Small Data Read online

  Small Data

  The Tiny Clues That Uncover Huge Trends

  Martin Lindstrom

  St. Martin’s Press

  New York

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  Table of Contents

  About the Author

  Copyright Page

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  Foreword

  Chip Heath

  Co-author of Made to Stick and Switch

  In today’s business environment, Big Data inspires religious levels of devotion and Martin Lindstrom is an atheist.

  While many skeptics are bores, Martin is definitively not. Reading his book is like sitting down to dinner with one of those famous explorers of the nineteenth century, Sir Richard Francis Burton perhaps, who has returned from exotic journeys full of striking observations and tall tales. There is a wide, uncatalogued social world out there, and Martin notices everything . . . Russian homes have no mirrors, owners of Roomba robotic vacuum cleaners frequently give them names, American hotel windows never open, cultures as diverse as those in Saudi Arabia and Siberia turn to refrigerator magnets to convey important family values.

  Martin is anything but a passive observer. When he arrives at the airport in a new country, he hand-picks a taxicab driven by a nonnative to drive him into town, and spends the trip grilling the driver for observations about the locals. He notes that outsiders often see a culture’s idiosyncrasies better than the natives. As an outsider, he visits real people in their homes and watches what they are doing and how they shape their spaces.

  Martin did not sit down to write an explicit critique of Big Data. But by showing the virtues of Small Data he throws into stark relief some problems you should be aware of when you consider Big Data. Consider two:

  Big Data doesn’t spark insight. New ideas typically come from juxtaposition—combining two things that previously haven’t been combined. But Big Data typically lives in databases that are defined too narrowly to create insight. When a firm explores Big Data from its online customers, it typically looks only at online purchases. Frequently that database doesn’t track purchases customers make at brick and mortar stores (those are in a separate database, jealously guarded by its owners), and neither database is linked to information on the timing of the firm’s advertisements. The book describes a breakthrough shopping experience Martin developed for a French retailer that was trying to attract the fickle attention of teenaged girls; Martin’s solution arose by triangulating across time diaries, phone records, interviews, personal photo diaries, and mall shopping observations. When psychologist Phil Tetlock studied Superforecasters, people who were far superior in predicting political and economic events, he found that they had a similar tendency to explore across data sources, looking for triangulation. Unfortunately our Big Data databases are not really “Big,” they are less like robust cross-trained athletes and more like gawky nerds who have one splinter skill and are mostly ineffective at everything else. They are too narrow to create the juxtaposition that leads to breakthroughs.

  Big Data is data, and data favors analysis over emotion. It’s hard to imagine data capturing many of the emotional qualities we most value: beautiful or friendly or sexy or awesome or cute. If data fostered better emotional decisions, then accountants, not poets, would be the cultural prototype for great lovers. Kevin Roberts of Saatchi and Saatchi argued that great brands have two advantages: (1) they evoke respect for their technological performance, durability, and effectiveness; and (2) they evoke love because, well, . . . we love them. Brands like HP and Duracell are “respect” brands and Big Data can often help make decisions about increasing respect (Given our history are customers likely to spend 20% more if we make our batteries last 15% longer?), but brands such as Disney, Cheerios, and Geek Squad are respected and loved, and Big Data is pretty incompetent at suggesting how to increase the love.

  At one point Martin is asked by the makers of the robotic vacuum cleaner, the Roomba, to help overcome a drop in revenues. Martin focused on Small Data about emotion. He followed Roomba owners into their homes and watched how they interacted with their machines. Surprisingly, owners treated their Roombas like a pet; they named it, they took pride in showing it to their guests (when is the last time you showed off your vacuum cleaner?). When owners stored the Roomba, they didn’t stick it in a closet, they left it peeking out from under the sofa, as though it were frozen in mid-action.

  Unfortunately, Roomba’s leaders had moved away from its initial “cute” factor. Roomba was inspired by R2D2 in Star Wars, but over time designs changed, making Roomba look less like R2D2 and more like an appliance. In the first model, the Roomba made sounds; when it accidentally bumped the wall, by accident it said “uh-oh,” but at some point the noises were cut by some engineer seeking a simpler design or a manager seeking lower costs. See chapter 7 for Martin’s clever advice to the Roomba managers, inspired by the world’s cutest automobile, the BMW Mini Cooper, on how to return emotional excitement to their brand.

  In sum, Big Data has problems, and Martin is successful at showing how Small Data is essential to overcoming them.

  I’ve talked about the many virtues of the book, so let me also list a couple of cautions. The book should not be read as a work of social science. When Martin quotes statistics, I don’t always know when he is serious and when he’s pulling my leg (are 60 percent of the toothbrushes sold in the world really red? Do teenage girls in France really spend 80 percent of their waking hours mulling their outfits for today and tomorrow?). And while Martin is clearly a careful observer, he often extrapolates to grand conclusions that I suspect are bogus (Do Americans have a conflict-aversion that causes us to prefer round cakes to square ones? And does breaking the cake rules by providing a square cake really allow consumers permission to break their diets?). Leaping this far beyond the data would get a masters’ student in Anthropology a failing grade.

