Episode 27
Predicting retention and turnover with David Allen and Brooks Holtom
December 19, 2019
Our understanding of retention and turnover has gotten incredibly complex in the age of big data. As internal and external data sources build a more complete picture, the question turns to how organizations should be deploying these predictions to their benefit. Professors David Allen and Brooks Holtom have built a model that predicts when employees will quit and conducted an in-depth study with live data. In this episode, they share some surprising findings about turnover and the reasons people stay in their jobs.
You can connect with David on Twitter or via email, and Brooks is available via email. Both are also on LinkedIn: David and Brooks.
Retention and Turnover: Key takeaways from episode 27
The study of retention and turnover is at an exciting inflection point because of the growing ability to gather data throughout the employee lifecycle. If you’re in HR, this is your opportunity to look beyond the standard exit interview and pull in many other data points, both external and internal.
According to research, about 60% of turnover is preceded by a major event, either in the employee’s personal life or at the organization level. Many of these events can be predicted—a family change, like marriage or the birth of a child; completing a graduate degree; or a merger or acquisition in the organization.
The concept of embeddedness, or how connected an employee is in a work environment, comes to play when one of these big events occurs. In brief, the higher a person’s embeddedness score, the greater the likelihood he or she will stay in the job through the transition.
It turns out that tenure is a much stronger predictor of retention than age or generation. If you’re involved in onboarding programs, you’re meeting people at the highest risk of leaving, as that first year on the job tends to be a tenuous time. Real-time assessments of embeddedness are particularly important for this group.
Collecting and analyzing data about retention and turnover is important, but it’s equally critical to develop strategies for acting on the findings. If your analysis identifies a flight risk, what do you expect the manager to do with that information? Making good use of data requires a thoughtful and nuanced approach.
Those of you whose HR organizations aren’t embracing big data need to start somewhere, even if it’s a small effort. Get started, see what you can learn, and refine your models over time.
Take time to step back from your day-to-day and read broadly. There are so many resources out there. Stay engaged and look for opportunities to learn about new fields, because you never know which seeds are going to sprout in your mind and lead you down a new path.
Resources mentioned in the episode
“Better ways to predict who’s going to quit” By Brooks Holtom and David Allen, Harvard Business Review (August 2019)
Employee Retention and Turnover: Why employees stay or leave By Peter Hom, David Allen, and Rodger Griffeth
Academic journals for HR practitioners
