How to decide who to market? Clustering or Decision Tree? I am working with a dataset that has enough observations and ~ 10 variables,


half of the variables are numeric
another half of the variables are categorical with 2-3 levels (demographics)
one ID variable
one last variable that has sales value, 0 for no sale and bill amount for sale


Using this information, I want to understand which segments of my customers to market. I am using R for code but that's not relevant here. :)

I am confused about which statistical technique to use. Since I want to identify what types of customers I want to acquire and build my campaigns, I was initially thinking of using k-means clustering, i.e. go with unsupervised learning.

However, considering I know who purchased and how much they purchased from the sales value figures, I believe it's worth including this information and have decided to go with predictive modeling instead. Here regression will only tell the importance of variables, but I am interested in nodes (for example, I want rules that can support my marketing campaign such as age 45+, from LA region...etc.) so decision tree would be a better fit.

What are your thoughts? Clustering or decision tree? Or actually something else?
Since you have label data (i.e., sale amount), you can apply supervised machine learning.
After a model has been fit, the features that contributed to predicting targets can be found.
Decisions trees would be a relatively simple option because it easily handle the different types of features and can a yield a decision path.