Inventory Allocation for Online Graphical Display Advertising

TL;DR

This paper presents a multi-objective optimization model for allocating online graphical ad inventory, balancing revenue, future sales, and fairness, with demonstrated effectiveness on real data.

Contribution

It introduces a flexible multi-objective model for ad inventory allocation that considers multiple campaign types and objectives, including fairness.

Findings

Model effectively balances revenue and fairness.

Experimental results show improved allocation efficiency.

Flexible framework adaptable to various objectives.

Abstract

We discuss a multi-objective/goal programming model for the allocation of inventory of graphical advertisements. The model considers two types of campaigns: guaranteed delivery (GD), which are sold months in advance, and non-guaranteed delivery (NGD), which are sold using real-time auctions. We investigate various advertiser and publisher objectives such as (a) revenue from the sale of impressions, clicks and conversions, (b) future revenue from the sale of NGD inventory, and (c) "fairness" of allocation. While the first two objectives are monetary, the third is not. This combination of demand types and objectives leads to potentially many variations of our model, which we delineate and evaluate. Our experimental results, which are based on optimization runs using real data sets, demonstrate the effectiveness and flexibility of the proposed model.