Machine Learning and Data Privacy in Digital Advertising

Abstract

Digital advertising provides great advantages such as lower advertising costs, fast and reliable feedbacks from customers, increased efficiency, and the ability to create detailed databases of customers, which make it increasingly more important for companies. Production of contents is mainly based on intuition and experience for conventional advertising, while it based on data for digital advertising. This makes it possible to offer targeted advertisements that are customized according to the digital trails of consumers. Targeted advertising has become the focus of digital advertising, and methods that have been developed in this field open new horizons for both companies and researchers. To provide targeted advertisements for digital advertising, bidding machines or pricing engines that offer customized prices and promotions are typically generated by means of a machine learning algorithm. Machine learning provides companies with more power to control advertisements; but the most important matter of debate is the customization of advertisements and, as a result thereof, the possibility that data privacy is compromised. This paper discusses the matter with a holistic approach by means of focusing on the concerns of data privacy in addition to the benefits of targeted advertisements and machine learning algorithms for businesses. This paper also discusses the steps that would prevent consumers from not proceeding with a purchase due to concerns about data privacy, while maintaining the high level of profitability gained thanks to targeted advertisements.