A New Method to Aid Copy Testing of Paid Search Text Advertisements
提出一种结合配对比较和文本内容指标的方法，用于评估和预测付费搜索广告的感知效果和点击表现，帮助广告主优化文案。
The authors propose a new approach to evaluate the perceptions and performance of a large set of paid search ads. This approach consists of two parts. First, primary data on hundreds of ads are collected through paired comparisons of their relative ability to generate awareness, interest, desire, action, and click performance. The authors use the Elo algorithm, a statistical model calibrated on paired comparisons, to score the full set of ads on relative perceptions and click performance. The estimated scores validate the theoretical link between perceptions and performance. Second, the authors predict the perceptions and performance of new ads relative to the existing set using textual content metrics. The predictive model allows for direct effects and interactions of the text metrics, resulting in a “large p, small n” problem. They address this problem with a novel Bayesian implementation of the VANISH model, a penalized regression approach that allows for differential treatment of main and interaction effects, in a system of equations. The authors demonstrate that this approach ably forecasts relative ad performance by leveraging perceptions inferred from content alone.