Deloitte’s 2024 Global Human Capital Trends report found that while 79% of business leaders believe their organization has a responsibility to create value for workers as human beings, only 43% of employees feel their organization has left them better off than when they started—highlighting an ongoing gap between leadership priorities and actual employee experience.
Employee retention involves numerous factors that are difficult to predict or strategically change. Therefore companies are turning to workforce retention analytics software to fill these gaps. Predictive analytics for employee turnover uses deep learning to identify trends in employee behaviour and create strategies for reducing the risk of staff leaving.
Data-driven insights allow managers to identify what their employees want from their position, including advancement opportunities, remote work opportunities, realistic expectations for their work, and to feel like their skills are needed as they “upskill” in their field. Analytics help businesses guide their investments toward strategies that tailor these factors to employee expectations.
For example, the HR team might lead a focused intervention on certain risk factors, such as a lack of employee appreciation or a lack of socialization in the workplace. On the other hand, top management could use the data to enact more global strategies, such as by using company resources to increase employee skill training via a company LMS.
6 ways to use predictive analytics to improve employee retention
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