Hands-on Survival Analysis with Python

What companies can learn from employee turnover data

Photo by Boxed Water Is Better on Unsplash

Survival analysis is a popular statistical method to investigate the expected duration of time until an event of interest occurs. We can recall it from medicine as patients' survival time analysis, from engineering as reliability analysis or time-to-failure analysis, and from economics as duration analysis.

Besides these disciplines, survival analysis can also be used by HR teams to understand and create insights about their employee engagement, retention, and satisfaction — which is a hot topic nowadays 🌶 🌶 🌶

According to Achievers’ Employee Engagement and Retention Report, 52% of workers plan on looking for new jobs in 2021 and a recent survey participated by over 30,000 workers in 31 countries shows that 40% of employees are thinking of quitting their jobs. Forbes calls this trend “Turnover Tsunami”, mostly driven by pandemic burnout and Linkedin experts predict the arrival of big talent migration and discuss under #GreatResignation and #GreatReshuffle topics.

As always data can help to understand employee engagement & retention to reduce turnover and build more engaged, committed, and satisfied teams.

Some examples of what HR teams can dig in the employee turnover data are: