ion problem.) The CCI that we are describing provides us with nothing more than a snapshot of the condition of the customer's propensity to churn. Understanding Churn Prediction Models On the other hand, there is predictive modeling for churn. When we do predictive modeling for churn, we employ the skills of a data-mining specialist. This statistical expert examines a number of predictive and behavioral characteristics, including the history of the behavior of people who have churned in the past, and this expert creates a specific prediction about who is likely to churn in the near future. In other words, predictive models include several factors that the CCI does not. The predictive model includes varieties such as: A time frame evaluating the validity of the prediction, along with evaluating a large number of intangible and not directly measurable characteristics which can be worked into a predictive model, but cannot be worked into our CCI. As a matter of fact, our CCI will, no doubt, be of great value to the developer of a predictive churn Predictive chum models recast who is lik ely to ch urn and when, but not why. Figure 14-3: Predictive Churn Models eXcellence in Telecommunications The Telco Churn Handbook model because the factors it measures help strengthen the model's accuracy. The biggest difference between the CCI and a predictive churn model is that churn models try to predict when a customer will churn, without explaining why, whereas the CCI tries to explain why, without helping us determine when. MAKING Use OF THE CUSTOMER CHURN INDEX Assuming that we were able to create a single number, a customer churn index number that could help us understand exactly how susceptible a customer is to churn at any given point in time, in any number of different situations, whatever would we do with it? Although the idea of creating such an indicator might seem, at first glance, to be attractive and interesting, this does not mean that it would be useful. So, before we create this index, let's consider how we cou