Navigating the complexities of marketing decision-making in uncertain supply chains: A quantitative exploration

Making rational decisions in times of uncertainty is an important capability required of marketing managers. However, choosing the precise quantitative decision-making technique or a combination of such tools usually presents managers with a score of challenges. To lessen this challenge, this single case research demonstrates how the choice of three Bayesian decision models (Laplace criterion, Savage minimax regret, and Hurwicz coefficient of realization) can reduce uncertainty in content marketing, search engine optimization (SEO), and referral marketing decision. We employed the case study approach to enable direct access to the decision-making process of the case organization, and to collect relevant data relating to the three marketing decision variables. Results show that, although the Laplace criterion may not have a direct effect on content marketing decisions, it influences the decision-making process leading to the creation and promotion of content. The Savage minimax regret had a direct bearing on SEO decision-making. Hurwicz's coefficient of realization yielded no direct impact on referral marketing but shows the possibility of influencing decision-making processes that led to the development and implementation of referral marketing campaigns. The implications are discussed.