The latest retail media trends: Lessons from Big Tech platforms

As retailers invest in the growth of their retail media networks (RMNs), there are crucial lessons to be learned from how Google, Meta, and Amazon achieved their leading market share in digital media. It’s not just about creating another revenue stream but transforming your digital presence into a powerful retail advertising platform that delivers value for brands and consumers. 

This analysis examines how retailers can build effective advertising businesses focusing on the four latest retail media platform trends: first-party data, machine learning, self-service automation, and outcomes-based performance.

1. The power of first-party data in the age of AI

Big Tech platforms have shown how valuable first-party data is in driving advertising success. Google used search intent signals, Meta leveraged social connections, and Amazon built its success on purchase data. Retailers can learn from these models by tapping into their first-party data to create personalized and highly effective ad campaigns.

Unlike traditional digital advertising platforms, retailers possess invaluable insights into purchasing behavior, product preferences, and shopping patterns. This treasure trove of data becomes even more powerful when properly leveraged through advanced AI capabilities. 

Establishing a first-party data strategy

The breakout winners in retail media maximize revenue first from their owned-and-operated channels. Highly valued onsite ad inventory, powered by first-party data, enhances customer experience and grows share of advertisers, spend, and margin. From there, RMNs can extend into omnichannel media (e.g. offsite digital), while being thoughtful about what data they expose to competing networks or walled gardens and their AI models. 

Retailers can grow their first-party data advantage by leveraging key differentiators such as:

• Purchase intent signals: Real-time understanding of customer shopping behavior and purchase patterns.
  
• Cross-channel intelligence: Unified view of customer interactions across digital and physical touchpoints.
  
• Loyalty program data: Rich customer profiles built on authenticated shopping history.

2. Investment in machine learning to drive growth

If first-party data is the fuel for digital advertising growth, machine learning (ML) is the engine that transforms raw energy into actionable insights and positive ad outcomes, and unlocks incremental marketing investment. 

Again, we can see examples in Google’s evolution from simple keyword matching to sophisticated prediction models. Meta developed advanced content ranking algorithms, and Amazon mastered product recommendations. Each company demonstrates how the capabilities of machine learning in advertising create compound advantages over time, a playbook that retailers must follow to compete with walled gardens for major budgets.

Key capabilities for ML in retail media

Leading retail media networks are investing in sophisticated ML capabilities to:

• Optimize ad targeting: Deploy predictive models identifying customers’ propensity to convert throughout the buying journey.
  
• Deliver sophisticated recommendations: Automatically adjust product and brand recommendations based on real-time shopping signals.
  
• Enhance yield management: Maximize revenue potential based on predicted clicks (not just bids) and balance advertiser outcomes with customer experience.
  
• Automate campaign optimization: Continuously refine ad delivery based on performance data and business rules.

3. Scaling ad platforms with self-serve automation

To effectively scale any RMN business, retailers must look beyond their marquee supplier list and find ways to enable spend across the long tail of suppliers. This generates incremental revenue that can rival your blue-chip accounts and increase bid density — which means better product discovery and personalization across your onsite ads.

Big Tech platforms have repeatedly proven that automation is essential for efficiently managing high volumes of advertisers while maintaining quality and performance. Google Ads and Meta scaled their advertising platforms through best-in-class self-service seller portals, and Amazon later followed suit.

Components of self-serve RMN platforms

Retailers need self-service capabilities to activate advertisers more quickly, reduce operational overhead, and enable suppliers to optimize their campaigns in real time.

• Intuitive campaign management: Easy-to-use interfaces that allow advertisers to launch and manage campaigns independently.
  
• Automated bidding strategies: ML-powered bidding algorithms that automatically optimize campaigns toward advertiser goals.
  
• Smart budget allocation: Automated distribution of spend across placements and audiences to maximize target outcomes.
  
• Campaign health monitoring: Proactive alerts and recommendations for performance improvement and creative optimization.
  

4. Outcome-based advertising and reporting

Arguably, the biggest recent trend in advertising has been the push for greater accountability. While a certain amount of media waste was always expected in traditional broadcast channels, budgets in digital advertising are under intense scrutiny to prove a return. 

Google and Meta set the digital ad performance measurement standard with their goal-based, ML-powered campaign settings — “Performance Max” and “Advantage+,” respectively. Amazon elevated the game further by directly connecting digital ad spend to sales – a critical closed-loop attribution that retailers can leverage to earn seller trust and spend.

The rise of outcomes in retail media

The most successful retail media platforms are moving beyond traditional ad pricing models, such as cost per mille (CPM) for impressions, and instead embracing outcome-based currencies that align with advertiser objectives. This requires:

• Performance-based ad models: Flexible buying models can optimize for sales, cost-per-order, or advertiser outcomes such as target return on ad spend (ROAS).
  
• Real-time campaign optimization: Continuous adjustment of campaigns based on automated performance insights and real-time ML models.
  
• Shared performance visibility: Intuitive dashboards and transparent reporting that give advertisers immediate insight into campaign effectiveness and ROI.
  
• Closed-loop measurement: Direct correlation between ad exposure and actual purchases, revealing high-impact audience segments.

The future of retail media platform growth

As retail media platform trends continue to evolve, success increasingly depends on the ability to harness technology and develop seller relationships. RMN winners will be those who:

1. Invest in AI infrastructure: Build or partner with sophisticated AI-powered retail media platforms that can process and act on complex data signals.
   
2. Embrace automation: Deploy intelligent automation across all aspects of retail media operations, from onboarding to optimization.
   
3. Focus on outcomes: Align retail media offerings with advertiser objectives through performance-based solutions.
   
4. Maintain agility: Build flexible systems that can adapt to changing market conditions and requirements.

By learning from Big Tech and the recent history of digital advertising, retail media platforms can transform their unique first-party data into powerful advertising solutions and sustainable growth.

Build a high-margin retail ad business with Moloco

Moloco built the industry’s first AI-native commerce media platform, giving retailers and marketplaces the same tools as Big Tech to fuel retail media growth. Learn how you can build a high-margin ad business that delights shoppers and advertisers alike.