Inductive Fuzzy Classification in Marketing Analytics

1. Front Matter

   Pages i-xx

   PDF

2. Gradual Concept of Truth

   • Michael Kaufmann

   Pages 1-5

3. Fuzziness and Induction

   • Michael Kaufmann

   Pages 7-34

4. Analytics and Marketing

   • Michael Kaufmann

   Pages 35-58

5. Prototyping and Evaluation

   • Michael Kaufmann

   Pages 59-75

6. Precisiating Fuzziness by Induction

   • Michael Kaufmann

   Pages 77-82

7. Back Matter

   Pages 83-125

   PDF

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.

• Data mining
• Fuzzy classification
• Fuzzy set
• Inductive logic
• Marketing analytics
• Segmentation

• Engineering and Architecture, Lucerne University of Applied Sciences and Arts, Horw, Switzerland

  Michael Kaufmann

Michael Kaufmann is a computer scientist with specialization in analytics and machine learning. Currently he is working as a business analyst at FIVE Informatik, where he consults executive boards of small and medium enterprises. He was data architect at Swiss Mobiliar and a Data Warehouse Analyst at Post Finance. He is a postdoctoral researcher publishing scientific articles on applications of fuzzy classification. He got his degree of Doctor Scientiarum Informaticarum (Dr. sc. Inf.) in 2012 and his Master's and Bachelor's degrees in Computer Science in 2004 and 2005, respectively, from the University of Fribourg, Switzerland.