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 National Basketball Association 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 brought 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.
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 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 with established predictive analytics practices that are now moving outside of traditional niches such as marketing and risk management. They're predicting website click-through rates and overall behavior, and helping HR anticipate which employees are likely to churn. Another area is help desk call routing, where models can be used to determine which agent is likely to do the best job of answering a given customer 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 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 that business decision-makers can understand and, above all, make sure the business is willing and able to act based on those predictions.