Why use mean revenue in a split test?
If you've got prior information then you should certainly not use simple mean in a split test. I assume you're trying to just predict which group will produce the greatest amount of revenue overall, by trying to emulate the underlying distribution.

Firstly, it's worth noting that any metrics you choose will actually reduce to mean in a pretty trivial way. Eventually mean will necessarily work out, though using a standard bayesian method to estimate the mean is probably your best bet.

If you've got a prior then using a standard bayesian approach to update the prior on your mean revenue is probably the best way to do it. Basically, just take the individual results you get and update a multinomial distribution representing your prior in each case.

If you want some more full background on multinomial distributions as bayesian priors are pretty well, this Microsoft paper does a pretty good job of outlining it. In general, I wouldn't care so much about the fact that your distribution is technically discrete, as a multinomial distribution will effectively interpolate across your solution space, giving you a continuous distribution that is a very good approximation of your discrete space.