The Top Factor That Determines How Long Employees Stay with a Job

Experienced data scientist Brian Richmond possesses extensive knowledge and expertise in statistics, experimental design, machine learning, and analytics. As a senior data scientist at Aura Health, Brian Richmond is particularly interested in people analytics as a rapidly-growing field that uses data to create and monitor company culture.

Employee turnover is very costly for companies, often costing over 20% of an employee’s salary to replace a position. An independent study on job hoppers sheds light on the reasons some employees don’t stay on a particular job for long. A job-hopper is defined as someone who has stayed for less than two years in his or her two previous jobs.

There are many variables to use in this study, but the researcher narrowed down on one single measure: fit. Fit refers to the alignment of an employee’s values with the values of his or her work environment. The study used three metrics of fit: culture fit, job fit, and manager style fit.

Culture fit describes how an employee’s values are aligned with the values of the organization she or he works for. Job fit measures how an employee fits into the role she or he is doing. Manager style fit refers to how an employee’s preferred style of being managed is aligned with how his or her superior manages him or her.

Job hoppers with low job fit have higher turnover rates. Job hoppers emphasize opportunities for learning and full utilization of their skills as primary factors for staying with a job longer than two years. Moreover, job hoppers with low culture fit at supervisory levels have higher turnover rates. The study also found that a manager style fit affects job hoppers and non-job hoppers in the same way. Therefore, manager style fit is not in any way related to job-hopping.

Applications for People Analytics in Change Management

After working at WeWork’s New York City headquarters, Brian Richmond relocated to California and joined the early-stage startup Aura Health. In addition to his position as a senior data scientist for Aura Health, Brian Richmond participates in the meetup group Bay Area People Analytics, which gathers professionals in the field to discuss novel applications of people analytics in HR, such as change management.

Change management is a multi-faceted process that helps employers prepare their workers for newly implemented systems and solutions. By implementing people analytics methods and tools, HR professionals can collect and analyze timely information on the sources of change resistance and develop immediate solutions.

Likewise, people analytics allows for employers to provide instant feedback on training initiatives, workshops, and other methods used to raise awareness of the incoming changes. Outcomes such as employee attrition after a change can also be measured, and provide a more comprehensive picture of the effects of the new system or solution. The success of change management strategies cannot be measured without data, so people analytics is critical to driving improvements in change management.

Using People Analytics to Measure Talent

During his time at WeWork, data scientist Brian Richmond established the company’s first people analytics team, which earned him the prestigious Employee Excellence Award. Brian Richmond maintains a strong interest in investigating the potential of people analytics to revolutionize how data can be harnessed to create and maintain the right company culture, improve diversity and inclusion, and hire and keep the best people.

The rapid collection and assessment of data enabled by people analytics has the ability to refine and improve the parameters used to identify high-performing employees.

Traditional assessment models usually depend on the opinions of management, which can be biased and inaccurate. People analytics allow employers to measure other aspects of an employee’s performance that can be used to assess their contributions, including successful collaborations with their colleagues.

Furthermore, by automating the data collection process, HR can identify underperforming employees in real time and deliver feedback in a timely matter. Managers can also use this data to develop personal development plans for their employees and make one-on-one feedback session more relevant. Machine learning can even be used to predict employee turnover, and take steps to reward and retain top employees.