Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
This addresses budget allocation for online marketing campaigns using offline data, offering a practical solution for industrial deployment, though it is incremental as it builds on existing reinforcement learning methods.
The paper tackles the marketing budget allocation problem using offline data by proposing a novel game-theoretic offline value-based reinforcement learning method with mixed policies, which reduces stored policies from infinite to constant and achieves nearly optimal efficiency, outperforming baselines in experiments with tens-of-millions of users and over one billion budget.
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.