The latest retail media trends: Lessons from Big Tech platforms

Summary

The article argues that retailers can build successful retail media networks (RMNs) by adopting strategies from Big Tech platforms like Google, Meta, and Amazon. Four key trends are identified: 1) First-party data: Retailers possess rich purchase intent signals, cross-channel intelligence, and loyalty data, which when combined with AI, enable personalized ad campaigns that outperform traditional digital ads. 2) Machine learning: Investment in ML optimizes targeting, product recommendations, yield management, and campaign automation, creating a competitive advantage against walled gardens.

3) Self-service automation: To scale beyond top-tier suppliers, RMNs need intuitive campaign management, automated bidding, and smart budget allocation, reducing operational overhead and enabling real-time optimization. 4) Outcomes-based performance: Shifting from CPM to outcome-based currencies (e.g., closed-loop attribution linking ad spend to sales) builds advertiser trust and justifies budgets. Data points highlight that retailers using these approaches see increased revenue from owned-and-operated channels, improved bid density, and higher advertiser ROI.

Actionable takeaways include building AI infrastructure, embracing automation across operations, focusing on performance-based solutions, and maintaining system agility. The article concludes by promoting Moloco's AI-native platform as a tool for retailers to achieve these capabilities.