Putting predictive analytics to work

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

By , Computerworld |  Business Intelligence, Analytics

The Orlando Magic's analytics team spent 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 NBA franchise. While his team was in fact working on predictive analytics well before that, Perez added, their tools weren't powerful enough to give them insights they needed, and the group needed to scale up its efforts. So Perez bought in new, more powerful software from SAS and began climbing the learning curve.

Today the established practice is not only helping optimize ticket sales but is also providing tools to help the coaching staff predict the best lineups for each basketball game, and which potential players 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 took that information and adjusted prices to maximize attendance -- and profits. "This 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.

Nine steps to predictive success

Follow these best practices to ensure a successful foray into predictive analytics.

Sources: Guy Peri, P&G; George Roumeliotis, Intuit; Dean Abbott, Abbott Analytics; Eric Siegel, Prediction Impact; Jon Elder, Elder Research; Anne Robinson, The Institute for Operations Research and the Management Sciences.

Now those models are run and prices fine-tuned every day. Ask him about how the models are used to predict the best team match-ups and the game strategy, 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 tackling the learning curve with 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 telecommunications, financial services and retail, says Gareth Herschel, an analyst at Gartner Inc. "But overall it's still a relatively small percentage of organizations that use it -- maybe 5%."


Originally published on Computerworld |  Click here to read the original story.
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