This work presents "A Logit Model of Brand Choice Calibrated on Scanner Data." It explores the application of the logit model to understand and predict consumer brand choices, utilizing data obtained from scanner technology. This research is relevant to the fields of marketing, econometrics, and statistics, offering insights into consumer behavior and the effectiveness of marketing strategies.
The study by Guadagni and Little contributes to the quantitative analysis of consumer choices, providing a framework for marketers and analysts to model and predict brand preferences based on observed purchasing patterns. The use of scanner data allows for a granular and data-driven approach to understanding consumer behavior, making it a valuable resource for those interested in data-driven marketing and econometrics.
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.
This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.
As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Paperback. Etat : new. Paperback. This work presents "A Logit Model of Brand Choice Calibrated on Scanner Data." It explores the application of the logit model to understand and predict consumer brand choices, utilizing data obtained from scanner technology. This research is relevant to the fields of marketing, econometrics, and statistics, offering insights into consumer behavior and the effectiveness of marketing strategies. The study by Guadagni and Little contributes to the quantitative analysis of consumer choices, providing a framework for marketers and analysts to model and predict brand preferences based on observed purchasing patterns. The use of scanner data allows for a granular and data-driven approach to understanding consumer behavior, making it a valuable resource for those interested in data-driven marketing and econometrics.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781024187861
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