Mastering Data Mining: The Art and Science of Customer Relationship Management
Michael J. A. Berry, Gordon S. Linoff
- 出版商: Wiley
- 出版日期: 1999-12-28
- 售價: $931
- 語言: 英文
- 頁數: 512
- 裝訂: Paperback
- ISBN: 0471331236
- ISBN-13: 9780471331230
-
相關分類:
Data-mining
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相關主題
商品描述
"Berry and Linoff lead the reader down an enlightened path of best practices." -Dr. Jim Goodnight, President and Cofounder, SAS Institute Inc.
"This is a great book, and it will be in my stack of four or five essential resources for my professional work." -Ralph Kimball, Author of The Data Warehouse Lifecycle Toolkit
Mastering Data Mining
In this follow-up to their successful first book, Data Mining Techniques, Michael J. A. Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. Their first book acquainted you with the new generation of data mining tools and techniques and showed you how to use them to make better business decisions. Mastering Data Mining shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management.
In this book, you'll learn how to apply data mining techniques to solve practical business problems. After providing the fundamental principles of data mining and customer relationship management, Berry and Linoff share the lessons they have learned through a series of warts-and-all case studies drawn from their experience in a variety of industries, including e-commerce, banking, cataloging, retailing, and telecommunications.
Through the cases, you will learn how to formulate the business problem, analyze the data, evaluate the results, and utilize this information for similar business problems in different industries.
Berry and Linoff show you how to use data mining to:
* Retain customer loyalty
* Target the right prospects
* Identify new markets for products and services
* Recognize cross-selling opportunities on and off the Web
The companion Web site at http://www.data-miners.com features:
* Updated information on data mining products and service providers
* Information on data mining conferences, courses, and other sources of information
* Full-color versions of the illustrations used in the book
"This is a great book, and it will be in my stack of four or five essential resources for my professional work." -Ralph Kimball, Author of The Data Warehouse Lifecycle Toolkit
Mastering Data Mining
In this follow-up to their successful first book, Data Mining Techniques, Michael J. A. Berry and Gordon S. Linoff offer a case study-based guide to best practices in commercial data mining. Their first book acquainted you with the new generation of data mining tools and techniques and showed you how to use them to make better business decisions. Mastering Data Mining shifts the focus from understanding data mining techniques to achieving business results, placing particular emphasis on customer relationship management.
In this book, you'll learn how to apply data mining techniques to solve practical business problems. After providing the fundamental principles of data mining and customer relationship management, Berry and Linoff share the lessons they have learned through a series of warts-and-all case studies drawn from their experience in a variety of industries, including e-commerce, banking, cataloging, retailing, and telecommunications.
Through the cases, you will learn how to formulate the business problem, analyze the data, evaluate the results, and utilize this information for similar business problems in different industries.
Berry and Linoff show you how to use data mining to:
* Retain customer loyalty
* Target the right prospects
* Identify new markets for products and services
* Recognize cross-selling opportunities on and off the Web
The companion Web site at http://www.data-miners.com features:
* Updated information on data mining products and service providers
* Information on data mining conferences, courses, and other sources of information
* Full-color versions of the illustrations used in the book
