Fuzzy methods for customer relationship management and marketing: applications and classifications
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Hershey, PA
Business Science Reference
2012
|
| Series: | Premier reference source
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XXI, 365 S. Ill., graph. Darst. 29 cm |
| ISBN: | 9781466600959 9781466600973 |
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| adam_text | Titel: Fuzzy methods for customer relationship management and marketing
Autor: Meier, Andreas
Jahr: 2012
Detailed Table of Contents
Foreword..............................................................................................................................................xii
Preface.................................................................................................................................................xiv
Acknowledgment................................................................................................................................xxi
Chapter 1
Applying Fuzzy Logic and Fuzzy Methods to Marketing..
Laurent Dome, University ofFribourg, Switzerland
Andreas Meier, University ofFribourg, Switzerland
The chapter introduces fundamental concepts such as fuzzy sets, fuzzy logic, and computing with linguistic
variables and terms. This set of fuzzy methods can be applied in marketing and customer relationship
management. In the conclusion, future research directions are given for applying fuzzy logic to market-
ing and customer relationship management.
Section 1
Fuzzy Modeling
The first section is principally theoretical in orientation. Chapter 2 introduces a fuzzy set of operators
designed to retrieve human-focused network information. Chapter 3 next examines the conceptualiza-
tion of dynamic groups and their fuzzification. Finally, in Chapter 4 a process for the validation of fuzzy
models is presented.
Chapter 2
Fuzzy Soft Social Network Modeling and Marketing..........................................................................16
Ronald R. Yager, Iona College, USA
Rachel L. Yager, Metropolitan College of New York, USA
The chapter focuses on the modeling of social networks. The authors provide a language that can be used
to query a social network. Using fuzzy set theory and granular computing, the authors have constructed
a bridge between the human analyst and the formal model of the network.
Chapter 3
Fuzzy Dynamic Groups: Measures and Implications for Television Audiences..................................41
Jose-Domingo Mora, University of Massachusetts-Dartmouth, USA
The characteristics of the individuals in a group and / or the individual memberships in the group may
change. These effects, which are due to time, introduce fuzziness into the definition of the group. The
authors attempt to model dynamic groups of this kind. Analysis of members of a family watching televi-
sion is offered as one application of such modeling.
Chapter 4
Using Case Data to Ensure Real World Input Validation within Fuzzy Set Theory Models..............61
Sara Denize, University of Western Sydney, Australia
Sharon Purchase, University of Western Australia, Australia
Doina Olaru, University of Western Australia, Australia
As fuzzy set theories open up new ways of investigating business networks, it becomes necessary to
validate these fuzzy models with real-world data. The chapter illustrates a fuzzy model validation process
and concludes with recommendations to assist researchers in validating their fuzzy models.
Section 2
Customer Relationship Management and Web Analytics
There are five chapters in the second section, which is the cornerstone of the book. These chapters
address the main focus of the book: Customer Relationship Management. In Chapter 5, fuzzy cluster-
ing is applied to Web user profiles. In Chapter 6, a second application of fuzzy clusters is given, for e-
Elections. A framework for a fuzzy online reputation analysis is demonstrated in Chapter 7. In Chapter
8, the concept of fuzzy target groups is used, and finally, in Chapter 9, an inductive fuzzy classification
method is proposed.
Chapter 5
Fuzzy Clustering of Web User Profiles for Analyzing their Behavior and Interests............................91
Stanislav Kreuzer, Goethe University, Germany
Natascha Hoebel, Goethe University, Germany
This chapter addresses the problem of how to define, calculate, and visually represent fuzzy clusters
of Web visitors with respect to their behavior and supposed interests. The authors introduce a fuzzy
clustering approach named CORD (Clustering of Ordinal Data). A fuzzy k-modes algorithm extension
for categorical data is the basis of this method. The results can be presented in two ways: in a table or
with an animation.
