How good has information technology gotten at predicting the future?
For years, companies have mined historical data in order to better craft future plans. By and large, those plans were based on the quality of managers' gut intuition. But as the complexity of markets grows, managers are seeking out new tools to help make critical decisions.
Consider the efforts of Center Parcs Europe to stimulate traffic to its 16 short-stay vacation villages in the Netherlands, Belgium, Germany and France. Center Parc's primary marketing effort was always a bit like Popeye's nemesis, Bluto: bulky and bruising, but lacking in the wiliness and cunning of his spinach-eating competitor. Twice each year, the company would blanket the continent with 5 million brochures. The strategy worked, but the collateral damage was becoming unacceptably high. "We were hurt badly by mailing and printing costs," says Richard Verhoeff, technology and e-business director at Center Parcs Europe N.V., a subsidiary of Pierre & Vacances Group.
Center Parcs Europe attracts more than 3 million visitors a year and generates revenues of more than $600 million; its 10,000 or so bungalows enjoy an average occupancy rate of about 90 percent. But Verhoeff believed that a more targeted campaign could raise occupancy rates even higher, while reducing marketing costs.
The plan was to shift away from the bulk mailings to many leaner campaigns. To be effective, however, the company had to segment customers so they received the right brochures at the right time. Verhoeff points out, for example, that customers are loyal to their home countries, and sensitive about driving long distances. Families prefer parks with lots of activities for kids, while older people seek out quieter surroundings. And mailings must be timed to coincide with the period when people typically make vacation plansnot too early and not too late.
These and a dozen or so other criteria were not difficult. "This isn't brain surgery. We knew what drives customers to register for one of our parks," Verhoeff points out. The problem was more fundamental: He couldn't find a way to take all the criteria into account and come up with a rating that would indicate which brochures to send to which customers, and when.
To solve his problem, Verhoeff turned to PredictiveMarketing, a predictive-analytics product from Chicago-based SPSS Inc. The application uses data from Center Parcs's customer database to rank prospects by the likelihood that they will respond to a particular campaign. Now, when the company wants to mail a brochure about a facility, the predictive-analytics application produces a list of prospects ranked by how likely they are to respond.
The results have been dramatic. In Germany alone, the number of Center Parcs mailings dropped from 2 million to 450,000, while occupancy increased by 10 percent.
Like other forms of business intelligence such as data warehousing, online analytical processing (OLAP), data mining and CRM, predictive analytics looks at historical data. But unlike traditional business intelligence, which involves the questioning of past trends and then using your gut to determine future actions, predictive analytics adds an oracular element. By applying statistical algorithms, predictive analytics finds patterns that can be used to predict future actions. These predictions, often expressed in the form of a ranking or a score, can foretell consumer behavior, market trends, the likelihood that a customer will make a repeat purchase, the effectiveness of products or services, and virtually any other factor that can be statistically analyzed.
Are you having problems making successful predictions?
What business intelligence tools are you currently using?
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