This week the Danish toymaker LEGO posted a 25% jump in revenues and a 31% rise in net profits for 2015, a far cry from 2003 when the company was in deep 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 then, “We are on a burning platform, losing money with negative cash flow, and a real risk of debt default which could lead to a breakup of the company.” How had the Danish toymaker fallen so far so fast then and jumped so high this week?
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 began advising LEGO in 2004, years after the company first hired me as an adolescent, 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 of 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 ankle. 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 sneakers, 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 chance observation of 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 in turn 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.[1]
Today, intriguingly, we are turning the tables on the Internet by circling back and finding human – not digital – insights about ourselves based on our own unconscious online behaviors.
In 2013, for example, using data accumulated from 250,000 people over a period of ten years, a study appeared in the Journal of Personality and Social Psychology examining music consumption tastes as they evolve over the course of a lifetime. Music, it appears, adapts to whatever “life challenges” or psychosocial needs we face as we get older.[2] The study divided music consumption patterns into five “empirically derived” categories dubbed the “MUSIC model” – an acronym that stands for mellow, unpretentious, sophisticated, intense and contemporary. Perhaps unsurprisingly, the first significant age of music-listening is adolescence, a time defined by intense, which possibly reflected increased hormonal activity or the creation of the teenaged “self.” Intense intersects with a rise incontemporary music-listening, a trend that lasts until early middle age, when two other “preference dimensions” – Electronic and R & B – enter the mix, both of which are “romantic, emotionally positive and danceable.”[3] The final musical age of humans is dominated by sophisticated – jazz and classical music – and unpretentious – country, folk and blues. These latter two musical forms are relaxing, positive and link indirectly to listeners’ social status and perceived intellect.
What do the sports we love the most say about us? A study carried out by Mind Lab surveyed 2, 000 UK adults and found that bicyclists are “laid back and calm” and less likely than runners or swimmers to be stressed or depressed. Runners tended to be extroverted, enjoyed being the center of attention and preferred “lively, upbeat music.” Swimmers, the study concluded, were charitable, happy and orderly, whereas walkers generally preferred their own company, didn’t like drawing attention to themselves and were comparatively unmaterialistic.
Are you aware that people with a lot of Facebook friends tend to have lower-than-average self-esteem? Or that the more neurotic Facebook users are, the more likely they are to post mostly photos? Last year, an article in the New York Times Magazine analyzed the significance of the passwords we use to get online and access certain websites. The article reported that in the same way we leave a trail of emotional DNA in our wake, we also distill emotion inside our passwords – and that many of our passwords ritualize a regular encounter with a meaningful memory, or time in our lives, that we seldom have occasion to recall anywhere else. “Many of {our passwords} are suffused with pathos, mischief, sometimes even poetry. Often they have rich back stories. A motivational mantra, a swipe at the boss, a hidden shrine to a lost love, an inside joke with ourselves, a defining emotional scar – these keepsake passwords, as I came to call them, are like tchotchkes of our inner lives.”
Big data might find it hard to find meaning, or relevance, in insights like these. In every big data study I mention to company executives there is a missing question: How might these findings be combined with small data to affect or transform a brand or business? My own research might reveal that a 16-year-old Canadian girl who listens to “intense” music might find it a poor fit with her teenaged identity, and a 45-year-old Englishman who listens to John Coltrane and Chopin might tell you he pines for the intensity of his adolescence and, in fact, wears a black rubber band around his wrist as a badge of rebellion. But you would never know this until you sat across from these people in their living lives or bedrooms.
Nor, it seems, could an unnamed banking institution truly comprehend the behavior of its customers even after leveraging a big data analytics model designed to prevent customer “churn,” a term referring to customers who move money around, refinance their mortgages or generally show signs they are on the verge of exiting the bank. Thanks to the analytics model, the bank soon found evidence of church, and promptly drafted letters asking customers to reconsider. Before sending them out, though, the bank executive who had hired me discovered something surprising. Yes, indeed, “big data” had seen evidence of churning. Thing is, it wasn’t because customers were dissatisfied with the bank or its customer service. No: most were getting a divorce, which explained why they were shifting around their assets. A parallel small data study could have figured this out in a day or less.
Then there are the issues facing Google’s new self-driving cars, most of which it seems can be credited to the mismatch between technology and humanity. According to the New York Times, last year as one of Google’s new cars approached a crosswalk, it did as it was supposed to and came to a complete stop. The pedestrian in front crossed the street safely, at which point the Google car was rammed from behind by a second non-Google automobile. Later, another self-driving Google car found that it wasn’t able to advance through a four-way stop, as its sensors were calibrated to wait for other drivers to make a complete stop, as opposed to inching continuously forward, which most did. Noted theTimes, “Researchers in the fledgling field of autonomous vehicles say that one of the biggest challenges facing automated cars is blending them into a world in which humans don’t behave by the book.”
As accurate, then, as big data can be while connecting millions of data points to generate correlations, big data is often compromised whenever humans act like, well, humans. As big data continues helping us cut corners and automate our lives, humans in turn will evolve simultaneously to address and pivot around the changes technology creates. Big data and small data are partners in a dance, a shared quest for balance – and information.
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