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Commercial Data Mining

Book Description

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.

Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book.



  • Illustrates cost-benefit evaluation of potential projects
  • Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools
  • Approachable reference can be read from cover to cover by readers of all experience levels
  • Includes practical examples and case studies as well as actionable business insights from author's own experience

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright page
  5. Acknowledgments
  6. Chapter 1: Introduction
    1. Abstract
  7. Chapter 2: Business Objectives
    1. Abstract
    2. Introduction
    3. Criteria for Choosing a Viable Project
    4. Factors That Influence Project Benefits
    5. Factors That Influence Project Costs
    6. Example 1: Customer Call Center – Objective: IT Support for Customer Reclamations
    7. Example 2: Online Music App – Objective: Determine Effectiveness of Advertising for Mobile Device Apps
    8. Summary
  8. Chapter 3: Incorporating Various Sources of Data and Information
    1. Abstract
    2. Introduction
    3. Data about a Business’s Products and Services
    4. Surveys and Questionnaires
    5. Loyalty Card/Customer Card
    6. Demographic Data
    7. Macro-Economic Data
    8. Data about Competitors
    9. Financial Markets Data: Stocks, Shares, Commodities, and Investments
  9. Chapter 4: Data Representation
    1. Abstract
    2. Introduction
    3. Basic Data Representation
    4. Advanced Data Representation
  10. Chapter 5: Data Quality
    1. Abstract
    2. Introduction
    3. Examples of Typical Data Problems
    4. Relevance and Reliability
    5. Quantitative Evaluation of the Data Quality
    6. Data Extraction and Data Quality – Common Mistakes and How to Avoid Them
    7. How Data Entry and Data Creation May Affect Data Quality
  11. Chapter 6: Selection of Variables and Factor Derivation
    1. Abstract
    2. Introduction
    3. Selection from the Available Data
    4. Reverse Engineering: Selection by Considering the Desired Result
    5. Data Mining Approaches to Selecting Variables
    6. Packaged Solutions: Preselecting Specific Variables for a Given Business Sector
    7. Summary
  12. Chapter 7: Data Sampling and Partitioning
    1. Abstract
    2. Introduction
    3. Sampling for Data Reduction
    4. Partitioning the Data Based on Business Criteria
    5. Issues Related to Sampling
  13. Chapter 8: Data Analysis
    1. Abstract
    2. Introduction
    3. Visualization
    4. Associations
    5. Clustering and Segmentation
    6. Segmentation and Visualization
    7. Analysis of Transactional Sequences
    8. Analysis of Time Series
    9. Typical Mistakes when Performing Data Analysis and Interpreting Results
  14. Chapter 9: Data Modeling
    1. Abstract
    2. Introduction
    3. Modeling Concepts and Issues
    4. Neural Networks
    5. Classification: Rule/Tree Induction
    6. Traditional Statistical Models
    7. Other Methods and Techniques for Creating Predictive Models
    8. Applying the Models to the Data
    9. Simulation Models – “What If?”
    10. Summary of Modeling
  15. Chapter 10: Deployment Systems: From Query Reporting to EIS and Expert Systems
    1. Abstract
    2. Introduction
    3. Query and Report Generation
    4. Executive Information Systems
    5. Expert Systems
    6. Case-Based Systems
    7. Summary
  16. Chapter 11: Text Analysis
    1. Abstract
    2. Basic Analysis of Textual Information
    3. Advanced Analysis of Textual Information
    4. Commercial Text Mining Products
  17. Chapter 12: Data Mining from Relationally Structured Data, Marts, and Warehouses
    1. Abstract
    2. Introduction
    3. Data Warehouse and Data Marts
    4. Creating a File or Table for Data Mining
  18. Chapter 13: CRM – Customer Relationship Management and Analysis
    1. Abstract
    2. Introduction
    3. CRM Metrics and Data Collection
    4. Customer Life Cycle
    5. Example: Retail Bank
    6. Integrated CRM Systems
    7. Customer Satisfaction
    8. Example CRM Application
  19. Chapter 14: Analysis of Data on the Internet I – Website Analysis and Internet Search
    1. Abstract
  20. Chapter e14: Analysis of Data on the Internet I – Website Analysis and Internet Search
    1. Abstract
    2. Introduction
    3. Analysis of Trails left by Visitors to a Website
    4. Search and Synthesis of Market Sentiment Information on the Internet
    5. Summary
  21. Chapter 15: Analysis of Data on the Internet II – Search Experience Analysis
    1. Abstract
  22. Chapter e15: Analysis of Data on the Internet II – Search Experience Analysis
    1. Abstract
    2. Introduction
    3. The Internet and Internet Search
    4. Data Mining of a User Search Log
    5. Summary
  23. Chapter 16: Analysis of Data on the Internet III – Online Social Network Analysis
    1. Abstract
  24. Chapter e16: Analysis of Data on the Internet III – Online Social Network Analysis
    1. Abstract
    2. Introduction
    3. Analysis of Online Social Network Graphs
    4. Applications and Tools for Social Network Analysis
    5. Summary
  25. Chapter 17: Analysis of Data on the Internet IV – Search Trend Analysis over Time
    1. Abstract
  26. Chapter e17: Analysis of Data on the Internet IV – Search Trend Analysis over Time
    1. Abstract
    2. Introduction
    3. Analysis of Search Term Trends Over Time
    4. Data Mining Applied to Trend Data
    5. Summary
  27. Chapter 18: Data Privacy and Privacy-Preserving Data Publishing
    1. Abstract
    2. Introduction
    3. Popular Applications and Data Privacy
    4. Legal Aspects – Responsibility and Limits
    5. Privacy-Preserving Data Publishing
  28. Chapter 19: Creating an Environment for Commercial Data Analysis
    1. Abstract
    2. Introduction
    3. Integrated Commercial Data Analysis Tools
    4. Creating an Ad Hoc/Low-Cost Environment for Commercial Data Analysis
  29. Chapter 20: Summary
    1. Abstract
  30. Appendix: Case Studies
    1. Case Study 1: Customer Loyalty at an Insurance Company
    2. Case Study 2: Cross-Selling a Pension Plan at a Retail Bank
    3. Case Study 3: Audience Prediction for a Television Channel
  31. Glossary
  32. Glossary
  33. Bibliography
  34. Index