Commercial Data Mining: Processing, Analysis and Modeling for Predictive Analytics Projects

450 Pages·2014·14.48 MB·English

by  David Nettleton

Commercial Data Mining
Processing, Analysis and Modeling for
Predictive Analytics Projects
David Nettleton
Table of Contents
Cover image
Title page
Copyright page
Acknowledgments
Chapter 1: Introduction
Abstract
Chapter 2: Business Objectives
Abstract
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
Abstract
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
Abstract
Introduction
Basic Data Representation
Advanced Data Representation
Chapter 5: Data Quality
Abstract
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
Abstract
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
Abstract
Introduction
Sampling for Data Reduction
Partitioning the Data Based on Business Criteria
Issues Related to Sampling
Chapter 8: Data Analysis
Abstract
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
Abstract
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
Abstract
Introduction
Query and Report Generation
Executive Information Systems
Expert Systems
Case-Based Systems
Summary
Chapter 11: Text Analysis
Abstract
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
Abstract
Introduction
Data Warehouse and Data Marts
Creating a File or Table for Data Mining
Chapter 13: CRM – Customer Relationship Management and Analysis
Abstract
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
Abstract
Chapter e14: Analysis of Data on the Internet I – Website Analysis and Internet
Search
Abstract
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
Abstract
Chapter e15: Analysis of Data on the Internet II – Search Experience Analysis
Abstract
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
Abstract
Chapter e16: Analysis of Data on the Internet III – Online Social Network
Analysis
Abstract
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
Abstract
Chapter e17: Analysis of Data on the Internet IV – Search Trend Analysis over
Time
Abstract
Introduction
Analysis of Search Term Trends Over Time
Data Mining Applied to Trend Data
Summary
Chapter 18: Data Privacy and Privacy-Preserving Data Publishing
Abstract
Introduction
Popular Applications and Data Privacy
Legal Aspects – Responsibility and Limits
Privacy-Preserving Data Publishing
Chapter 19: Creating an Environment for Commercial Data Analysis
Abstract
Introduction
Integrated Commercial Data Analysis Tools
Creating an Ad Hoc/Low-Cost Environment for Commercial Data Analysis
Chapter 20: Summary
Abstract
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
Glossary
Bibliography
Index