Commercial Data Mining
1st Edition
Processing, Analysis and Modeling for Predictive Analytics Projects
Table of Contents
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
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.
Key Features
- 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
Readership
Data mining professionals in business & IT.
Details
- No. of pages:
- 304
- Language:
- English
- Copyright:
- © Morgan Kaufmann 2014
- Published:
- 19th February 2014
- Imprint:
- Morgan Kaufmann
- Paperback ISBN:
- 9780124166028
- eBook ISBN:
- 9780124166585
Reviews
"...a mandatory volume for anyone who runs data mining projects, since all the steps and most important details that should not be forgotten are described here...I strongly recommend this book for anyone even slightly involved with data mining projects."--IEEE Communications Magazine, Commercial Data Mining
"I strongly disagree with Bellin’s statement that the book will not help practitioners, and one can only conclude that the reviewer is not familiar with what data mining practitioners need." -Computing Reviews, Oct 13, 2014
Ratings and Reviews
About the Authors
David Nettleton Author
David F. Nettleton has more than 25 years of experience in IT system development, specializing in databases and data analysis. He has a Bachelor of Science degree in Computer Science, Master of Science degree in Computer Software and Systems Design and a Ph.D. in Artificial Intelligence. He has worked for IBM as a Business Intelligence Consultant, among other companies. In 1995 he founded his own consultancy dedicated to commercial data analysis projects, working in the Banking, Insurance, Media, Industry and Health Sectors. He has published over 40 articles and papers in journals, national and international congresses and magazines, and has given many presentations in conferences and workshops. He is currently a contract researcher at the Universitat Pompeu Fabra, Barcelona, Spain and at the IIIA-CSIC, Spain, specializing in data mining applied to online social networks and data privacy. Dr. Nettleton was born in England and lives in Barcelona, Spain since 1988.
Affiliations and Expertise
Contract Researcher at the Universitat Pompeu Fabra, Barcelona, Spain and at the IIIA-CSIC, Spain