Chapter 6
Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections..................115
Luis Terdn, University ofFribourg, Switzerland
Andreas Ladner, Institut de Hautes Etudes en Administration Publique, Switzerland
Jan Fivaz, University of Bern, Switzerland
Stefani Gerber, University of Bern, Switzerland
The authors propose a fuzzy recommender system for e-Elections. A user-friendly interface has been
developed to help the voters to find the most similar candidates according to the preferences of the vot-
ers. A fuzzy c-mean algorithm is applied to constitute these clusters of similar candidates.
Chapter 7
Fuzzy Online Reputation Analysis Framework..................................................................................139
Edy Portmann, University ofFribourg, Switzerland
Tarn Nguyen, National University of Singapore, Singapore
Jose Sepulveda, National University of Singapore, Singapore
Adrian David Cheok, National University of Singapore, Singapore
The authors have set up a fuzzy online reputation analysis framework (foRa) in order to permit com-
munications operatives to search the Social Web to find meaningful information in a straightforward
manner. An interactive user interface known as the dashboard allows the browsing of related topics.
Chapter 8
Fuzzy Target Groups in Analytic Customer Relationship Management.............................................168
Michael Kaufmann, Swiss Mobiliar Insurance Pensions, Switzerland
Cedric Graf, ITpearls AG, Switzerland
An inductive fuzzy classification based on normalized likelihood ratios permits the authors to generate
customer profiles based on a target class and relevant customer attributes. The method is particularly
well suited for selection, visualization, and prediction in the field of analytics. A case study is described,
together with an implementation of the method proposed.
Chapter 9
Web Analytics with Fuzziness............................................................................................................193
Darius Zumstein, University ofFribourg, Switzerland
As Web metrics are not adequately classified in a sharp manner, the author proposes an inductive fuzzy
classification. This method seems particularly adapted to Web mining. It makes it possible to rank Web
pages using agradual degree of membership in classes. As a consequence, the results are easier to interpret.
Section 3
Performance Analysis
The third section focuses on the economic activity or the performance of a firm. Chapter 10 examines
a data warehouse and shows how to integrate fuzzy concepts as meta-tables. The assessment of online
customers can be realized with a fuzzy logic approach, as described in Chapter II. A hybrid fuzzy multiple
objectives approach can also help to develop a model for lotsizing, pricing, marketing, and planning.
This is presented in Chapter 12.
Chapter 10
Fuzzy Data Warehouse for Performance Analysis..............................................................................217
Daniel Fasel, University ofFribourg, Switzerland
Khurram Shahzad, Royal Institute of Technology (KTH), Sweden
In constructing a fuzzy data warehouse, the authors integrate fuzzy concepts as meta-tables without affect-
ing the core of a classical data warehouse. Despite the integration of the fuzziness, the time-invariability
of the data is preserved. This approach allows analyses of data in both sharp and fuzzy modes. A case
study demonstrates the validity of the approach.
Chapter 11
A Fuzzy Logic Approach for the Assessment of Online Customers...................................................252
Nicolas Werro, Swisscom AG, Switzerland
Henrik Stormer, Edorex InformatikAG, Switzerland
This chapter is devoted to the hierarchical fuzzy classification of online customers. A combination of
relational databases and fuzzy logic allows the classification of a customer into several classes at the same
time. As a result, customer equity is greatly improved, which can help to refine marketing campaigns.
Chapter 12
A Hybrid Fuzzy Multiple Objective Approach to Lotsizing, Pricing, and Marketing Planning
Model..................................................................................................................................................271
R Ghasemy Yaghin, Amirkabir University of Technology, Iran
S.M.T. Fatemi Ghomi, Amirkabir University of Technology, Iran
The imprecise and fuzzy nature of parameters, such as unit costs and marketing functions, has led the
authors to develop a fuzzy multiple objectives model. The objective functions considered are profit,
return on inventory investment, and a qualitative objective related to customer satisfaction. The model
is able to determine the joint pricing, lotsizing, and marketing plan of the manufacturer.
