Plasma Experimentation, A,B testing features with on chain and off chain data.
I have lived through enough cycles to understand that trust no longer comes from slogans, it comes from how a product measures itself and corrects its own course, how ironic it is, the more promises there are, the less signal remains.
With Plasma, I think what is most worth talking about right now is A,B testing at the real points of friction, not to make a dashboard look pretty, but to expose the exact leaks in the funnel, maybe it all starts with approval to submit rate, then tx success rate, then time to first tx, and finally D7 retention by cohort.
I want to look on chain to see the outcome, whether the tx hash lands successfully, why it reverts, how much fee is actually paid, latency by block and by RPC, and I want to look off chain to see the cause, whether the user stalls on the signing screen or the fee screen, how many times they adjust before they hit submit, how many seconds before they exit the app, whether they return within the same day.
I am tired of inflating new wallet counts only to watch them disappear, I am skeptical of numbers pumped by short term incentives, but I still believe Plasma can win if every feature ships with a hypothesis, a metric, and a cold conclusion.
If you could choose only one metric to tell the truth about Plasma in this phase, which one would you choose. #plasma @Plasma $XPL
