Recent estimates put the rate of consumer data capture at about 800 new records per person per day. Consider for example the metadata describing one simple mobile call—that is, the information about the call, not the information within the call. The telco stores the caller and receiver IDs, their locations, the time of day and the duration of the call. As an isolated datum, pretty uninspiring. But as an SK Telecom representative described at MobileWorld in Hong Kong last year, a consistent pattern of short calls between the same two numbers during daytime is a strong predictor of a marriage; long calls in the evening is a predictor of unwed lovers. And of course with that, a great marketing opportunity for the telco.
This practice of acquiring, analyzing and interpreting ridiculously huge amounts of data is now known as ‘Big Data’. And it has the technology and marketing worlds buzzing. Here we go again. Another catchphrase that simply repackages an existing idea. Big Data? Big deal.
But this time, there’s meaning in the meme. The term 'Big Data' means much more than companies and government agencies collecting lots of personal information about people. The data stores are so huge that our brains simply cannot comprehend the patterns and correlations contained within. Try to visualise a billion people, or the 150 million kilometers that takes you to the sun. Big numbers are hard enough on their own. Big Data introduces a new set of challenges resulting from amassing sets of data so large and complex that traditional data management and analytics can’t handle them. Mastering Big Data offers the promise of great rewards for those who invest. The hardest thing is knowing what question to ask and what problem you’re solving.
One of my favourite examples is American retailer Target’s ability to predict customer pregnancy based on product purchases, as published in The New York Times in February this year. Whilst it may sound relatively easy, identifying pregnant customers is harder than it sounds. Starting with transaction data from women who signed up for the Baby Registry, Target identified a shift from scented to unscented body-lotion purchases, as well as increasing purchases of mineral supplements like calcium and magnesium. The analytics team built a mathematical model based on these parameters, and found it to be highly predictive of second-trimester pregnancy.
The genius in this example is not that Target managed to successfully cross-sell baby products to expectant mothers. It’s that they were addressing a much bigger problem: how to change people’s ingrained buying habits. In the 1980s, Professor Alan Andreasen of UCLA studied purchasing patterns of FMCGs like soap, toothpaste and toilet paper, and found that most consumers made these purchases habitually, with little decision making. This makes it very hard for marketers to persuade shoppers to change, no matter how attractive the offer or incentive. But becoming a parent is one of those times in life where habits do change. Exhausted parents need easier, cheaper and less time-consuming shopping experiences, so loyalty is up for grabs. As birth records are public, new parents are inundated with offers at the time of a baby’s birth. The key is to secure loyalty early, as Target has done by predicting pregnancy in the second trimester and commencing their targeted campaigns from this point.
In response to a more recent New York Times article about the data-collection practices of Axciom, eight members of US Congress have opened an inquiry into the data brokering industry. Letters of inquiry have been sent to nine leading industry players (including my company Experian), asking for information about how they collect, enhance and sell consumer data. Gaining consent, securing consumer data and being transparent about business practices are of course the keys to building consumer and government trust. As Big Data gets hotter, the greater will be government and regulatory scrutiny, even though government and the public sector are some of biggest proponents of Big Data in a post 9-11 world. This month the US Federal Trade Commission issued its biggest ever civil penalty of $22.5 million to Google for misrepresenting how Apple Safari users were having their Internet activity tracked. (Incidentally, Google earns this much in five hours.) Locally, ASEAN has become one of the most active regions in the world from a privacy regulatory perspective. And an alleged violation of local consumer privacy law in China was the downfall of Dun & Bradstreet's Shanghai Roadway subsidiary earlier this year.
Science fiction authors like William Gibson and Neal Stephenson saw the Big Data world coming in the early 1990s. In their respective novels Neuromancer and Snow Crash, the coolest and most valued professionals were the leather-clad data jockeys who could interpret geospatial visualisations of huge databases (think Google Earth meets Second Life, or Tom Cruise working the data screens in Minority Report), and in real-time pluck out million-dollar insights. We’re entering this era now; but whilst there is an industry forming around Big Data, the reality is that few people know what to do with it, at least from a marketing perspective. Big Data promises to give us personalised medicine as a result of the genomics initiatives coming out of Human Genome Project. The failure of US intelligence agencies to predict 9-11 has driven a push for Big-Data competency in national security. But few marketing managers and analysts have the skills to ask the right questions, let alone generate actionable insights. In 20 years time, every university will have a dedicated Data Analytics department. But in the coming years, the talent shortage will be the marketing data scientist. It might finally be time to take my ‘Data is Cool’ T-shirt out of the wardrobe.
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