In performance based digital advertising, one of the key technical tools is to predict the expected value of post ad click purchases(a.k.a. conversions). Some of the salient aspects of this problem such as having a non-binary label and advertisers reporting the label in different scales make it a much harder problem than predicting probability of a click. In this paper we ask what is a good way to model the label and extract as much information as possible. A label can affect the model in multiple ways and we consider three such directions and come up with new techniques for each of them. The first is that the label scale can affect how the model capacity is devoted to different advertisers, the second is how labels for outliers can affect over-fitting and the third is if we can use information in the distribution of the label and not just the mean.