Predictive analysis in human resources is becoming a lot more helpful in determining how well-suited potential employees may be for a particular company and a specific job. But what can it tell us about employee engagement?
According to a recent Wall Street Journal article regarding a soon-to-be published study by Deloitte Consulting LLP, about 5% of companies with 25,000 or more employees are now using predictive analysis in human resources. This analytical information can go a long way towards learning about who to hire and for what positions to better ensure they are likely to succeed in the job and stay at the company.
At Google, where numbers are crunched for just about everything, they use analytics to identify the skills, behaviors and values of ideal candidates for specific roles. Humans still scan resumes, but this is no longer the primary method for finding the right people. ConAgra Foods has gone further and used analytic software to help predict which key employees were likely to leave the company and why. They studied departments with particularly high turnover as well as those with low turnover and looked at more than 200 factors that may contribute to employees leaving the company.
The results surprised them: two of the strongest indicators as to whether an employee would likely leave the company were his or her relationship with their supervisor and the degree to which they were recognized for their work. Compensation wasn’t even in the top ten.
As I wrote in a previous post, what employees are looking for can vary greatly from what managers assume they want. This disparity can greatly diminish productivity as well as cause employees to leave the company. Learning to correct for this disparity can improve employee engagement and organizational performance. One should expect that data will be used more and more for determining existing employees’ suitability for promotions and succession planning as well as the overall impact on them through mergers and acquisitions. Studying human capital data may not only help verify the difference between what we think and what actually does motivate employees, it may help us understand how to best engage them as well.
In the same way the Hawthorne effect demonstrated at least a minimal short-term positive impact by simply paying attention to employees, I think predictive analysis could be used to generate some greater understanding of how to best engage employees. And this can have much longer term implications. Ideas to do this may include finding effective ways to first quantifiably measure employee engagement as well as emotional intelligence and job satisfaction levels. Using this data, organizations can then analyze how it compares to the productivity of individual employees. If a direct correlation can be drawn from the results, then creating effective ways to raise any of the variables may result in a predictable increase in employee productivity.
This is not meant to reduce employees into statistics, but only as a way of verifying in a quantitative way what we may already suspect qualitatively. Understanding and verifying how to best engage and motivate employees can then effectively raise an organization’s productivity, reduce the high cost of turnover and make for a better workplace environment.