An employee’s resignation impacts companies greatly, dwindling both costs and productivity. In recent years, the average cost to hire an employee is $4,129 dollars – the losses suffered can double when an employee leaves suddenly. The departure also dampens workflow efficiency, operation management and morale in other employees. But now with Machine Learning,  it’s no longer a surprise that we can now find ways to strengthen employee retention strategies. 

How does an employee replacement affect recruiters and employers?

The replacement cost of an employee is surprisingly high. Studies have shown that it costs a company about $9,444.47 per $8 employee turnover. With employees only staying for about 1.1 years on average in one of the worldwide largest technology companies,  we can imagine how low retention financially drains organizations rapidly.

From a recruiting perspective, the higher number of employees to replace reduces the efficiency of searching for potential candidates. With the uptick in talent acquisition costs that companies now have to bear, hiring teams may find themselves working with limited resources per hire to keep within budgets. It can also lead to an increase in the time it takes to fill a position. 

Job-hopping can ultimately bring damage to a company’s brand. Work environments and relationships deteriorate when remaining employees become uncertain about their future in the organization. Over the last few years, the average time to fill a position has been 42 days. Without even accounting for possible delays in the process with strained resources, existing employees are already expected to take on additional projects for a long period of time. 



Why do employees switch jobs?

There are many reasons why employee retention isn’t easy.  On many occasions, candidates end up transferring from a big company to a smaller one. Their reason for doing so? Smaller companies in large cities tend to give some talents more flexibility in building their work-life balance. The employee experience takes the front seat when it comes to these decisions, and they’re not just synonymous with salary increases or higher positions. Employees want a working experience that gives space for both career growth and personal growth. The modern workforce also tend to favor opportunities to play a role in making an impact  – people want brands to have stronger ties to social justice and community empowerment.

How does machine learning limit job-hopping?

Algorithms created by machine learning can predict the possibility of an employee’s resignation.  We partnered with a group of graduate students at the University of Maryland, College Park who analyzed the state of employment of a group of people. Using Machine Learning, each person in the group was analyzed based on various factors such as their length of employment as well as their time spent completing their tertiary education. Tallying up these factors allowed Machine Learning technology to come up with an approximate prediction on how likely it is for a particular worker to leave their current job within the next six months. The suggestions made by these algorithms ultimately help lower replacement costs in the long run. 

These results give organizations the opportunity to better the employee experience for the candidates they select. Predicting a candidate’s tenure with a company gives employers a big-picture idea of what the talent pool is looking for. Machine Learning not only gives employers access to committed talent. It gives them the opportunity to build holistic work environments that employees choose.

To read more on this study, click the link below to download our latest white paper, People Retention and Machine Learning!