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Perhaps the only universal truth about big data analytics is that it is a new field and everyone is still learning. That was true Monday as data scientists, quants and business intelligence managers gathered in San Francisco to swap ideas about what's working.

While retail corporations dominated Monday's schedule, the Predictive Analytics World conference also drew financial executives, marketing managers and others who know all too well that the success of their companies may depend on making better use of the river of data flooding in from social media, transaction records, web surfing, retail outlets and other sources.

Two early presentations came from executives at Sears Holdings and eBay, two of the companies that have led the industry in growing successful online operations. Their presentations were minutes apart, and both stressed the importance of experimentation, but they covered widely divergent areas from different perspectives.

The Sears Approach

In a presentation for Sears Holdings, the 10th largest retailer in the US, Robin Glinton,  director of Sears' data sciences lab in Silicon Valley, said the company is trying hard to ensure all customers from all its brands can buy any item either online or in-store and pay for it in any way they'd like.

"Instead of focusing on bringing  in new customers, we focus on getting our existing customers to buy more from our other brands," he said. His presentation showed how Sears creates 3-D models based on customer segments and testing them against various spending "threshholds" and value propositions to optimize offers that yield the maximum benefit for its brands.

For example, a deep discount may attract many shoppers. But what if you want to entice a particular type of shopper to buy more. What is the minimum they must spend, and what must the value be to them? Through careful  analysis and testings, Sears has found it can generate a 20 percent increase in email open rates with a 15 percent increase in offer redemptions.

Over at eBay

"If you go on eBay  right now, you'll see hundreds, if not thousands, of experiments going on," said Gayatri Patel, director of eBay's data strategies. It's all part of eBay's effort to make more relevant data available to employees at all levels. Using a variety of BI tools like Microstrategy, Tableau, SAS and R, the company provides dashboards for executives and portals for others managers who want to create "personal workspaces" to analyze any of the data in eBay's system.

EBay's goal over the next two to three years, she said, is to increase how quickly the data can be analyzed and used to know more about the customer in real time, near time and post time.

For example, she gave the example of a customer who loses in an eBay auction. The company would like to let that customer know  about similar auctions that are still going on,  but first it must understand if the customer only wants new items, wants a particular brand or only wants used goods in near-perfect condition. That's a big challenge for when selling everything from antique furniture to fashion-forward handbags.

Traditional retailers may be worried about online stores like Amazon, but many are working with eBay to test different geographical markets or customer segments. EBay shares its data with its retail clients to help them evaluate what works best, she said.

Came to Learn

"I want to hear how cutting edge analysis is working,  hear what new vendors have to offer and to network with my peers," said Shyam Gottapati, director of analytics at Visa. While the lessons from Sears or other retailers don't translate directly into  lessons for financial services providers, he noted the retailers are in the forefront of establishing best practices in big data.