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Multi-Click Attribution in Sponsored Search Advertising: An Empirical Study in Hospitality Industry

机译:赞助搜索广告中的多点归因:酒店业的实证研究

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摘要

Sponsored search advertising has become a dominant form of advertising for many firms in the hospitality vertical, with Priceline and Expedia each spending in excess of US$2 billion in online advertising in 2015. Given the competition in online advertising, it has become essential for advertisers to know how effectively to allocate financial resources to keywords. Central to budget allocation for keywords is an attribution of revenue (from converted ads) to the keywords generating consumer interest. Conventional wisdom suggests several ways to attribute revenues in the sponsored search advertising domain (e.g., last-click, first u26 last-click, or evenly distributed approach). We develop a multi-click attribution methodology using a unique multi-advertiser data set, which includes full advertiser and consumer-level click and purchase information. We add to the literature by developing a two-stage multi-click attribution methodology with a specific focus on sponsored search advertising in the hospitality industry with which we develop a parametric approach to calculate the value function from each stage of the estimation process. Given our multi-advertiser data set, we are able to illustrate the inefficiency of single-click attribution approaches, which undervalue assist clicks while overvaluing converted clicks.
机译:赞助搜索广告已成为许多酒店业垂直广告的一种主要形式,Priceline和Expedia在2015年的在线广告支出均超过20亿美元。鉴于在线广告的竞争,对于广告客户而言,它已成为必不可少的知道如何有效地将财务资源分配给关键字。关键字预算分配的核心是将收入(来自转化广告)分配给产生消费者兴趣的关键字。传统观点提出了几种在赞助搜索广告领域中分配收入的方式(例如,最终点击,首次最终点击或平均分配方式)。我们使用唯一的多广告商数据集开发了一种多点击归因方法,其中包括完整的广告商和消费者级别的点击和购买信息。我们通过开发一种两阶段的多点击归因方法来增加文献资料,该方法特别关注于酒店行业中的赞助搜索广告,通过该方法我们开发了一种参数方法来从估计过程的每个阶段计算价值函数。鉴于我们的多广告客户数据集,我们能够说明单次点击归因方法的效率低下,这种方法低估了辅助点击,却高估了转化的点击。

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