f the individuals that will leave our company. Although predicting the who and when of churn can certainly be useful, prediction of raw churn numbers is equally important. It is the gross number of people that are expected to leave during a given time frame. Predicting Who Will Churn in a Given Time Frame The more classical approach to churn prediction is trying to predict which people will leave, SO that, in theory at least, we can do something to prevent this event. In the following sections we will take a closer look at each of these churn prediction models. PREDICTING RAW CHURN NUMBERS The raw churn prediction model that is very useful because it predicts how many people will leave in a given time frame. A model like this will not identify who the individuals are that will leave, but it will provide us with some useful information nonetheless. Prediction History Current Month Figure 15-1: Raw Churn Prediction Chart eXcellence in Telecommunications The Telco Churn Handbook Figure 15-1 shows a typical raw churn prediction chart, giving the actual number of customers that have churned for each month up to the current month, and then predicting what the churn will be in the months ahead. Ironically, even though the raw churn prediction model is less specific than the targeted model, it is usually both easier to build and more accurate. This is simply one of the truisms of statistical analysis. It is easier to predict the behaviors of a large population of people than to predict the behavior of any one individual in that group. Reasons To Generate "Raw" Churn Numbers Even though the development of raw churn numbers is easy and accurate, that does not necessarily mean that it creates useful information. There are, however, several areas where this kind of information can be extremely valuable. Capacity Planning Raw churn numbers can be used to provide assistance in capacity planning for the different channels. Typically, the customer service organization is the one hardest hit by churn activity, and any way that we