I have a problem that seems like it should be simple, but I'm having a hard time nailing down the right test. I'm analyzing sample data from a pricing experiment in which some customers are shown a higher price. The customers seeing the higher price are less likely to buy, but when they do buy they contribute more revenue. I have a dataset that tells me how much revenue was generated by each user (including those that see the price but do not buy). Here are some samples below. In real life, control and test both contain thousands of data points.
control <- (0, 0, 15, 0, 25, 0, 17, 0, 0, 12, 0) test <- (0, 0, 0, 0, 30, 0, 19, 0, 0, 20, 0)
In this example, control had a higher conversion rate, but test generated more revenue per user. I want a test that can help me understand whether test or control are likely to generate more total revenue (or if I don't have enough data to say either way). So far I've tried running a simple t-test on the two datasets, since I was hoping that I had enough data points to not worry about the data not being normally distributed. I've also been looking at the Wilcoxon Test, but I'm having a hard time understanding it!
Sorry if this is a basic question, but any advice at all would be super helpful!