The data warehouse provides the enterprise with a memory. But, memory is of little use without intelligence. Intelligence allows us to comb through our mem ories, noticing patterns, devising rules, coming up with new ideas, figuring out 6 Chapter 1 the right questions, and making predictions about the future. This book describes tools and techniques that add intelligence to the data warehouse. These techniques help make it possible to exploit the vast mountains of data generated by interactions with customers and prospects in order to get to know them better. Who is likely to remain a loyal customer and who is likely to jump ship? What products should be marketed to which prospects?
THE ROLE OF DATA MINING
What determines whether a person will respond to a certain offer? Which telemarketing script is best for this call? Where should the next branch be located? What is the next product or service this customer will want? Answers to questions like these lie buried in corporate data. It takes powerful data mining tools to get at them. The central idea of data mining for customer relationship management is that data from the past contains information that will be useful in the future. It works because customer behaviors captured in corporate data are not random, but reflect the differing needs, preferences, propensities, and treatments of customers. The goal of data mining is to find patterns in historical data that shed light on those needs, preferences, and propensities. The task is made dif ficult by the fact that the patterns are not always strong, and the signals sent by customers are noisy and confusing. Separating signal from noise recognizing the fundamental patterns beneath seemingly random variations—is an important role of data mining.