In today’s tightening labor market where finding skilled labor is tough, turnover, whether it is planned or not, can be quite a challenge for employers. Hiring and training new workers, managing through temporary skill and capacity gaps during vacancies, and even surviving the loss of valuable institutional knowledge can be costly and difficult for companies to recover from. However, this new study shows that a data-driven approach can be applied to help employers predict, and potentially reduce, unplanned departures.
“It has always been important for employers to minimize turnover, but it is more critical now than ever before given the current state of labor market,” said Ahu Yildirmaz, co-head of the ADP Research Institute. “Unemployment is at a 17-year low, and job switching is at a record high. If employers can use data to identify flight-prone employees and understand what’s driving their departure, they will have an important advantage in a highly competitive market for talent.”
ADPRI analyzed two years of monthly aggregated and anonymized payroll data from more than 41,000 companies and 12.5 million employees. From a sub-set of that data a model was developed to help predict employee turnover and shed light on the factors driving it. In fact, the study determined that when a company’s historical turnover rate is applied to the probability model established in the research, the model can identify “at risk” employees at a rate of five to six times higher than guesswork.
The study explored the overall landscape of turnover and determined the following:
•There is a seasonality to turnover: The majority of turnover (voluntary and involuntary) takes place in the month of September and March has the lowest rate of turnover. However, there are some exceptions. Education turnover, understandably, peaks in July when the academic year ends and Manufacturing has the lowest turnover in January.
•Turnover rates vary by industry: The highest average monthly turnover (voluntary and involuntary) is in leisure and hospitality (9.1 percent), followed by construction (6.2 percent), and education and health (5.5 percent). The lowest turnover rates are in manufacturing (3.4 percent), with the finance (3.8 percent) and information (4.1 percent) industries close behind.
•The majority of turnover is voluntary: Looking at when employees choose to leave, the highest voluntary turnover (72 percent) is found in accommodation and food services (a sub-set of the leisure and hospitality industry) and the lowest is found in administrative, support, waste management, and remediation services (58 percent).
•More than 40 different factors can contribute to voluntary turnover: These factors fall into six buckets that include pay, promotion, overtime/premium time, commuting, experience and tenure, and other job characteristics. They can have varying degrees of impact, depending on the industry and the specific company. Yet, overall, pay and promotion are, as might be expected, the lead drivers of voluntary turnover. The next most influential factors are related to overtime and premium-time. Interestingly, commute time tends to be a more important factor than experience and tenure.
Marc Rind, chief data scientist at ADP, said, “Data can say things that employees might not during exit interviews, and shed light on causes of voluntary turnover that even the best expert may miss. In today’s challenging talent market, unbiased, insights derived from actual workforce data can enable employers to identify potential flight risks before it’s too late.”
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