Ask in pairs on open text. We say, what's great about working for this organization and what would you do to improve working at this organization? You need t keep it open enough that people rite feel that they can write stuff in without having just sort of leading them in but not so open, like, is there anything else you want to tell us? Where that generally prompts people to be sarcastic and joking and not give you any serious responses. So we've got model and training data of somewhere in the region of 230 themes that people talk about in open questions. And then we analyze every single one of those themes by those categories using binary classification models.
"The people" are often the most valuable asset for a company, so getting the ones who are a good fit, supporting them in their work and their careers, and figuring out what motivates (and demotivates) them is critical. And data—both quantitative and qualitative—can help with that. It's a topic we've wanted to tackle for a long time (well, Moe and Michael have; Tim was confused, as he thought it couldn't be that hard to analyze a data set consisting of a single "Do they do their f***ing job?" boolean flag), and we finally got to it, with Andrew Marritt from OrganizationView! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.