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INTRODUCTION
Online advertising has grown at a fast pace over the past decade reaching a revenue of US$21.2 billion in 2007 (IAB, 2007). The growth is not expected to slow down in the near future as users spend more time online, rich media technology increases user interaction and online publishers start discovering ways to monetise their remnant inventory (Hof, 2008).
The value proposition of digital marketing is to provide real-time feedback on customer behaviour and closely monitor the performance of advertising campaigns. Online advertising has changed the nature of marketing, requiring more quantitative approaches with sophisticated tools and algorithms (Booz Allen Hamilton, 2007) as well as dynamic adjustment of spending, in contrast to the traditional up-front spending in broadcast advertising (Araman and Popescu, 2007). Hence, different approaches are needed.
The online advertising space can be divided into two parts: Search word advertising and display advertising. Search word advertising is done by search engine websites that post advertisements (ads) along with search word results. Display advertising, on the other hand, is done by web publishers who post advertisement banners on their websites.
In this paper, we consider a web publisher who seeks to maximise its advertising revenues by delivering display ads on its website. Taking into account traffic uncertainty, the publisher needs to decide which advertising contracts to accept and how to fulfil them. In practice, contract decisions and advertisement delivery are often done independently by the sales team and delivery engines (for example, Dart by DoubleClick) respectively, with little coordination between the two functions.
We propose a unified approach where the web publisher dynamically decides which advertising requests to accept by taking into account the available advertising inventory and dynamically delivers the promised advertising contract to the viewers, so as to maximise its revenues over a finite horizon. We formulate the problem as a dynamic program (DP) and characterise its structural properties. We then propose a Certainty Equivalent Control (CEC) heuristic, similar to Bertsimas and Popescu (2003). Using a real case study, we show that our heuristic outperforms other approaches that are commonly used in practice. Our analysis thus highlights the importance of accounting for the opportunity cost of capacity allocation in advertising contract negotiation for globally maximising online publishers' revenues.
The paper is organised...