Combinatorial Auctions
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Pricing guaranteed contracts in online display advertising
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Yield optimization of display advertising with ad exchange
Proceedings of the 12th ACM conference on Electronic commerce
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Display advertising is the graphical advertising on the World Wide Web (WWW) that appears next to content on web pages, instant messaging (IM) applications, email, etc. Over the past decade, display ads have evolved from simple banner and pop-up ads to include various combinations of text, images, audio, video, and animations. As a market segment, display continues to show substantial growth potential, as evidenced by companies such as Microsoft, Yahoo, and Google actively vying for market share. As a sales process, display ads are typically sold in packages, the result of negotiations between sales and advertising agents. A key component to any successful business model in display advertising is sound pricing. Main objectives for on-line publishers (e.g. Amazon, YouTube, CNN) are maximizing revenue while managing their available inventory appropriately, and pricing must reflect these considerations. This paper addresses the problem of maximizing revenue by adjusting prices of display inventory. We cast this as an inventory allocation problem. Our formal objective (a) maximizes revenue using (b) iterative price adjustments in the direction of the gradient of an appropriately constructed Lagrangian relaxation. We show that our optimization approach drives the revenue towards local maximum under mild conditions on the properties of the (unknown) demand curve. The major unknown for optimizing revenue in display environment is how the demand for display ads changes to prices, the classical demand curve. This we address directly, by way of a factorial pricing experiment. This enables us to estimate the gradient of the revenue function with respect to inventory prices. Overall, the result is a principled, risk-aware, and empirically efficient methodology. This paper is based on research undertaken on behalf of one of Google's clients.