time the customer responded to or ignored a recommendation. Traditional retailers simply couldn't access this kind of information, let alone act on it in a timely manner. It's no wonder that Amazon has put so many brick-and-mortar bookstores out of business.

The familiarity of the Amazon story almost masks its power. We expect companies that were born digital to accomplish things that business executives could only dream of a generation ago. But in fact the use of big data has the potential to transform traditional businesses as well. It may offer them even greater opportunities for competitive advantage (online businesses have always known that they were competing on how well they understood their data). As we'll discuss in more detail, the big data of this revolution is far more powerful than the analytics that were used in the past. We can measure and therefore manage more precisely than ever before. We can make better predictions and smarter decisions. We can target more-effective interventions, and can do so in areas that so far have been dominated by gut and intuition rather than by data and rigor.

As the tools and philosophies of big data spread, they will change long-standing ideas about the value of experience, the nature of expertise, and the practice of management. Smart leaders across industries will see using big data for what it is: a management revolution. But as with any other major change in business, the challenges of becoming a big data-enabled organization can be enormous and require hands-on-or in some cases hands-off-leadership. Nevertheless, it's a transition that executives need to engage with today.

What's New Here?

Business executives sometimes ask us, "Isn't 'big data' just another way of saying 'analytics'?" It's true that they're related: The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage. However, there are three key differences:

Volume.

As of 2012, about 2.5 exabytes of data are created each day, and that number is doubling every 40 months or so. More data cross the internet every second than were stored in the entire internet just 20 years ago. This gives companies an opportunity to work with many petabyes of data in a single data set-and not just from the internet. For instance, it is estimated that Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. A petabyte is one quadrillion bytes, or the equivalent of about 20 million filing cabinets' worth of text. An exabyte is 1,000 times that amount, or one billion gigabytes.

Velocity.

For many applications, the speed of data creation is even more important than the volume. Real-time or nearly real-time information makes it possible for a company to be much more agile than its competitors. For instance, our colleague Alex "Sandy" Pentland and his group at the MIT Media Lab used location data from mobile phones to infer how many people were in Macy's parking lots on Black Friday-the start of the Christmas shopping season in the United States. This made it possible to estimate the retailer's sales on that critical day even before Macy's itself had recorded those sales. Rapid insights like that can provide an obvious competitive advantage to Wall Street analysts and Main Street managers.

Variety.

Big data takes the form of messages, updates, and images posted to social networks; readings from sensors; GPS signals from cell phones, and more. Many of the most important sources of big data are relatively new. The huge amounts of information from social networks, for example, are only as old as the networks themselves; Facebook was launched in 2004, Twitter in 2006. The same holds for smartphones and the other mobile devices that now provide enormous streams of data tied to people, activities, and locations. Because these devices are ubiquitous, it's easy to forget that the iPhone was unveiled only five years ago, and the iPad in 2010. Thus the structured databases that stored most corporate information until recently are ill suited to storing and processing big data. At the same time, the steadily declining costs of all the elements of computing-storage, memory, processing, bandwidth, and so on-mean that previously expensive data-intensive approaches are quickly becoming economical.

As more and more business activity is digitized, new sources of information and ever-cheaper equipment combine to bring us into a new era: one in which large amounts of digital information exist on virtually any topic of interest to a business. Mobile phones, online shopping, social networks, electronic communication, GPS, and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. Each of us is now a walking data generator. The data available are often unstructured-not organized in a database-and unwieldy, but there's a huge amount of signal in the noise, simply waiting to be released. Analytics brought rigorous techniques to decision making; big data is at once simpler and more powerful. As Google's director of research, Peter Norvig, puts it: "We don't have better algorithms. We just have more data."

How Data-Driven Companies Perform

The second question skeptics might pose is this: "Where's the evidence that using big data intelligently will improve business performance?" The business press is rife with anecdotes and case studies that supposedly demonstrate the value of being data-driven. But the truth, we realized recently, is that nobody was tackling that question rigorously. To address this embarrassing gap, we led a team at the MIT Center for Digital Business, working in partnership with McKinsey's business technology office and with our colleague Lorin Hitt at Wharton and the MIT doctoral student Heekyung Kim. We set out to test the hypothesis that data-driven companies would be better performers. We conducted structured interviews with executives at 330 public North American companies about their organizational and technology management practices, and gathered performance data from their annual reports and independent sources.

