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 →

Michael Beebe 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 5 · C 5 · P 5 · Cm 4 4.85

Q So the day, uh, sorry, I don't mean to cut you off, but it's a really short podcast. Why, why are you better than other people? Is it a unique data source? Is it something about how you analyze the data? Why are you unique?

A What makes us unique, I think, is, um, the, not necessarily, uh, the data that we ingest or any of the particular data sources. It's the, uh, quantity and variety of data that we ingest. We ingest not only the bid stream, which is ad-supported, um, uh, behavioral data, but non-ad-supported behavioral data, which we trade or purchase, uh, for so we can see what people are doing outside of the ad-supported internet. And we also incorporate, um, mobile data, location-based data, app data. Um, that variety of different data, um, is sort of the raw ingredients to a consumer behavioral profile that is unique in the integration of all those data and those data sets, as well as the processes that we use to, um, clean that data of all the fraud and the misleading behaviors or non-human behaviors. So it's really the intermediate data, that consumer user behavioral profile, which makes us different and we think better.

AI assessment note: “consumer user behavioral profile, which makes us different and we think better.”

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