The metric they're using (average tenure) is not useful for comparing employee retention across companies. E.g. a company that's growing will automatically have a lower average tenure than one that's not growing.
Now, it's understandable why they're using an inappropriate metric. It's the only thing they can even remotely reliably derive from their data set of scraped Linkedin profiles. But when your data is bad, the right thing to do is to just not do the study. Not to write a content marketinb blog post that's so blatantly incorrect that it has to be intentionally dishonest.
> Now, it's understandable why they're using an inappropriate metric. It's the only thing they can even remotely reliably derive from their data set of scraped Linkedin profiles.
I don't think even that is true.
They could do a better metric like "Among the employees that were employed by company X ten years ago, what percentage are still employed there?"
This also solves the "average tenure at a growing company" problem.
I had to look a few times to convince myself they weren't at least using tenure of ex-employees. But I'm not sure they could actually search linkedin that effectively on anything but current employees without hitting monetized APIs, etc.
The metric they're using (average tenure) is not useful for comparing employee retention across companies. E.g. a company that's growing will automatically have a lower average tenure than one that's not growing.
Now, it's understandable why they're using an inappropriate metric. It's the only thing they can even remotely reliably derive from their data set of scraped Linkedin profiles. But when your data is bad, the right thing to do is to just not do the study. Not to write a content marketinb blog post that's so blatantly incorrect that it has to be intentionally dishonest.