Learn how LaSoft\u2019s HR Insights system uses AI with predictive analytics to help forecast employee turnover. Discover how you can save money by using proactive employee retention strategies.<\/p>\n\n
Life is full of uncertainty, but artificial intelligence (AI)<\/a> can make it a little more predictable. After all, machine learning algorithms can forecast sales trends and maintenance issues. Can they also predict who might soon quit their job, even before a person decides to?<\/p>\n Our CEO asked exactly that question, and rightly so \u2014 employee turnover poses a challenge for many C-level executives, HR managers, and even finance departments. So, that\u2019s what we at LaSoft\u2019s Digital Innovation Lab set out to discover. We found that predictive analytics for HR can help you understand turnover and improve your employee retention rate. Let\u2019s see how.<\/p>\n The basic answer is that turnover costs time and money. Let\u2019s look at some numbers. Recruiting, hiring, and training new employees is expensive, so it\u2019s simply more cost-effective to retain people than to hire replacements. In the US, when a person leaves their job, a company might spend 50-60% of the employee\u2019s annual salary<\/a> to find and train someone new. Even more dire, the Society for Human Resource Management (SHRM) estimates the total cost of replacing an employee can range from 90% to 200% of their annual salary.<\/p>\n But the consequences of high turnover go beyond just costing you money. The Global Talent Climate report from The Josh Bersin Company claims that, on average, it takes 44 days to fill a position<\/a>. One repercussion of that time lag is that another team member has to temporarily cover that chunk of work on top of their own. Plus, turnover disrupts team dynamics and collaborative efforts, affecting overall morale and efficiency.<\/p>\n It can be very disruptive to lose a key team member. Top performers are 400% more productive<\/a> than the average employee, yet 47% of them left their companies<\/a> in 2022. When key employees leave, they take valuable knowledge and expertise with them, which can impact project continuity and productivity.<\/p>\n It\u2019s often HR\u2019s job to sense (or directly ask) if an employee is exploring other job opportunities. However, with the average HR department managing 150 employees, keeping tabs on who\u2019s talking to recruiters is almost impossible. Plus, so many employees now work remotely, making it even more difficult for managers to know about their employees\u2019 intentions.<\/p>\n Without close in-person communication and collaboration, forming genuine connections is hard. And that\u2019s probably why fully remote workers are more likely to quit than in-office employees, according to the Wall Street Journal<\/a>. Often, people may not even realize that they might want to switch companies in the near future.<\/p>\n Two words: predictive analytics. It may not completely solve the employee retention problem, but it will definitely help channel resources wisely for the 98% of HR managers who experience burnout<\/a>.<\/p>\n One of the most significant benefits of using AI to predict employee turnover is the ability to take preemptive action to retain employees at risk of leaving. By identifying at-risk individuals, HR managers can engage them with targeted retention strategies, such as career development opportunities, mentorship programs, raises, or even simple recognition of their contributions.<\/p>\n We\u2019re not saying that AI is the solution for HR woes just because LaSoft developed analytics software that can predict turnover. In 2022, Gartner surveyed 118 HR leaders, and nearly half identified HR technology as their top investment priority<\/a> for 2023.Why is high turnover a problem?<\/h2>\n
So, what\u2019s the solution?<\/h2>\n
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