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Big Data and TV Advertising

August 20, 2018 / Taylor Kilduff

This study by Malthouse, Maslowska and Franks determines how predictive categories of consumer data are of product interest in response to advertising. The study does this for the purpose of seeing what consumer data is most relevant for TV advertisers who wish to more specifically target consumers through addressable and or programmatic advertising. Digital advertising has been using this targeted approach to advertising for a while now, and while TV advertising has hued closer to the traditional mass advertising model, TV advertisers are beginning to use consumer data to more specifically target their ads to consumers more likely to have product interest. The study looked at three data sets, consumer characteristics (race, age, income etc.), consumer media consumption, and past behavioral data of consumers. What the study found was that while each data set did have an effect on product interest, consumers’ past behavior had the largest effect on their product interest and consumers’ characteristics had the smallest effect.


Takeaway for Managers:


Firstly, for managers generally, this study reinforces the notion that past behavior is the best predictor of future behavior.  Additionally, consumer data relating to their past behavior is extremely useful in specifically targeting consumers that are most likely to have an interest in a particular product or brand. Secondly, managers that are looking to improve their TV advertising methods should utilize all types of consumer data to target their optimal audiences, but at the same time should not put too much emphasis on the use of consumer characteristics to predict their potential product interest.


Reference:

Malthouse, E. C., Maslowska, E., & Franks, J. (2018). The Role of Big Data in Programmatic TV Advertising. Advances in Advertising Research IX, 29-42. doi:10.1007/978-3-658-22681-7_3

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