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 →

Enzo Ottens no published score: no usable exchanges 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.

clear all ✕
1exchanges match
0on raw tape
0redirected or not addressed
Answered produced feed D 4 · C 4 · P 4 · Cm 4 4.00

Q Well, let me paint a picture for you, right? If you spend a hundred bucks on a TikTok ad to get a 35 dollar a month user and they cancel after the second month, you only made 70 bucks. So you're losing money. How do you know if you're losing money or making money if you're not tracking retention?

A I'm tracking retention in terms of monthly active usage. I'm tracking retention in terms of because we're talking about the subscription, but we're not only just a subscription business. We also have a freemium business model that people move into subscription. So what I really care about is how many of these users are using our app every single month and looking at that in terms of churn. If somebody churns after months too without ever having touched the app, I don't, I wonder whether the problem is because we didn't do the service right, product wasn't right. I'm looking more into Um, kind of the same kind of strategy that the guys from superhuman did. Look at the customers that would be really sad if you would no longer exist. What are you getting right? And how can I replicate for those customers that aren't getting that value into the same scenario as them? That's how I'm looking at it. I'm not looking at total figures yet.

AI assessment note: “I'm tracking retention in terms of monthly active usage.”

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