To many business executives, marketing mix models remain shrouded in mystery. There are many advertising agencies and market research companies, plus countless online media firms, which promote their particular marketing mix model in an effort to support marketing decision makers. However, there is a remarkable lack of universally accepted and standardized methods that marketing practitioners can draw upon. In fact, performing a search for books on Amazon.com regarding “marketing mix models” yields only three relevant titles and, as it turns out, they are mostly geared towards an academic audience. In contrast to the sparse array of books, there is no shortage of academic papers on all aspects of marketing mix modeling and optimization. Without doubt, many of these peer-reviewed journal articles have progressed the field of marketing science, but the often abstract nature of the proposed methods keep them far removed from practical implementation.
Given this lack of textbook references in this field, plus the fairly inaccessible nature of the academic literature, decision makers have to rely almost exclusively on the persuasion skills of research vendors and consultants in determining the validity of any proposed marketing mix model.
Marketing models based on Bayesian networks are not automatically a solution to this quandary, but their inherently visual nature plus their computational transparency make them much more accessible to a broad range of stakeholders. To interpret and validate a Bayesian network requires, most importantly, a “common-sense” understanding of the domain and not necessarily a degree in statistics.
It is our objective to use the framework of Bayesian networks, plus the features of the BayesiaLab software package, to create sound marketing mix models that can be implemented by many and interpreted by all. More specifically, we will focus on how to optimize marketing mix models with BayesiaLab’s algorithms and to derive policy recommendations for decision makers.