 to Strengthen the Validity of Churn Predictions The use of historical information and the stability of factors allow us to create churn prediction models with a certain level of accuracy and reliability. Getting a bare minimum of predictability is nice and certainly better than nothing, but what we would really like to do is construct predictive models that are strong and have a high degree of probability. We can take several steps to increase the accuracy of our predictions. Optional Principle No. 3 - Identification of Causal Factors We use the term causal factors to describe whatever actually causes the customers to switch companies and churn. What makes predictive models SO powerful, however, is that we do not actually have to identify any causal factors to create accurate or useful models. We can develop churn models that tell us what we want to know without telling us why. However, when we can identify causal factors and can associate them with our other predictive capabilities, we strengthen the model appreciably. In Chapter 14, we talked about the development of the CCI (customer churn index). This index is a collection of the best causal factors that we can identify for wireless churn. When the model developer can incorporate CCI factors into the churn prediction model, the strength of the model is increased significantly. What We Can Predict The next step in the process is to get even more specific. There are many situations we can predict, all of which are important and can be used for various business purposes. What is especially interesting about these various types of churn prediction models is that each requires different input information to generate them, and each can use different techniques to generate results. This means that different kinds of models will have different levels of accuracy and confidence, using very different kinds of input information. Chapter 15 Predicting How Many People Will Churn When we think about churn prediction, we do not necessarily assume that we can identify all o