Algorithmic Digital Transformation in Name-Personalized Retargeting

Abstract

Digital marketing is rapidly evolving and given the recent emergence of algorithmic personalization, especially in retargeted advertising, it is timely to study and understand its use. In this research, we introduce name-personalized retargeting which is a type of algorithmic-driven digital transformation that seeks to embed and personalize, in real time, the name of the user in the ads to enhance the level of engagement and customer conversions. Ads can be personalized through, for example, machine learning, data analytics, and real-time bidding, which optimizes consumer experience in real time, ultimately, improving customer retention. However, there are significant ethical and privacy concerns about algorithmic personalization in advertising as consumers struggle to understand how their personal data is collected and used in marketing. Although personalization has been shown that ad personalization can enhance efficiency and effectiveness through algorithms, the personalization-privacy paradox suggests that more targeting could create consumer push-back and regulatory issues. This study considers the practices, effectiveness, and ethical considerations for name-personalized retargeting practices with the goal to better understand how personalization can be achieved without sacrificing consumer trust. Taken together, the article presents empirical reviews to make recommendations to marketers, draw conclusions, and frame discussions in a larger context on the future of advertising as driven by new AI technologies. In sum, this research adds to the discussion of ethics in digital marketing and the implications we can expect from AI approaches in advertising.