Predictive models or 'Scorecards' helps marketers plan and manage the future - be it in building strategies for identifying Credit Card Customers most likely to default on their next payment, or in identifying prospects most likely to respond to a direct mail offer.

Click here for a Case Study on how a Consumer Finance company lowered its portfolio default rates by predicting for Prospects most likely to default on their first payment, and thereby declining their loan application.

Click here for a Case Study on how an Auto OEM was able to identify 'Hot' leads; and thereby increase its sales by 12%.

If we take the example of how predictive modeling can be used for profitably acquiring new customers, it involves:

- Building ‘Response’ or ‘Targeting’ scorecards that will help you identify prospects that you want to acquire – either those that are most likely to respond to a particular product offering or those that will engage at a pre-determined level once they accept a given product.

- Uplift or 'Incremental' Models - very often models are built that target Prospects/Customers that would have responded in any case. Uplift models save precious resources by 'only' targeting those prospects that will respond given an offer.

- Adding a layer of complexity, the concept of ‘Lifetime Customer Value’ can be developed on a predictive basis and overlayed on ‘Response’ dimension leading to a holistic, profit-based customer acquisition strategy.

To see how this works in a real business scenario, please check out the following Case Study: Building a Profitable HELOC portfolio