Not everyone was embracing data-driven decision making. In fact, we found a broad spectrum of attitudes and approaches in every industry. But across all the analyses we conducted, one relationship stood out: The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results. In particular, companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors. This performance difference remained robust after accounting for the contributions of labor, capital, purchased services, and traditional IT investment. It was statistically significant and economically important and was reflected in measurable increases in stock market valuations.

Often someone coming from outside an industry can spot a better way to use big data than an insider, just because so many new, unexpected sources of data are available. One of us, Erik, demonstrated this in research he conducted with Lynn Wu, now an assistant professor at Wharton. They used publicly available web search data to predict housing-price changes in metropolitan areas across the United States. They had no special knowledge of the housing market when they began their study, but they reasoned that virtually real-time search data would enable good near-term forecasts about the housing market-and they were right. In fact, their prediction proved more accurate than the official one from the National Association of Realtors, which had developed a far more complex model but relied on relatively slow-changing historical data.

This is hardly the only case in which simple models and big data trump more-elaborate analytics approaches. Researchers at the Johns Hopkins School of Medicine, for example, found that they could use data from Google Flu Trends (a free, publicly available aggregator of relevant search terms) to predict surges in flu-related emergency room visits a week before warnings came from the Centers for Disease Control. Similarly, Twitter updates were as accurate as official reports at tracking the spread of cholera in Haiti after the January 2010 earthquake; they were also two weeks earlier.

So how are managers using big data? Let's look in detail at two companies that are far from Silicon Valley upstarts. One uses big data to create new businesses, the other to drive more sales.

Improved Airline ETAs

Minutes matter in airports. So does accurate information about flight arrival times: If a plane lands before the ground staff is ready for it, the passengers and crew are effectively trapped, and if it shows up later than expected, the staff sits idle, driving up costs. So when a major U.S. airline learned from an internal study that about 10% of the flights into its major hub had at least a 10-minute gap between the estimated time of arrival and the actual arrival time-and 30% had a gap of at least five minutes-it decided to take action.

At the time, the airline was relying on the aviation industry's long-standing practice of using the ETAs provided by pilots. The pilots made these estimates during their final approach to the airport, when they had many other demands on their time and attention. In search of a better solution, the airline turned to PASSUR Aerospace, a provider of decision-support technologies for the aviation industry. In 2001 PASSUR began offering its own arrival estimates as a service called RightETA. It calculated these times by combining publicly available data about weather, flight schedules, and other factors with proprietary data the company itself collected, including feeds from a network of passive radar stations it had installed near airports to gather data about every plane in the local sky.

PASSUR started with just a few of these installations, but by 2012 it had more than 155. Every 4.6 seconds it collects a wide range of information about every plane that it "sees." This yields a huge and constant flood of digital data. What's more, the company keeps all the data it has gathered over time, so it has an immense body of multidimensional information spanning more than a decade. This allows sophisticated analysis and pattern matching. RightETA essentially works by asking itself "What happened all the previous times a plane approached this airport under these conditions? When did it actually land?"

After switching to RightETA, the airline virtually eliminated gaps between estimated and actual arrival times. PASSUR believes that enabling an airline to know when its planes are going to land and plan accordingly is worth several million dollars a year at each airport. It's a simple formula: Using big data leads to better predictions, and better predictions yield better decisions.

Speedier, More Personalized Promotions

A couple of years ago, Sears Holdings came to the conclusion that it needed to generate greater value from the huge amounts of customer, product, and promotion data it collected from its Sears, Craftsman, and Lands' End brands. Obviously, it would be valuable to combine and make use of all these data to tailor promotions and other offerings to customers, and to personalize the offers to take advantage of local conditions. Valuable, but difficult: Sears required about eight weeks to generate personalized promotions, at which point many of them were no longer optimal for the company. It took so long mainly because the data required for these large-scale analyses were both voluminous and highly fragmented-housed in many databases and "data warehouses" maintained by the various brands.

In search of a faster, cheaper way to do its analytic work, Sears Holdings turned to the technologies and practices of big data. As one of its first steps, it set up a Hadoop cluster. This is simply a group of inexpensive commodity servers whose activities are coordinated by an emerging software framework called Hadoop (named after a toy elephant in the household of Doug Cutting, one of its developers).

