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

XXV, 643 S.

graph. Darst.

Includes index

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Second Edition; Acknowledgments; About the Authors; Introduction; Contents; Chapter 1: Why and What Is Data Mining?; Analytic Customer Relationship Management; What Is Data Mining?; What Tasks Can Be Performed with Data Mining?; Why Now?; How Data Mining Is Being Used Today; Lessons Learned; Chapter 2: The Virtuous Cycle of Data Mining; A Case Study in Business Data Mining; What Is the Virtuous Cycle?; Data Mining in the Context of the Virtuous Cycle

A Wireless Communications Company Makes the Right ConnectionsNeural Networks and Decision Trees Drive SUV Sales; Lessons Learned; Chapter 3: Data Mining Methodology and Best Practices; Why Have a Methodology?; Hypothesis Testing; Models, Profiling, and Prediction; The Methodology; Step One: Translate the Business Problem into a Data Mining Problem; Step Two: Select Appropriate Data; Step Three: Get to Know the Data; Step Four: Create a Model Set; Step Five: Fix Problems with the Data; Step Six: Transform Data to Bring Information to the Surface; Step Seven: Build Models

Step Eight: Assess ModelsStep Nine: Deploy Models; Step Ten: Assess Results; Step Eleven: Begin Again; Lessons Learned; Chapter 4: Data Mining Applications in Marketing and Customer Relationship Management; Prospecting; Data Mining to Choose the Right Place to Advertise; Data Mining to Improve Direct Marketing Campaigns; Using Current Customers to Learn About Prospects; Data Mining for Customer Relationship Management; Retention and Churn; Lessons Learned; Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools; Occam's Razor; A Look at Data; Measuring Response

Multiple ComparisonsChi-Square Test; An Example: Chi-Square for Regions and Starts; Data Mining and Statistics; Lessons Learned; Chapter 6: Decision Trees; What Is a Decision Tree?; How a Decision Tree Is Grown; Tests for Choosing the Best Split; Pruning; Extracting Rules from Trees; Taking Cost into Account; Further Refinements to the Decision Tree Method; Alternate Representations for Decision Trees; Decision Trees in Practice; Lessons Learned; Chapter 7: Artificial Neural Networks; A Bit of History; Real Estate Appraisal; Neural Networks for Directed Data Mining; What Is a Neural Net?

Choosing the Training SetPreparing the Data; Interpreting the Results; Neural Networks for Time Series; How to Know What Is Going on Inside a Neural Network; Self-Organizing Maps; Lessons Learned; Chapter 8: Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering; Memory Based Reasoning; Challenges of MBR; Case Study: Classifying News Stories; Measuring Distance; The Combination Function: Asking the Neighbors for the Answer; Collaborative Filtering: A Nearest Neighbor Approach to Making Recommendations; Lessons Learned

Chapter 9: Market Basket Analysis and Association Rules

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