Structured Plus Unstructured Data Equals Personalized Product Offers
Many banks are focused on using big data to gain a better understanding of their customers' purchasing behaviors. The best way to do that is to combine data from as many sources as possible, both structured and unstructured. NGDATA, in Ghent, Belgium, specializes in doing just that. The company's platform, dubbed Lily, is designed specifically for large enterprises and gathers structured and unstructured data for analysis. This helps clients take data sets that were previously siloed and analyze them together to get a 360-degree view of their customers, NGDATA CEO Luc Burgelman (pictured at right) says. The platform's recommendation engine then uses that analysis to create personalized product and service offerings for the customer.
Lily's recommendation engine is twice as successful at enticing customers to buy products as the next-to-buy algorithms used by companies such as Amazon and Netflix to recommend movies and books, Burgelman reports. And Lily analyzes data in real time, an ability that Burgelman says is key to its success.
"Real time equals timely offers. Without real-time analytics you can never capture the customer when they're at the store or on your website," he explains.
NGDATA released its second version, Lily 2.0, in January. It includes the ability to link to new applications such as QuickView, SAP Business Objects, SAS and Tableau. The company is already looking into adding more prepackaged connections to similar applications for its next release, as well as adding new churn-modeling capabilities to help understand why customers leave their bank, telco provider or utility service. -- J.C.
Katherine Burger is Editorial Director of Bank Systems & Technology and Insurance & Technology, members of UBM TechWeb's InformationWeek Financial Services. She assumed leadership of Bank Systems & Technology in 2003 and of Insurance & Technology in 1991. In addition to ... View Full Bio