Sears started using the cluster to store incoming data from all its brands and to hold data from existing data warehouses. It then conducted analyses on the cluster directly, avoiding the time-consuming complexities of pulling data from various sources and combining them so that they can be analyzed. This change allowed the company to be much faster and more precise with its promotions. According to the company's CTO, Phil Shelley, the time needed to generate a comprehensive set of promotions dropped from eight weeks to one, and is still dropping. And these promotions are of higher quality, because they're more timely, more granular, and more personalized. Sears's Hadoop cluster stores and processes several petabytes of data at a fraction of the cost of a comparable standard data warehouse.

Shelley says he's surprised at how easy it has been to transition from old to new approaches to data management and high-performance analytics. Because skills and knowledge related to new data technologies were so rare in 2010, when Sears started the transition, it contracted some of the work to a company called Cloudera. But over time its old guard of IT and analytics professionals have become comfortable with the new tools and approaches.

The PASSUR and Sears Holding examples illustrate the power of big data, which allows more-accurate predictions, better decisions, and precise interventions, and can enable these things at seemingly limitless scale. We've seen big data used in supply chain management to understand why a carmaker's defect rates in the field suddenly increased, in customer service to continually scan and intervene in the health care practices of millions of people, in planning and forecasting to better anticipate online sales on the basis of a data set of product characteristics, and so on. We've seen similar payoffs in many other industries and functions, from finance to marketing to hotels and gaming, and from human resource management to machine repair.

Our statistical analysis tells us that what we're seeing is not just a few flashy examples but a more fundamental transformation of the economy. We've become convinced that almost no sphere of business activity will remain untouched by this movement.

A New Culture of Decision Making

The technical challenges of using big data are very real. But the managerial challenges are even greater-starting with the role of the senior executive team.

Muting the HiPPOs.

One of the most critical aspects of big data is its impact on how decisions are made and who gets to make them. When data are scarce, expensive to obtain, or not available in digital form, it makes sense to let well-placed people make decisions, which they do on the basis of experience they've built up and patterns and relationships they've observed and internalized. "Intuition" is the label given to this style of inference and decision making. People state their opinions about what the future holds-what's going to happen, how well something will work, and so on-and then plan accordingly. (See "The True Measures of Success," by Michael J. Mauboussin, in this issue.)

For particularly important decisions, these people are typically high up in the organization, or they're expensive outsiders brought in because of their expertise and track records. Many in the big data community maintain that companies often make most of their important decisions by relying on "HiPPO"-the highest-paid person's opinion.

To be sure, a number of senior executives are genuinely data-driven and willing to override their own intuition when the data don't agree with it. But we believe that throughout the business world today, people rely too much on experience and intuition and not enough on data. For our research we constructed a 5-point composite scale that captured the overall extent to which a company was data-driven. Fully 32% of our respondents rated their companies at or below 3 on this scale.

New roles.

Executives interested in leading a big data transition can start with two simple techniques. First, they can get in the habit of asking "What do the data say?" when faced with an important decision and following up with more-specific questions such as "Where did the data come from?," "What kinds of analyses were conducted?," and "How confident are we in the results?" (People will get the message quickly if executives develop this discipline.) Second, they can allow themselves to be overruled by the data; few things are more powerful for changing a decision-making culture than seeing a senior executive concede when data have disproved a hunch.

When it comes to knowing which problems to tackle, of course, domain expertise remains critical. Traditional domain experts-those deeply familiar with an area-are the ones who know where the biggest opportunities and challenges lie. PASSUR, for one, is trying to hire as many people as possible who have extensive knowledge of operations at America's major airports. They will be invaluable in helping the company figure out what offerings and markets it should go after next.

As the big data movement advances, the role of domain experts will shift. They'll be valued not for their HiPPO-style answers but because they know what questions to ask. Pablo Picasso might have been thinking of domain experts when he said, "Computers are useless. They can only give you answers."

You don't need to make enormous up-front investments in IT to use big data (unlike earlier generations of IT-enabled change). Here's one approach to building a capability from the ground up.

1. Pick a business unit to be the testing ground. It should have a quant-friendly leader backed up by a team of data scientists.

2. Challenge each key function to identify five business opportunities based on big data, each of which could be prototyped within five weeks by a team of no more than five people.

3. Implement a process for innovation that includes four steps: experimentation, measurement, sharing, and replication.

4. Keep in mind Joy's Law: "Most of the smartest people work for someone else." Open up some of your data sets and analytic challenges to interested parties across the internet and around the world.

