Despite hype around offsite, CTV, and in-store media, onsite retail media advertising is poised to continue driving over 80% of retail media ad spending for years. Onsite ads engage high-intent shoppers in their purchase journey, yielding significantly higher ROAS than offsite placements. They also offer superior profit margins (often 90%, 3-4x higher than offsite) and a brand-safe environment.
Leading RMNs like Amazon and Walmart derive the majority of their media revenue from onsite inventory, which has an outsized impact on overall profitability. For example, 68% of Amazon's profits come from digital advertising. Growth in onsite revenue is achievable without large traffic increases by leveraging machine learning (ML) that automatically improves ad performance, often unlocking 5x more monetization from the same inventory.
Key strategies to maximize onsite growth include: 1) adopting advanced ML for personalized, profitable ads; 2) scaling the advertiser base through self-serve portals that expand TAM and increase auction density; and 3) enabling outcomes-based targeting with probabilistic auctions, allowing advertisers to set target ROAS or CPO. By focusing on these levers, retailers can maximize value from their most valuable media asset.
First-party data, collected directly from users with consent, is crucial for marketers due to privacy regulations limiting third-party data. It enables accurate personalization, compliance, and cost savings. Key steps include ethical collection, maintaining clean data, and using it internally for product/marketing optimization and externally via commerce media networks.
Retail media networks (RMNs) are poised for major growth in 2025, with personalized, AI-driven onsite ads becoming top priority. Advertisers demand performance-based outcomes like CPO and tROAS, while retailers invest in self-serve platforms and go-to-market teams. Key shifts include mid-funnel formats, regional variations (US in-store, EU onsite), and tech partnerships to scale. RMNs that combine ML personalization with streamlined operations will dominate.
Amazon launches Retail Ad Service, offering contextual ads, native demand, and ad management tools for retailers. While the tech is compelling, conflicts of interest, data privacy risks, and Amazon's incentive to privilege its own ads raise concerns. Large retailers may prefer independent solutions like Moloco for ML-based automation without competitive risks.
Retailers building retail media networks (RMNs) can learn from Google, Meta, and Amazon by leveraging first-party data, machine learning, self-service automation, and outcomes-based performance. Key insights include using purchase intent signals and loyalty data for personalization, investing in AI for targeting and optimization, automating campaign management to scale advertiser participation, and moving to outcome-based pricing like closed-loop attribution. These strategies transform RMNs into high-margin ad platforms that deliver value for brands and shoppers.
Retail media networks (RMNs) are rapidly growing but face myths about fragmentation and demand sourcing. Many consider building an SSP to attract demand, but this approach fails to scale. SSPs don't improve inventory performance, shift risk to advertisers, and risk commoditizing inventory. RMNs should instead leverage their unique first-party data and machine learning to drive relevance and performance, following models like Amazon, Meta, and Google to achieve sustainable growth.
The mobile advertising industry is optimistic heading into 2025, with 80% of marketers expecting the year to be as strong or stronger than 2024. Non-gaming apps are driving growth, with downloads up 12% YoY and IAP revenue increasing 20%+. Marketers are prioritizing profitability and ROAS, with over half reporting more aggressive KPIs. Generative AI is already benefiting creative production and optimization. iOS re-engagement remains underleveraged, and most marketers are still adapting to SKAN. Budgets are increasing, with a focus on ad networks and self-attributing networks.
Retail media networks report ROAS using different methodologies, causing up to 63% fluctuation across networks for the same campaign. This isn't a data quality issue but a structural one. Brands using multiple networks face a fragmentation tax where each network is its own source of truth, and budget decisions based on these irreconcilable figures are misleading. Independent measurement, applying the same attribution logic across all networks, is needed to reconcile data and enable confident cross-channel decisions. The signal infrastructure for this already exists from mobile measurement.
This guide helps app marketers select a Mobile Measurement Partner (MMP) by covering essential features like privacy-first measurement, unified attribution, fraud protection, and advanced analytics. It emphasizes choosing an MMP that integrates easily, scales with business growth, and provides reliable data for optimizing marketing ROI across teams.
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