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Data mining techniques for marketing, sales, and customer relationship management

Gordon S. Linoff Micahel J. A Berry

3rd ed.. 2011

Online access

  • Title:
    Data mining techniques for marketing, sales, and customer relationship management
  • Author: Gordon S. Linoff
  • Micahel J. A Berry
  • Description: Data Mining Techniques; Contents; Introduction; Chapter 1 What Is Data Mining and Why Do It?; What Is Data Mining?; Data Mining Is a Business Process; Large Amounts of Data; Meaningful Patterns and Rules; Data Mining and Customer Relationship Management; Why Now?; Data Is Being Produced; Data Is Being Warehoused; Computing Power Is Affordable; Interest in Customer Relationship Management Is Strong; Every Business Is a Service Business; Information Is a Product; Commercial Data Mining Software Products Have Become Available; Skills for the Data Miner; The Virtuous Cycle of Data Mining
    A Case Study in Business Data MiningIdentifying BofA's Business Challenge; Applying Data Mining; Acting on the Results; Measuring the Effects of Data Mining; Steps of the Virtuous Cycle; Identify Business Opportunities; Transform Data into Information; Act on the Information; Measure the Results; Data Mining in the Context of the Virtuous Cycle; Lessons Learned; Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management; Two Customer Lifecycles; The Customer's Lifecycle; The Customer Lifecycle; Subscription Relationships versus Event-Based Relationships
    Event-Based RelationshipsSubscription-Based Relationships; Organize Business Processes Around the Customer Lifecycle; Customer Acquisition; Who Are the Prospects?; When Is a Customer Acquired?; What Is the Role of Data Mining?; Customer Activation; Customer Relationship Management; Winback; Data Mining Applications for Customer Acquisition; Identifying Good Prospects; Choosing a Communication Channel; Picking Appropriate Messages; A Data Mining Example: Choosing the Right Place to Advertise; Who Fits the Profile?; Measuring Fitness for Groups of Readers
    Data Mining to Improve Direct Marketing CampaignsResponse Modeling; Optimizing Response for a Fixed Budget; Optimizing Campaign Profitability; Reaching the People Most Influenced by the Message; Using Current Customers to Learn About Prospects; Start Tracking Customers Before They Become "Customers"; Gather Information from New Customers; Acquisition-Time Variables Can Predict Future Outcomes; Data Mining Applications for Customer Relationship Management; Matching Campaigns to Customers; Reducing Exposure to Credit Risk; Predicting Who Will Default; Improving Collections
    Determining Customer ValueCross-selling, Up-selling, and Making Recommendations; Finding the Right Time for an Offer; Making Recommendations; Retention; Recognizing Attrition; Why Attrition Matters; Different Kinds of Attrition; Different Kinds of Attrition Model; Predicting Who Will Leave; Predicting How Long Customers Will Stay; Beyond the Customer Lifecycle; Lessons Learned; Chapter 3 The Data Mining Process; What Can Go Wrong?; Learning Things That Aren't True; Patterns May Not Represent Any Underlying Rule; The Model Set May Not Reflect the Relevant Population
    Data May Be at the Wrong Level of Detail
    The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition-more than 50% new and revised- is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving respons
  • Publication Date: 2011
  • Publisher: Indianapolis, Ind. : Wiley Pub., Inc.
  • Format: 1 online resource (889 p.).
  • Identifier: ISBN 1-280-68619-7;ISBN 9786613663139;ISBN 1-118-08745-3;ISBN 1-118-08750-X
  • Subjects: Data mining; Marketing -- Data processing; Business -- Data processing; Electronic books
  • Language: English
  • Source: 01DAL UDM ALMA

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