Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model
This work addresses the need for robust advertising-sales analysis in marketing, particularly for fast-food franchises, but it is incremental as it applies an existing model to a specific dataset.
The authors tackled the problem of quantifying the impact of advertising expenditures on sales for a fast-food franchise network by implementing a Bayesian structural time series model based on the Nerlove-Arrow framework, resulting in a method that allows for incorporating prior managerial views and updating with data to inform budget scheduling strategies.
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 country-wide fast-food franchise network with its weekly sales. Thanks to the flexibility and modularity of the model, it is well suited to generalization to other markets or situations. Its Bayesian nature facilitates incorporating \emph{a priori} information (the manager's views), which can be updated with relevant data. This aspect of the model will be used to present a strategy of budget scheduling across time and channels.