Digital Exhaust and the Next Frontier in Social Data Analytics.
By Doug Palmer, Vikram Mahidhar and Dan Elbert Infographics by Ilovedust
Saturday, 17th September 2011
In 2010, Google's Eric Schmidt said that 'I don't believe society understands what happens when everything is available, knowable and recorded by everyone all the time'.

He was referring to the fact that in the digital world, data are everywhere. We create them constantly, often without our knowledge or permission, and with the bytes we leave behind, we leak information about our actions, whereabouts and characteristics.

This revolution in sensemaking—in deriving value from data—is having a profound and disruptive effect on everything from supply chains to corporate strategy. In particular, it is generally forcing executives to rethink how they understand, reach and even influence their customers. Simply put, with so much data available, especially on social networks, the ability to know the people you sell to and to monetize that knowledge has never been greater.

That said, most companies are only beginning to scratch the surface of what's possible with social data. Many are still operating in the pre-social media age, simply trying to make sense of the data they have—rather than the variety of sources that exist. But even those that are best-of-breed have only started to tap into the true potential of this information: developing an intimate and real-time knowledge of customers' relationships and behavior.

"The next big data revolution will be around mobile. If you think about mobile devices as sensors, our phones know more about us than our partners do."

Whole new opportunities are available with these kinds of insights in hand. Banks can evaluate loan applicants based on the credit worthiness of others in their social network, the notion being that people who pay off their loans tend to group together. Telecoms can find their most influential customers—those who might switch providers and take a number of friends with them—and target them for early upgrades or deals. Even human resource departments can benefit, by understanding which applicants are most connected, or even most passionate, in a given field. The data points have typically been limited only by technology's ability to capture and store them, a constraint that is rapidly fading away.

Still, very few companies are able to act on this kind of information, let alone accumulate it. However, as we will explore, what it takes goes beyond just an analytics solution.

Three waves

Customer analytics is hardly news to most executives; organizations have been gradually embracing it over the last 20 years. Amazon.com was an early leader in this space, for instance, pioneering the art of using buyer behavior to personalize its site. For Amazon.com, no click goes unnoticed. Its model of the customer has very little to do with your name, age and address, and everything to do with what the company been able to learn about you by studying your data trails.

But even some organizations that long predate the digital age are masters at analysis. Credit card issuers, for instance, have for decades used data to predict everything from spending patterns to risky behavior. Continental Airlines, which was founded in 1934 and recently merged with United, is another example. For its most profitable customers, the company tracks every kind of "flight disruption" that might lead them to switch carriers. Whenever one of them is delayed, or if the airline loses their bag, cabin crews are automatically alerted the next time they fly.2

These examples highlight what we think of as the first of three waves playing out in the space of big data. In this wave, organizations can tap an incredible amount of information—purchasing histories, demographics, measures of engagement—that make customer targeting more feasible. They can use everything from clicks and flights to credit card transactions, made on and off the Web. The key feature of this wave, however, is that the data inside it are not social – they are drawn from closed or proprietary mechanisms instead of what is often stored openly on the Internet. More importantly, because these data aren't social, they lack a broader context about the relationships and behaviors of the people creating them.

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