The Role of Bayesian Priors in a Marketing Mix Model: A Scholarly Exploration
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The Role of Bayesian Priors in a Marketing Mix Model: A Scholarly Exploration
Varun Chivukula
varunvenkatesh88@berkeley.edu
Abstract
Bayesian inference has increasingly become a cornerstone of modern data analytics, including applications in marketing mix modeling (MMM). The inclusion of Bayesian priors allows for the incorporation of prior knowledge into the modeling process, thereby addressing issues of limited data and model uncertainty. This paper examines the role of Bayesian priors in MMM, detailing their theoretical foundation, practical implementation, and implications for decision-making in marketing. Additionally, this study highlights the advantages and challenges associated with Bayesian approaches and provides insights into how they enhance model robustness and interpretability.
Keywords: Marketing Mix Model (MMM), Bayesian Priors, Markov Chain Monte Carlo Methods