Programmatic advertising, defined generally as “an automated, technology-driven method of buying, selling or fulfilling advertising” (American Association of Advertising Agencies, 2015), is a relatively new instrument within media and advertising in general that has the potential to disrupt the business models of the various actors in the media industry (Busch 2015). Tasks traditionally performed by human actors can be automated using programmatic solutions, and in this sense, programmatic has the characteristics of a production technology that could alter the business models of actors in the industry. In the traditional business model, advertising space is bought and sold by human actors as insertions in a media (magazines, television, and so forth) that will reach and expose a given audience to a general message. With programmatic advertising, each individual in a given audience can be reached, bought, and sold independently and separately from other media users (Kosorin, 2016).
Programmatic Advertising is a novel technique which has been developed in recent years, and uses large amounts of data, or big data. It has stimulated growth and investment in graphic advertising on the Internet (Kireyev, Pauwels, & Gupta, 2016). Programmatic Advertising, when compared to traditional models of buying and selling advertising space on the Internet, has led to models which use the number of user impressions, the cost of banner clicks and creative advertising (Aslam & Karjaluoto, 2017). The technology that drives Programmatic Advertising analyses millions of pieces of real time data, which allow the adverts to accurately reflect the precise interests of a user at the exact moment at which they are most likely to make a purchase or click on an ad (Huang, 2018).
This can be emphasized further that, for these online advertising techniques to work properly, companies need to have at their disposal data about the users browsing their web pages, as well as information about their interests and the types of products they are willing to buy (Hargittai & Marwick, 2017). Companies even customize programmatic advertising depending on the interests and offers suitable for each user (Saura, Reyes-Menendez, & Alvarez-Alonso, 2018). In these cases, users are not aware that their online behaviour patterns, along with millions of pieces of data about their search history and Internet browsing are being analyzed in order to increase programmatic advertising effectiveness (Yang & Nair, 2013).
Programmatic advertising allows the exact audience for each advert to be precisely defined according to their visit history. This type of advertising allows the profiles of the product or service audience to be selected and limited (Klein & Ford, 2003). Information such as age, sex or previous searches for similar products or services, is useful information to help target and personalize the advertisements that are shown to consumers (Bennett, Yábar, & Saura, 2017).
In this way, thanks to the complex management of large amounts of data, the audience can be correctly selected at the time and place when and where they are most likely to be receptive. This is the point at which terms related with this sector such as user privacy and massive data collection are interesting for research into finding out how users feel about these practices (Kannan & Li, 2017). We must take into account that Li and Huang (2016), found that users consider the perceived usefulness of programmatic advertising as positive, as long as it is personally related to them and offers them better savings, products and Internet services. However, the Perceived Usefulness of Programmatic Advertising is negative when the segmentation does not have the above qualities.
In coming chapters, effort will be made to consider the impact of programmatic advertising on products and services using Tecno and Ren Money as case studies.