Five Management Challenges

Companies won't reap the full benefits of a transition to using big data unless they're able to manage change effectively. Five areas are particularly important in that process.

Leadership.

Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions. Big data's power does not erase the need for vision or human insight. On the contrary, we still must have business leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with customers, employees, stockholders, and other stakeholders. The successful companies of the next decade will be the ones whose leaders can do all that while changing the way their organizations make many decisions.

Talent management.

As data become cheaper, the complements to data become more valuable. Some of the most crucial of these are data scientists and other professionals skilled at working with large quantities of information. Statistics are important, but many of the key techniques for using big data are rarely taught in traditional statistics courses. Perhaps even more important are skills in cleaning and organizing large data sets; the new kinds of data rarely come in structured formats. Visualization tools and techniques are also increasing in value. Along with the data scientists, a new generation of computer scientists are bringing to bear techniques for working with very large data sets. Expertise in the design of experiments can help cross the gap between correlation and causation. The best data scientists are also comfortable speaking the language of business and helping leaders reformulate their challenges in ways that big data can tackle. Not surprisingly, people with these skills are hard to find and in great demand. (See "Data Scientist: The Sexiest Job of the 21st Century," by Thomas H. Davenport and D.J. Patil, in this issue.)

Technology.

The tools available to handle the volume, velocity, and variety of big data have improved greatly in recent years. In general, these technologies are not prohibitively expensive, and much of the software is open source. Hadoop, the most commonly used framework, combines commodity hardware with open-source software. It takes incoming streams of data and distributes them onto cheap disks; it also provides tools for analyzing the data. However, these technologies do require a skill set that is new to most IT departments, which will need to work hard to integrate all the relevant internal and external sources of data. Although attention to technology isn't sufficient, it is always a necessary component of a big data strategy.

Decision making.

An effective organization puts information and the relevant decision rights in the same location. In the big data era, information is created and transferred, and expertise is often not where it used to be. The artful leader will create an organization flexible enough to minimize the "not invented here" syndrome and maximize cross-functional cooperation. People who understand the problems need to be brought together with the right data, but also with the people who have problem-solving techniques that can effectively exploit them.

Company culture.

The first question a data-driven organization asks itself is not "What do we think?" but "What do we know?" This requires a move away from acting solely on hunches and instinct. It also requires breaking a bad habit we've noticed in many organizations: pretending to be more data-driven than they actually are. Too often, we saw executives who spiced up their reports with lots of data that supported decisions they had already made using the traditional HiPPO approach. Only afterward were underlings dispatched to find the numbers that would justify the decision.Without question, many barriers to success remain. There are too few data scientists to go around. The technologies are new and in some cases exotic. It's too easy to mistake correlation for causation and to find misleading patterns in the data. The cultural challenges are enormous, and, of course, privacy concerns are only going to become more significant. But the underlying trends, both in the technology and in the business payoff, are unmistakable.

The evidence is clear: Data-driven decisions tend to be better decisions. Leaders will either embrace this fact or be replaced by others who do. In sector after sector, companies that figure out how to combine domain expertise with data science will pull away from their rivals. We can't say that all the winners will be harnessing big data to transform decision making. But the data tell us that's the surest bet.

A version of this article appeared in the October 2012 issue of Harvard Business Review.

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There's much wisdom in that saying, which has been attributed to both W. Edwards Deming and Peter Drucker, and it explains why the recent explosion of digital data is so important. Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.

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No Deje que Big Data Entierre a su Marca

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Idea in Brief

A Classic Tension

Marketers have always had to strike a careful balance between brand building and short-term sales promotion. Depend on too much of the latter, and-as Capital One learned-you'll wind up with a brand name that doesn't stand for much.

Made Worse by Big Data

Today that balance is harder to strike than ever because of big data and analytics and the highly targeted promotions they enable. The temptation to neglect brand investments in the pursuit of sure sales is nearly irresistible.

The Advice

To protect your brand, make every message do double duty. Use insights from data to make the long-term case for brand investments. Don't pursue sales offers you can't defend in brand terms. And go to new lengths to ensure productive collaboration between your brand mavens and your data analysts.

Deep into the second quarter, the chief marketing officer of a restaurant chain arrives at work to find that the CEO has dropped by. In this business, as in many others, "CMO" means chief revenue officer to the CEO, who's here to talk sales. "There's only a month left," he says, "and I need a boost to compensate for what we lost because of the weather. The data analysts over in IT tell me we get the highest response to burger and apps offers. So, time for some coupons?"

