ASE: Applebee’s, Travelocity, and Others: Data Mining…

CASE: Applebee’s, Travelocity, and Others: Data Mining for Business Decisions. Randall Parman, database architect at restaurant chain Applebee’s International and head of Teradata’s user the group, opened Teradata’s annual user conference in Las Vegas with a warning to those who aren’t making the best use of their data. “Data are like gold,” Parman noted. “If you don’t use the gold, you will have someone else who will come along and take the opportunity,” speaking to a room packed with almost 3,900 attendees. Parman drew an analogy to the story about Isaac Newton’s discovery of gravity after he was hit on the head with an apple. “What if Newton had just eaten the apple?” he asked. “What if we failed to use the technology available, or failed to use these insights to take action?” Applebee’s, which has 1,900 casual dining restaurants worldwide and grossed $1.34 billion in revenue last year, has a four-node, 4-terabyte data warehouse system. Although the company has a staff of only three database administrators working with the system, “we have leveraged our information to gain insight into the business,” he said. “Some of those insights were unexpected, coming out of the blue while we were looking in a completely different direction.” For example, Applebee’s had been using the data warehouse to analyze the “back-of-house performance” of restaurants, including how long it took employees to prepare food in the kitchens. “Someone had the unanticipated insight to use back-of-house performance to gauge front-of-house performance,” he said. “From looking at the time the order was placed to when it was paid for by credit card and subtracting preparation meal time, we could figure out how long servers were spending time with customers,” Parman added that the information is being used to help the company improve customer experiences. Applebee’s has also advanced beyond basic business decisions based on data—such as replenishing food supplies according to how the much-finished product was sold daily—to developing more sophisticated analyses. His department, for example, came up with a “menu optimization quadrant” that looks at how well items are selling so that the company can make better decisions about not only what to order, but about what products to promote. Meanwhile, technology vendors see untapped potential for businesses to spend money on software and hardware that lets them use data to make more sophisticated business decisions. “Companies who operate with the greatest speed and intelligence will win,” says Teradata CEO Michael Koehler. Like many companies, Travelocity.com has lots of unstructured data contained in e-mails from customers, call center representative notes, and other sources that contain critical nuggets of information about how customers feel about the travel site. To offset the inability of business intelligence tools to search for unstructured data, Travelocity has launched a new project to help it mine almost 600,000 unstructured comments so that it can better monitor and respond to customer service issues. The online travel site has begun to install new text analytics software that will be used to scour some 40,000 verbatim comments from customer satisfaction surveys, 40,000 e-mails from customers, and 500,000 interactions with the call center that result in comments to surface potential customer service issues. “The truth is that it is very laborious and extremely expensive to go through all that verbatim customer feedback to try to extract the information we need to have to make business decisions,” notes Don Hill. Travelocity’s director of customer advocacy. “The text mining capability . . . gives us the ability to go through all that verbatim feedback from customers and extract meaningful information. We get information on the nature of the comments and if the comments are positive or negative.” Travelocity will use text analytics software from Attensity to automatically identify facts, opinions, requests, trends, and trouble spots from the unstructured data. Travelocity will then link that analysis with structured data from its Teradata data warehouse so the company can identify trends. “We get to take unstructured data and put it into structured data so we can track trends over time,” adds Hill. “We can know the frequency of customer comments on issue ‘x’ and if comments on that topic are going up, going down, or staying the same.” Unlike other text analytics technology, which requires manual tagging, sorting, and classifying of terms before analysis of unstructured data, Attensity’s technology has a natural language engine that automatically pulls out important data without a lot of predefining terms, notes Michelle de Haaff, vice president of marketing at the vendor. This allows companies to have an early warning system to tackle issues that need to be addressed, she added. VistaPrint Ltd., an online retailer based in Lexington, Massachusetts, which provides graphic design services and custom-printed products, has boosted its customer conversion rate with Web analytics technology that drills down into the most minute details about the 22,000 transactions it processes daily at 18 Web sites. Like many companies that have invested heavily in online sales, VistaPrint found itself drowning, more than a year ago, in Web log data tracked from its online operations. Analyzing online customer behavior and how a new feature might affect that behavior is important, but the retrieval and analysis of those data were taking hours or even days using an old custom-built application, says Dan Malone, senior manager of business intelligence at VistaPrint. “It wasn’t sustainable, and it wasn’t scalable,” Malone says. “We realized that improving conversion rates by even a few percentage points can have a big impact on the bottom line.” So VistaPrint set out to find a Web analytics package that could test new user interfaces to see whether they could increase conversion rates (the percentage of online visitors who become customers), find out why visitors left the site, and determine the exact point where users were dropping off.

 

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