In this work we analyze data from Google AdWords, today's most important platform for online advertisements. There, the success of an online ad campaign depends on (time-global) seasonal factors as well as on (time-local) events, such as the importance of Valentine's Day for an online flower shop. These components are, however, difficult to assess using existing key figures and methods. In order to reveal both components from the data, we build upon the recent advances in the literature on the functional linear regression model with points of impact. Our proposed model contributes a yearly perspective on the click-through rate -- one of the most important measures for evaluating Google AdWords campaigns. For estimating the model parameters, we provide an adjusted estimation algorithm that leads to a significant improvement over the original estimation procedure.

翻译：在这项工作中,我们分析谷歌AdWords的数据,谷歌AdWords是今天最重要的在线广告平台。在那里,在线广告运动的成功取决于(时间-全球)季节性因素以及(时间-当地)活动,例如情人节对在线花店的重要性。然而,这些组成部分很难利用现有的关键数字和方法进行评估。为了从数据中揭示这两个组成部分,我们以功能线性回归模型文献的最新进展及其影响点为基础。我们提议的模型每年提供点击率的视角,这是评价谷歌AdWords运动的最重要措施之一。为了估算模型参数,我们提供了经调整的估计算法,从而大大改进了原始估算程序。