The CMO's plan was to use the coming months to strengthen brand equity, through ads promoting the quality of the food and the heritage of the brand, while moving away from the emphasis on discounts forced by a tough first quarter. But when the boss asks for something, what can you do?

This all too realistic scenario illustrates the classic tension for CMOs and, we might say, the main reason their high-level job should even exist: to achieve just the right balance between short-term revenue pursuit and long-term brand building. Advanced marketing analytics and big data make that job much harder today. If it was difficult before to defend branding investments with indefinite and distant payoffs, it is doubly so now that near-term sales can be so precisely engineered. Analytics allows a seeming omniscience about what promotional offers customers will find appealing. Big data allows impressive amounts of information to be obtained about the buying patterns and transaction histories of identifiable customers. Given marketing dollars and the discretion to invest them in either direction, the temptation to keep cash registers ringing is nearly irresistible.

We can speak to the power of analytics directly, because one of us led marketing efforts for many years at Capital One, and the other is a longtime adviser to that company and others. Capital One was "born analytical" in 1988 as a credit card company wholly invested in the data-crunching capabilities that were just then emerging. Recognizing how the credit card industry could be transformed by means of computer analysis and continual scientific testing, its founders outlined an information-based strategy that would not only give it a competitive edge as it went to market, but ultimately define its entire culture. The ability to gain granular insights into the behavioral economics of very narrowly defined segments allowed Capital One to make credit card offers to far more consumers at far more attractive rates with far less financial risk. Back then the offers weren't electronic-but direct mail provided more than enough closed-loop feedback for analysis, and Capital One was able to out-innovate its competition by learning from it.

That history makes the direction that Capital One's marketing has recently taken rather surprising. After a decade of promotions that drew on ever-expanding digital data sources, which resulted in growth that catapulted the company into the Fortune 200, Capital One's CEO, Rich Fairbank, decided that the company should invest more in brand building.

Most companies have gone in the opposite direction in recent years. Inspired by success stories like Capital One's, organizations around the world concluded that they were overinvested in brand-building campaigns with unclear returns and started pushing toward a greater reliance on promotional analytics. But Fairbank saw a troubling future for a company whose name held no relevance for customers and relied instead on the appeal of the Visa and MasterCard brands. Indeed, he commissioned a brand-equity study to understand how consumers saw Capital One, and it identified only one attribute the brand overwhelmingly owned: "They send me lots of mail."

In the past few years we've learned a lot about how to strike the necessary balance-both through Capital One's course correction and through insights gained from other marketers we respect. Here we offer some practices that amount to a playbook written by the smartest CMOs we know, whose companies target specific customers with short-term promotions but also depend on strong brands.

Marketing News

Many students of marketing are familiar with the classic case of the weekly newsmagazine that promoted its way to irrelevance. Time enjoyed a circulation of 4.6 million at its peak, in 1988. Despite a 31% increase in the U.S. population since then, that number today is below 3.3 million-a 30% drop. In 2014 Time was finally spun off by Time Warner, the company that grew from its once strong foundation. The magazine was no longer earning its keep.

The first challenge came in the form of all-news cable channels, which by accelerating news cycles made it difficult for a print weekly to carry sufficiently current information. Time's marketers responded with a TV ad that made things much worse: "Hi, I'm Judy, one of the operators here at Time magazine. Remember, if you call now, you'll get Time at almost half off the cover price. And this exclusive Time AM/FM walkabout, free. This offer ends soon, so call right now. Our operators are standing by."

Desperate to replace the subscribers it was losing, Time ran blatantly promotional ads that were disconnected from the traditional image campaign it had under way-one that emphasized the heritage and quality of Time journalism and highlighted the brand's personality. Thus the magazine unwittingly set up an experiment, and the results quickly flooded in. The promotional ads were so effective that Time soon killed off its brand campaign. A truly sad aspect of the case is that the editors had been working hard to create a better Time for a sped-up era. They knew they had one of the strongest brands for news in a world that had suddenly become news-obsessed. Their circulation-focused colleagues, by doubling down on the "call now" offers, not only failed to communicate that-they implicitly conveyed a lack of faith in the brand.

