← HR Metrics·Workforce Composition
Average Age
Mean age of active employees
How it’s computed
AVG(age)
What the evidence shows
Evidence (effect sizes, priors, validity) is syncing from Principia.
What this metric can show you
Average Age can tell roughly 25 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 workforce composition
{mover} is becoming a bigger share of who you are
workforce-composition · T1
{mover} is fading from the mix
workforce-composition · T1
A few people hold the whole network together
org-networks · T1
One unit is over-managed
workforce-composition · T1
Spans are stretched thin in one corner
workforce-composition · T1
The mix is holding steady
workforce-composition · T1
The organization you have isn't the one you had
workforce-composition · 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.
“(8)According to a study by Culture Amp on retention intention at Box, a worker’s pay or relationship with his boss matters far less than how connected the worker feels to his team. (9) Corporate Culture’s impact on Attrition Research have shown that there is a relationship…”
— Predictive HR Analyticsmatch 51%
“6 in 10 employees indicate that they know what is expected of them at work, but if that ratio can be increased to 8 in 10, your business can achieve a 14% reduction in staff turnover and a 7% increase in productivity. (6) Engagement A study by Praful Tickoo, the head of people…”
— People Analytics Text Mining with Rmatch 50%
“It is calculated simply as the difference between an employee’s date of hire and that employee’s date of birth. Dates of birth and dates of hire are nearly universally maintained in an organization’s human resources databases. Further, one would expect that age at hire would be…”
— Compensating Employees Fairlymatch 47%
Resources: Predictive HR Analytics · People Analytics Text Mining with R · Compensating Employees Fairly