Any companies worried about employee turnover can actually predict when workers will want to leave, the Wall Street Journal reported on Friday.
Companies like Wal-Mart, Credit Suisse, and Box have created data-based algorithms to find out when their employees are likely to quit, according to the article. Data considered in the algorithms range from employee personality to team size to job location.
Although there is not one deciding factor, the ability to predict when an employee will quit helps companies reduce turnover, plan for open positions, and save money.
For Credit Suisse, savings range between $US75 million to $US100 million even in a one-point reduction in the percentage of people leaving the firm. The bank has tracked data on employment factors for the past three years, according to WSJ. This led Credit Suisse to expand efforts to hire internally, and target specific employees for promotions.
Since then, about 300 people have taken promotions within the firm, the global head of talent acquisition William Wolf told the Journal.
“We believe we’ve saved a number of them from taking jobs at other banks,” Wolf said.
Data has also helped the bank determine how job titles, promotions, or maternity leave might affect women who want to leave the company.