ral complaining calls to customer service Chapter 15 two months previous to the churn event. We might also find that customers tend to churn during a certain time of the year. No one knows what kind of factors we are going to identify when we do this analysis, but we are looking for the strongest association possible between the actual churn event and anything else that happened in these customers' environments. When we find a pattern of behavior or conditions that seem to occur on a regular basis before a customer churn event, then we have identified a predictive factor. Strong predictive factors allow us to build strong predictive models. Principle No. 2 - Stability of Causal Factors The second factor that underlies our ability to create good predictive models is an assumption related to the stability and consistency of the environment that we are using to develop our predictions. We must make sure that the environment described by our historical review is similar to the environment for which we are trying to make predictions. In other words, predictive models will be of little value if major changes in the customer's environment have occurred between the time the history information was collected and the time for which we are trying to make predictions. Stability of Factors Example For example, let's say that we looked at the customer churn behavior over the past several months and found that we had a regular churn rate of three percent per month and that most people who left had three different predictive factors that we could identify. As long as business conditions remain the same, we will probably be able to build a very strong churn prediction model based on these factors. However, if one of our competitors goes out of business, causing prices to increase for most competitors and leaving less churn options available to our customers, then we can probably expect to see a lower churn rate and a shift in the factors leading up to the churn event. eXcellence in Telecommunications The Telco Churn Handbook Ways