The Four Traps of Predictive Analytics
Management consultant James Taylor explains how to avoid common mistakes of predictive analytics.
Competing With Data & Analytics
If the name James Taylor makes you think of “Fire and Rain,” Carly Simon and adult contemporary radio, you’re probably not into business analytics. On the other hand, if you are into business analytics, or more specifically predictive analytics, the name means something very, very different. The other James Taylor is British and the CEO of Decision Management Solutions, an analytics and management consultancy in Palo Alto, California — and, to the right audience, he’s a rock star.
Taylor was in Boston recently performing his “greatest flops” — a countdown of the things companies fail at when starting out to do predictive analytics (drumroll, please):
The First Trap: Magical Thinking
Taylor said companies see analytics as a kind of magic — plug in some data and reap profit windfalls. The truth is, companies must understand what they want before they go analyzing things helter skelter, especially when it comes to making predictions. He points out that there are really only four things businesses can use analytics to predict: risk, opportunity, fraud and demand.
Companies also can’t just build a model once and apply it everywhere. Each of the four areas will almost certainly need different models, and companies may find they need a different model for every question they ask, Taylor said.
The Second Trap: Starting at the Top
Organizations often try to start using predictive analytics at the top of the organization to gain buy-in, Taylor said. But top executives make the kind of decisions that don’t lend themselves to analytics, he argues. Predictive analytics works best on decisions that get made repeatedly, but top executives most often make strategic decisions, which, Taylor said, tend to be one-time situations. Other top-level decisions are often tactical, which are also relatively complex and hard to formalize.
But operational decisions, such as those in which companies choose a supplier or determine whether to extend credit, lend themselves well to predictive analytics. So companies need to recognize that predictive analytics works best for prompting decisions about operations, rather than initiating their use at the executive level.
Companies also need to frame their predictive analytics around actions. “Don’t look at how good a customer is. Look at, what action should I offer to a customer? Should I change suppliers?” Taylor said.