 measures can give us tangible facts that eXcellence in Telecommunications The Telco Churn Handbook we can use to make important decisions. Because of this, the science of creating predictive analyses is subject to all of the rules of good, sound, statistical analysis, along with good business sense. Basic Operating Assumptions for Churn Prediction In summary, then, our primary objective is to predict with some degree of certainty the churn behavior of individuals in our customer population. To accomplish this we have to make certain assumptions based upon certain fundamental principles that underlie scientifically provable and accurate predictions. Principle No. 1 - Historical Precedent The most important principle that underlies our ability to create reliable predictions is the rule of historical precedent. This rule says that predictive models can be developed based on historical analysis of the behavior of individuals who are members of the population that we want to analyze. This rule tells us that, if we can look at how individuals in a population have behaved in the past, we might be able to discover a predictive pattern that we can use to anticipate the behavior of similar individuals in the future. To create predictive models we must, of course, have access to historical information about the same behaviors from our population. Good predictive models cannot be generated without this basic input. Example of Historical Precedent in Use An example can illustrate this principle. Assume that a company has experienced churn of three percent per month for the past fifteen months. The first step toward developing our predictive model is to identify which customers left during each of these months. Once we identify these customers, we look at the conditions that were prevalent in these customers' environments at the time of, and leading up to, the churn event. Examination of these factors may lead to any number of interesting insights. We may discover that customers who churn tend to have a history of making seve