This question is in tandem with my earlier question here:
https://stats.stackexchange.com/questions/188799/using-ml-approaches-to-build-a-recommender-engine-for-sales-team
However, now I'd like to discover insights about x features as opposed to predicting future y values.
To clarify, here's a bit of context: I'm a developer at a startup, where most of our revenue comes from ad purchases. We have a sales team that approaches our leads to sell them ads. I wanted to make this process more efficient by looking at past data (particularly those leads that converted) and discovering patterns about their featues. That is, if we find out that leads with certain features (xk or xj) are more likely to convert, the sales team would spend more time and focus on those leads.
So, to formulate the problem, I guess we can say, we have the following data from past sales efforts: {(x1, y1), …, (xn, yn)}, where x represents the sales leads and y is from a binary set of {0,1}, where 1 indicates that the lead converted and 0 denotes that she didn’t. And each sales lead has the following feature vector for xi in Rd: xi = {x1, ... ,xd}
I believe we'll only need to focus on the Xs that did convert, right?
How would you go about solving this problem?