Data Mining Techniques For Marketing, Sales, and Customer Relationship Management

Name: Data Mining Techniques For Marketing, Sales, and Customer Relationship Management
Price: 7.79 USD
Availability: InStock
ISBN: 9780471470649

ISBN-10: 0471470643

ISBN-13: 9780471470649

Edition: 2nd 2004 (Revised)

Authors: Michael J. A. Berry, Gordon S. Linoff

List price: $50.00

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Description:

Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for…

Book details

List price: $50.00
Edition: 2nd
Copyright year: 2004
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/9/2004
Binding: Paperback
Pages: 672
Size: 9.25" wide x 7.25" long x 1.00" tall
Weight: 2.2
Language: English



Acknowledgments


About the Authors


Introduction



Why and What Is Data Mining?



The Virtuous Cycle of Data Mining



Data Mining Methodology and Best Practices



Data Mining Applications in Marketing and Customer Relationship Management



The Lure of Statistics: Data Mining Using Familiar Tools



Decision Trees



Artificial Neural Networks



Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering



Market Basket Analysis and Association Rules



Link Analysis



Automatic Cluster Detection



Knowing When to Worry: Hazard Functions and Survival Analysis in Marketing



Genetic Algorithms



Data Mining throughout the Customer Life Cycle



Data Warehousing, OLAP, and Data Mining



Building the Data Mining Environment



Preparing Data for Mining



Putting Data Mining to Work


Index