Data mining techniques in CRM: inside customer segmentation / Konstantinos Tsiptsis and Antonios Chorianopoulos.

Includes bibliographical references and index.

Acknowledgements. 1. Data Mining in CRM. The CRM Strategy. What Can Data Mining Do? The Data Mining Methodology. Data Mining and Business Domain Expertise. Summary. 2. An Overview of Data Mining Techniques. Supervised Modeling. Unsupervised Modeling Techniques. Machine Learning/Artificial Intelligence vs. Statistical Techniques. Summary. 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. Summary. 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. Summary. 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. Summary. 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. Summary. 8. Segmentation for Retailers. Segmentation in the Retail Industry. The RFM Analysis. Grouping Customers According to the Products They Buy. Summary. Further Reading. Index.

"Data Mining Techniques in CRM: Inside Customer Segmentation presents a comprehensive guide to the use of Data Mining Techniques in the CRM framework, combining a technical and a business perspective and 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." "The book 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. Recommendations for the utilization of the data mining results for effective marketing are made." "Data mining algorithms are presented in a simple and comprehensive way for the business users with no technical expertise." "Lists of recommended input fields are provided to serve as the basis 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."--BOOK JACKET.