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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This textbook demonstrates the application of recent advancements in data science to address various marketing issues. It provides a unique framework for transforming marketing problems into data science problems, which is a crucial first step that is often overlooked in books that focus solely on data analytical tools. It also emphasizes the intuitive understanding of the analytical tools and data science methodologies, in addition to presenting their limitations and best use cases. Students will learn why certain data science tools work well for particular marketing problems, while others may not. Finally, it explores how to translate the insights gained from these analytics tools into business decisions and how they can be used to inform the final decisions related to critical business questions.Blending technical fields such as statistics, econometrics, and machine learning with business areas like marketing and customer understanding, this textbook provides solutions to various marketing and customer-centered questions using data analytical models and techniques, with each chapter covering a specific type of question. It will helps upper-level marketing student understand the technical aspects of data science in a way that is relevant and applicable to their future careers. 248 pp. Englisch.
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This textbook demonstrates the application of recent advancements in data science to address various marketing issues. It provides a unique framework for transforming marketing problems into data science problems, which is a crucial first step that is often overlooked in books that focus solely on data analytical tools. It also emphasizes the intuitive understanding of the analytical tools and data science methodologies, in addition to presenting their limitations and best use cases. Students will learn why certain data science tools work well for particular marketing problems, while others may not. Finally, it explores how to translate the insights gained from these analytics tools into business decisions and how they can be used to inform the final decisions related to critical business questions.Blending technical fields such as statistics, econometrics, and machine learning with business areas like marketing and customer understanding, this textbook provides solutions to various marketing and customer-centered questions using data analytical models and techniques, with each chapter covering a specific type of question. It will helps upper-level marketing student understand the technical aspects of data science in a way that is relevant and applicable to their future careers.
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Ajouter au panierTaschenbuch. Etat : Neu. Hierarchical Bayesian Method in the Study of Individual Level Behavior | In the Context of Discrete Choice Modeling with Revealed and Stated Preference Data | Xiaojing Dong | Taschenbuch | 68 S. | Englisch | 2012 | AV Akademikerverlag | EAN 9783639415056 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Langue: anglais
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ISBN 10 : 3836423537 ISBN 13 : 9783836423533
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware - A major breakthrough in travel demand modeling in the early 1970's was modeling based on disaggregate (individual) level data (McFadden 2001). Although the disaggregate model focuses on individual level behavior, the estimated model parameters are fixed across individuals. To incorporate unobserved taste variations across individuals, recent developments allow for the parameters to vary across individuals, such as the Mixed Logit model, where the parameters are assumed to follow a distribution. The mixed logit model recognizes the differences among individuals, but it does not distinguish individuals who respond differently to travel service changes. This study focuses on the application of the Hierarchical Bayesian method to obtain individual level inferences. We demonstrate the advantage of this method by obtaining a more reasonable distribution of value of travel time relative to the distribution obtained from the mixed logit model. In addition, the HB method helps us to combine information from both revealed and stated preference data, where the revealed preference data is limited to properties of only the chosen alternatives.
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Edité par Springer-Verlag Gmbh Mai 2026, 2026
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This textbook demonstrates the application of recent advancements in data science to address various marketing issues. It provides a unique framework for transforming marketing problems into data science problems, which is a crucial first step that is often overlooked in books that focus solely on data analytical tools. It also emphasizes the intuitive understanding of the analytical tools and data science methodologies, in addition to presenting their limitations and best use cases. Students will learn why certain data science tools work well for particular marketing problems, while others may not. Finally, it explores how to translate the insights gained from these analytics tools into business decisions and how they can be used to inform the final decisions related to critical business questions.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 233 pp. Englisch.
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Ajouter au panierTaschenbuch. Etat : Neu. Marketing Analytics and Data Science | Tools and Models | Xiaojing Dong | Taschenbuch | xiv | Englisch | 2026 | Springer | EAN 9783032111296 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Langue: anglais
Edité par Springer-Verlag Gmbh Mai 2026, 2026
ISBN 10 : 3032111293 ISBN 13 : 9783032111296
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook demonstrates the application of recent advancements in data science to address various marketing issues. It provides a unique framework for transforming marketing problems into data science problems, which is a crucial first step that is often overlooked in books that focus solely on data analytical tools. It also emphasizes the intuitive understanding of the analytical tools and data science methodologies, in addition to presenting their limitations and best use cases. Students will learn why certain data science tools work well for particular marketing problems, while others may not. Finally, it explores how to translate the insights gained from these analytics tools into business decisions and how they can be used to inform the final decisions related to critical business questions.Blending technical fields such as statistics, econometrics, and machine learning with business areas like marketing and customer understanding, this textbook provides solutions to various marketing and customer-centered questions using data analytical models and techniques, with each chapter covering a specific type of question. It will helps upper-level marketing student understand the technical aspects of data science in a way that is relevant and applicable to their future careers.
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -This textbook demonstrates the application of recent advancements in data science to address various marketing issues. It provides a unique framework for transforming marketing problems into data science problems, which is a crucial first step that is often overlooked in books that focus solely on data analytical tools. It also emphasizes the intuitive understanding of the analytical tools and data science methodologies, in addition to presenting their limitations and best use cases. Students will learn why certain data science tools work well for particular marketing problems, while others may not. Finally, it explores how to translate the insights gained from these analytics tools into business decisions and how they can be used to inform the final decisions related to critical business questions.Blending technical fields such as statistics, econometrics, and machine learning with business areas like marketing and customer understanding, this textbook provides solutions to various marketing and customer-centered questions using data analytical models and techniques, with each chapter covering a specific type of question. It will helps upper-level marketing student understand the technical aspects of data science in a way that is relevant and applicable to their future careers. 248 pp. Englisch.
Langue: anglais
Edité par VDM Verlag Dr. Mueller E.K., 2007
ISBN 10 : 3836423537 ISBN 13 : 9783836423533
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Langue: anglais
Edité par VDM Verlag Dr. Mueller E.K., 2007
ISBN 10 : 3836423537 ISBN 13 : 9783836423533
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Ajouter au panierPAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Langue: anglais
Edité par AV Akademikerverlag Mai 2012, 2012
ISBN 10 : 3639415051 ISBN 13 : 9783639415056
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Revision with unchanged content. A major breakthrough in travel demand modeling in the early 1970's was modeling based on disaggregate (individual) level data (McFadden 2001). Although the disaggregate model focuses on individual level behavior, the estimated model parameters are fixed across individuals. To incorporate unobserved taste variations across individuals, recent developments allow for the parameters to vary across individuals, such as the Mixed Logit model, where the parameters are assumed to follow a distribution. The mixed logit model recognizes the differences among individuals, but it does not distinguish individuals who respond differently to travel service changes. This study focuses on the application of the Hierarchical Bayesian method to obtain individual level inferences. We demonstrate the advantage of this method by obtaining a more reasonable distribution of value of travel time relative to the distribution obtained from the mixed logit model. In addition, the HB method helps us to combine information from both revealed and stated preference data, where the revealed preference data is limited to properties of only the chosen alternatives. 68 pp. Englisch.
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Langue: anglais
Edité par VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2007
ISBN 10 : 3836423537 ISBN 13 : 9783836423533
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Ajouter au panierEtat : New. Print on Demand pp. 80 67:B&W 6.69 x 9.61 in or 244 x 170 mm (Pinched Crown) Perfect Bound on White w/Gloss Lam.