Consider a publisher of online display advertising that sells its ad resources in both an up-front market and a spot market. When planning its ad delivery, the publisher needs to make a trade-off between earning a greater short-term profit from the spot market and improving advertising effectiveness in the up-front market. To address this challenge, we propose an integrated planning model that is robust to the uncertainties associated with the supply of advertising resources. Specifically, we model the problem as a distributionally robust chance-constrained program. We first approximate the program by using a robust optimization model, which is then transformed into a linear program. We provide a theoretical bound on the performance loss due to this transformation. A clustering algorithm is proposed to solve large-scale cases in practice. We implement ad serving of our planning model on two real data sets, and we demonstrate how to incorporate realistic constraints such as exclusivity and frequency caps. Our numerical experiments demonstrate that our approach is very effective: it generates more revenue while fulfilling the guaranteed contracts and ensuring advertising effectiveness.
- Ad delivery planning
- Display advertising
- Distributionally robust chance-constrained optimization
- Targeted advertising
ASJC Scopus subject areas
- Computer Science Applications
- Management Science and Operations Research