AI-Driven Strategies to overcome Media-Planning Challenges in Retail Media Network (RMN)s

Abstract

Retail Media Networks (RMNs), have emerged as a pivotal channel through which brands can leverage  first-party data and connect with consumers at the point of purchase. However, media planning that comes with RMNs has some unique challenges. These include fractured RMN environment, a changing data privacy regulatory environment, inconsistent performance measurement and organization misalignment, complicating critical areas of media planning such as budget allocation, audience , tactics planning, and strategic implementation. The literature review is a synthesis of knowledge on academic sources and industry reports to shed light on the identified obstacles in RMN media planning and how artificial intelligence (AI) and analytics drive solutions can be beneficial. Through the analysis of peer-reviewed research and the experiences of practitioners, we discover valuable AI use cases, including advanced segmentation, a better budget optimization, and built-in analytics environments to achieve cross-measurement. Our findings show that AI and analytics have the potential to significantly tackle the issues of fragmentation and measurement, in addition to making the targeting more accurate and enabling real-time decisions. We conclude with research gaps—particularly around AI ethics, integration complexities, and ROI transparency—and propose future directions to refine RMN media planning frameworks.