Hiding in Plain View: How to Gain Actionable Insights from Big Data Analytics

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Eric Green
By Eric Green
Jun 23 2014

When it comes to leveraging actionable insights from big data analytics, vendors and retailers should take a page from one of the world’s most creative and successful tech entrepreneurs, Oren Etzioni. As Etzioni has shown time and again, the best insights are right in front of you. 

Learning From the Best

When most people hear the term big data, they think of companies like Google or IBM. But when it comes to translating data into value, perhaps the biggest success story belongs to one astonishingly prolific entrepreneur with a knack for solving problems.

When Oren Etzioni sold Decide.com to eBay last September, it was his sixth successful exit as co-founder and CTO of a profitable tech startup. How does one person thrive in so many markets? He lets insights flow from real information, rather than forcing them to fit his ideas.

Etzioni’s genius for useful and innovative software applications reaches back to the earliest days of the Internet. He created one of the first search engines, called MetaCrawler (purchased by Infospace), in 1994… four years before Google. The next year, he created the first shopping comparison tool, which was snapped up by Excite, and then shortly after that he invented a text summary application called ClearForest, which was eventually bought by Reuters.

But the discovery that would ultimately open the door to big data analytics came to Etzioni during a moment of irritation on an airplane in 2003. He’d purchased his ticket several months in advance, assuming like most people that he would save money. But when he checked his theory with his seatmate, he discovered that the man had only bought his ticket that week, and paid much less!

If you’re like Etzioni and you’re in the habit of making the online world a faster, smarter, and easier place for everybody, this is the kind of problem you want to solve. Most people would approach it head-on and try to understand why the airplane priced its tickets so unpredictably.

But Etzioni wasn’t interested in why the prices didn’t fit his theory, or which theory would accurately explain the airline’s rationale; he just wanted to know when the prices went down. In other words, he needed the big data analytics.

Etzioni created a program that scraped travel sites and tracked ticket prices, and before long, patterns started to emerge. He didn’t need to understand the factors that were creating those patterns — the reasons for the price fluctuations were irrelevant to the success of the application as a predictive tool. He just wanted to know when tickets were cheapest, and that’s exactly what the data told him. The resulting website, Farecast.com, sold to Microsoft $110 million in 2008.

Let the Data Speak for Itself

Etzioni knew how to capture the value of the data he gleaned, but he certainly didn’t corner the market on innovation. Once companies recognized the opportunities for evidence-based customer insight to be found in big data analytics, they came rushing through the door that Etzioni opened.

One of the clearest and most obvious applications for big data is marketing. Some of the world’s most successful retailers are also the most efficient at using big data to encounter their customers directly — wherever they are, and just as they are:

  • Amazon extends unique content and flexible pricing to its customers based on big data analytics.
  • Target gives each customer a track-able identity that allows the company to focus its marketing efforts on those most likely to be interested.
  • WalMart’s Shoppycat tool uses Facebook to recommend purchases based on the interests of the customer’s social network.

In all of these cases, the company doesn’t need to understand why customers buy what they buy or do what they do. Once the data streams are flowing smoothly, consumers just do whatever it is they naturally do, and if the analytics are working properly, vendors and retailers are going to respond to that information in real time and continually make the experience easier and more natural.

Predictions about shopper behavior come after the fact, not before.

The seemingly incomprehensible pricing patterns of an airline company are a lot like the purchasing patterns of consumers. You could spend a long time trying to replicate the calculations an airline makes in order to fill seats, just as you could try to enter the mindset of a shopper who’s stepping out to run some errands on a Tuesday. Or you could just look at what they actually do.

As Etzioni’s many successes have made abundantly clear, good big data analytics beats theories every time.

To get a clear perspective on what your customers are doing, bring your retailer data into Askuity for a free trial.

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