Time's is hardly the only case that teaches a simple truth about maintaining balance. Michelob beer, for example, was for many years the premium brew that "weekends were made for"-until price promotions sent it spiraling down to the bottom of the barrel. Brand building is necessary to sustain the healthy margins that allow a business to keep fulfilling its brand promise in the long run. But because of the natural tension between brand equity and sales promotion, any pressure to spur revenue in the short term will threaten brand-building investments.

With that tension in mind, consider how great the pressure has become. Big data and analytics can put promotions on steroids. As a startling New York Times story revealed, they can enable a retailer to use a customer's history of buying pregnancy-related goods to seize the opportune moment for promoting new-baby goods. As recent moves in the personal insurance market show, they allow the tailoring of price offers to individuals' revealed habits to replace large-group risk pooling. In ways we encounter as consumers every day, they enable sellers to dangle offers with uncanny insight into what will make us bite.

Make Every Piece of Messaging Do Double Duty

At Subway, whose franchisees operate more than 40,000 restaurants in some 100 countries, CMO Tony Pace managed to get the balance right during his tenure: The Subway name stands for a set of brand promises and simultaneously drives sales to the 40 million customers the company serves every week. Pace told us his secret: Rather than try to balance promotions and image-building campaigns at the portfolio level, his organization strikes that balance in every element. For example, "Our five-dollar footlong promotion also features some of our 'famous fans' [such as the star athletes Robert Griffin III and Michael Phelps]," he says. "Their presence in the ads is for branding, not just sales." In fact, what began in 2008 as a four-week sales-seeking promotion turned into a strategic $4 billion brand asset, complete with its own catchy jingle, logo, and hand gestures signifying "five dollars" and "footlong."

Subway is a perfect example of a company whose marketing could easily be taken over by analytics-driven promotions. With a strong loyalty program and many cardholders subscribing to receive electronic promotional messages, it has an enviable ability to track and influence its customers' behavior. Pace points out, however, that if analytics-driven promotions are programmed to maximize sales in the short run, they will always push to a given customer whatever that customer has ordered most often. (Other offers have a lesser chance of prompting immediate action.) That is destructive in the long term, because, as the company knows, the bigger a customer's "repertoire" (sandwich types ordered more than once), the more loyal that customer remains to Subway.

Even worse long-term effects, Pace notes, would come from overly automated media buying and ad placement. "Programmatic marketing can lead to activities that are off brand strategy and brand-destructive," he told us. "I'm not going to abdicate the messaging decisions."

Yet if a marketing team stays focused on the right long-term goals, big data can often help pursue them. Subway's new ability to uncover sales relationships among the 20 different sandwiches it offers, for example, means that it can see opportunities for growing a customer's repertoire. The unprecedented quantity of data available to his marketers, Pace says, "does lead to enhanced precision of hypothesis making."

Pull Branding-Level Insights from the Data

As we looked for other companies that had effectively balanced branding and sales promotion, it was hard to ignore what Mark Addicks managed to achieve, both for General Mills and personally. By the time he retired, in 2015, he had been with the company 26 years, making him, as Advertising Age reported, "one of the longest-tenured CMOs in the food industry."

Today Addicks describes General Mills' use of big data as evenly balanced between the pursuit of short-term sales and the building of long-term brands. But it wasn't always that way, he says. "Initially, data was used simply to drive sales: advertising on the right day of the week, adding precision on when to engage, knowing what offer to put in front of whom, which pie to focus on for which part of the country." Indeed, Addicks was more comfortable than many other CMOs with "letting the data tell us the consumer logic" for a promotion. He offered one example of a pattern revealed by web data: Many shoppers who looked at a certain kind of yogurt went on to consult certain chicken recipes. "It may seem odd, but we are now starting to follow the logic without questioning it," Addicks says.

Over time, such assumption-challenging patterns have yielded insights that have helped General Mills deepen its brand relationships and generate content that is more relevant and more meaningful. In addition to enabling targeted offers, granular, data-driven understanding of consumer behavior and segments can reveal the shared concerns and underserved needs of subsets of customers, such as those who have young children or those who are responding to cholesterol guidance from a physician. "This is hard for people to get their heads around," Addicks says. "People worry about taking their eye off the brand when you get that granular." But when a marketer's message can strengthen the connection between how consumers perceive a brand and the particular problem they need to solve, he says, it "drives sales, but also links to the broader brand positioning."

