بهینه سازی پاسخ مستقیم در صفحه نمایش تبلیغات اینترنتی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|2164||2012||12 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Electronic Commerce Research and Applications, Volume 11, Issue 3, May–June 2012, Pages 229–240
Internet display advertising has grown into a multi-billion dollar a year global industry and direct response campaigns account for about three-quarters of all Internet display advertising. In such campaigns, advertisers reach out to a target audience via some form of a visual advertisement (hereinafter also called “ad”) to maximize short-term sales revenue. In this study, we formulate an advertiser’s revenue maximization problem in direct response Internet display advertisement campaigns as a mixed integer program via piecewise linear approximation of the revenue function. A novelty of our approach is that ad location and content issues are explicitly incorporated in the optimization model. Computational experiments on a large-scale actual campaign indicate that adopting the optimal media schedule can significantly increase advertising revenues without any budget changes, and reasonably sized instances of the problem can be solved within short execution times.
Since the first banner ad appeared on the Internet in 1994, Internet advertising has become a multi-billion dollar a year global industry; significantly surpassing radio advertisement and becoming the third largest market right behind TV and newspapers (Silverman 2010). The top two forms of Internet advertising are paid-search and display advertising. In paid-search advertising, advertisers pay an advertising fee, usually based on ad views or click-throughs, to have their websites shown in top placement on search engine result pages. In Internet display advertising (IDA), which is the subject of this study, advertisers reach out to a target Internet audience via some form of a visual advertisement such as display banner ads, flash-based rich media, or digital video. One study indicates that IDA has grown into a $17 billion global industry in 2009 (Soriano 2010), and another one reports that US. Internet users received a total of 4.9 trillion display ads in 2010 (Radwanick 2011). Traditionally, objective of an IDA campaign is characterized as being either branding or direct response. Branding campaigns are long-term advertisement investments with goals such as boosting brand awareness, generating new customer lead, and improving customer relationship (Hollis 2005). In practice, branding campaigns aim to maximize the reach of the campaign, i.e., the proportion of the target audience exposed to at least one ad. In contrast, the goal in a direct response campaign is to achieve a measurable, direct, and immediate response. In general, direct response campaigns try to maximize revenue obtained by click-through or view-through conversions.
نتیجه گیری انگلیسی
Internet display advertising is a multi-billion dollar a year industry that just keeps growing along with the rest of Internet advertising. Of the two types of Internet display advertising, direct response and branding, the former accounts for a vast majority of the ad dollars. This study, which is the first of its kind, presents a mathematical model for the revenue maximization problem in direct response Internet display advertising. The model presented is a mixed integer program that is based on piecewise linear approximation of individual insert revenue functions. In particular, the assumption of statistical independence among inserts makes the objective function separable and allows approximation of individual revenue functions via special ordered sets. A novelty of our approach is that we explicitly incorporate ad placement and creative issues in our problem formulation. In addition, our model takes into account differences in CTRs across different exposures for an insert. These exposure CTRs are computed using an empirical exposure CTR ratio. Our model can be used in optimization of direct response campaigns tracking both click-through and view through conversions. In our methodology, we use the negative binomial distribution for modeling exposure distribution, i.e., percent of the target population exposed to different number of ads. Of central use in our approach is the scaling property of the negative binomial distribution to compute exposure distribution for different number of impressions for an insert. Our computational experiments on a large-scale real world Internet advertising campaign indicate that (i) only a small number of intervals is required in the piecewise linear approximation in practice, and (ii) reasonably sized instances of the problem can be solved within short execution times. Our results also indicate that adopting the optimal schedule can result in significant increase in revenues without making any changes in the advertising budget.