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