Artificial Intelligence In Digital Marketing Management: A Comparative Study Of Advertising Platforms

Abstract

Artificial intelligence (AI) has revolutionized digital advertising by shifting away from insights based on rules, media buying, audience modeling, and real-time budget allocation, and continually predicting audiences, automating creative assembly, and automating media buying. However, when comparing platforms, it is sometimes based on isolated information from the vendor, which can be hard to move from goal to goal. Drawing on the published impact of peer-reviewed advertising research and platform features from the five top ad ecosystems – Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads and Amazon Ads – this study wants to provide a structured comparison of the different ad ecosystems from a managerial perspective. A secondary research approach was used in a comparative manner. Seven criteria were synthesized from the literature: intent capture, targeting/personalization, Automation, Measurement, Creative intelligence, business-to-business suitability, and managerial control. All criteria were marked on a five-point anchored scale and explained by capability, radar, heatmap, portfolio, and maturity models (MM). The analysis showed that none of the platforms automatically dominated. Google Ads and Amazon Ads are best suited for ads featuring a clearly evident commercial intent, while Meta Ads and TikTok Ads are better suited for an environment focused on discovery or creative learning, and LinkedIn Ads is best suited for an environment focused on the professional identity and B2B context. Automation in all ecosystems is accelerating and scaling up, but it is diminishing management discretion to mysterious optimization systems. Thus, the best approach is a controlled portfolio in which the platforms are chosen according to the journey role, required data quality, creative needs, and risk appetite. This study proposes an auditable management decision-making process and research agenda with topics related to incrementality, algorithmic transparency, privacy, and human-AI collaboration.