← HR Metrics·Engagement & Retention
Exit Reason Distribution
Distribution of reasons for employee departures
How it’s computed
COUNT(exits) GROUP BY reason
What the evidence shows
Evidence (effect sizes, priors, validity) is syncing from Principia.
What this metric can show you
Exit Reason Distribution can tell roughly 27 pre-built stories — each a designed scene the data either confirms or it doesn’t. Bring your numbers and the Story Finder runs every one of these shapes against them.
specific to engagement & retention
Engagement is eroding
engagement · T1
It's two companies, split by manager
leadership-quality · T1
Most are fine — a tail is struggling
engagement · T1
On this trajectory, you breach the benchmark
regretted-loss · T1
One condition is the binding constraint
cams · T1
One exit reason towers over the rest
exit-knowledge · T1
Retention is working
retention · T1
The workforce is splitting in two
engagement · T1
Top talent is quietly leaving
regretted-loss · T1
universal shapes — any single metric can take these
A few large values are doing the talking
any focus · T1
A one-time event, not a trend
any focus · T1
It doesn't track — the premise is false
any focus · T1
It's concentrated — one group stands apart
any focus · T1
Scenes are pre-built; your data is the toggle. Browse the full deck or watch one play end-to-end in The Quiet Exodus.
Run it on your data
This metric is computed in the People Analytics Toolbox on your own numbers. See pricing — posted, no quotes.
sources: toolbox:metrics-catalog
What the literature says
The measurement literature behind this signal — sourced, so you can defend it.
“You can use dozens, if not hundreds, of different characteristics to describe your employee population or any given employee in that population. The provided examples are a simplified variation of data that exists in every human resources information system (HRIS) or HR…”
— People Analytics For Dummiesmatch 58%
“References: (1) Rachel Emma Silverman and Nikki Waller (2015) The Algorithm That Tells the Boss Who Might Quit. https://www.visier.com/the-algorithm-that-tells-the-boss-who-might-quit/ (26 November 2018) (2) Erik van Vulpen, Predictive Analytics in Human Resources - Tutorial and…”
— Predictive HR Analyticsmatch 54%
“Stagnating in a role for an additional 10 months raises the odds that employees will leave the company for their next role by about one percentage point, a statistically significant effect. (16) Corporate Culture Research have shown that there is a relationship between Corporate…”
— People Analytics Text Mining with Rmatch 52%
Resources: People Analytics For Dummies · Predictive HR Analytics · People Analytics Text Mining with R