Assessing the effect of advertising expenditures upon sales: A Bayesian structural time series model.

Abstract

We propose a robust implementation of the Nerlove-Arrow model using a Bayesian structural time series model to explain the relationship between advertising expenditures of a countrywide fast-food franchise network with its weekly sales. Due to the flexibility and modularity of the model, it is well suited to generalization to other markets or situations. Its Bayesian nature facilitates incorporating a priori information reflecting the manager's views, which can be updated with relevant data. This aspect of the model will be used to support the decision of the manager on the budget scheduling of the advertising firm across time and channels.