Suppose I have a customer dataset. My independent variables are various types of customer attributes (age, where they live, gender, price of the item they are potentially buying) and my dependent variable is "not buy" vs "buy" (0 or 1).
What I'm trying to understand is, how much each customer attribute affects the probability of buying. For example, I want to be able to say "customer 25 years old is x% more likely to buy then....".
My first thought of this is to some sort of classification model (logistic regression, trees etc). In my mind, if I can fit this model as best as possible on both the training and test set, then the coefficients of my model will tell me how much impact each customer attribute has on the probability of buying.
Is this the best way to think about this? Is using a predictive model for explanatory purposes accurate?
My end goal is not to predict a future customer's action, but just to understand my current customer's attribute. To me, they sound the same, but I want to make sure that they are one and the same.
Are there other statistical techniques I can use to do what I want to do?