Muutke küpsiste eelistusi

E-raamat: Fuzzy Classification of Online Customers

  • Formaat: PDF+DRM
  • Sari: Fuzzy Management Methods
  • Ilmumisaeg: 26-Feb-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319159706
  • Formaat - PDF+DRM
  • Hind: 55,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Sari: Fuzzy Management Methods
  • Ilmumisaeg: 26-Feb-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319159706

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book introduces a fuzzy classification approach, which combines relational databases with fuzzy logic for more effective and powerful customer relationship management (CRM). It shows the benefits of a fuzzy classification in contrast to the traditional sharp evaluation of customers for the acquisition, retention and recovery of customers in online shops. The book starts with a presentation of the basic concepts, fuzzy set theory, and the combination of relational databases and fuzzy classification. In its second part, it focuses on the customer perspective, detailing the central concepts of CRM, its theoretical constructs and aspects of analytical, operational and collaborative CRM. It juxtaposes fuzzy and sharp customer classes and shows the implications for customer positioning, mass customization, personalization, customer assessment and controlling. Finally, the book presents the application and implementation of the concepts in online shops. A detailed case study presents the application and a separate chapter introduces the fuzzy Classification Query Language (fCQL) toolkit for implementing these concepts. In its appendix the book lists the fuzzy set operators and the query language’s grammar.

Arvustused

The book provides the reader systematic yet incremental exposure to the concepts of fuzziness in decision making through grounding theories of set theory and extended relational database schema with fuzzy classification. the book, which is based on Werros thesis work, will attract the attention of researchers and practitioners. Case-based discussions enhance the usefulness of the concepts and will benefit readers. (Harekrishna Misra, Computing Reviews, October, 2015)

1 Introduction
1(6)
1.1 Motivation
1(1)
1.2 Research Issues
2(1)
1.3
Chapters' Overview
3(1)
1.4 Published Work
4(3)
Part I Conceptual Perspective
2 Fuzzy Set Theory
7(20)
2.1 Human Beings and Fuzziness
7(2)
2.2 Concept of Fuzzy Sets
9(3)
2.3 Properties of Fuzzy Sets
12(3)
2.4 Operations on Fuzzy Sets
15(5)
2.5 Application Fields
20(7)
2.5.1 Possibility Theory
20(1)
2.5.2 Fuzzy Control Theory
21(1)
2.5.3 Fuzzy Expert Systems
22(1)
2.5.4 Fuzzy Classification
23(1)
2.5.5 Fuzzy Database Systems & ER Models
24(3)
3 Relational Databases & Fuzzy Classification
27(24)
3.1 Databases and Fuzziness
27(1)
3.2 Extension of the Relational Model
28(11)
3.2.1 Database Schema with Contexts
29(3)
3.2.2 Context Based Relational Algebra
32(2)
3.2.3 Fuzzy Classification Database Schema
34(2)
3.2.4 Aggregation Operator
36(3)
3.3 Fuzzy Classification Query Language fCQL
39(5)
3.3.1 fCQL Principles
40(1)
3.3.2 fCQL Syntax
40(1)
3.3.3 fCQL Query Examples
41(3)
3.4 Other Fuzzy Query Languages
44(7)
3.4.1 SQLf
45(1)
3.4.2 FQUERY
46(1)
3.4.3 FSQL
47(4)
Part II Customer Perspective
4 Customer Relationship Management
51(16)
4.1 CRM Concept
51(3)
4.2 CRM Theoretical Constructs
54(4)
4.2.1 Customer Value, Lifetime Value and Equity
54(2)
4.2.2 Customer Satisfaction, Loyalty and Retention
56(2)
4.3 CRM Systems
58(6)
4.3.1 Analytical CRM
60(2)
4.3.2 Operational CRM
62(1)
4.3.3 Collaborative CRM
63(1)
4.4 CRM Systems and Fuzzy Classification
64(3)
5 Fuzzy Customer Classes
67(22)
5.1 Fuzzy Versus Sharp Customer Classes
68(2)
5.2 Customer Positioning
70(4)
5.2.1 Planning and Optimizing Marketing Campaigns
71(2)
5.2.2 Monitoring Customers
73(1)
5.3 Mass Customization and Personalization
74(6)
5.3.1 Personalized Discount
75(2)
5.3.2 Fuzzy Product Portfolio
77(3)
5.4 Customer Assessment and Controlling
80(9)
5.4.1 Hierarchical Decomposition
81(2)
5.4.2 Customer Lifetime Value and Equity Calculation
83(1)
5.4.3 Controlling of the Customer Relationships
84(5)
Part III Application and Implementation Perspective
6 Fuzzy Classification Applied to Online Shops
89(18)
6.1 The E-Business Framework
90(1)
6.2 E-Commerce and Online Shops
91(3)
6.2.1 Online Shops for SMEs
92(1)
6.2.2 Webshop Processes and Repositories
92(2)
6.3 Analysis of Online Customers
94(3)
6.3.1 Customer Profiles
94(1)
6.3.2 Decomposition of the Kiel & Co Customer Profiles
95(2)
6.4 Kiel & Co Case Study
97(10)
6.4.1 Decomposition of the Profitability Concept
98(3)
6.4.2 Decomposition of the Loyalty Concept
101(1)
6.4.3 Customer Lifetime Value Classification
102(5)
7 fCQL Toolkit
107(20)
7.1 Architecture of the fCQL Toolkit
107(2)
7.2 User Interface---A Guided Tour
109(15)
7.2.1 Database Connection
110(1)
7.2.2 Data Analysis Panels
110(3)
7.2.3 Fuzzy Classification Definition Wizard
113(6)
7.2.4 Fuzzy Querying Process
119(2)
7.2.5 Results Evaluation
121(3)
7.3 Database Schema of the Meta-Tables
124(3)
8 Conclusion
127(4)
8.1 Summary
127(2)
8.2 Outlook
129(2)
Appendix A Fuzzy Set Operators 131(2)
Appendix B Query Languages' Grammar 133(2)
References 135
Nicolas Werro is currently "Head of Business Analytics" in the "Big Data and Business Intelligence" department of Swisscom AG. He achieved his PhD thesis in 2008 at the University of Fribourg in the research group "Information Systems" led by Professor Andreas Meier, where he has been assistant until the end of 2007. Before starting his doctoral studies, Nicolas Werro achieved a diploma degree in Computer Science and Economics as well as a master degree in Computer Science from the University of Fribourg.