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 percent 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."
Ultimately, however, predictive analytics is forcing a showdown between data-driven and intuition-based decision making, says Eric Siegel, president of the analytics training firm and conference organizer Prediction Impact Inc. "That's the big ideological battle. It's a religious debate."
Data: Getting to good enough
On the technology side, both building the model and preparing the data can be stumbling points. Predictive analytics is an art as well as a science, and it takes time and effort to build that first model and get the data right, says Abbott. "But once you build the first one, the next one is much less expensive to model" -- assuming you're using the same data. Analysts building a new, entirely different model that uses different data might find that project just as time consuming as the first. Nonetheless, he says, "The more experience one gains, the faster the process becomes."
Data preparation issues can quickly derail a project, says Siegel. "The software vendors skip that point because all of the data in the demo has already been put into the correct format. They don't get into it because it's the biggest obstacle on the technical side of project execution -- and it can't be automated. It's a programming job."
When the Magic's Perez got started in 2010 he grossly miscalculated the time it would take to prepare the data. "We didn't set the right expectations. All of us were thinking that it would be easier than it was," he says. Pulling together data from Ticketmaster, concession vendors and other business partners into a data warehouse took much longer than anticipated. "We went almost the entire season without a fully functional data warehouse. The biggest thing we learned was that this really requires patience," he says.