ations The Telco Churn Handbook of time. Information about payment schedules, phone utilization, increases or decreases in revenues, customer service contacts, and everything else that can be discovered about a customer's interaction with the firm is scrutinized. Step Two - Identification of Churn Causal Factors The next step is to find anything about the behavior or activities of the customer before churning that seems to be related to the churn event. For example, you might find that customers who churn show a marked reduction in their minutes used in the three months prior to contract termination. Or you might discover that some of the customers who churn typically make several calls to the customer service organization to complain about coverage six weeks before they leave. All such potentially predictive events are identified and categorized. Each is given a weight indicating its probable strength or weakness as a predictive factor. Step Three - Calculating the Churn Probabilities Once the factors are identified and weighted, the information is built into a formula (model), which is then applied to all customers. The formula is run, and all of the factors that apply to existing customers are evaluated. The more causal factors that show up, the more likely that person will be to churn, the fewer the factors, the less likely to churn. MAKING USE OF INDIVIDUAL CHURN PREDICTIONS When the individual churn prediction process is run, the analyst is provided with a list of the names of every customer in the firm. Associated with each customer's name is a number that indicates his or her likelihood to churn. With such a list, the churn manager now has that key ingredient that was missing from our churn management Chapter 15 formula, an idea about the risk of churn associated with the customer and the time frame for which that risk applies. This Is Not an Exact Science The good news is that churn prediction models provide the firm with some good clues about where a large population of potential churners might be found