Correlation analysis while detecting outliers

I have simple dataset here. Supposed I want to find out which customers who bought a certain item are more likely to come back after 10 months.

I have 2 sets of data

1. The repeat purchase % of users who bought various items
2. The # of users who bought various items

In the example dataset below, in total 150,000 users bought items, and 20% of them returned after 10 months to buy something.

In the first table, it represents how many users purchased that item X times. For example, D3 means that 60,000 purchased Item A 3 times.

In the second table, it represents how likely the user are to come back after 10 months if they purchased that item X times. For example, D12 means that if a user purchased Item A 3 times, the likelihood they will come back is 34%

My question is - given that there is a natural correlation between people who buy more things and people who come back, how do we find the outliers, and the inflexion points?

I am particularly puzzled by how to use these 2 tables together. Multiplying the related cells together somewhat works, but i wonder if there is a more significant way to do that.