To be effective, data mining must occur within a context that allows an organi zation to change its behavior as a result of what it learns. It is no use knowing that wireless telephone customers who are on the wrong rate plan are likely to cancel their subscriptions if there is no one empowered to propose that they switch to a more appropriate plan as suggested in the sidebar. Data mining should be embedded in a corporate customer relationship strategy that spells out the actions to be taken as a result of what is learned through data mining. When low-value customers are identified, how will they be treated?
Are there programs in place to stimulate their usage to increase their value? Or does it make more sense to lower the cost of serving them? If some channels consis tently bring in more profitable customers, how can resources be shifted to those channels? Data mining is a tool. As with any tool, it is not sufficient to understand how it works; it is necessary to understand how it will be used. Why and What Is Data Mining? This sidebar explores the example from the main text in slightly more detail. An analysis of attrition at a wireless telephone service provider often reveals that people whose calling patterns do not match their rate plan are more likely to cancel their subscriptions. People who use more than the number of minutes included in their plan are charged for the extra minutes—often at a high rate. People who do not use their full allotment of minutes are paying for minutes they do not use and are likely to be attracted to a competitor’s offer of a cheaper plan.