Data Mining Techniques in CRM: Inside Customer Segmentation / Edition 1

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This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

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Editorial Reviews

From the Publisher
"The book is written in a language that is easily accessible to business users who are not fluent in statistical methods and who have no prior exposure to the data mining or customer segmentation domain . . . This book is poised to become a standard reference, and I unconditionally recommend it to anyone working in this field." (Computing Reviews, 23 June 2011)

"This is an excellent book for any data miner or anybody involved in CRM. The text is clear and pictures are well done and funny which is rare enough to be mentioned. From basic to advanced topics, the book is a very pleasant journey inside data mining with a clear focus on customer segmentation. Really advised if you're not a fan of formulas." (Data Mining Research, 18 March 2011)

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Product Details

  • ISBN-13: 9780470743973
  • Publisher: Wiley
  • Publication date: 3/8/2010
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 372
  • Sales rank: 1,516,205
  • Product dimensions: 6.90 (w) x 9.90 (h) x 1.00 (d)

Meet the Author

Konstantinos Tsiptsis, CRM and Customer Intelligence Manager, Eurobank, EFM, Greece

Antonios Chorianopoulos, Greek Ministry of Economy and Finance, Data Analysis Unit, MIS Service, Greece.

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Table of Contents


1. Data Mining in CRM.

The CRM Strategy.

What Can Data Mining Do?

The Data Mining Methodology.

Data Mining and Business Domain Expertise.


2. An Overview of Data Mining Techniques.

Supervised Modeling.

Unsupervised Modeling Techniques.

Machine Learning/Artificial Intelligence vs. Statistical Techniques.


3. Data Mining Techniques for Segmentation.

Segmenting Customers with Data Mining Techniques.

Principal Components Analysis.

Clustering Techniques.

Examining and Evaluating the Cluster Solution.

Understanding the Clusters through Profiling.

Selecting the Optimal Cluster Solution.

Cluster Profiling and Scoring with Supervised Models.

An Introduction to Decision Tree Models.


4. The Mining Data Mart.

Designing the Mining Data Mart.

The Time Frame Covered by the Mining Data Mart.

The Mining Data Mart for Retail Banking.

The Mining Data Mart for Mobile Telephony Consumer (Residential) Customers.

The Mining Data Mart for Retailers.


5. Customer Segmentation.

An Introduction to Customer Segmentation.

Segmentation Types in Consumer Markets.

Segmentation in Business Markets.

A Guide for Behavioral Segmentation.

Segmentation Management Strategy.

A Guide for Value-Based Segmentation.

Designing Differentiated Strategies for the Value Segments.


6. Segmentation Applications in Banking.

Segmentation for Credit Card Holders.

Segmentation in Retail Banking.

The Marketing Process.

Segmentation in Retail Banking; A Summary.

7. Segmentation Applications in Telecommunications.

Mobile Telephony.

The Fixed Telephony Case.


8. Segmentation for Retailers.

Segmentation in the Retail Industry.

The RFM Analysis.

Grouping Customers According to the Products They Buy.


Further Reading.


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Customer Reviews

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Sort by: Showing 1 Customer Reviews
  • Posted March 19, 2010

    I Also Recommend:

    Data mining in action

    If you are looking for a comprehensive guide on data mining this is your book. A real hands-on approach, packed with guidelines and tips. It presents a robust and comprehensive data mining process model, supplemented with real world application examples. This is also the first book that deals with the crucial issue of what data to use and presents full lists of input fields for banking, telcos and retail.

    Was this review helpful? Yes  No   Report this review
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