Commercial Data Mining

Processing, Analysis and Modeling for Predictive Analytics Projects

By

  • David Nettleton, Contract Researcher at the Universitat Pompeu Fabra, Barcelona, Spain and at the IIIA-CSIC, Spain

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.

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Audience

Data mining professionals in business & IT.

 

Book information

  • Published: February 2014
  • Imprint: MORGAN KAUFMANN
  • ISBN: 978-0-12-416602-8

Reviews

"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




Table of Contents

1. Introduction

2. Business Objectives

3. Data Quality

4. Data Representation

5. Possible Sources of Data and Information

6. Selection of variables and factors

7. Data Sampling

8. Data Analysis

9. Modeling

10. The Data Mart – structured data warehouse

11. Querying, Report Generation and Executive Information Systems

12. Analytical CRM – Customer Relationship Analysis

13. Website analysis and Internet search

14. Online social network analysis

15. Web search trend analysis

16. Creating your own environment for commercial data analysis

17. Summary

Appendices, Case Studies