Mastering Data Mining: The Art and Science of Customer Relationship Management

Michael J. A. Berry

ISBN: 9788126518258

512 pages

INR 759


Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with "mining" or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of "database marketing" managers.




Part I: Setting The Focus

  • Data Mining in Context
  • Why Master the Art?
  • Data Mining Methodology: The Virtuous Cycle Revisited
  • Customers and Their Lifecycles


Part II: The Three Pillars Of Data Mining

  • Data Mining Techniques and Algorithms
  • Data, Data Everywhere...
  • Building Effective Predictive Models
  • Taking Control: Setting Up a Data Mining Environment


Part III: Case Studies

  • Who Needs Bag Balm and Pants Stretchers
  • Who Gets What? Building a Best Next Offer Model for an Online Bank
  • Please Don't Go! Churn Modeling in Wireless Communication
  • Converging on the Customer: Understanding Customer Behavior in the Telecommunications Industry
  • Who Is Buying What? Getting to Know Supermarket Shoppers
  • Waste Not, Want Not: Improving Manufacturing Processes.
  • The Societal Context: Data Mining and Privacy