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 .
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 incorporatinga 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 .
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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
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
A
it is well suited to generalization to other markets or situations. Its Bayesian nature facilitates incorporating
A
a priori
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information (the manager's views),
R
help the firm to manage their advertisement plans, thus leading to a robust, risk-aware optimization of their revenue
be updated with relevant data. This aspect of the model will be used to present a strategy of budget scheduling across time and channels