IBM investing heavily on predictive algorithms to curb attrition

BENGALURU: Top technology services companies across the world such as IBM and Accenture have grappled with the problem of high employee turnover in an industry that typically witnesses high attrition rates.

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And the likes of IBM are investing heavily on such predictive analytical tools, as they look to save hundreds of millions of dollars annually, which otherwise is wasted on hires that may not have been the best fit for the company in the first place.

“You can do an analysis and you can look at whether the benefits in your compensation program is really targeted at the people who are the most productive and the most likely to stay because you can spend a lot of money on people who have a low probability of staying with you,” said Kevin Cavanaugh, vice president for Smarter Workforce Engineering at IBM.

“So you’re largely going to be wasting that compensation. And we’ve done some work in IBM where we found that we could save millions of dollars by targeting our benefits and compensation for the people who are most productive and most likely to stay with us,” Cavanaugh told ET on Thursday. Attrition rates are typically higher across the global technology services industry.

According to a Deloitte study, in FY15, the highest voluntary attrition across sectors was seen in the IT services sector at 21.9%, whereas the lowest was in the energy and natural resources sector at 10.5%.

Companies like IBM have, therefore, been forced to find newer ways of retaining employees and have had to take a fresh look at traditional technology industry metrics. And the results are starting to show, as companies are now deploying a more strategic approach towards hiring and only investing on talent that is likely to help drive long-term growth.

“We’ve saved millions and millions of dollars as a result of (these tools) and we’ve had a stable workforce. We’re pretty sure that we can replicate those capabilities — a lot of this work has been done on a case-by-case, company-by-company basis. And one of our challenges at this point is to try to find some general models that will bring down the cost of doing this work, so that we can apply it in a less bespoke manner, in a more general manner to help people,” said Cavanaugh.

In the past few years, companies have gathered socioeconomic data from incoming engineers such as the educational qualifications of parents and household incomes. Armed with such information, human resource (HR) departments are able to use algorithms and analytics in recruitment.