Analytics used to predict who will leave a job!

22 03 2013

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.

Capture

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.

Operations and Supply Chain Management

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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Introduction to Human Resource Analytics

12 11 2012

Human Resource analytics do not require an advanced degree in mathematics, economics or statistics. Any operations, HR or business management professional can become an expert in their client or companies people based metrics. Just tracking and reviewing some of these metrics is often all the evidence you need to prove or quantify what you already knew about operations, recruitment, benefits and organizational development.

Lets take a look at a simple broad metric, Revenue Per Employee (“RPE”).  So compare two similar companies with similar workforces but one has double the revenue per employee. It paints a very different picture of productivity, profitability and compensation at these companies. Based on economic theory and my understanding of scalability, software and manufacturing companies would lead the pack here when it comes to RPE. This is likely due to scalability and the nature of their industry and products. The RPE laggards on the list should be food and professional services (low-tech). The implication is that these low RPE companies and industries often need twice or three times as many employees to produce the same revenue as the higher RPE companies.

A later post will delve deeper into an industry comparison of data I gathered from fast growing Oregon businesses across several industries.








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