# Ideas for prospect scoring model

I have to think about a model to identify prospects (companies) that have a high chance of being converted into clients, and I'm looking for advice on what kind of model could be of use.

The databases I will have are, as far as I know (I don't have them yet), the list of current clients (in other words, converted prospects) and their features (size, revenue, age, location, stuff like that), and a list of prospects (that I have to score) and their features. However, I don't think I'll have a list of the companies that used to be prospects but for which the conversion to clients failed (if I had, I think I could have opted for a random forest. Of course I could still use a random forest, but I feel it would be a bad idea to run a random forest on the union of my two databases, and treat the clients as converted and the prospects as non-converted...)

So I need to find, in the list of prospects, those who look like the already existing clients. What kind of model can I use to do that ?

(I'm also thinking about things such as "evaluating the value of the clients and apply this to the similar prospects", and "evaluating the chance each prospect has of going out of business" to further refine the value of my scoring, but it's kinda out of the scope of my question).

Thanks