After a first article on how Survival Analysis can help understand employee churn, this part focuses on the notion of censoring.
AI and Machine Learning are powerful techniques to generate insights into your workforce. As many organisations are only now starting to use it, blindly trusting predictions coming out of black-box models might still sound quite daunting to most. This is where Model Explainability comes in.
You need to learn about your churn!”. But what is churn? Am I going to talk about machines and techniques to improve butter production? Well, no, I am talking about the other type of churn, specifically employee churn, or turnover – the rate at which employees leave a workforce and are replaced.
This year, the People Analytics & Future Of Work (PAFOW) Conference was held online instead of a conference hall. More than 300 HR managers enjoyed the three-day online event hosted by Al Adamsen and David Green. Here are my four takeaways.
“That’s astounding” — a common response from the ever-enthusiastic Al Adamsen, the PAFOW founder. Coming from a physics background, and several years attending scientific conferences, attending PAFOW Europe 2020 was certainly an interesting introduction to the world of HR.
“Daddy, did you check the weather forecast?” This is the question my daughter asks me almost every morning. Maybe I should do this more often. Just like not checking the weather forecast often leads to us coming home drenched in rain, ignoring good HR data & analytics can also leave us out in the cold.
Investing in people analytics leads to a win-win for the business as well as its people. Here are 7 useful steps to consider when undertaking workforce analytics.