Commercial Data Mining

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