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Data Mining Techniques in CRM Inside Customer Segmentation

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ISBN-10: 0470743972

ISBN-13: 9780470743973

Edition: 2010

Authors: Konstantinos Tsiptsis, Antonios Chorianopoulos

List price: $134.95
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Description:

The book aims to be a complete and comprehensive handbook for the use of Data Mining Techniques in the CRM framework. It combines a technical and a business perspective, bridging the gap between data mining & business professionals. By using non-technical language it focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes.It guides readers through all phases of the data mining process, from the understanding of the business objective and the setting of the data mining goal to the model development, evaluation and deployment.Methodological and technical guidelines are supplemented by real-world application examples from all major industries, including Telecommunications, Banking and Retailing and recommendations for the use of the data mining results for effective marketing.Data mining algorithms are presented in a simple and comprehensive way for the business users with no technical expertise.Mining data marts for Telecommunications, Banking and Retailing and full lists of recommended input fields are proposed that can serve as the base for the implementation of data mining applications.The book is mainly addressed to business users who are looking for a practical guide on data mining. It presents the authors' knowledge and experience from the 'data mining trenches', demystifying the secrets for data mining success.
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Book details

List price: $134.95
Copyright year: 2010
Publisher: John Wiley & Sons, Limited
Publication date: 1/15/2010
Binding: Hardcover
Pages: 372
Size: 7.25" wide x 9.75" long x 1.00" tall
Weight: 1.738
Language: English

Data Mining In Crm
The CRM Strategy
What Can Data Mining Do?
The Data Mining Methodology
Data Mining and Business Domain Expertise
Summary
An Overview Of Data Mining Techniques
Supervised Modeling
Unsupervised Modeling Techniques
Machine Learning/Artificial Intelligence vs. Statistical Techniques
Summary
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
Summary
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
Summary
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
Summary
Segmentation Applications In Banking
Segmentation for Credit Card Holders
Segmentation in Retail Banking
Segmentation Applications In Telecommunications
Mobile Telephony
The Fixed Telephony Case
Summary
Segmentation For Retailers
Segmentation in the Retail Industry
The RFM Analysis
Grouping Customers According to the Products They Buy
Summary
Index