Chance Constrained Optimization for Targeted Internet Advertising

TL;DR

This paper presents a chance constrained optimization model for targeted internet advertising, accounting for viewer supply uncertainty, with theoretical bounds and computational methods demonstrated through Monte Carlo sampling and convex approximations.

Contribution

It introduces a novel chance constrained model for ad allocation that incorporates supply uncertainty, with theoretical bounds and practical computational techniques.

Findings

The model effectively handles supply uncertainty in ad campaigns.

Monte Carlo sampling and convex approximations enable practical computation.

Theoretical bounds provide insights into model performance.

Abstract

We introduce a chance constrained optimization model for the fulfillment of guaranteed display Internet advertising campaigns. The proposed formulation for the allocation of display inventory takes into account the uncertainty of the supply of Internet viewers. We discuss and present theoretical and computational features of the model via Monte Carlo sampling and convex approximations. Theoretical upper and lower bounds are presented along with a numerical substantiation.