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Business & Management - Business Information Systems | Inductive Fuzzy Classification in Marketing Analytics

Inductive Fuzzy Classification in Marketing Analytics

Kaufmann, Michael

2014, XX, 125 p. 35 illus.

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  • Provides a solid foundation of fuzzy classification and inductive logic and their application in marketing
  • Includes a case study of a real world application at a financial institute
  • Visualizes the abstract concepts with numerous illustrations
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.

Content Level » Research

Keywords » Data mining - Fuzzy classification - Fuzzy set - Inductive logic - Marketing analytics - Segmentation

Related subjects » Business Information Systems - Database Management & Information Retrieval - Marketing - Theoretical Computer Science

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