The data analysis tools he used explained where his money had gone the year before, but offered no explanation for why costs had risen, and no clear indication what they would be in the future-let alone what he could do about it. As a practicing physician who sees patients in one of Pitney Bowes’ onsite clinics and a passionate advocate for employee health, Mahoney wanted to be sure that when he cut costs, he maximized patient benefits.

What Mahoney needed was a crystal ball. He found the next best thing in the form of predictive modeling: technology that employs either rules-based algorithms or artificial intelligence to predict (in this case) future health-care expenses.

Backed by a health-care savvy CEO, Mahoney and his HR counterpart, David Hom, set aside their pet theories about health-care cost drivers, resisted the temptation of scope creep and convinced skeptical colleagues to trust the models’ predictions. Then they implemented programs based on those predictions that improved care and lowered costs. In an era when health-care costs are swelling 10 percent to 15 percent per year, predictive modeling has helped Pitney Bowes slash the median cost of care for diabetic and asthmatic employees by as much as 15 percent since 2001. And it forecast a dramatic cost increase in one region early enough to give Mahoney and Hom a chance to counteract it.

Now, Pitney Bowes, famous for inventing the postage meter back in 1920, is pioneering the use of predictive modeling in a way that may garner competitive advantage. Hom, who is vice president for employment brand total rewards, wants to pinpoint the ideal combination of benefits that motivate employees to perform better at work-ultimately increasing profits for the $4.6 billion integrated mail services and document management company. Predictive modeling will have the biggest influence on defining the benefits package and the engaged workforce, he claims.

Cynthia Burghard, a Gartner research director specializing in health care, says Pitney Bowes is innovative in its use of predictive modeling because it has developed programs to address the issues its models uncovered. Even among health insurers who use predictive modeling to forecast costs, she says, "There’s a lot of-so now what do I do?"