Estimating expected revenue generation
Why not just use quantile regressions?

Usually, in regressions, the coefficients are estimated for the average case: $\min_\beta \sum_i||X_i\cdot\beta -y_i||^2$.

If you want, you can estimate your coefficients so that they tell you things like $P(R=y)&gt;p$. You give a probability $p$, and the model gives you $y$. You can even build probability density function out of these estimation techniques.

In quantile regressions, you solve this optimization problem for every quantile $\tau$ that you want, $\min_\beta \sum_{i|y_i\geq X_i\cdot\beta}\tau|X_i\cdot\beta-y_i| + \sum_{i|y_i&lt; X_i\cdot\beta}(1-\tau)|X_i\cdot\beta-y_i|$.

This is a more robust approach to modeling the revenue distribution. Usually people use quantile regressions because software is readily available, but they can be implemented for other models as well. I work in an institute that does exactly that.