Putting predictive analytics to work

Contrary to popular opinion, you don't need a huge budget to get started.

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Now departments come to him with requests, and they assign someone from their own group to work with Jones' team. Once the output is ready, the person returns to their group and teaches others how to use it. Jones doesn't have hard return on numbers yet but will be reporting on increased arrests, dollars recovered and other metrics.

Bryan Jones
Bryan Jones, who heads up analytics at the Inspector General's office at the U.S. Postal Service, says, "You're dead in the water if you don't have that support from the top."

There were also unanticipated benefits. "Now people understand that we can be proactive. That was a return we didn't have on our list," he says, and new investigators that don't have 25 years of experience and connections can get up to speed quickly. "These tools can level the playing field," Jones says.

Jones' advice: Get close to your customer, get professional help building your first model and present the results in a compelling, easy-to-understand way. "We didn't have the right people or expertise to begin with. We didn't know what we didn't know," Jones says, so he turned to an outside data-mining expert to help with the models. "That relationship helped us understand why we failed and kept us from making the same mistakes again."

Overcoming business skepticism

While hiring a consultant can help with some of the technical details, that's only part of the challenge, says John Elder, principal with Elder Research, a consultancy that worked with Jones' team. "Over 16 years we have solved over 90% of the technical problems we've been asked to help with, but only 65% of the solutions have gone on to be implemented."

The problem, generally, is that the people the model is intended to help don't use it. "We technical people have to do a better job making the business case for the model and showing the payoff," Elder says.

Convincing decision-makers to use the results can be as difficult as getting them to go along with the project in the first place, because the predictions may be the exact opposite of what their business intuition tells them, says Anne Robinson, president-elect for the Institute for Operations Research and the Management Sciences (Informs), the professional society for business analytics. "As you get more involved with analytics, it becomes counter-intuitive. But it's those deviations from what you're doing that bring the rewards, because when the results are intuitive you find that most people are already doing them."

Several years ago, Cisco Systems created "propensity to buy" models -- to figure the probability that customers will buy this quarter or next, or never. The models cover every product in every sales territory. The salespeople felt they already knew what some of the people identified by the model were going to buy, so Cisco excluded those sales when calculating the return on its effort. "The first year we did it, we generated $1 billion in sales uplift," says Theresa Kushner, Cisco's senior director of customer and influencer intelligence. "We had an experience to line up against what they thought they believed."

Peri learned the hard way that 80% of a predictive analytics project is cultural. "I came in naively thinking that if I had a model that does all of these great things, it will just work. But you have to be aware of how people make decisions and how it will transform that process."

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