Section 4
Market Analysis
The concluding section is addressed to marketers. The question of segmentation is revisited in chapter
13 with the introduction of fuzzy concepts. And finally, in Chapter 14, a fuzzy set qualitative compara-
tive analysis, particularly well adapted to the analysis of complex configurations of causal conditions,
is applied to the identification of market mavens.
Chapter 13
A Fuzzy Segmentation Approach to Guide Marketing Decisions......................................................291
Monica Casabayo, ESADE-URL, Spain
NuriaAgell, ESADE-URL, Spain
This chapter presents a fuzzy segmentation model, which combines statistical and artificial intelligence
techniques to identify and quantify multifaceted consumers. This LAMDA technique is proposed as an
alternative to non-overlapping segmentation techniques. The fuzzy method appears to have a number
of advantages, as is demonstrated in a genuine Spanish business case study.
Chapter 14
Causal Recipes Sufficient for Identifying Market Gurus versus Mavens...........................................312
Miri Chung, University of Rhode Island, USA
Arch G. Woodside, Boston College, USA
The authors apply a fuzzy set qualitative comparative analysis to distinguish between Market Mavens
and Market Gurus using multi-year data from a national U.S. omnibus survey. This technique makes
possible the analysis of complex configurations of causal conditions as explanations of an outcome
condition. Truth tables are constructed to display all possible combinations of causal conditions and case
distributions, and this allows the discovery of multiple pathways to an outcome.
Compilation of References...............................................................................................................332
About the Contributors....................................................................................................................356
Index...................................................................................................................................................362
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| spelling | Fuzzy methods for customer relationship management and marketing applications and classifications Andreas Meier ... [ed.] Hershey, PA Business Science Reference 2012 XXI, 365 S. Ill., graph. Darst. 29 cm txt rdacontent n rdamedia nc rdacarrier Premier reference source Includes bibliographical references and index Customer relations Management Marketing Fuzzy logic Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf Kundenmanagement (DE-588)4236865-0 gnd rswk-swf Marketing (DE-588)4037589-4 s Kundenmanagement (DE-588)4236865-0 s Fuzzy-Logik (DE-588)4341284-1 s b DE-604 Meier, Andreas 1951- Sonstige (DE-588)115753613 oth Erscheint auch als Online-Ausgabe 978-1-466-60096-6 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025745699&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Fuzzy methods for customer relationship management and marketing applications and classifications Customer relations Management Marketing Fuzzy logic Fuzzy-Logik (DE-588)4341284-1 gnd Marketing (DE-588)4037589-4 gnd Kundenmanagement (DE-588)4236865-0 gnd |
| subject_GND | (DE-588)4341284-1 (DE-588)4037589-4 (DE-588)4236865-0 |
| title | Fuzzy methods for customer relationship management and marketing applications and classifications |
| title_auth | Fuzzy methods for customer relationship management and marketing applications and classifications |
| title_exact_search | Fuzzy methods for customer relationship management and marketing applications and classifications |
| title_full | Fuzzy methods for customer relationship management and marketing applications and classifications Andreas Meier ... [ed.] |
| title_fullStr | Fuzzy methods for customer relationship management and marketing applications and classifications Andreas Meier ... [ed.] |
| title_full_unstemmed | Fuzzy methods for customer relationship management and marketing applications and classifications Andreas Meier ... [ed.] |
| title_short | Fuzzy methods for customer relationship management and marketing |
| title_sort | fuzzy methods for customer relationship management and marketing applications and classifications |
| title_sub | applications and classifications |
| topic | Customer relations Management Marketing Fuzzy logic Fuzzy-Logik (DE-588)4341284-1 gnd Marketing (DE-588)4037589-4 gnd Kundenmanagement (DE-588)4236865-0 gnd |
| topic_facet | Customer relations Management Marketing Fuzzy logic Fuzzy-Logik Kundenmanagement |
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