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
- The repeat purchase % of users who bought various items
- 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.