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The Next Frontier in Social Data Analytics

Sep 22, 2011   |   5 min read

Knowledge Center  ❯   Blog

The following is a summary of a piece, Making Sense of Social Data, which was recently published in Deloitte’s Review.

In the world of technology and innovation today, there is no doubt one topic that continues to be a hot concept- the rise of data. In particular, it’s forcing businesses 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’re selling to and to monetize that knowledge has never been greater.

In 2010, Google’s Eric Schmidt response about data was:

“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 is 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.

Many companies are still catching up and are simply trying to make sense of the data they have, and 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.

We’ve already acknowledged Amazon as an early adopter in this space, pioneering the art of using buyer behavior to personalize its site. Its model of the customer has very little to do with your name, age and address, and everything to do with what the company being 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. (wonder why you get so many offers from them?!) For Continental Airlines (now merged with United) 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.

These examples are a glimpse into what is seen as the first of three waves playing out in the space of big data. In this wave, organizations can tap into 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 is not social – it is drawn from closed or proprietary mechanisms instead of what is often stored openly on the Internet. More importantly, because the data isn’t social, it lacks a broader context about the relationships and behaviors of the people creating them.

The second wave adds a bit more color with the rise of social media. Twitter, Facebook and LinkedIn are familiar platforms we all know in this wave. Broadly speaking, their data is valuable because they tap into the voice of the consumer, telling companies things like who they connect with and what they like. For companies looking to target ads or customize their products, this information can be incredibly powerful. It can also drive new forms of digital listening. For example, many organizations are starting to build social media “command centers” to take in social data commentary and react to them in real-time.

However, the voice of the customer is by and large explicit. It is all tell, no show. In fact, this kind of implicit analysis of social data is only beginning to emerge – and constitutes what we see as the third and most powerful wave of data analytics. In this wave, the focus shifts from the voice of the customer to the individual’s behavior. But more importantly, it looks at that behavior in the context of who is around them and how they interact. Many of the most interesting examples of this third wave are coming from the edge: startups in Silicon Valley, labs at the Massachusetts Institute of Technology (MIT). These players are constantly evolving and exploring powerful new ways to help link data and identity – and in the process, are redefining how companies can know and even influence who they sell to.

An important point about the third wave, however, is that it is hardly limited to data from social networks. In fact, some of the most compelling opportunities in social analytics lie far outside our relationships on Facebook. Instead, companies will need to figure out not just how to extract information about their customers, but how to structure their organizations to act on it in real time; how to customize offerings on the fly; how to respond quickly to changes in consumer behavior; how to intelligently manage privacy and risk; how to segment, price and market effectively, as new information comes in. In short, the way Netflix operates-as a dynamic product that different for everyone who uses it-may foreshadow how leading firms behave down the line.

This doesn’t mean companies should marginalize the first wave or skip over the second to get to the third. Far from it. Instead, we think each wave reinforces and is made more powerful by the preceding. Companies can achieve better results by striking the right balance among the three. And like all things data, even this will need a tailored fit. Easy as 1, 2, 3?!

In this sense, perhaps the most important conclusion from this and the other examples in the whole article is that the universe of data we can draw upon to understand our customers is almost limitless. Social data needn’t be from social networks, and social analytics is much bigger than just listening to buzz on Twitter. To take the next step, companies will likely have to develop a deep and real-time understanding of their customers through a variety of digital trails.

How can you start to develop these insights within your own organization? How can you finely tune your offerings or ads based on the data your customers create? Download the full article and let us know what you think!

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