Cuốn sách này mô tả các công cụ và kỹ thuật thông minh để các kho dữ liệu. Những kỹ thuật này giúp làm cho nó có thể để khai thác vùng núi rộng lớn của dữ liệu được tạo ra bởi các tương tác với khách hàng và triển vọng để có được biết họ tốt hơn. | 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 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 difficult 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. This book covers all the most important data mining techniques and the strengths and weaknesses of each in the context of customer relationship management. The Role of the Customer Relationship Management Strategy To be effective data mining must occur within a context that allows an organization 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 .
