Putting predictive analytics to work
Contrary to popular opinion, you don't need a huge budget to get started.
Computerworld - 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.
Who owns your analytics group?
"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.
- 15 Non-Certified IT Skills Growing in Demand
- How 19 Tech Titans Target Healthcare
- Twitter Suffering From Growing Pains (and Facebook Comparisons)
- Agile Comes to Data Integration
- Slideshow: 7 security mistakes people make with their mobile device
- iOS vs. Android: Which is more secure?
- 11 sure signs you've been hacked
- The value of smarter oil and gas fields With global energy requirements continuing to rise, the exploration, development and production of new oil and gas resources are shifting to increasingly challenging...
- Smarter Environmental Analytics Solutions: Offshore Oil and Gas Installations Example This IBM Redbooks® Solution Guide describes a solution for implementing smarter environmental monitoring and analytics for oil and gas industries. The solution implements...
- Piecing Together the Business Intelligence Puzzle Business intelligence (BI) technology collects and analyzes company data, delivering relevant information to corporate decision-makers in an effort to produce favorable outcomes.
- Harness IT -- An Introduction to Business Intelligence Solutions Learn the key selection criteria required to provide your organization with the capability to address structured data, unstructured data and mobile demands so...
- Live Webcast Increasing the Value of Your Reports and Dashboards Learn how incorporating other analytical capabilities such as predictive modeling and visualization can increase the value of your reports and dashboards by providing...
- The Software-Defined Data Center: Is your ADC ready? Data center transformation is accelerating beyond virtualization to next-generation cloud architectures and software-defined data centers, bringing new challenges for application performance, scalability and...
- Application Acceleration: Optimize the End-User Experience Watch this on-demand webcast and learn how you can optimize your web content, accelerate performance across any device and browser combination, and offload... All Business Intelligence/Analytics White Papers | Webcasts