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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.
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