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Fuzzy Methods for Customer Relationship Management and Marketing

Book Description

Information overload has made it increasingly difficult to analyze large amounts of data and generate appropriate management decisions. Furthermore, data is often imprecise and will include both quantitative and qualitative elements. For these reasons, it is important to extend traditional decision making processes by adding intuitive reasoning, human subjectivity, and imprecision.Fuzzy Methods for Customer Relationship Management and Marketing: Applications and Classifications explores the possibilities and advantages created by fuzzy methods through the presentation of thorough research and case studies. This book covers a variety of possible fuzzy logic approaches to customer relationship management and marketing, making it a valuable resource for not only students and researchers but also executives, managers, marketing experts, and project leaders who are interested in applying fuzzy classification to managerial decisions.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Editorial Advisory Board and List of Reviewers
    1. LIST OF REVIEWERS
  5. Foreword
  6. Preface
    1. OVERVIEW OF BOOK CHAPTERS
  7. Acknowledgment
  8. Chapter 1: Applying Fuzzy Logic and Fuzzy Methods to Marketing
    1. ABSTRACT
    2. BRIDGING ART AND SCIENCE
    3. CRISP SETS VS. FUZZY SETS
    4. FUZZY LOGIC AND LINGUISTIC VARIABLES
    5. APPLYING FUZZY LOGIC TO MARKETING
    6. CONCLUSION
  9. Section 1: Fuzzy Modeling
    1. Chapter 2: Fuzzy Soft Social Network Modeling and Marketing
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND: BASIC RELATIONAL NETWORK THEORY
      4. MODELLING SOCIAL NETWORK NOTIONS WITH FUZZY CONCEPTS
      5. VECTOR VALUED NODES AND SOCIAL NETWORK DATABASES
      6. FUTURE RESEARCH DIRECTIONS: SOLUTION FOR DETECTING TRENDSETTERS AND OPINION LEADERS
      7. CONCLUSION
    2. Chapter 3: Fuzzy Dynamic Groups
      1. ABSTRACT
      2. INTRODUCTION
      3. GROUPS, FAMILIES AND TELEVISION
      4. GROUP SIZE AND TV CONSUMPTION: AN EMPIRICAL STUDY
      5. CONCLUSION AND FUTURE RESEARCH
    3. Chapter 4: Using Case Data to Ensure ‘Real World’ Input Validation within Fuzzy Set Theory Models
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. VALIDATION AND VALIDATION APPROACHES
      5. SIMULATION CONTEXT
      6. ILLUSTRATING A VALIDATION PROCESS
      7. DISCUSSION
      8. CONCLUSION
      9. Appendix
  10. Section 2: Customer Relationship Management and Web Analytics
    1. Chapter 5: Fuzzy Clustering of Web User Profiles for Analyzing their Behavior and Interests
      1. ABSTRACT
      2. INTRODUCTION
      3. MOTIVATION: USER PROFILES AND SCALE OF INTEREST
      4. BACKGROUND
      5. FUZZY CLUSTERING OF WEB USER PROFILES USING CORD
      6. THE GUGUBARRA FRAMEWORK AND THE VISUALIZATION OF FUZZY CLUSTERS AND THEIR CENTROIDS
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    2. Chapter 6: Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections
      1. ABSTRACT
      2. 1 MOTIVATION
      3. 2 ELECTRONIC GOVERNMENT AND ELECTRONIC DEMOCRACY
      4. 3 SMARTVOTE: A VOTING ADVICE APPLICATION
      5. 4 FUZZY-BASED CLUSTER ALGORITHM FOR RECOMMENDIND E-ELECTIONS
      6. 5 DISCUSSION
      7. 6 OUTLOOK
      8. 7 CONCLUSION
    3. Chapter 7: Fuzzy Online Reputation Analysis Framework
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. THE USE OF SEARCH ENGINES FOR ONLINE REPUTATION MANAGEMENT
      5. THE FUZZY ONLINE REPUTATION ANALYSIS FRAMEWORK
      6. YOUREPUTATION: A REPUTATION ANALYSIS TOOL
      7. FUTURE RESEARCH DIRECTIONS
      8. CONCLUSION
    4. Chapter 8: Fuzzy Target Groups in Analytic Customer Relationship Management
      1. ABSTRACT
      2. INTRODUCTION
      3. APPLICATION OF INDUCTIVE FUZZY CLASSIFICATION TO ANALYTICS
      4. MEMBERSHIP FUNCTION INDUCTION FOR ANALYTIC CRM
      5. SOFTWARE IMPLEMENTATION
      6. CONCLUSION
    5. Chapter 9: Web Analytics with Fuzziness
      1. ABSTRACT
      2. 1. WHY WEB ANALYTICS WITH FUZZINESS?
