Advertising can be a profitable business and in the United States alone, advertising is a $200 billion industry. As consumers, we are all exposed to advertising on a daily base, either on the TV, via e-mail, social networks, or through other related online content. Yet advertising remains poorly understood by economists. This is mainly because offline data has been insufficient for business and academics to measure the true impact of advertising on consumer purchasing behavior (Lewis et al., 2014). In 2013, for the first time in the history of advertising in the United States, digital advertising surpassed TV broadcast advertising, which for a long period of time has been considered the best mass-marketing medium (IAB, 2014).
Because now a day people are more and more online and get more often exposed to online digital advertising, a lot of valuable data is generated which allows businesses and academics to reduce the information gap that is present in the advertising world.
In this study, Ghose & Todri-Adamopuolos (2016) go beyond the existing literature and with the use of individual-level data, research the effectiveness of online display advertising and the effects display advertising has on different consumer behaviors online. Studying the latter is a novelty compared to historical research of display advertising. In this case to understand consumers’ response to advertising, not just a probable exposure to it, often simple proxies were used like click-through-rates. Ghose & Todri-Adamopuolos (2016) surpass these relative simple methods and proxies and use an experimental framework that allows to compare the online behavior of two groups of users: those who view the display advertisements and those who do not view the display advertisements. What the data shows is that if consumers are just exposed to display advertisement this already significantly increases the interest of consumers to search for the displayed brand or product. Subsequently, the increased interest results in either active online searching for the product/brand or an increased likelihood to click on a related future display advertisement. Secondly, the longer a consumer looks at the advertisement the higher the probability a consumer goes directly to the website of the specific brand or product (36% bigger chance than average) instead of using search engines like google. Lastly, after seeing a display advertisement consumers are 7,1% more likely to buy the advertised product.
Practical implications for business
Ghose & Todri-Adamopuolos (2016) propose a model that demonstrates how advertisers can divide resources across the different types of display advertising. This model allows advertisers to use big data analytics in order to move advertising budget from less effective and cost efficient channels/media towards more effective advertising and increase the overall effectiveness and return on investment of the digital marketing strategy. Furthermore, as the results signal that search advertising exposure only happens in a consumer’s funnel path after the consumer launches a search session that shows his or her interest for a brand/product, it is important for advertisers who would like to control the presence and the frequency of search advertising exposures that they examine what triggers consumers to initiate a search session before examining anything else. Lastly, it is important that potential consumers are targeted by businesses sooner in their shopping journey, as this could increase the effectiveness of the advertisements up to four times.
Ghose, A., & Todri, V. (2015). Towards a Digital Attribution Model: Measuring the Impact of Display Advertising on Online Consumer Behavior. (pp. 889-910) MIS Quarterly
Interactive Advertising Bureau (IAB). 2014. “IAB Internet Advertising Revenue Report: 2013 Full Year Results,” PricewaterhouseCoopers LLP.
Lewis, R., Rao, J. M., & Reiley, D. H. (2014). Measuring the effects of advertising: The digital frontier. In Economic Analysis of the Digital Economy (pp. 191-218). University of Chicago Press.