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E-raamat: Data Mining Techniques in CRM - Inside Customer Segmentation: Inside Customer Segmentation [Wiley Online]

(Greek Ministry of Economy and Finance, Greece), (Eurobank, Greece)
  • Formaat: 372 pages
  • Ilmumisaeg: 15-Jan-2010
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 470685816
  • ISBN-13: 9780470685815
Teised raamatud teemal:
  • Wiley Online
  • Hind: 103,56 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Data Mining Techniques in CRM - Inside Customer Segmentation: Inside Customer SegmentationSuurem pilt
  • Formaat: 372 pages
  • Ilmumisaeg: 15-Jan-2010
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 470685816
  • ISBN-13: 9780470685815
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470685815
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.
1 DATA MINING IN CRM
1.1 The CRM Strategy
1.2 What Can Data Mining Do?
1.3 The Data Mining Methodology
1.4 Data Mining and Business Domain Expertise
1.5 Summary
2 AN OVERVIEW OF DATA MINING TECHNIQUES
2.1 Supervised Modeling
2.2 Unsupervised Modeling Techniques
2.3 Machine Learning/Artificial Intelligence vs. Statistical Techniques
2.4 Summary
3 DATA MINING TECHNIQUES FOR SEGMENTATION
3.1 Segmenting Customers with Data Mining Techniques
3.2 Principal Components Analysis
3.3 Clustering Techniques
3.4 Examining and Evaluating the Cluster Solution
3.5 Understanding the Clusters through Profiling
3.6 Selecting the Optimal Cluster Solution
3.7 Cluster Profiling and Scoring with Supervised Models
3.8 An Introduction to Decision Tree Models
3.9 Summary
4 THE MINING DATA MART
4.1 Designing the Mining Data Mart
4.2 The Time Frame Covered by the Mining Data Mart
4.3 The Mining Data Mart for Retail Banking
4.4 The Mining Data Mart for Mobile Telephony Consumer (Residential) Customers
4.5 The Mining Data Mart for Retailers
4.6 Summary
5 CUSTOMER SEGMENTATION
5.1 An Introduction to Customer Segmentation
5.2 Segmentation Types in Consumer Markets
5.3 Segmentation in Business Markets
5.4 A Guide for Behavioral Segmentation
5.5 Segmentation Management Strategy
5.6 A Guide for Value-Based Segmentation
5.7 Designing Differentiated Strategies for the Value Segments
5.8 Summary
6 SEGMENTATION APPLICATIONS IN BANKING
6.1 Segmentation for Credit Card Holders
6.2 Segmentation in Retail Banking
7 SEGMENTATION APPLICATIONS IN TELECOMMUNICATIONS
7.1 Mobile Telephony
7.2 The Fixed Telephony Case
7.3 Summary
8 SEGMENTATION FOR RETAILERS
8.1 Segmentation in the Retail Industry
8.2 The RFM Analysis
8.3 Grouping Customers According to the Products They Buy
8.4 Summary
INDEX
Konstantinos Tsiptsis, CRM and Customer Intelligence Manager, Eurobank, EFM, Greece Antonios Chorianopoulos, Greek Ministry of Economy and Finance, Data Analysis Unit, MIS Service, Greece.
Püsilink: https://www.kriso.ee/db/9780470685815_pe.html
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