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.

How Data Analysis Can Be Used in Workforce Planning

  Brian Richmond  is a former senior data scientist at WeWork where he was responsible for leading and providing a vision for the people analytics team of the company. In 2018, Brian Richmond joined Aura Health as a senior data scientist to apply data to drive product improvements in the health and wellness space.

One of the many fields where data and statistical analysis are starting to shine is in providing a reasonable expectation of workplace evolution. Based on the rate of innovation and changes in the workplace, most future jobs haven’t been invented yet. In that kind of environment, it is difficult to know for upcoming members of the workforce where to focus their efforts. Predictive data analysis can help in this area.

Using large data sets across long periods of time, predictive analysis can provide some insight into gaps that tend to crop up in a technologically changing marketplace. When this type of analysis is combined with reliable qualitative input, it can generate very reliable data that can help shape the workforce of the future.

Frequently Asked Questions About Meditation

A senior data scientist at Aura Health, Brian Richmond specializes in applying data-driven models to generate personalized recommendations in meditation and mindfulness applications. Brian Richmond’s work in data science started in 2017 and he has become a dedicated practitioner and expert in the field.

Meditation has many benefits and it’s growing in popularity, but many people don’t really know where to start with meditation. Here are some questions that come up often.

Q: When and where should I meditate?

A: Most experts recommend meditating first thing in the morning in a quiet place with no distractions. Short, 3-minute meditation breaks at work can greatly improve focus and productivity, and meditation can really help people settle their minds at night so they can sleep better. However, any time is fine.

Q: Do I have to remain seated?

A: A seated or lying position is fine. If someone is meditating for a purpose other than sleep, lying down could make that challenging.

Q: How do I start meditating?

A: There are a number of resources that describe as many methods and approaches, but a simple and straightforward way to start is with a meditation app such as Aura.

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.