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

TL;DR

This paper introduces a Bayesian structural time series model to analyze how advertising expenditures influence weekly sales in a fast-food franchise, allowing for flexible, data-informed budget strategies.

Contribution

It presents a robust Bayesian implementation of the Nerlove--Arrow model, enabling flexible analysis and strategic budget scheduling for advertising impacts.

Findings

Model effectively captures advertising-sales relationship

Facilitates incorporation of managerial prior beliefs

Supports strategic advertising budget planning

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 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.