We are told that we live in an Information Economy. “Business intelligence” is now the life-blood of a competitive enterprise. But, it appears that having current information is no longer good enough. The next wave of using information is to predict the next wave. According to InformationWeek “Businesses Mine Data To Predict What Happens Next”:
Real-time information, once a competitive differentiator that produced more timely and relevant business decisions, is now a commodity. Even midsize companies process transactions as fast as the New York Stock Exchange, while decision makers communicate and collaborate over broadband networks as if they were in the same office. Sheer speed isn’t the advantage it once was.
So what’s next? What’s next is what’s next–the ability to forecast where events are heading, then make informed decisions based on that assessment. Predictive analytics, the scientific name for using a data warehouse as a crystal ball, is where business intelligence is going. It involves running historical data through mathematical algorithms–neural networks, decision trees, Bayesian networks–to identify trends and patterns and predict future outcomes. Will product demand surge? Will a patient relapse? Will a customer take his business elsewhere? Our ability to make such educated guesses is key to improving service, cutting costs, and exploiting new market opportunities.
Much of this is what is now old-fashioned data-mining:
Alumni donations to the University of Utah’s David Eccles School of Business increased 73% last year after the school used predictive analysis software from Kintera to determine which of the 300,000 people in its alumni database were most likely to respond to its annual appeal for donations. “It’s always a question of who do we want to reach given the limited resources we have,” says Erika Marken, development research director at the university.
But some applications do have real predictive capabilities:
Tom Wicinski, managing director of customer marketing analytics at FedEx, will happily take the 65% to 90% accuracy rate he says the package-shipping company’s predictive analysis system is providing. FedEx uses SAS Institute’s Enterprise Miner and other tools to develop models that predict how customers will respond to price changes and new services, which customers are at risk of jumping to a competitor, and how much revenue will be generated by new storefront or drop-box locations. Accuracy, Wicinski says, depends not just on a problem’s complexity and the number of variables, but also on the amount and quality of the supporting data.
FedEx began using predictive analytics for customer prospecting in the 1990s. But the company has broadened its use of the technology, applying it to more complex business problems. Applications, including the customer-at-risk system, are relatively new. “It’s becoming a more mainstream business process,” Wicinski says.
FedEx next will deploy predictive analytics in real-time operational settings such as call centers, he says, helping customer service reps identify at-risk customers and take the necessary steps to make them happy. Today, FedEx call-center agents and other front-line personnel must alert a sales rep when red flags go up–and that process may not be fast enough.
The article notes other, somewhat more controversial applications – like predicting when someone’s health is too risky to fly an airplane or predicting where the next crime wave will occur. While such applications have a large public benefit, they raise potentials for abuse of information and questions of privacy. These are issues we will have to work through as all of public life becomes more data intensive.
But on the company-side, look for more and more usage of these predictive analytics.
Staying one step ahead your rivals has always been the competitive advantage of good information.
Staying one step ahead of your customers’ needs is a superior competitive advantage in the I-Cubed Economy. After all, isn’t that what innovation is all about?