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 →

Dominic Edmonds 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 5 5.00

Q Yep. And, and your model, so what, how are you making money in that model and when did you shift it to more SaaS?

A So we've had a number of iterations. So we started out as performance. Now I would still class performance as SaaS for us, because having done that for kind of three, four years in the early days, we understood seasonality down to an art, meaning that we understood What to expect from a client depend on their sale cycle, excuse the pun. So that was fairly predictable. However, taking something then to a blend of a base fee, which allows us to talk about servicing, allowed us to talk about the implementation fees, the costs that go with it, and then applying a performance fee on top was our second iteration. And now we've moved into a 100% flat fee model, which again matches with the same kind of fees people would have been paying us on average, but it smooths it out rather than making it choppy, I guess.

AI assessment note: “we started out as performance... second iteration... moved into a 100% flat fee model”

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