TV Attribution examines the effects of television ads on any online goal such as website traffic, app download, sales and forms The most efficient channel, program and content type is determined for the advertiser. The effect of variables such as hour of the day, day of the week, and creative type is measured.
Which TV spots provided the most user interaction and sales on the website?
On which channels or programs should one continue to invest, and which should not?
At what time of the day or on which day of the week do TV ads provide the most conversion on the website? It is not possible to get an answer to these questions with the traditionally used audience measurement method. However, mobile devices, which have been increasingly used in recent years, are opening a new door for measurement. According to a study conducted in 2018 by Nielsen, data on the use of smartphones, tablets or laptops while watching TV is as in the graphic below. 9% of the subjects stated that they always use a second screen while watching TV, whereas 36% of the subjects claim that they use a second screen very often while watching TV. This data is actually a clear proof that there is an organic path between TV advertising and digital platforms.
Therefore, it is possible to have an increase in your online goals with the changes you make in your TV campaign. The way to do this is through accurate measurement. Tvlyzer TV Attribution makes the digital data more meaningful by integrating it into mobile analytics platforms such as Adjust and Appsflyer in addition to web analytics platforms. It clearly shows the effect of TV spots with statistical modeling methods and decomposes the effect of TV spots that coincide with machine learning. It also provides live data streaming to advertisers thanks to its video recognition technology.
If you are a TV advertiser with goals such as online sales, forms, app downloads or website traffic, you should definitely have a TV Attribution perspective.
Most digital marketers have witnessed an increase in online performance during the TV campaign period. You can even see the traffic increase clearly on the Google Analytics real time screen as soon as the TV spot is broadcasted. However, the most extensive time reporting is done on an hourly basis in the Google Analytics platform, and the minute data is not reflected in the dashboard. In addition, it is not possible to manually determine which traffic or sales come from TV advertising and which other from marketing channels. Many advertisers have TV spots broadcast in different channels in a row, sometimes even simultaneously, at prime time. The effect of these overlapping spots can only be parsed with a machine learning algorithm. Tvlyzer examines variables such as channel, schedule, day, or time of overlapping spots, determines their behavior in situations where they do not overlap, and accordingly, decomposes the effects of these spots.
Thanks to the insights of Tv Attribution, the campaign on the air can be optimized as well as an accumulation of knowledge can be obtained for future campaigns. In this way, advertisers will be able to evaluate their past performance better and have the opportunity to achieve more in the future.
