Zooming In on Paid Search Ads—A Consumer-Level Model Calibrated on Aggregated Data
基于搜索引擎提供的聚合数据，构建了一个两阶段消费者层面的付费搜索广告响应模型，用于评估广告属性对效果的影响，并提出一种根据广告展示位置提供优惠券的定向方案，以提升转化收入。
We develop a two-stage consumer-level model of paid search advertising response based on standard aggregated data provided to advertisers by major search engines such as Google or Bing. The proposed model uses behavioral primitives in accord with utility maximization and allows recovering parameters of the heterogeneity distribution in consumer preferences. The model is estimated on a novel paid search data set that includes information on the ad copy. To that end, we develop an original framework to analyze composition and design attributes of paid search ads. Our results allow us to correctly evaluate the effects of specific ad properties on ad performance, taking consumer heterogeneity into account. Another benefit of our approach is allowing recovery of preference correlation across the click-through and conversion stage. Based on the estimated correlation between price- and position-sensitivity, we propose a novel contextual targeting scheme in which a coupon is offered to a consumer depending on the position in which the paid search ad was displayed. Our analysis shows that total revenues from conversion can be increased using this targeting scheme while keeping cost constant.