Investigators could click on the circles to see the details of the contracts and related contracts that were at risk. "That's when people started to notice that we really had something that could help them," says Jones.
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," he 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," Jones says.
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, a principal at Elder Research, a consultancy that worked with Jones and his team. "Over 16 years, we have solved over 90 percent of the technical problems we've been asked to help with, but only 65 percent of the solutions have gone on to be implemented."
The problem, generally, is that the people that 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.
Persuading 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 of the Institute for Operations Research and the Management Sciences (Informs), a professional society for business analytics. "As you get more involved with analytics, it becomes counterintuitive. 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 that were designed to help calculate the probability that customers would buy this quarter, next quarter 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."
Ultimately, predictive analytics is forcing a showdown between data-driven and intuition-based decision-making, says Eric Siegel, president of Prediction Impact, an analytics training firm and conference organizer. "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 blocks. 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 an entirely different model with new data might find the second 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. "Software vendors skip that point," he says, noting that "all of the data in a 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."