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Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition

Book Description

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 response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.

  • Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems

  • Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately

  • Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more

  • Provides best practices for performing data mining using simple tools such as Excel

Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. About the Authors
  6. Credits
  7. Acknowledgments
  8. Introduction
  9. Chapter 1: What Is Data Mining and Why Do It?
    1. What Is Data Mining?
    2. Why Now?
    3. Skills for the Data Miner
    4. The Virtuous Cycle of Data Mining
    5. A Case Study in Business Data Mining
    6. Steps of the Virtuous Cycle
    7. Data Mining in the Context of the Virtuous Cycle
    8. Lessons Learned
  10. Chapter 2: Data Mining Applications in Marketing and Customer Relationship Management
    1. Two Customer Lifecycles
    2. Organize Business Processes Around the Customer Lifecycle
    3. Data Mining Applications for Customer Acquisition
    4. A Data Mining Example: Choosing the Right Place to Advertise
    5. Data Mining to Improve Direct Marketing Campaigns
    6. Using Current Customers to Learn About Prospects
    7. Data Mining Applications for Customer Relationship Management
    8. Retention
    9. Beyond the Customer Lifecycle
    10. Lessons Learned
  11. Chapter 3: The Data Mining Process
    1. What Can Go Wrong?
    2. Data Mining Styles
    3. Goals, Tasks, and Techniques
    4. Formulating Data Mining Problems: From Goals to Tasks to Techniques
    5. What Techniques for Which Tasks?
    6. Lessons Learned
  12. Chapter 4: What You Should Know About Data
    1. Occam's Razor
    2. Looking At and Measuring Data
    3. Measuring Response
    4. Multiple Comparisons
    5. Chi-Square Test
    6. An Example: Chi-Square for Regions and Starts
    7. Case Study: Comparing Two Recommendation Systems with an A/B Test
    8. Data Mining and Statistics
    9. Lessons Learned
  13. Chapter 5: Descriptions and Prediction: Profiling and Predictive Modeling
    1. Directed Data Mining Models
    2. Directed Data Mining Methodology
    3. Step 1: Translate the Business Problem into a Data Mining Problem
    4. Step 2: Select Appropriate Data
    5. Step 3: Get to Know the Data
    6. Step 4: Create a Model Set
    7. Step 5: Fix Problems with the Data
    8. Step 6: Transform Data to Bring Information to the Surface
    9. Step 7: Build Models
    10. Step 8: Assess Models
    11. Step 9: Deploy Models
    12. Step 10: Assess Results
    13. Step 11: Begin Again
    14. Lessons Learned
  14. Chapter 6: Data Mining Using Classic Statistical Techniques
    1. Similarity Models
    2. Table Lookup Models
    3. RFM: A Widely Used Lookup Model
    4. Naïve Bayesian Models
    5. Linear Regression
    6. Multiple Regression
    7. Logistic Regression
    8. Fixed Effects and Hierarchical Effects
    9. Lessons Learned
  15. Chapter 7: Decision Trees
    1. What Is a Decision Tree and How Is It Used?
    2. Decision Trees Are Local Models
    3. Growing Decision Trees
    4. Finding the Best Split
    5. Pruning
    6. Extracting Rules from Trees
    7. Decision Tree Variations
    8. Assessing the Quality of a Decision Tree
    9. When Are Decision Trees Appropriate?
    10. Case Study: Process Control in a Coffee Roasting Plant
    11. Lessons Learned
  16. Chapter 8: Artificial Neural Networks
    1. A Bit of History
    2. The Biological Model
    3. Artificial Neural Networks
    4. A Sample Application: Real Estate Appraisal
    5. Training Neural Networks
    6. Radial Basis Function Networks
    7. Neural Networks in Practice
    8. Choosing the Training Set
    9. Preparing the Data
    10. Interpreting the Output from a Neural Network
    11. Neural Networks for Time Series
    12. Can Neural Network Models Be Explained?
    13. Lessons Learned
  17. Chapter 9: Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering
    1. Memory-Based Reasoning
