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3 Ways to Cut Your Employee Turnover Rate

Erin Iafelice
Woman at computer with papers

The COVID-19 crisis has created economic chaos for businesses across the U.S., but not all of it is terrible news. Companies in logistics, technology, grocery, and essential retail have seen customer demand surges and are hiring at record rates. By the end of March, Walmart was hiring 5,000 new employees a day, and by mid-April, Amazon had brought on an additional 175,000 employees. Of course, not every organization is hiring at this scale. And, employee turnover can negatively impact every business, especially in the first 180 days. Here are three ways HR teams can reduce their employee turnover rate with strategies and technology deployed during hiring.  

Use pre-hire assessments to identify candidates most likely to stay or to leave.

Validated, proven pre-hire assessments can effectively cut attrition, especially when they’re integrated into an enterprise hiring platform, so they scale. Modern Hire has built job-specific assessments based on more than a decade of validation research and the hiring and performance data from millions of applicants. Some organizations choose the ready-to-implement assessments to gain a predictor of candidates’ on-the-job performance. Others engage Modern Hire to customize the assessments to their particular organization and role, seeking specific strategies to reduce early turnover:

  • For one client that needed 10,000 new entry-level retail employees per year, Modern Hire found that new hires who had scored highest on the pre-hire assessment were more likely to stay on the job than those who scored lower. By understanding this relationship and how individual candidates performed on the assessment, the client could hire good- and even weak-fit entry-level candidates over poor-fit candidates, realizing millions of dollars in business impact by reducing turnover.
  • Another client hiring 15,000 warehouse employees per year sought a different approach to retention. The client used Modern Hire’s customized pre-hire assessments to filter candidates with the highest risk of turnover. Applicants who scored in the bottom 5% of the assessment were found to leave at a rate of 30% during the first 90 days, compared to a turnover rate of 20% for all other applicants. Use of a Modern Hire customized pre-hire assessment enabled this client to make more informed hiring decisions and continually adjust its filter levels according to workforce needs and the applicant pool’s size at the time.

Select pre-hire assessments that leverage AI rather than traditional hiring algorithms.

Artificial intelligence (AI) and machine learning technology have been shown to improve the prediction of turnover significantly, in some cases, by a factor of three. What is the difference-maker between AI and traditional algorithms? Traditional scoring models linearly score questions, whereas machine learning models can detect non-linear patterns and adjust the scoring appropriately. They can better account for the complexity of the reasons employees leave and the organizational factors that contribute to turnover risk. Find out more about the power of AI to predict outcomes in the Modern Hire white paperAI in Hiring: 5 Critical Questions.

Enable candidates to make more informed decisions.

Approach reduction of turnover from multiple angles by helping candidates self-select in or out of the hiring process. Hiring is a two-way street, and modern candidates want a high level of transparency from potential employers. The Modern Hire enterprise hiring platform helps recruiters create that transparency with every candidate interaction, from realistic job previews and job simulations in pre-hire assessments to opportunities to embed content about their organization’s culture and employment experience. A transparent hiring process creates a better candidate experience and can help narrow the applicant pool organically. To learn more about achieving a measurable reduction in turnover with Modern Hire’s enterprise hiring platform, read our case study.