      3. 2. WEB ANALYTICS – AN OVERVIEW
      4. 3. CONTROLLING LOOP FOR WEB ANALYTICS
      5. 5. WEB ANALYTICS WITH FUZZINESS
      6. 6. CASE: FUZZY CLASSIFICATION OF REAL WEB DATA
      7. 7 CONCLUSION AND OUTLOOK
  11. Section 3: Performance Analysis
    1. Chapter 10: Fuzzy Data Warehouse for Performance Analysis
      1. ABSTRACT
      2. INTRODUCTION
      3. BASIC CONCEPTS
      4. EXISTING APPROACHES OF FUZZY DATA WAREHOUSE
      5. FUZZY DATA WAREHOUSE: INTEGRATING FUZZY CONCEPTS IN META-TABLES STRUCTURE
      6. A METHOD FOR MODELING FUZZY DATA WAREHOUSE
      7. OPERATIONS, AGGREGATION AND PROPAGATION OF FUZZY CONCEPTS
      8. A CASE STUDY IN PERFORMANCE ANALYSIS OF A MOVIE RENTAL COMPANY
      9. DISCUSSION AND CONCLUSION
    2. Chapter 11: A Fuzzy Logic Approach for the Assessment of Online Customers
      1. ABSTRACT
      2. INTRODUCTION
      3. FUZZY CLASSIFICATION TOOLKIT
      4. FUZZY CUSTOMER CLASSES
      5. PRACTICAL EXAMPLE
      6. CONCLUSION AND OUTLOOK
    3. Chapter 12: A Hybrid Fuzzy Multiple Objective Approach to Lotsizing, Pricing, and Marketing Planning Model
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. MAIN FOCUS OF THE CHAPTER
      5. 4. PROBLEM DEFINITION AND FORMULATION
      6. 5. FUZZY MATHEMATICAL ANALYSIS
      7. 6. A FUZZY GOAL PROGRAMMING MODEL FOR H-FMOJPLM
      8. 7. PARTICLE SWARM OPTIMIZATION
      9. 8. NUMERICAL EXAMPLE: A REAL-WORLD INDUSTRIAL CASE STUDY
      10. 9. CONCLUDING REMARKS
  12. Section 4: Market Analysis
    1. Chapter 13: A Fuzzy Segmentation Approach to Guide Marketing Decisions
      1. ABSTRACT
      2. 1. INTRODUCTION
      3. 2. BACKGROUND
      4. 3. PROPOSED SOLUTION
      5. 4. FIELD STUDY
      6. 5. CONCLUSION
    2. Chapter 14: Causal Recipes Sufficient for Identifying Market Gurus versus Mavens
      1. ABSTRACT
      2. INTRODUCTION
      3. MARKET MAVEN AND MARKET GURU
      4. FUZZY SET QUALITATIVE COMPARARIVE ANALYSIS
      5. DATASET
      6. CAUSUAL CONDITIONS AND FUZZY SET PROPOSITIONS
      7. CALIBRATION OF SCORES
      8. ANALYSIS
      9. FINDINGS
      10. CONCLUSION
      11. LIMTATIONS AND FUTURE RESEARCH IMPLICATIONS
  13. Compilation of References
  14. About the Contributors