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Analyze this! HR professionals are delving into data to develop broader and deeper workforce analytics.

Article Excerpt
The oilfield services industry has an annual turnover rate of about 35 percent among its largely blue-collar workforce. Executives have long been resigned to this fact because they didn't know what caused it, how much it cost or what to do about it.

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In 2007, soon after Ray Lieber was hired as the HR vice president at Superior Energy Services Inc. in New Orleans, he calculated revenue lost for each job type that quit. That got managers' attention: The business model for the oilfield services and equipment provider is based on billable hours, so turnover among its 4,300 employees equals lost revenue.

"The moment we lose an operator, revenue starts to drop," Lieber says. "We've always known it but never quantified it. Now we quantify it."

He also calculated turnover by job type. According to conventional wisdom, turnover was mostly among semiskilled workers, but he found that nearly half of the people who quit were skilled operators or supervisors with a higher impact on revenue. Having made his case, Lieber sold some of the business unit managers on the next step: Using predictive modeling, the process of creating a statistical model for predicting the probability of an outcome, he identified action with the best chance of stemming turnover--more supervisor training, especially one-to-one coaching skills.

"Turnover is down significantly in two of the three business units where we conducted the analysis and put in new processes and practices," Lieber says. "We were at 34 percent in one, and we're going to hit 26 percent to 28 percent this year. Even an improvement of 3 to 4 percentage points will show up in the profit and loss statement."

Most HR departments have spent years acquiring gigabytes of data about their employees and installing technology to store and organize that information. But most are still only using the data for transactional purposes--to more efficiently process payroll and administer benefits. They're not using these rich veins of data to make better human capital decisions.

Lieber and a growing number of HR professionals are using quantitative methods and various business intelligence (BI) tools to analyze the data from their HR databases, corporate financial statements, employee surveys and other sources to make fact-based human capital management (HCM) decisions that impact the bottom line. These pioneers are forging a culture of inquiry and quantification in HR.

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