The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Daniel Chait no published score: only 1 usable exchange on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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1exchanges match
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Answered raw tape D 4 · C 5 · P 4 · Cm 3 4.15

Q Do you offer that type of analytics outside of DNI as well? Is that, is that part of the product?

A So a big part of greenhouse generally is that customers will collect lots of information at the hiring process and then use that to like, Either make better decisions in the moment about which candidates to, to go forward with, or overall at their process. Like, how's the process going? Uh, what trends are getting better or worse? You know, where are we say spending money on candidates that are, uh, you know, not productive versus spending money on candidates that are, you know, that are making their way through the funnel, uh, which of our interviewers, uh, always say yes, which of our interviewers always say no, and how do we sort of calibrate the interview process? So there's lots of kind of data collection that we provide in overall in the, In the product because, um, you know, hiring is one of the, you know, super impactful. Every time you make a hire, you're basically placing a six figure annual bet on that person. And I can't think of another place in any organization where you make as big of a decision with as much financial impact with as little information as when most companies hire someone. They have a data sheet on like this table when they buy it, but often they hire people based on kind of like gut feel and, ah, he seemed like a good guy, went to the right college. So we try to bring a data perspective there.

AI assessment note: “there's lots of kind of data collection that we provide in overall in the”

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