Ideas for prospect scoring model
I faced almost exactly the same scenario a year and a half ago -- basically what you have is a variation of the one-class classification (OCC) problem, specifically PU-learning (learning from Positive and Unlabelled data). You have your known, labelled positive dataset (clients) and an un-labelled dataset of prospects (some of which are client-like and some of which are not client-like). Your task is to identify the most client like of the prospects and target them... this hinges on the assumption that prospects that look most like clients are more likely to convert than prospects that look less like clients.

The approach we settled upon used a procedure called the Spy-technique. The basic idea is that you take a sample from your known positive class and inject them into your unlabelled set. You  then train a classifier on this combined data and then run the unlabelled set back through the trained classifier assigning each instance a probability of being a positive class member. The intuition is that the injected positives (so-called spies) should behave similarly to the positive instances (as reflected by their posterior probabilities). By setting a threshold this allows you to extract reliable negative instances from the unlabelled set. Now, having both positive and negative labelled data, and you can build a classifier using any standard classification algorithm you choose. In essence, with the spy technique, you boot-strap your data to provide you with the needed negative instances for proper training.



For starters you should look into the work of Li and Liu who have a number of papers exploring the topic of OCC and PU-learning.