    2. Challenges of MBR
    3. Case Study: Using MBR for Classifying Anomalies in Mammograms
    4. Measuring Distance and Similarity
    5. The Combination Function: Asking the Neighbors for Advice
    6. Case Study: Shazam — Finding Nearest Neighbors for Audio Files
    7. Collaborative Filtering: A Nearest-Neighbor Approach to Making Recommendations
    8. Lessons Learned
  18. Chapter 10: Knowing When to Worry: Using Survival Analysis to Understand Customers
    1. Customer Survival
    2. Hazard Probabilities
    3. From Hazards to Survival
    4. Proportional Hazards
    5. Survival Analysis in Practice
    6. Lessons Learned
  19. Chapter 11: Genetic Algorithms and Swarm Intelligence
    1. Optimization
    2. Genetic Algorithms
    3. The Traveling Salesman Problem
    4. Case Study: Using Genetic Algorithms for Resource Optimization
    5. Case Study: Evolving a Solution for Classifying Complaints
    6. Lessons Learned
  20. Chapter 12: Tell Me Something New: Pattern Discovery and Data Mining
    1. Undirected Techniques, Undirected Data Mining
    2. What is Undirected Data Mining?
    3. Methodology for Undirected Data Mining
    4. Lessons Learned
  21. Chapter 13: Finding Islands of Similarity: Automatic Cluster Detection
    1. Searching for Islands of Simplicity
    2. Customer Segmentation and Clustering
    3. The K-Means Clustering Algorithm
    4. Interpreting Clusters
    5. Evaluating Clusters
    6. Case Study: Clustering Towns
    7. Variations on K-Means
    8. Data Preparation for Clustering
    9. Lessons Learned
  22. Chapter 14: Alternative Approaches to Cluster Detection
    1. Shortcomings of K-Means
    2. Gaussian Mixture Models
    3. Divisive Clustering
    4. Agglomerative (Hierarchical) Clustering
    5. Self-Organizing Maps
    6. The Search Continues for Islands of Simplicity
    7. Lessons Learned
  23. Chapter 15: Market Basket Analysis and Association Rules
    1. Defining Market Basket Analysis
    2. Case Study: Spanish or English
    3. Association Analysis
    4. Building Association Rules
    5. Extending the Ideas
    6. Association Rules and Cross-Selling
    7. Sequential Pattern Analysis
    8. Lessons Learned
  24. Chapter 16: Link Analysis
    1. Basic Graph Theory
    2. Social Network Analysis
    3. Mining Call Graphs
    4. Case Study: Tracking Down the Leader of the Pack
    5. Case Study: Who Is Using Fax Machines from Home?
    6. How Google Came to Rule the World
    7. Lessons Learned
  25. Chapter 17: Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining
    1. The Architecture of Data
    2. A General Architecture for Data Warehousing
    3. Analytic Sandboxes
    4. Where Does OLAP Fit In?
    5. Where Data Mining Fits in with Data Warehousing
    6. Lessons Learned
  26. Chapter 18: Building Customer Signatures
    1. Finding Customers in Data
    2. Designing Signatures
    3. What a Signature Looks Like
    4. Process for Creating Signatures
    5. Dealing with Missing Values
    6. Lessons Learned
  27. Chapter 19: Derived Variables: Making the Data Mean More
    1. Handset Churn Rate as a Predictor of Churn
    2. Single-Variable Transformations
    3. Combining Variables
    4. Extracting Features from Time Series
    5. Extracting Features from Geography
    6. Using Model Scores as Inputs
    7. Handling Sparse Data
    8. Capturing Customer Behavior from Transactions
    9. Lessons Learned
  28. Chapter 20: Too Much of a Good Thing? Techniques for Reducing the Number of Variables
    1. Problems with Too Many Variables
    2. The Sparse Data Problem
    3. Flavors of Variable Reduction Techniques
    4. Sequential Selection of Features
    5. Other Directed Variable Selection Methods
    6. Principal Components
    7. Variable Clustering
    8. Lessons Learned
  29. Chapter 21: Listen Carefully to What Your Customers Say: Text Mining
    1. What Is Text Mining?
    2. Working with Text Data
    3. Case Study: Ad Hoc Text Mining
    4. Classifying News Stories Using MBR
    5. From Text to Numbers
    6. Text Mining and Naïve Bayesian Models
    7. DIRECTV: A Case Study in Customer Service
    8. Lessons Learned
  30. Index