October 08, 2012, 11:08 AM — The Orlando Magic's analytics team spent nearly two years honing its skills on the business side.
"Eighteen to 20 months ago, we knew virtually nothing about predictive analytics," says Anthony Perez, director of business strategy for the National Basketball Association franchise. While his team members were in fact working on predictive analytics well before that, Perez adds, their tools weren't powerful enough to give them the insights they needed, and the group had to scale up its efforts. So Perez brought in new, more powerful software from SAS and began climbing the learning curve.
Today, the established analytics practice is helps optimize ticket sales and provides the coaching staff with tools that help predict the best lineups for each game and identify players that offer the best value for the money.
Perez's team began by using analytic models to predict which games would oversell and which would undersell. The box office then used that information to adjust prices to maximize attendance -- and profits. "This [past] season we had the largest ticket revenue in the history of our franchise, and we played only 34 games of the 45-game season due to the lockout," he says.
Now those models are run and prices are fine-tuned every day. Ask how the models are used to predict the best player matchups and game strategies, however, and Perez is less forthcoming. "That's the black box nobody talks about," he says.
Although it's still fairly early going, other organizations are beginning to embrace predictive analytics, the forward-looking data-mining discipline that combines algorithmic models with historical data to answer questions such as how likely a given customer will be to renew a season ticket. The models assign probabilities to each person. Armed with that data, the business can prepare to take action. Additional analysis can then be applied to predict how successful different courses of action will be.
The use of predictive analytics is common in industries such as telecommunications, financial services and retail, says Gareth Herschel, an analyst at Gartner. "But overall, it's still a relatively small percentage of organizations that use it -- maybe 5%."
Nonetheless, interest is high in organizations that are still focused on historical "descriptive analytics" and in businesses that are expanding the focus of established predictive analytics practices beyond traditional niches such as marketing and risk management. Analytics models are being used to predict website click-through rates and help HR anticipate which employees are likely to leave the company. They're also used to optimize help desk call routing, by determining which agent is likely to do the best job of answering a given user question.
"There's more interest because there's more data," says Dean Abbott, president of consultancy Abbott Analytics. "The buzz is about momentum. People are saying, 'This is something I need to do.' "
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 veterans, to share their experiences.
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.