Similarity matching between two distinct datasets (marketing case study)
If I understand your question correctly, you have two groups of people: Group A, each of whom has purchased the product, say yogurt; and Group B, each of whom has not purchased yogurt. Your problem at hand is to find all people in Group B who will be likely to purchase your yogurt, if they have a similar profile as people in Group A.
This seems to be a very common problem in causal inference, where you need to match the treated people with the control group, but since one person could not be both treated and untreated, we need to find &quot;similar&quot; people on both sides such that they are comparable (in terms of their characteristics, or variables) so that we can make a causal inference from there.
Now, returning to your problem, I don't think it is necessary to do the clustering for matching. Instead, you could consider the &quot;matching&quot; approach commonly used for causal inference. Here is an r packages that comes to my mind: MatchIt.
In essence, what you need to do is to regard Group A as treatment and Group B as control. I believe they provide many different ways of matching algorithm and you can certainly play around to see which one works for you the best.