43% Revenue Growth and 150% ROAS: How a Retailer Scaled Its Media Network with AI
A MarTech-forward retailer partnered with ShyftLabs to launch a modern Retail Media Network that could meet the demands of high-volume data, real-time personalization, and multi-jurisdictional compliance. Built on a scalable architecture, the platform created new monetization streams while elevating customer experience and partner outcomes.
The Challenge
The client faced structural and technological barriers that made it difficult to execute a competitive retail media strategy. A fragmented legacy environment across multiple systems slowed down innovation and limited the ability to unify customer signals in real time. Manual targeting solutions underperformed, leaving brand partners with inconsistent ROAS and the retailer with missed revenue opportunities estimated at $3.2 million annually.
Handling real-time personalization across thousands of shopping sessions required a system that could process large-scale behavioral data with low latency. At the same time, operating across Canadian provinces and U.S. states introduced a complex mix of privacy regulations, including PIPEDA and CCPA, which legacy systems were not designed to manage.
The organization needed a reliable, intelligent platform that could deliver measurable results quickly while maintaining governance across all data flows.
The Solution
ShyftLabs delivered an enterprise-grade Retail Media Network designed for performance, privacy, and personalization. Built on a cloud-native infrastructure, the solution seamlessly integrated with the client's existing systems and enabled immediate monetization through brand partnerships.
The platform was anchored by four integrated components:
A cloud-based data integration hub that unified data from legacy systems using real-time pipelines, automated validation, and a centralized data layer.
A compliance management framework that enforced jurisdiction-specific privacy rules through automated content classification and secure data handling.
An AI-powered ad targeting engine combining multiple machine learning models to deliver real-time, personalized ad recommendations based on browsing context, purchase history, and behavioral signals.
A high-performance ad delivery system that optimized bids, minimized latency, and consistently rendered personalized ads in under 5 milliseconds.
Built using a microservices architecture, the platform is scalable, secure, and ready for continued innovation.
The Results
Within six months, the AI-powered retail media platform generated over $20 million in revenue and delivered a 43 percent increase in advertising income from brand partners. Real-time personalization and sub-5 millisecond ad delivery improved engagement, while campaigns saw an average 160 percent lift in return on ad spend across more than 60 brands. With closed-loop attribution, cross-channel campaign management, and intelligent personalization in a single system, the retailer established a scalable foundation for media monetization and partner growth.
$20M+ Revenue Generated
Achieved within six months through enhanced ad targeting, real-time optimization, and operational efficiency.
43% Revenue Growth
Increase in overall advertising revenue from brand partners using AI-driven targeting.
160% ROAS Improvement
Lift in return on ad spend across campaigns for over 60 partner brands.
5ms Ad Response Time
Personalized ad experiences delivered in under 5 milliseconds to preserve user experience and engagement.
Key insights
Integration Strategy Matters
Building adaptable layers for data consolidation is more effective than trying to replace core systems.
Real-Time Performance Drives Results
Milliseconds matter in digital advertising. Faster decisioning leads to better engagement and higher revenue.
Compliance Should Be Embedded, Not Added On
A built-in privacy framework ensures scalable operations across regions without sacrificing agility.
Data Quality Is the Foundation for Machine Learning
AI models only perform as well as the data feeding them. Unified, clean inputs led to significantly improved targeting precision.