For Addicks, perhaps, this was a natural direction for analytics to take. Somewhat atypically, when big data began to generate all the hype, he pushed hard to build the company's capabilities with it. But when his team made its first pitch to the rest of the C-suite, its emphasis on the predictable sales payoff "really hurt our efforts to get the investment," he says. The company "so wants to focus on building a brand and serving customers that a focus on data seemed inconsistent with that." Now that it's clear that data can be just as useful in brand building, the investment is less controversial.

Use Data to Make the Case for Brand Building

Mark Addicks was in a company that understood and loved brand building, and he had to advocate for data's role in that. More often, as we've said, the brand builders are on the defensive, because others in their company want to see more sales emphasis. That's why we were very interested by a company that makes a strong case for brand building with data.

Caesars Entertainment gained a reputation early on for data-driven decision making. Under the leadership of Gary Loveman, its culture became both sales-oriented and deeply analytical, with a general bias toward elements that are measurable and transactional. So when Tariq Shaukat became the CMO, he was prepared to drive even more accountability and granular analytics-but he was also determined to put them at the service of brand building. "With the precision available now in zip+4 TV ad targeting, we're taking a direct-marketing approach to advertising," he explains. That means the company can rigorously test and compare the effects of brand-focused versus promotion-focused advertising. And once customers attracted by either type walk in the door, transactions inside the casino reveal "how they behave."

Given the nature of Caesars's business, says Shaukat, the real brand building is done on each property, as guests experience what the company calls its "total service." The big data Shaukat's team uses includes not only transactions with guests, but also the answers to more than 500,000 feedback surveys every year. Together these offer deeply tactical insights as to how components of the experience drive brand perceptions and where investments will have the most impact. Caesars can also discover how customers' brand perceptions correlate with their lifetime value to the company-and therefore can predict how an investment that increases brand ratings will translate into incremental revenue gains. In other words, Shaukat says, "if we can move from a B to an A rating of our brand, that will have x impact on results."

It's gratifying to see that interrogating larger data sets with better questions can lead to very different marketing decisions. Today Caesars's TV ads are almost wholly branding-oriented, designed to heighten anticipation and emotional appeal. (A five-second promotional offer at the end allows them to do the double duty that Tony Pace advises.) Although the corporation has run into trouble from its debt load, the brand and core business remain strong.

Don't Do It If You Can't Defend It

We've mentioned Mark Addicks's willingness to "follow the logic" of data analysis that makes surprising connections and suggests nonintuitive marketing moves. In, say, a grocery setting, the risk is small enough that it's worth trying out a novel promotional idea. At Capital One, however, being part of customers' financial lives means that computerized decision making could backfire if it produced outcomes that were inconsistent with the company's values or brand.

One ad campaign that effectively melds branding's meaning with big data's targeting features a green navigation line, the brainchild of Fidelity's EVP of corporate communications services, Jim Speros. When the campaign was launched, in the depths of the recent recession, Fidelity was responding to an environment in which many savers and investors questioned their own acumen. The company wanted them to know it could serve as an adviser as well as a provider of mutual funds. Although each customer's financial situation and goals were unique, Fidelity could put people on the right track and help them stay there. As Speros tells it, the metaphor came to him after he moved to Boston and had to constantly refer to his car's GPS system: "Why couldn't Fidelity be that kind of guide?" he wondered.

Marketing programs and advertising campaigns are typically designed either to burnish the brand (by creatively communicating its essence, associations, reputation, and so forth) or to generate sales (by providing specific information about an offering, often with a call to action or an incentive to buy right away). The green line has endured as a powerful business builder because it bridges the gap. Jim Speros received the Financial Communications Society's "Financial Marketer of the Year" award in 2009; the green line campaign won a Cannes Gold Media Lion in 2011; and the campaign is still vibrantly pushing its point and Fidelity's products in 2015.

Some CMOs apply the standard "Can I intuitively explain this before simply following the dictates of the data?" CMOs whose companies, like Capital One, are in regulated industries need also to ask, "Could I explain this to regulators, community leaders, and other stakeholders?" Taking a follow-the-data approach could lead to marketing initiatives that generate strong ROI but unwittingly expose the company to allegations of inappropriate targeting, or unfair exclusion, or using data-driven correlations that in hindsight appear discriminatory. In a human-driven, hypothesis-first model, this risk is minimized by training and awareness and oversight. In a data-driven, automated world, the risk of unintended missteps grows significantly in the absence of an appropriate judgment screen.

