But you have to walk before you can run, and with its data-heavy demands, predictive analytics isn't something to take up lightly, or haphazardly. We asked businesses that are new to the game as well as seasoned professionals to share their experiences. Start small, they say, partner closely with the business to define the problem, continuously test and refine the model, put results in terms business decision makers can understand and, above all, make sure the business is willing and able to act based on those predictions.
Making the business case
Consumer products company Procter & Gamble Co. 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.
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 closeBryan 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 health care 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.