## 20 Metrics in 20 Days- Day 5: Transition Probabilities

Day 5 in our series of 20 consecutive posts on HR metrics: Transition Probabilities

## Definition

Transition probability is the probability of someone in one role (or state) transitioning to another role (or state) within some fixed period of time.

The year is the typical unit of time but as with other metrics that depend on events with a lower frequency, I recommend also you look at longer periods too (e.g. 2 years).

## Description

We’ll get concrete and start with a group of 100 employees working at a call center as of Jan 1st. Over the course of the next 12 months we have the following changes:

• 27 quit the company
• 14 are “invited to flourish elsewhere”
• 9 are promoted
• 9 move to another role in the company
• 41 remain in their current role

Framed in terms of transition probabilities and given that we have 100 employees we would just say the following:

• .27 probability of quiting the company (27/100)
• .14 probability of being “invited to flourish elsewhere” (14/100)
• .09 probability of being promoted (9/100)
• .09 probability of moving to another role in the company (9/100)
• .41 probability of remaining in their current role (41/100)

The concept is that simple.

As another example, if I start with 85 employees and then 10 leave within the next year, 7 are promoted, and the rest stay in the same role I would have the following transition probabilities:

• .117 probability of leaving (10/85)
• .082 probability of being promoted (7/85)
• .8 probability of staying in the same role (68/85)

Most HR organizations are not probably not talking about transition probabilities but I honestly believe they give you a simple but serious advantage in understanding employee movement, especially when combined with some quality visualizations.

If you want to get WAY into the math on this kind of thing check out Markov Chains. It’s not critical but some of you might like to scratch that mathematical itch and fully extend the value of understanding and tracking transition probabilities.

## Why You Should Care

It forces you see all the transitions in the same terms and at the same time.

There is no debating definitions.

Just cold, informative numbers.