Originally Posted by

**BGM**
Some comments:

You may try to google "zero-inflated" for some relevant issues here.

I second Karabiner's idea here. I think you need to clarify your goal - to compare which campaign is better - in what sense/measured by what variable. For example, if you look at the number of purchases only, then as Karabiner points out you just need to compare the proportion by proportion test.

And empirically if the number of purchases is not too small, you may try to focus on the revenue generated (those non-zero data) and try to plot and see if a certain parametric distribution fit it well. Then you may try to conduct an appropriate parametric test for the mean. Of course a non-parametric alternative maybe appropriate as well as the data should not be as highly skewed as before.

If the number of purchases is very small, I do not think you can do much to compare the "magnitude" of the purchases. Actually your situation should be very common in insurance company where they face those zero-inflated data a lot. In such case unless you can rely on external resources to give you additional information for the distribution of revenue, and use it in your analysis to estimate the mean. Without these I think you can just look at those rare purchases case by case.