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 →

Konstantin Bayondin 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 They're all still with you. That's great. So what are you, I mean, give me a backstory here. You have a, uh, based off your bio, a lot of different things you've had your hands on. What year did you launch, Tommy?

A Yeah, so I had always been a data geek and, you know, science-y guy and turned into a marketer, and I worked for more than five years in e-commerce, in leading positions in marketing, and at some .3 years ago, I moved back to United States, to New York, and worked at Compass in paid digital marketing and marketing technology, and I spotted a huge opportunity because, you know, doing digital marketing in real estate is, you know, it's real pain. And, uh, as I, as I said, the key problem is that, uh, long conversion cycles offline and very rare conversions that you cannot use for optimization for ads, Google, Facebook. And I grasped the opportunity for predictive marketing approach where you can score every website visitor in real time and use this, uh, scoring, uh, the expected probability to, to pay later.

AI assessment note: “I had always been a data geek and, you know, science-y guy”

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