Analytics can be used to guide the customer acquisition efforts. However a typical difficulty with acquisition models is the availability of input data. The amount of information available for people who do not yet have a relationship with the organization is generally limited compared to information about existing customers. Without data you can not build predictive models. Thus data on prospects must be collected. Most often buying data on prospects at an individual or postal code level can resolve this issue.
A usual approach in such cases is to run a test campaign on a random sample of prospects, record their responses and analyze them with predictive models (classification models like decision trees for example) in order to identify the profiles associated with increased probability of offer acceptance.