Putting predictive analytics to work

Contrary to popular opinion, you don't need a huge budget to get started.

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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 rear-view-mirror-looking company, but what happened six months ago isn't actionable. Now we're using advanced analytics to be more forward-looking and to manage by exception," he says, which 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. "We focus the business on what really matters," Peri says.

Guy Peri
"We're using advanced analytics to be more forward looking and to manage by exception," says Guy Peri, director of business intelligence for one of Procter & Gamble's business units.

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, he 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 accommodate the needs of other business leaders.

Those requests required a more granular level of detail, but his 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: 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 the 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," he says, and you don't need to make that investment for your first couple of projects. Instead, invest in training staff on advanced spreadsheet modeling.

"All of this can be done with Excel to get started." Only when you are ready to scale up do you need to investigate 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 was a failure. Jones, director of countermeasures and performance evaluations at the Office of Inspector General within the U.S. Post Office, wanted to use predictive analytics 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 mailed to each office was largely ignored by investigators.

Fortunately, Jones' group had the unwavering support of the Inspector General. "You're dead in the water if you don't have that 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.

U.S. Post Office heat map
Heat maps such as this one, used at the U.S. Postal Service, make data much more actionable than raw numbers alone, implementers say.

And they made those results more 'real' to users. For a team investigating contract fraud, for instance, 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 the highest risk for fraud.

Investigators could then click on the circles to drill down on the details of each contract, as well as related contracts, that are at risk. "That's when people started to notice that we really had something that could help them," he says.

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