Abstract
This paper describes an optimization model for setting bid levels for certain types of advertisements on web pages. This model is non-convex, but we are able to obtain optimal or near-optimal solutions rapidly using branch and cut open- source software. The financial benefits obtained using the prototype system have been substantial.
Highlights
Advertising on the World Wide Web is ubiquitous and a big business
A great deal of attention has been paid to “Sponsored Search”, where text advertisements with hypertext links are placed next to the search results produced by search engines, and these do account for a large fraction of the revenue generated by web advertising
Return on Investment (ROI) is computed as the ratio of the imputed income to the delivery cost, minus 1
Summary
Advertising on the World Wide Web is ubiquitous and a big business. Recently a great deal of attention has been paid to “Sponsored Search”, where text advertisements with hypertext links are placed next to the search results produced by search engines (see [1]), and these do account for a large fraction of the revenue generated by web advertising. Long as House businesses can profitably compete for ads under revenue types 1 or 2, in principle, and all other things being equal, they should have unlimited budgets since the more they spend, the more Yahoo! This involves observing the bid levels of other groups of ads and re-computing our bids in the relevant auctions in such a way as to maximize expected return for the model horiz- on This requires choosing among discrete bids, which can be modeled as Special Ordered Sets [2] of type 1. Our only “handle” in the Class 2 context is the bid levels to be set for the auction procedure which affect the serving policy of the ad server This implies some form of discrete model, since a bid either exceeds another bid, or it does not, and the outcome of that will depend on these relative bid levels. In the remainder of this paper we will outline some of the terminology used with these models, discuss the data available, formulate our model, propose some solution strategies and give computational experience
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