Before revision: In this work we propose a data-driven modelization approach for the management of advertising investments of a firm. First, we propose an application of dynamic linear models to the prediction of an economic variable, such as global sales, which can use information from the environment and the investment levels of the company in different channels. After we build a robust and precise model, we propose a metric of risk ,   which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .
After revision: 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 , which can help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue .