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."
P&G once developed a model designed to provide an "early warning" on how each business was going to perform. "It was actually quite accurate, but the warnings were given in such as way that people didn't understand how to take action on them, and so we didn't get the proactive decisions we wanted," he says. Lesson learned: "Analytics is only valuable when you take action on the insight."
People can also feel threatened by analytics. "There's a concern initially that the model is designed to take over decision-making or doesn't respect my business knowledge," Peri says. Users need to understand that the predictive model serves as a decision support tool and how to use the output in their own decision-making processes.
Don't waste time trying to get people to believe in the model, says Cisco's Kushner. Instead, do a test and present the results. In this way you're not countering their knowledge: The science is. "This is math; this is fact; this is statistics. You have an experiment to line up against what they thought they believed."