Summary:

Data collected can be useful to retailers in many ways, but not necessarily in the ways that one might expect, as discussed at Structure:Data. For example, did you know that there’s a correlation between the music you listen to and the things you might buy?

STS Prasad of @WalmartLabs, Jed Kolko of Trulia, George John of Rocket Fuel, and Phil Hendrix of GigaOM Pro at Structure:Data 2012

Big data is being used everywhere to create efficiencies and to maximize sales. But how it’s applied can make all the difference. Thursday at GigaOM’s Structure:Data conference in New York, executives from Rocket Fuel, Trulia and Walmart Labs discussed what their companies and the companies they work with can learn from leveraging big data, and how they use that data to increase sales.

STS Prasad of @WalmartLabs, Jed Kolko of Trulia, George John of Rocket Fuel, and Phil Hendrix of GigaOM Pro at Structure:Data 2012

(c) 2012 Pinar Ozger. pinar@pinarozger.com

The key takeaway is that data collected can be useful in many ways, but not necessarily in the ways that one might expect. Rocket Fuel CEO George John gave the example of how using anonymous user data can help better target advertising for retailers, so that someone looking for a car on Edmunds.comwould be served an appropriate car ad on the next site they showed up on, rather than getting a non-targeted ad.

Jed Kolko, Chief Economist and Head of Analytics at real estate startup Trulia, said that the data it collects can be used to help real estate agents improve their listings, and can also be used to tell consumers when the right times are to list their home or buy a new one. (Not surprisingly, Kolko says consumers typically wait too long to sell, and buy too soon.)

In the case of Walmart Labs, using data has helped the retailer better target the needs of local consumers, by helping them understanding what their needs are and identifying purchase intent. A wealth of social data now also allows Walmart to provide more opportunities to interact with customers, for instance, by suggesting gifts for Facebook friends when users link up their social graph.

Those seem like pretty straightforward examples, but there’s deeper insight to be gained if businesses look closely enough. John, for example, mentioned how having a huge data set allows Rocket Fuel and its partners to draw inferences about consumer interests based upon non-intuitive data, like what kind of music they listen to. And Kolko mentioned that analyzing inbound and outbound requests for housing in different markets can be used to predict supply and demand all over.

Not everyone was on the same page about how that data could be applied, however. When the New York Times reported that Target could predict whether or not its customers were pregnant based on purchase data, John said Rocket Fuel customers had fairly polarizing reactions to the power of big data:

“Some of our customers called us and said, ‘Oh that’s creepy, you don’t do that, do you?,’” John said, “While others called and said ‘That’s awesome, can you do that?’”

Watch the livestream of Structure:Data here.

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