  But Martin is an explorer and raconteur, not a social scientist, so as a reader I am willing to forgive his excesses. And that’s easier to do because he so frequently manages to provoke his clients in new directions that are clearly better, as in the Roomba case above.

  Here’s another of my favorite cases: In the 1990s LEGO’s sales were declining and executives were scared by big research studies showing that Digital Natives were increasingly distractible and in search of instant gratification. Swayed by this data, LEGO was considering dumbing down its toys, making the kits simpler and even perhaps increasing the size of its iconic brick. But then Small Data convinced LEGO to do an abrupt pivot, going the other direction completely, after senior leaders visited the homes of their young users and talked to them about hobbies and leisure. In the introduction to this book, you will read about how the critical “datapoint” was an old pair of Adidas worn by an 11-year-old German skateboarder. LEGO leaders eventually embraced the aspirational desires of the geeks who wanted kits that were worthy of their talents, and designed larger kits with more complex features.

  A few years ago, a 10-year-old family friend proudly showed me h
is completed LEGO model of the Taj Mahal. It remains the largest set LEGO has ever produced, with over 5900 bricks. When he received the kit, construction commenced immediately. I’m not even sure he waited for the end of his party. The next day he awakened spontaneously at 4 a.m. and worked until school started at 7, and the first thing he did after school at 3:00 p.m. was to head to his room to start again. And he did that the next day. And the next and the next. He finished in 4 days. The price of the kit? $300 at the time. For a kid’s toy. And today the kits are collectors’ items with prices of $3000 or more. LEGO executives, hearing my young friend’s reaction to this complex challenge would suffer from a sweaty brow and dry mouth, thinking about how narrowly they dodged the Big Data–inspired mistake of dumbing down their kits.

  Our businesses will not improve through Big Data alone. We need to follow Martin and explore Rich Data. Deep Data. Even if it comes in the form of Small Data.

  Our businesses will be better for it.

  —Chip Heath, January 2016

  Introduction

  The solution to LEGO’s problems—the thing that may have rescued it from potential bankruptcy—lay in an old pair of sneakers.

  It was early 2003, and the company was in trouble, having lost 30 percent of its turnover over the past year. In 2004, another 10 percent vanished. As Jørgen Vig Knudstorp, LEGO’s CEO, put it, “We are on a burning platform, losing money with negative cash flow, and a real risk of debt default which could lead to a break up of the company.”1

  How had the Danish toymaker fallen so far so fast? Arguably, the company’s problems could be traced back to 1981, when the world’s first handheld game, Donkey Kong, came to market, inspiring a debate within the pages of LEGO’s internal magazine, Klodshans, about what so-called “side-scrolling platform games” meant for the future of construction toys. The consensus: platforms like Atari and Nintendo were fads—which turned out to be true, at least until the advent of computer games for PCs launched their wildly successful second wind.

  I had begun advising LEGO in 2004 when the company asked me to develop its overall branding strategy. I didn’t want the company to move away from what it had been doing well for so long, but no one could deny the increasing everywhere-ness of all things digital. From the mid-1990s on, LEGO began moving away from its core product, i.e., building blocks, and focusing instead on its loosely knit empire of theme parks, children’s clothing lines, video games, books, magazines, television programs and retail stores. Somewhere during this same period, management decided that considering how impatient, impulsive and fidgety millennials were, LEGO should begin manufacturing bigger bricks.

  Every big data study LEGO commissioned drew the exact same conclusions: future generations would lose interest in LEGO. LEGOs would go the way of jackstraws, stickball, blindman’s bluff. So-called Digital Natives—men and women born after 1980, who’d come of age in the Information Era—lacked the time, and the patience, for LEGOs, and would quickly run out of ideas and storylines to build around. Digital natives would lose their capacity for fantasy and creativity, if they hadn’t already, since computer games were doing most of the work for them. Each LEGO study showed that the generational need for instant gratification was more potent than any building block could ever hope to overcome.

  In the face of such a prognosis, it seemed impossible for LEGO to turn things around—but, in fact, the company did. It sold off its theme parks. It continued successful brand alliances with the Harry Potter, Star Wars and Bob the Builder franchises. It reduced the number of products while entering new and underserved global markets.

  Still, probably the biggest turnaround in LEGO’s thinking came as the result of an ethnographic visit LEGO marketers paid in early 2004 to the home of an 11-year-old boy in a midsized German city. Their mission? To figure out what really made LEGO stand out. What executives found out that day was that everything they thought they knew, or had been told, about late twentieth- and early twenty-first-century children and their new digital behaviors—including the need for time compression and instantaneous results—was wrong.

