Making the business case
Consumer products company Procter & Gamble makes extensive use of analytics to project future trends, but it wasn't always that way, says Guy Peri, director of business intelligence for P&G's Global Business Services organization. "This used to be a rearview-mirror-looking company," he says. "Now we're using advanced analytics to be more forward-looking and to manage by exception." Than means separating out the anomalies to identify and project genuine trends.
P&G uses predictive analytics for everything from projecting the growth of markets and market shares to predicting when manufacturing equipment will fail, and it uses visualization to help executives see which events are normal business variations and which require intervention.
The place to start is with a clear understanding of the business proposition, and that's a collaborative process. "Be clear on what the question is and what action should be taken" when the results come back, Peri says.
It's also important to keep the scope focused. Mission creep can destroy your credibility in a hurry, Peri says. Early on, P&G developed a model to project future market shares for regional business leaders in a line of business he declined to identify. It was successful until the company tried to use the same model to help other business leaders.
The other leaders required a more granular level of detail, but Peri's group tried to make do with the same model. "The model became unreliable, and that undermined the credibility of the original analysis," which had been spot-on, he says.
New users need to take several steps to get started with predictive analytics, says Peri. They should hire a trained analyst who knows how to develop a model and apply it to a business problem, find the right data to feed the models, win the support of both a business decision-maker and an executive sponsor in the business who are committed to championing the effort -- and take action on the results.
"Notice I didn't mention tools," Peri says. "Resist the temptation to buy a million-dollar piece of software that will solve all of your problems. There isn't one." And, he adds, you don't need to make that kind of investment for your first couple of projects. Instead, train staffers in advanced spreadsheet modeling.
"All of this can be done with Excel," says Peri. Only when you're ready to scale up do you need bigger, platform-level types of tools, he says.
Keeping users close
Bryan Jones started on a shoestring budget -- but that's not why his first effort at predictive analytics failed. Jones, director of countermeasures and performance evaluations in the Office of the Inspector General at the U.S. Postal Service, wanted to help investigators determine which healthcare claims were most likely to be fraudulent.
After eight months, he had a working model, but the independent analytics group working on the project wasn't fully engaged with the department that would be using the tool. As a result, the raw spreadsheet output was largely ignored by investigators.
Fortunately, Jones' group had the support of the inspector general. "You're dead in the water if you don't have support from the top," he says.
The second time around, Jones hired a consultant to help with modeling and data prep, and embedded an analyst within the group that would be using the results.
And they made those results more "real" to users. For an investigation of contract fraud, for example, his team placed the results in a Web-based interactive heat map that showed each contract as a circle, with larger circles representing the biggest costs and red circles being the highest risks for fraud (see map, at left).