Bank Financial Corp. Gets a Lesson in Predictive Analytics Predictive analytics, a relatively new form of data analysis, is becoming increasingly popular with many businesses, Predictive analytics enables companies to derive new insight or new information from existing information. It takes data mining to the next level by applying artificial intelligence techniques, such as neural networks, to customer data to predict what your customer may do in the future. This new technology has developed as a result of increasing processor power, advances in Al technology, increased amounts of customer data to draw from, and heightened demand for valuable information to guide a company's future By applying artificial intelligence to data mining, companies are able to predict, for example, which of their customers are most likely to leave within the next month. Imagine the value of such information! Once provided with a list of hundreds or even thousands of customers that you are likely to lose, you could take action to keep them. Predictive analytics can lead to a high return on investment if managed intelligently Bank Financial Corp. in Chicago began using predictive analytics to develop models so that the bank can, for example, more accurately target promotions to customers and new prospects. The software it uses employs neural networks and regression routines to create these models. Regression routines are perhaps the most widely used statistics technique to estimate relationships between independent variables and dependent variables to help understand and explain relationships and predict outcomes. William Connerty. assistant vice president of market research for Bank Financial anticipates that the software will reduce the time it takes the bank to develop models by 50 to 75 percent "The biggest obstacle is getting transaction data and dealing with disparate data sources," Conherty says Neural nets require large amounts of data for maximum accuracy. Unfortunately, Bank Financial works with data that originates in several separate bank systems in a variety of formats. Much systems integration and interface work needs to be done before the bank will see the full fruits of its predictive analytics tools. Connerty hopes the effort will be worthwhile. "We need to increase our efficiency, our ability to deliver actionable information to decision makers," he says. "I'm under a lot of pressure to deliver." Some software tools have been developed to assist businesses in gathering and integrating data for predictive analytics. KXEN Inc., a software company in San Francisco, claims that its analytic Framewok product can greatly reduce the time it takes to define. develop, and run a model. Seymour Douglas, director of CRM and database marketing at Cox Communications in Atlanta, claims that it cuts data preparation time in half. Having clean, uniformly formatted data, however, is only the first obstacle to realizing success with predictive analytics. Robert Berry, president and CEO of Central Michigan University Research Corp. in Mount Pleasant, thinks that lack of organization is the fundamental stumbling block that keep businesses from capitalizing an predictive analytics. Berry says predictive modeling should involve collaboration among people who have IT, analytical, and business expertise. "You have to build a business-intelligence team," he says. "But companies are struggling with common issues like who owns it, who manages it, and so on. do you pull the business skills, the IT skills, and the analytical skills across corporate silos and create this team? It's not easy."
Predictive analytics is a valuable technique that no doubt will earn increasing attention of businesses of all sizes. But the current analytics systems require a sizeable, consistently formatted, integrated data warehouse - plus a staff of professional who understand its potential. Discussion Questions 1. Why predictive analytics is becoming increasingly popular with many businesses? 2. How can predictive analytics increase Bank Financial Corp's competitive advantage? 3. What is the biggest obstacle that Bank Financial Corp. is facing in operating their predictive analytics? Critical Thinking Questions 4. What types of data do you think must be analyzed to determine what customers are most likely to leave the bank during the coming month? 5. Why predictive modeling should involve collaboration among people with different expertise?
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