Inductive Fuzzy Classification in Marketing Analytics

Business & Finance, Industries & Professions, Information Management, Nonfiction, Computers, Database Management
Cover of the book Inductive Fuzzy Classification in Marketing Analytics by Michael Kaufmann, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Michael Kaufmann ISBN: 9783319058610
Publisher: Springer International Publishing Publication: June 4, 2014
Imprint: Springer Language: English
Author: Michael Kaufmann
ISBN: 9783319058610
Publisher: Springer International Publishing
Publication: June 4, 2014
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Yeasts in Natural Ecosystems: Ecology by Michael Kaufmann
Cover of the book National Space Legislation by Michael Kaufmann
Cover of the book Scalable Uncertainty Management by Michael Kaufmann
Cover of the book The NICE Cyber Security Framework by Michael Kaufmann
Cover of the book Ambient Assisted Living by Michael Kaufmann
Cover of the book Amorphous Drugs by Michael Kaufmann
Cover of the book Evolutionary Governance Theory by Michael Kaufmann
Cover of the book The Benefits of Natural Products for Neurodegenerative Diseases by Michael Kaufmann
Cover of the book Moral Ecologies by Michael Kaufmann
Cover of the book FM 2016: Formal Methods by Michael Kaufmann
Cover of the book Parenting and Family Processes in Child Maltreatment and Intervention by Michael Kaufmann
Cover of the book Stupid Humanism by Michael Kaufmann
Cover of the book After-School Programs to Promote Positive Youth Development by Michael Kaufmann
Cover of the book Proceedings of the International Conference on Martensitic Transformations: Chicago by Michael Kaufmann
Cover of the book Intergroup Helping by Michael Kaufmann
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy