Case: Pioneers in HR Analytics The power of HR metrics and analytics is an untapped resource for many organizations. Human resource information systems (HRIS) are commonly used to capture and store gigabytes of data about employees, but few organizations have mined their data to improve human capital decisions. Most business leaders and HR executives do not make people decisions with the same level of rigor and rationale as they do other business decisions, relying more on intuition and gut feelings. This propagates the myth that the impact of human resources on organizations is either not measurable or not significant. Financial, operational, and marketing decisions all depend heavily on detailed analysis and cost justification. The use of analytics in human resource management can enhance the strategic contribution of HR executives and lead to better decisions and organizational outcomes. At Superior Energy Services in New Orleans, careful analysis of turnover data shattered previous beliefs about which employees were most likely to quit. The organization was losing skilled oilfield operators and supervisors faster than semiskilled blue-collar workers. This discovery led to implementation of training and coaching programs for supervisory employees, which resulted in a 15% drop in turnover and improved the bottom line of the company. Without this analytic approach to turnover, attention would have been focused on retaining blue-collar workers, which would not have delivered such impressive results. Thrivent Financial for Lutherans in Minneapolis believed that turnover during the first year of new hires’ careers was related to the previous experience they had in their disciplines. The thinking was that if a customer service employee had previously worked in customer service, she was less likely to leave Thrivent in the first year. Analytics dispelled that theory and Thrivent found that the exact opposite was true. Employees with previous experience in the discipline were leaving at a faster rate than those without such experience. Although they have not determined the causes, this data will help Thrivent’s leaders to address the real issues. One answer will lead to additional questions and lines of inquiry. The food service and convenience company Wawa, Inc., assumed that turnover among store clerks was tied to their hourly wage rate. However, the number of hours worked in a week was a much more significant factor in turnover. Employees liked working part-time, and when their work hours exceeded 30 hours per week, they were more likely to quit. Wawa reduced in-store turnover by 60% by scheduling employees for less than 30 hours. Concerns about an aging workforce and a presumption that a high percentage of employees would retire in the near term led the University of Southern California to carefully analyze employee demographic data. To their surprise, HR found that the nontenured staff employees were, on average, too young to begin retiring en masse. Tenured faculty, while much older, are far more likely to work past the age of 70. The anticipated retirements are still a fact for USC to address. However, managers can plan for this and develop a longerterm transition plan because they are not facing massive retirements in the near future. The HR executives at Superior Energy Services, Thrivent, Wawa, and USC are harnessing the power of HR data and statistical models to better understand the challenges facing their organizations. Long-held beliefs about the patterns of employee actions and decisions can be analyzed and either supported or debunked. Either way, the organization can address the true issues only if HR looks beyond the surface and digs deeper into the sea of data. Overcoming the fear of number-crunching and developing expertise with metrics and analytics can separate winning organizations from those that get left behind. HR professionals who learn to interpret bits and bytes of employee data will help their organizations succeed well into the future. Questions: What are some reasons that more organizations do not implement HR analytics? How would you make the case for adopting HR analytics? How can HR professionals develop the needed skills to analyze and interpret metrics? What resources could an HR professional consult to begin building expertise in this area
Case: Pioneers in HR Analytics
The power of HR metrics and analytics is an untapped resource for many organizations. Human resource
information systems (HRIS) are commonly used to capture and store gigabytes of data about employees, but
few organizations have mined their data to improve human capital decisions. Most business leaders and HR
executives do not make people decisions with the same level of rigor and rationale as they do other business
decisions, relying more on intuition and gut feelings. This propagates the myth that the impact of human
resources on organizations is either not measurable or not significant. Financial, operational, and
decisions
management can enhance the strategic contribution of HR executives and lead to better decisions and
organizational outcomes.
At Superior Energy Services in New Orleans, careful analysis of turnover data shattered previous beliefs about
which employees were most likely to quit. The organization was losing skilled oilfield operators and
supervisors faster than semiskilled blue-collar workers. This discovery led to implementation of training and
coaching programs for supervisory employees, which resulted in a 15% drop in turnover and improved the
bottom line of the company. Without this analytic approach to turnover, attention would have been focused
on retaining blue-collar workers, which would not have delivered such impressive results.
Thrivent Financial for Lutherans in Minneapolis believed that turnover during the first year of new hires’
careers was related to the previous experience they had in their disciplines. The thinking was that if a
customer service employee had previously worked in customer service, she was less likely to leave Thrivent in
the first year. Analytics dispelled that theory and Thrivent found that the exact opposite was true. Employees
with previous experience in the discipline were leaving at a faster rate than those without such experience.
Although they have not determined the causes, this data will help Thrivent’s leaders to address the real
issues. One answer will lead to additional questions and lines of inquiry.
The food service and convenience company Wawa, Inc., assumed that turnover among store clerks was tied
to their hourly wage rate. However, the number of hours worked in a week was a much more significant
factor in turnover. Employees liked working part-time, and when their work hours exceeded 30 hours per
week, they were more likely to quit. Wawa reduced in-store turnover by 60% by scheduling employees for
less than 30 hours.
Concerns about an aging workforce and a presumption that a high percentage of employees would retire in
the near term led the University of Southern California to carefully analyze employee demographic data. To
their surprise, HR found that the nontenured staff employees were, on average, too young to begin retiring
en masse. Tenured faculty, while much older, are far more likely to work past the age of 70. The anticipated
retirements are still a fact for USC to address. However, managers can plan for this and develop a longerterm transition plan because they are not facing massive retirements in the near future.
The HR executives at Superior Energy Services, Thrivent, Wawa, and USC are harnessing the power of HR data
and statistical models to better understand the challenges facing their organizations. Long-held beliefs about
the patterns of employee actions and decisions can be analyzed and either supported or debunked. Either
way, the organization can address the true issues only if HR looks beyond the surface and digs deeper into
the sea of data. Overcoming the fear of number-crunching and developing expertise with metrics and
analytics can separate winning organizations from those that get left behind. HR professionals who learn to
interpret bits and bytes of employee data will help their organizations succeed well into the future.
Questions:
What are some reasons that more organizations do not implement HR analytics? How would you make the
case for adopting HR analytics?
How can HR professionals develop the needed skills to analyze and interpret metrics? What resources could
an HR professional consult to begin building expertise in this area
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