he definition and the tracking of churn will become evident Analysts preparing these models will spend a large amount of time simply poring through large volumes of customer activity data, trying to identify the true churn events. Investigation And Discovery Of Strongly Indicated Causal Factors Once the history of churn has been clearly identified, the analyst then begins the investigative part of the process. Analysis is performed to determine if there are any clues provided by the data that indicate that a churn event is likely to occur in the future. For example, it is a well-known fact that a customer who is bound by a contract for a specified amount of time has a very high likelihood of churning eXcellence in Telecommunications The Telco Churn Handbook shortly before or after that event. The expiration of the contract is the causal factor. Expiration of a contract is a relatively obvious and easy-to-identify factor, but the analyst specializing in churn investigation will work hard to discover as many factors as possible. Projection of Discovered Factors onto the Future Population Once the causal factor is identified, the next step in the process is to see how these factors can be used to predict the future churn event. For example, if we find out that fifty percent of the people whose contracts expire in a given month will leave and find another carrier, then we will have the necessary information to do predictions. To project this rule into the future, all we need to do is find out how many contracts will expire each month. We know then that, based on our historical analysis, half of them will churn. CREATING TARGETED PREDICTIVE MODELS Another predictive model that we develop is the targeted model. With targeted models we attempt to understand the actual churn activity of individuals, thereby helping us identify where to focus our retention activities. Reasons to Generate Targeted Predictive Models As in the case of the raw churn numbers, there are several reasons we might want to build predictive models t