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 Connections -- Neural 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 Models -- Step 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 Comparisons -- Chi-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 Set -- Preparing 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 -- Defining Market Basket Analysis -- Association Rules -- How Good Is an Association Rule? -- Building Association Rules -- Extending the Ideas -- Sequential Analysis Using Association Rules -- Lessons Learned -- Chapter 10: Link Analysis -- Basic Graph Theory -- A Familiar Application of Link Analysis -- Case Study: Who Is Using Fax Machines from Home? -- Case Study: Segmenting Cellular Telephone Customers -- Lessons Learned -- Chapter 11: Automatic Cluster Detection -- Searching for Islands of Simplicity -- K-Means Clustering -- Similarity and Distance -- Data Preparation for Clustering -- Other Approaches to Cluster Detection.

Evaluating Clusters -- Case Study: Clustering Towns -- Lessons Learned -- Chapter 12: Knowing When to Worry: Hazard Functions and Survival Analysis in Marketing -- Customer Retention -- Hazards -- From Hazards to Survival -- Proportional Hazards -- Survival Analysis in Practice -- Lessons Learned -- Chapter 13: Genetic Algorithms -- How They Work -- Case Study: Using Genetic Algorithms for Resource Optimization -- Schemata: Why Genetic Algorithms Work -- More Applications of Genetic Algorithms -- Beyond the Simple Algorithm -- Lessons Learned -- Chapter 14: Data Mining throughout the Customer Life Cycle -- Levels of the Customer Relationship -- Customer Life Cycle -- Business Processes Are Organized around the Customer Life Cycle -- Lessons Learned -- Chapter 15: Data Warehousing, OLAP, and Data Mining -- The Architecture of Data -- A General Architecture for Data Warehousing -- Where Does OLAP Fit In? -- Where Data Mining Fits in with Data Warehousing -- Lessons Learned -- Chapter 16: Building the Data Mining Environment -- A Customer-Centric Organization -- An Ideal Data Mining Environment -- Back to Reality -- The Data Mining Group -- Data Mining Infrastructure -- Data Mining Software -- Lessons Learned -- Chapter 17: Preparing Data for Mining -- What Data Should Look Like -- Constructing the Customer Signature -- Exploring Variables -- Deriving Variables -- Examples of Behavior-Based Variables -- The Dark Side of Data -- Computational Issues -- Lessons Learned -- Chapter 18: Putting Data Mining to Work -- Getting Started -- Choosing a Data Mining Technique -- How One Company Began Data Mining -- Lessons Learned -- Index.