We propose a modeling procedure for estimating immediate responses to TV ads and evaluating the factors influencing their size. First, we capture diurnal and seasonal patterns of website visits using the kernel smoothing method. Second, we estimate a gradual increase in website visits after an ad using the maximum likelihood method. Third, we analyze the non-linear dependence of the estimated increase in website visits on characteristics of the ads using the random forest method. The proposed methodology is applied to a dataset containing minute-by-minute organic website visits and detailed characteristics of TV ads for an e-commerce company in 2019. The results show that people are indeed willing to switch between screens and multitask. Moreover, the time of the day, the TV channel, and the advertising motive play a great role in the impact of the ads.

翻译：我们提出了一种用于估算电视广告即时响应并评估其规模影响因素的建模流程。首先，利用核平滑法捕捉网站访问量的昼夜和季节性模式；其次，采用最大似然法估计广告播出后网站访问量的渐进增长；第三，运用随机森林法分析广告特征与估计增长量之间的非线性依赖关系。将该方法应用于2019年某电商公司的分钟级有机网站访问量及电视广告详细特征数据集。结果表明，人们确实愿意跨屏切换并执行多任务操作。此外，广告播出时段、电视频道及广告动机对其效果具有显著影响。