  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.
