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.
