Many businesses still miss the valuable insights hiding in data on consumer behavior, Teradata exec says.
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The holiday travel and shopping season is upon us, creating a flurry of consumer activity. Armed with smartphones, tablets, and PCs, harried shoppers are on a not-so-jolly quest for great deals -- not only in gifts, but also in airline fares, hotel rooms, and other seasonal essentials. Not surprisingly, they're generating a lot of data too, but businesses aren't necessarily using this information to their advantage.
Companies can do a better job of extracting useful information from both consumer devices and sensors, said Tim Simmons, VP of global industry marketing at Teradata. In fact, the information is ripe for taking, altogether some effort and expense is involved.
"Consumers do things fairly consistently. We've got to uncover it," Simmons said in a phone interview with InformationWeek. "Within the big data space, you have to start with a hypothesis. You think there might be a relationship, but you can't prove it without the data helping you prove it."
For instance, online shoppers often leave a digital "trail of breadcrumbs" that businesses should follow to get a clearer understanding of their customers' behavior. To track this data, Teradata uses its Aster big data platform to translate user-generated comments from social networks and online forums into a usable "point score" for its corporate clients.
"What we're trying to do is help the customer gather this disparate, rather strange-looking data, and progressively translate into something that's consumable," said Simmons.
This is achieved, in part, via two big data applications: attribution and end path analysis. The former essentially monitors all of the possible interaction a customer could have, while the latter examines the entire path to purchase in order to help analysts determine "which bits mattered the most," Simmons said.
For instance, airlines use big data analytics to study consumer behavior, or "different points of influence," to see which online activities have the most impact. A potential customer might browse a carrier's website to explore flights to a specific destination, and arrange the options by price, number of stops, or other factors.
"You might stop browsing there, and you might not buy anything," said Simmons. But how might the airline enhance the customer's experience to improve the odds of a sale on the carrier's site?
"They want to bring the booking to their site, rather than to a travel agent, because they lose margin in what is not a very high margin business in the first place," Simmons added.
In the retail world, customer analysis can deliver benefits as well. Simmons provided a brief case study of one Teradata customer: Cabela's, a Nebraska-based outdoor merchandise retailing chain, which studied consumer behavior to determine if the first item placed in the online shopper's basket held any specific significance.
It turned out the answer was yes: First-in-basket items tended to drive purchases of ancillary products, the data showed.
"Somebody bought a tent. Well, chances are you need other things than a tent. You need chairs, a cooking device, a bunch of sleeping bags," and other camping-related items, said Simmons.
These data-driven insights helped Cabela's determine which of its 250,000 retail items would best drive purchases of other merchandise. It then built its promotional efforts around these so-called "driver items."
In addition to consumer-generated info, sensors also provide a vast, often untapped, source of big data. Enterprises face the daunting challenge of finding ways to extract value from those bits, rather than simply unload them.
Simmons recalled being at a tech industry conference recently, where an airline representative was telling the audience about the staggeringly large amount of data generated by an airplane in flight. It was mind-boggling," said Simmons. "It was like hundreds of terabytes of data before the aircraft landed."
Airline manufacturers and carriers, however, may dump that data because they're unaware of its potential value. "Nobody from a business analyst perspective has [said to] them, 'Hey, you've got this rich data, why don't you use it for maintenance? Why don't you unlock it this way?'" said Simmons. "It's a pretty good illustration of the kind of data out there that gets dumped -- it doesn't have to be dumped, obviously."
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