Commercial Data Mining

Acknowledgments

Chapter 1: Introduction

Chapter 2: Business Objectives

Introduction

Criteria for Choosing a Viable Project

Factors That Influence Project Benefits

Factors That Influence Project Costs

Example 1: Customer Call Center – Objective: IT Support for Customer Reclamations

Example 2: Online Music App – Objective: Determine Effectiveness of Advertising for Mobile Device Apps

Summary

Chapter 3: Incorporating Various Sources of Data and Information

Introduction

Data about a Business’s Products and Services

Surveys and Questionnaires

Loyalty Card/Customer Card

Demographic Data

Macro-Economic Data

Data about Competitors

Financial Markets Data: Stocks, Shares, Commodities, and Investments

Chapter 4: Data Representation

Introduction

Basic Data Representation

Advanced Data Representation

Chapter 5: Data Quality

Introduction

Examples of Typical Data Problems

Relevance and Reliability

Quantitative Evaluation of the Data Quality

Data Extraction and Data Quality – Common Mistakes and How to Avoid Them

How Data Entry and Data Creation May Affect Data Quality

Chapter 6: Selection of Variables and Factor Derivation

Introduction

Selection from the Available Data

Reverse Engineering: Selection by Considering the Desired Result

Data Mining Approaches to Selecting Variables

Packaged Solutions: Preselecting Specific Variables for a Given Business Sector

Summary

Chapter 7: Data Sampling and Partitioning

Introduction

Sampling for Data Reduction

Partitioning the Data Based on Business Criteria

Issues Related to Sampling

Chapter 8: Data Analysis

Introduction

Visualization

Associations

Clustering and Segmentation

Segmentation and Visualization

Analysis of Transactional Sequences

Analysis of Time Series

Typical Mistakes when Performing Data Analysis and Interpreting Results

Chapter 9: Data Modeling

Introduction

Modeling Concepts and Issues

Neural Networks

Classification: Rule/Tree Induction

Traditional Statistical Models

Other Methods and Techniques for Creating Predictive Models

Applying the Models to the Data

Simulation Models – “What If?”

Summary of Modeling

Chapter 10: Deployment Systems: From Query Reporting to EIS and Expert Systems

Introduction

Query and Report Generation

Executive Information Systems

Expert Systems

Case-Based Systems

Summary

Chapter 11: Text Analysis

Basic Analysis of Textual Information

Advanced Analysis of Textual Information

Commercial Text Mining Products

Chapter 12: Data Mining from Relationally Structured Data, Marts, and Warehouses

Introduction

Data Warehouse and Data Marts

Creating a File or Table for Data Mining

Chapter 13: CRM – Customer Relationship Management and Analysis

Introduction

CRM Metrics and Data Collection

Customer Life Cycle

Example: Retail Bank

Integrated CRM Systems

Customer Satisfaction

Example CRM Application

Chapter 14: Analysis of Data on the Internet I – Website Analysis and Internet Search

Introduction

Analysis of Trails left by Visitors to a Website

Search and Synthesis of Market Sentiment Information on the Internet

Summary

Chapter 15: Analysis of Data on the Internet II – Search Experience Analysis

Introduction

The Internet and Internet Search

Data Mining of a User Search Log

Summary

Chapter 16: Analysis of Data on the Internet III – Online Social Network Analysis

Introduction

Analysis of Online Social Network Graphs

Applications and Tools for Social Network Analysis

Summary

Chapter 17: Analysis of Data on the Internet IV – Search Trend Analysis over Time

Introduction

Analysis of Search Term Trends Over Time

Data Mining Applied to Trend Data

Summary

Chapter 18: Data Privacy and Privacy-Preserving Data Publishing

Introduction

Popular Applications and Data Privacy

Legal Aspects – Responsibility and Limits

Privacy-Preserving Data Publishing

Chapter 19: Creating an Environment for Commercial Data Analysis

Introduction

Integrated Commercial Data Analysis Tools

Creating an Ad Hoc/Low-Cost Environment for Commercial Data Analysis

Chapter 20: Summary

Appendix: Case Studies

Case Study 1: Customer Loyalty at an Insurance Company

Case Study 2: Cross-Selling a Pension Plan at a Retail Bank

Case Study 3: Audience Prediction for a Television Channel

Glossary

Bibliography

Index

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

Data mining professionals in business & IT.