Dynamic allocation of advertising budgets across Direct Buying and Programmatic types using reinforcement learning

Abstract

The advertising budget allocation problem is the problem of determining when, to which advertising media, and how much to allocate the budget to maximize advertising effectiveness. This problem changes its characteristics depending on the advertising media assumed. In this paper, we consider a setting that includes both direct buying advertising, such as TV, and programmatic advertising, such as the Internet. We formulate the problem as a Markov decision process and apply model-free reinforcement learning. We demonstrate the effectiveness of our approach by experimenting with real-world data.