HP ran a pilot program to use analytics to predict if employees might quit. Employers traditionally use exit interviews to figure out why and when employees leave for improvement (hopefully). Quantifying the sources and finding a companies risk factors before losing talent would benefit any business.
In my own research I find that most voluntarily exits by employees occur in July though September. See the chart for an example of data from the Bureau of Labor Statistics. Look for the highlighted spike in late summer. December on the other hand is one of the lowest periods of the year for turnover. Therefore January through May would be a great time to start analyzing the historic data and implementing changes to beat off those late Summer exits, or for whenever your high turnover period was. It is predictive analytics you can do without special software or a PHD in data science or statistics.
Thanks to the Operations and Supply Chain Management blog of F. Robert (Bob) Jacobs, Professor of Operations Management at the Kelley School of Business, Indiana University for the original story.
WSJ – March 14, 2013, 3:36 PM ET
Book: HP Piloted Program to Predict Which Workers Would Quit
Hewlett Packard Co. tested a predictive scoring system that attempted to grade the likelihood that individual workers would quit the company, according to a new book.
HP piloted the scoring system in 2011 aimed at lowering attrition through a better understanding of which workers were most likely to leave, according to Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie Or Die by Eric Siegel. The analytics model, Mr. Siegel says, looked at factors such as salaries, promotions and job rotations, and scored the likelihood that particular employees would leave. HP data scientists believed a companywide implementation of the system could deliver $300 million in potential savings “related to attrition replacement and productivity,” according to a November 2011 company presentation.
Data scientists made the presentation at a Predictive Analytics World…
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