  In addition to being a LEGO aficionado, the 11-year-old German boy was also a passionate skateboarder. Asked at one point which of his possessions he was the most proud of, he pointed to a pair of beat-up Adidas sneakers with ridges and nooks along one side. Those sneakers were his trophy, he said. They were his gold medal. They were his masterpiece. More than that, they were evidence. Holding them up so everyone in the room could see and admire them, he explained that one side was worn down and abraded at precisely the right angle. The heels were scuffed and planed in an unmistakable way. The entire look of the sneakers, and the impression they conveyed to the world, was perfect; it signaled to him, to his friends and to the rest of the world that he was one of the best skateboarders in the city.

  At that moment, it all came together for the LEGO team. Those theories about time compression and instant gratification? They seemed to be off base. Inspired by what an 11-year-old German boy had told them about an old pair of Adidas, the team realized that children attain social currency among their peers by playing and achieving a high level of mastery at their chosen skill, whatever that skill happens to be. If the skill is valuable, and worthwhile, they will stick with it until they get it right, never mind how long it takes. For kids, it was all about paying your dues and having something tangible to show for it in the end—in this case, a pair of tumbledown Adidas that most adults would never look at twice.

  Until that point, LEGO’s decision making was predicated entirely on reams of big data. Yet ultimately it was a small, chance insight—a pair of sneakers belonging to a skateboarder and LEGO lover—that helped propel the company’s turnaround. From that point on, LEGO refocused on its core product, and even upped the ante. The company not only re-engineered its bricks back to their normal size, it began adding even more, and smaller, bricks inside their boxes. The bricks became more detailed, the instruction manuals more exacting, the construction challenges more labor-intensive. For users, it seemed, LEGO was all about the summons, the provocation, the mastery, the craftsmanship and, not least, the hard-won experience—a conclusion that complex predictive analytics, despite their remarkable ability to parse “average” scores, had missed.

  Cut to ten years later when, during the first half of 2014, in the wake of the worldwide success of The Lego Movie and sales of related merchandise, LEGO’s sales rose 11 percent to exceed $2 billion. For the first time ever, LEGO had surpassed Mattel to become the world’s largest toy maker.2

  Believe it or not, almost every insight I come up with as a global branding consultant happens just this way. I might be developing a new car key for Porsche owners, designing a credit card for billionaires, creating a newfangled innovation for a weight-loss organization, helping reverse the fortunes of a stumbling American supermarket chain or trying to position the Chinese automotive industry to compete globally. There’s a well-known quote that says if you want to understand how animals live, you don’t go to the zoo, you go to the jungle. And so I do. In nearly every instance, after conducting what I call Subtext Research (which I occasionally shorten to Subtexting), a detailed process that involves visiting consumers in their homes, gathering small data offline and online, and crunching, or Small Mining, these clues with observations and insights taken from around the world, there almost always comes a moment where I uncover an unmet or unacknowledged desire that forms the foundation of a new brand, product innovation or business.

  Over the past 15 years, I’ve interviewed thousands of men, women and children in their homes in 77 countries. I’m on a plane, or inside a hotel room, 300 nights a year. The drawbacks of living a life like this are obvious. I can’t really call anyplace home, relationships are hard to sustain, and children and pets aren’t an option. Still, there are benefits. Among them is the ongoing opportunity to observe people and the cultures they inhabit from their perspe
ctives, and to try to answer questions like: How do groups of people form? What are their core beliefs? What do they aspire to, and why? How do they create social ties? How does one culture differ from another? Do any of these local beliefs, habits or rituals have a universal significance?

  Not least are the examples of odd behavior, or general truths, I stumble on all across the world. We are afraid, for example, of letting others know more about us than we know about ourselves, fearing most of all that our masks will slip, and we will lose control, letting others see us as we truly are. We are unable to perceive the people we love—husbands, wives, partners, children—aging physically in the same way we notice people we see less often getting older. All humans experience “candy moments”—an internal reward system that takes place while we’re working, reading, thinking or focusing, and that divides and re-energizes our routines and re-stimulates our attention. Relatedly, we “reward” ourselves in the wake of completing a big job, just as the generosity we feel toward others around the holidays results in our buying presents for ourselves. And, in a transparent, overpopulated world where we spill our inner lives online, more than ever the concept of “privacy” and “exclusivity” has become the greatest luxury of all.

  Why do most of us when we’re on our cell phones walk around in a circle as we’re speaking, as if somehow to create a moat, or wall, of privacy? Why, when we’re hungry or thirsty, do we open the refrigerator door, glance up and down at the contents, close the door and a few moments later repeat this same behavior? Why when we’re late for an appointment do we seek out clocks that tell a “better time,” thereby justifying our tardiness? Why in an airport or train station or rock concert do we perceive people in crowds as average members of “the masses”—not realizing that they are doing exactly the same with us? Why do so many people get their best ideas in the shower, or in the presence of water?