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 →

Roy Mann 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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Redirected raw tape D 2 · C 4 · P 3 · Cm 3 3.00

Q talk about why, but it's a fact of life, right? It's a, and I don't think Shopify got there without payments. We could Debate whether that's multi-product, but it probably is, but no one gets, so you've got to figure out that strategy, if for no other, if TAM is a big reason, but if you'll never get that stickiness without the second, third, fourth, fifth product to add, right?

A Yeah, I can share. We had a problem with that, with explaining it to investors, because we grew too fast, like, and when you have new cohorts, Okay. That they're not, uh, yet mature. It's very, it obscures everything when you're like, uh, cohorts from the new year kind of obscured the, all the rest and everything kind of like looks weird. Like you said, and we had to work so hard to show that even on the graph, like when we did the graph, it looked weird. Cause like the, all the small cohort looked flat, like physically, when you looked at the graph, they looked flat only when you removed the new courts, which were bigger, you, you saw the same image of exponential growth. So it looks like, Hey, all these courts are flat. Why are they flat? They're not flat. You just can't see it because the new ones like are three times bigger. So we did an animation in the IPO. We created an animation that we added the layers and everyone saw it's the same thing because everyone, no one understood it's exponential when, when it look appears like. Flatish because you're growing so fast and other companies, when you're not growing as fast, when you're adding the same number of customers over the years, you know, then you have like a, it looks very healthy, like a layer cake and that you can compare one to the other.

AI assessment note: “We had a problem with that, with explaining it to investors”

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