The quantitative ux research team at Google was created for exactly this problem: a service which became popular before the right metrics existed, meaning metrics need to be derived first, then optimized. We would observe users (irl), read their logs, then generate experiments to improve the behavior as measured by logs, and return to see if the experiment improves irl experiences. There were not many of us and we are around :)
Thanks for the reply! I am outside the forecasting sphere.
RMSLE gives proportional error (so, scale-invariant) without MAPE's systematic under-prediction bias. It does require all-positive values, for the logarithm step.
Disagree with the first piece about only using the top 0.1%. I grew up (through my 20's) shooting on a Pentax K1000, cheap workhorse of a camera, and I preferred its ergonomics to top-end mirrorless cameras I use today.