For example, a company might assume that a customer is more likely to leave them if they get a better price offer from a rival rather than if they have a negative experience with the company. The reality could be more nuanced. A closer examination of data could show that customers are far more likely to respond to a rival's offer if they have even one bad experience with the company.
Another area that could benefit from data-driven management of the sort espoused by Beane is customer contact centers, according to Merced Systems, a vendor of performance management systems.
Contact centers are "rich in underused data and under-tracked processes, especially compared with functions like finance or manufacturing," the company said in a recent whitepaper.
Often, it said, vital components of a contact center are not measured because the data is scattered across multiple data stores and silos. "As a result, many centers favor those management metrics that are the easiest to get to, rather than those that correlate highly with profitability and customer loyalty.
Examples of uncommon and non-obvious metrics that have a major impact on call center performance include coaching frequency, supervisor effectiveness, bonus calculations and the frequency with which statistics are used to bolster decisions.
"Finding these 'Moneyball Metrics' involves a disciplined process of gathering hard to get data," and correlating it with the desired outcomes, the whitepaper noted.
Even the federal government is taking a leaf from Moneyball.
In a blog post Wednesday, Shelley Metzenbaum, associate director of performance and personnel management at the Office of Management and Budget, called on federal agencies to follow Beane's example.
"Like Beane, who understood that his goal was to win games - not hit the most home runs, government agencies must learn to be clear about what they want to accomplish" and be willing to change processes, Metzenbaum said. Agencies need to look for and identify relevant data and factors that are most likely to create problems and increase costs, Metzenbaum said.
By applying Moneyball-like data analytics, the Department of Transportation discovered that 20% of motor vehicles crashes in 2009 resulted from distracted driving, making that a priority focus for the agency, Metzenbaum said.
Similarly, the Social Security Administration is applying predictive analytics approaches to determine the best criteria to apply in the process of qualifying people for disability payments, she said.
"There may never be a movie about the management of the federal government, but the Administration has been taking its own Moneyball approach to management," Metzenbaum said.
Jaikumar Vijayan covers data security and privacy issues, financial services security and e-voting for Computerworld. Follow Jaikumar on Twitter at @jaivijayan, or subscribe to Jaikumar's RSS feed. His email address is email@example.com.
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