models to make significant changes in the way they are able to manage churn. Chapter 6 RANDOM, DISORGANIZED RESPONSES TO CHURN Unfortunately, the majority of telcos have no idea of precisely how they should respond to churn. For many reasons, that we will discuss later, this lack of a clear approach to managing churn leaves these companies with no choice but to continuously respond to churn events with sporadic, disorganized, and usually spurious and ineffective techniques. Ironically, it is not the nature of the churn campaigns and programs themselves that create the problem. In fact, most of them are quite effective. It is the erratic combination of the programs that tend to actually do more harm than good. When the various parts of the, looking at different aspects of the churn problem, develop decidedly different perspectives of what the problem is and how it is best addressed, the results are campaigns that conflict with each other's objectives and confuse customers. The programs often do worse than cancel each other out; they actually stimulate more churn. A few examples will illustrate this point. The Churn Stimulation Campaign This situation occurs in far too many situations. Executives at the telco realize that they are having a problem with churn. They proceed to hire a data mining group to develop churn prediction models. These models look at information about the customers and determine which ones are more likely to churn. The models generally attempt to determine which customers are unhappy with price, quality, coverage, service, or any number of other variables and then create a "hot list" of customers who are most likely to churn in the near term. Great! So the data mining models are run, and the list of names of potential churners is given to the marketing and advertising departments who have much experience in building ads that attract customers to the firm and much collateral available to convince people to join up with the firm. What happens when a group like that is given a list of potential c