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Abstract | ||
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AbstractAdvertisers engaged in brand-building activities online often purchase significant volumes of impression-based online advertising (e.g., banner ads or video ads) from website publishers or their ad-technology partners who schedule and deliver online advertisements. Such advertising is typically targeted, which means that ads can only be shown to specific audience segments chosen by the advertiser (e.g., urban females from any U.S. city). In general, advertisers are not well served by publishers who further constrain an ad campaign’s targeting (e.g., by delivering disproportionately many impressions of urban females from Los Angeles and not from other cities). In “Planning Online Advertising Using Gini Indices,” Miguel Lejeune and John Turner consider a new objective function for spreading impressions of online advertising across targeted audience segments. The authors show how the Gini Index, which economists use to measure wealth or income inequality, can be deployed within a publisher’s optimization model to minimize the weighted sum of Gini indices across all ad campaigns, and thus maximally spread impressions across targeted audience segments, while limiting demand shortfalls. Key properties and solution structure are compared to a popular existing non-Gini-based ad spreading model developed at Yahoo, and a novel optimization-based decomposition scheme is developed to efficiently solve the Gini-allocation problem. Finally, the authors illustrate how Lorenz curves may be used to visualize Gini-based spread so that managers can effectively monitor the performance of a publisher's ad delivery system.We study an online display advertising planning problem in which advertisers’ demands for ad exposures (impressions) of various types compete for slices of shared resources, and advertisers prefer to receive impressions that are evenly spread across the audience segments they target. We use the Gini coefficient measure and formulate an optimization problem that maximizes the spreading of impressions across targeted audience segments, while limiting demand shortfalls. First, we show how Gini-based metrics can be used to measure spreading that publishers of online advertising care about and how Lorenz curves can be used to visualize Gini-based spread so that managers can effectively monitor the performance of a publisher’s ad delivery system. Second, we adapt an existing ad planning model to measure Gini-based spread across audience segments and compare and contrast our model to this baseline with respect to key properties and the structure of the solutions they produce. Third, we introduce a novel optimization-based decomposition scheme that efficiently solves our instances of the Gini-based problem up to 60 times faster than the commercial solver CPLEX directly solves a basic formulation. Finally, we present a number of model and algorithmic extensions, including (1) an online algorithm that mirrors the structure of our decomposition method to serve well-spread ads in real time, (2) a model extension that allows an aggregator buying impressions in an external market to allocate them to advertisers in a well-spread manner, and (3) a multiperiod model and decomposition method that spreads impressions across both audience segments and time. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1287/opre.2019.1841 | Periodicals |
Keywords | Field | DocType |
online advertising,Gini index,Lorenz curve,decomposition method,spreading impressions | Mathematical optimization,Advertising,Lorenz curve,Impression,Online advertising,Web banner,Mathematics | Journal |
Volume | Issue | ISSN |
67 | 5 | 0030-364X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel A. Lejeune | 1 | 253 | 21.95 |
| John G. Turner | 2 | 12 | 1.21 |