Inductive Fuzzy Classification in Marketing Analytics

  • Michael Kaufmann
Part of the Fuzzy Management Methods book series (FMM)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Michael Kaufmann
    Pages 1-5
  3. Michael Kaufmann
    Pages 7-34
  4. Michael Kaufmann
    Pages 35-58
  5. Michael Kaufmann
    Pages 59-75
  6. Michael Kaufmann
    Pages 77-82
  7. Back Matter
    Pages 83-125

About this book

Introduction

To enhance marketing analytics, approximate and inductive reasoning can be applied to handle uncertainty in individual marketing models. This book demonstrates the use of fuzzy logic for classification and segmentation in marketing campaigns. Based on practical experience as a data analyst and on theoretical studies as a researcher, the author explains fuzzy classification, inductive logic, and the concept of likelihood, and introduces a blend of Bayesian and Fuzzy Set approaches, allowing reasonings on fuzzy sets that ​are derived by inductive logic. By application of this theory, the book guides the reader towards a gradual segmentation of customers which can enhance return on targeted marketing campaigns. The algorithms presented can be used for visualization, selection and prediction. The book shows how fuzzy logic can complement customer analytics by introducing fuzzy target groups. This book is for researchers, analytics professionals, data miners and students interested in fuzzy classification for marketing analytics.

Keywords

Data mining Fuzzy classification Fuzzy set Inductive logic Marketing analytics Segmentation

Authors and affiliations

  • Michael Kaufmann
    • 1
  1. 1.Engineering and ArchitectureLucerne University of Applied Sciences and ArtsHorwSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-05861-0
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Business and Economics
  • Print ISBN 978-3-319-05860-3
  • Online ISBN 978-3-319-05861-0
  • Series Print ISSN 2196-4130
  • Series Online ISSN 2196-4149
  • About this book