The Impact of TV Advertising on Website Traffic

TL;DR

This paper introduces a comprehensive modeling approach to quantify the immediate effects of TV advertising on website traffic, considering temporal patterns and ad characteristics, with application to real e-commerce data.

Contribution

It combines kernel smoothing, maximum likelihood, and random forest methods to analyze TV ad impacts on website visits, providing a novel integrated framework.

Findings

Time of day and TV channel significantly influence ad effectiveness.

People tend to multitask, switching screens during TV ads.

Ad characteristics like motive affect the increase in website visits.

Abstract

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.