The retail media landscape has reached a pivotal moment. With global retail media spend projected to reach $166 billion in 2025—up from just $46 billion in 2023—brands are discovering that traditional advertising approaches fall short in the complex retail media network ecosystem. However, innovative AI solutions are now emerging to transform how brands optimize their ad performance across retail media networks, delivering unprecedented improvements in return on ad spend (ROAS) and operational efficiency.
The Challenge: Managing Fragmented RMNs
Retail media networks have created unique challenges that traditional digital advertising tools cannot address. Unlike managing campaigns across a few major platforms, marketers must now juggle dozens of completely different systems—from Amazon DSP and Walmart Connect to emerging networks like Target Roundel and local platforms such as Checkers’ Sixty60 and Pick n Pay’s Smart Shopper.
Each platform operates with distinct campaign setup processes, bidding mechanisms, targeting taxonomies, and performance metrics. The knowledge required for Amazon’s advertising platform bears little resemblance to what’s needed for success on other networks, creating operational complexity that scales exponentially with each additional platform.
“Some networks don’t provide real-time insights, making it difficult to optimize campaigns on the go,” explains Scott Reinders, COO of Connect. This fragmentation has left brands drowning in disconnected spreadsheets while manually attempting to aggregate performance data from dozens of sources.
A Data Silo Problem
Perhaps most critically, each retail media network operates as a “walled garden,” jealously guarding customer data and providing reporting only within its own ecosystem. This creates massive information silos that make holistic marketing analysis nearly impossible.
“Retailers might provide basic metrics like impressions and clicks but withhold granular sales attribution data,” notes Reinders. “This makes it hard for brands to calculate true ROI.” The lack of data integration between platforms prevents sophisticated marketing strategies like cross-platform frequency capping and makes attribution modeling across the customer journey virtually impossible.
AI-Powered Solutions
The operational complexity of retail media marketing has created significant demand for technology solutions that can help brands manage campaigns more efficiently while providing unified reporting and optimization capabilities. Leading-edge platforms like RMIQ are now developing multi-agent AI architectures specifically designed to address retail media complexity.
These innovative systems employ autonomous agents that can continuously learn, adapt, and optimize campaigns across multiple platforms without requiring manual intervention for routine optimization tasks. By leveraging machine learning algorithms that process vast amounts of data in real-time, these platforms identify optimization opportunities and implement changes faster and more effectively than human managers.
Unified Reporting
AI-driven platforms address one of the most time-consuming aspects of retail media management—aggregating and normalizing performance data from multiple sources. Rather than manually combining reports from dozens of different platforms, brands can now access comprehensive dashboards that provide holistic views of their retail media performance with standardized metrics and comparable data formats.
This unified approach enables marketers to understand true cross-platform ROAS, identify which networks deliver the best performance for specific product categories, and make data-driven decisions about ad spend allocation.
Real-Time Optimization
AI-driven budget optimization represents a significant advancement in retail media management technology. These systems continuously monitor performance across all campaigns and networks, automatically reallocating budgets to the highest-performing opportunities in real-time based on comprehensive analysis of ROI, inventory availability, competitive dynamics, and strategic priorities.
“The more data-driven brands become, the better they can justify—or challenge—the spend,” observes Reinders. This capability is particularly valuable given the pricing opacity that characterizes many retail media networks, where rates often feel arbitrary and bundled packages make cost comparison difficult.
Advanced Attribution
Modern AI solutions are tackling the attribution maze that has plagued retail media marketing. By processing customer journey data across multiple touchpoints, these platforms can provide more accurate attribution models that account for the complex paths consumers take from initial product discovery through final purchase.
This enhanced attribution capability helps brands understand the true value of their retail media investments and optimize their strategies based on comprehensive customer journey analysis rather than simplified last-click attribution models.
Intelligence and Optimization
AI platforms are also addressing the strategic challenges brands face when retailers use advertising revenue and customer data to compete directly with their advertising customers. Advanced analytics can help brands identify when their advertising data might be used to develop competing private-label products and adjust strategies accordingly.
These systems can monitor competitive dynamics across platforms, track private-label product launches, and provide early warning indicators when retail partners may be leveraging advertising data for competitive advantage.
Regional Adaptation
As retail media networks expand globally with varying levels of sophistication, AI solutions are proving particularly valuable for managing campaigns across markets with different maturity levels. While global platforms like Amazon offer self-service tools, emerging markets often require different approaches.
“While global platforms like Amazon offer self-service tools, South Africa’s retail media networks are still developing these capabilities,” notes Reinders. “Takealot is leading the charge with its self-service offering, but for many brands, the options remain limited.”
AI platforms can adapt to these varying capability levels, providing automated management for sophisticated networks while offering enhanced manual optimization tools for less developed platforms.
Measurable Performance
Early adopters of AI-powered retail media management platforms are reporting significant improvements in key performance metrics:
- Enhanced ROAS: Automated optimization algorithms consistently identify high-performing opportunities faster than manual management
- Reduced operational overhead: Elimination of manual campaign management tasks across multiple platforms
- Improved attribution accuracy: Better understanding of true campaign performance across the customer journey
- Strategic insights: Comprehensive analytics that inform long-term strategy development beyond tactical optimization
Empowering Retail Media Ad Performance
The continued evolution of AI solutions promises even greater improvements in retail media network ad performance. As machine learning algorithms become more sophisticated and data integration capabilities expand, brands will gain unprecedented visibility into their retail media investments and optimization opportunities.
The projected growth to $166 billion in global retail media spend by 2025 suggests that current challenges won’t slow platform expansion, but will accelerate development of AI solutions to address existing pain points. As competition intensifies among retail networks for advertiser investment, pressure will increase for improved transparency and better measurement capabilities—areas where AI solutions provide clear competitive advantages.
The complexity of retail media marketing requires more than traditional advertising expertise—it demands sophisticated technology solutions that can navigate fragmented platforms, integrate disparate data sources, and optimize performance across multiple networks simultaneously.
AI-powered platforms like RMIQ are transforming how brands approach retail media networks, turning operational complexity into competitive advantage through automated optimization, unified reporting, and strategic insights. As these solutions continue evolving, brands that embrace AI-driven retail media management will be positioned to capture significant performance improvements while their competitors struggle with manual processes and fragmented data.
The future of retail media success lies not just in increased ad spend, but in intelligent ad spend optimization powered by AI solutions that can navigate complexity and deliver measurable improvements in ROAS across all retail media networks.