That is why Shaukat, even though he is thoroughly grounded in Caesars's analytics-loving culture, insists that oversight by marketers who are comfortable using their intuition and judgment will always be needed. Surprising correlations are great for challenging a marketer's preconceptions or the conventional wisdom; but in the end, the marketer must be able rationally to accept the logic. "If the data tells us to do something that doesn't make intuitive sense, we don't do it," says Shaukat. "And if I can't explain to a customer why we're making a particular offer, we won't do that one either." This sounds like a good brand-protecting philosophy for any company.

Get Branders and Analysts to Collaborate

One last problem for CMOs who are attempting to strike the right balance between branding and promotion is that, increasingly, the two objectives are staffed by very different kinds of people. At the pizza chain Domino's, for example, we heard from Russell Weiner, then the CMO (and now the president), that the complexities of the new data environment can't be mastered by the jack-of-all-trades marketer of the past. "Big data requires such special skills," he said, that it "attracts a different kind of person, who can't rotate in and out of functions." Just as the most creative marketers aren't the best data people, analytic professionals usually lack the skills, the experience, and perhaps even the "internal wiring" to excel at brand, image, and creativity.

Like other CMOs we consulted, however, Weiner believes strongly that drawing on big data to create targeted offers can be instrumental to brand building. "Not showing a pepperoni pizza to a vegetarian is about both sales and branding," he said. "A double good when you get it right, and a double bad when you get it wrong." His company's recent effort to encourage customers to order online rather than by telephone so that more and better data can be captured is part of helping his team deliver that double good.

But the bigger key, according to Weiner, is seamless working relationships between data-savvy creative marketers and consumer-centric big data analysts. "It's like linking a quarterback with an offensive line, or Elton John with [Bernie Taupin] his lyricist," he told us. The teamwork isn't always harmonious, though. There can be "governance issues" about which side takes the lead and which plays backup. Weiner's solution strikes us as one that other CMOs might borrow: He brought a top market researcher with him when he moved to Domino's-someone whose skill set placed him right at the intersection of data crunching and customer understanding. This professional's insights into the whys of what the data showed allowed him to create collaborative space for the rest of the team.

Weiner said of one campaign that drew on both sides' strengths, "It's not about big data. It's about big marketing." In the world of sports, he pointed out, many organizations have become more data-obsessed since Moneyball, the book (and movie) that celebrated the number-crunching sophistication of the Oakland A's. But they don't all play better ball as a result. Why is that? "Maybe the numbers get you only so far," Weiner mused, "and after that it's about the people getting it right."

Between the Extremes

Another difference between marketing people correlates with age. Reliance on analytics and data-driven decisions may be second nature to the new hires in a large marketing organization, but very foreign to their veteran colleagues. In Mark Addicks's words, big data "fundamentally challenges what they do and how they think." Speaking from experience, he told us, "A junior person can look at the data and know more about the category than a senior executive. It can be very humbling when a junior associate in a meeting says, 'I'm sorry, but that's not how the category works.'"

It's not easy to resist the pressure to go all-in on data-driven promotions. Even Rich Fairbank encountered (and encouraged) substantial pushback to his thinking about the importance of branding at Capital One. Extensive internal debates preceded the company's strategic leap to make a significant multiyear investment in building the brand.

Yet much in the traditional marketer's knowledge and skill set is not prone to obsolescence. Every marketing program has some degree of impact on both sides. Sales-oriented marketing influences short-term actions; branding prompts feelings and understanding of what a brand represents. But even a coupon offer says something about the brand, and even an ad with no call to action changes the consumer's proclivity to buy. Good marketing might be defined as successfully navigating between the Scylla and Charybdis of the extremes. In the past marketers have sometimes disastrously pushed toward branding while neglecting sales (the infamous Pets.com comes to mind). Today many are steering just as frighteningly toward sales promotions at the risk of causing their brands to founder. The best way forward will always require a middle course.

A version of this article appeared in the November 2015 issue (pp.78-84, 86) of Harvard Business Review.

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Idea in Brief A Classic Tension Marketers have always had to strike a careful balance between brand building and short-term sales promotion. Depend on too much of the latter, and-as Capital One learned-you'll wind up with a brand name that doesn't stand for much.

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Cómo usar Big Data para Impulsar la Cadena de Abastecimiento

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Cómo usar Big Data para Impulsar la Cadena de Abastecimiento

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Nada S. Sanders

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How to use big data to drive your supply chain.pdf