The business problem: We have two different vendors that offer personalized recommender engines and want to do A/B testing with them. The recommendation will give the user a personalized offer via a push message on the phone. During the testing period, we should give each provider a dataset with different details regarding the customers (purchase history, in-app events, etc). Each vendor will receive a dataset with identical info but from different clients.
What is the best method to choose the two datasets so that they would be similar in terms of client behaviour?
I assume that giving them random data from our database wouldn't be a rigorous method so one idea that I have in mind is applying dbScan clustering on our database and further randomly picking clients from each cluster - I don't know if this is the best approach. The full database has 200k clients and each dataset should contain 5k clients.
Example: After dbScan clustering there are
k=10 clusters so I randomly pick elements from each cluster and split them into Dataset01 and Dataset02.