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 →

Thomas Walle 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
1on raw tape
0redirected or not addressed
Answered raw tape D 5 · C 5 · P 4 · Cm 4 4.60

Q to manage internal data. Uh, this concept is, is more that external data, which traditionally has been like competitive intelligence and surveys is becoming, um, uh, digital and therefore can be analyzed. How, how, how much of a new concept is that, especially for all those industries like retail and, uh, you know, like shopping malls and that type of thing. How ready are they to embrace something like this?

A Yeah, they are not as ready as we wish they, they, they were. So we see, we are kind of two types of, um, clients. We have the, the ones that have invested, uh, in, in an analytics team and they have the tools and they can kind of understand this data them, uh, themselves and we provide them the data sets. Uh, then you also have, especially in kind of real estate and, and retail, not very sophisticated clients that need this, Insights delivered. So we're kind of playing on two sides, uh, right now, but luckily we're seeing the trend that even like the large retail chains and the larger, like, let's say shopping mall operators, they're investing more and more now time to like merge all these data sets together. So you can build the data, but also transaction data and CRM data.

AI assessment note: “they are not as ready as we wish they, they, they were.”

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