Redefining retail media for retailers with Mirakl Ads
Legacy advertising stacks built on keyword matching and rule-based systems are hitting their limits. As catalogs grow to millions of SKUs and shoppers expect Amazon-level personalization, the gap between what ad technologies can deliver and what the market demands continues to widen.
Two forces are accelerating this shift. First and foremost, generative AI has transformed shopper expectations, with users accustomed to LLM-quality responses now expecting the same sophistication in search and recommendations. This means shoppers now search with intent, not specific keywords. At the same time, catalog explosion from marketplace growth means more products are competing for visibility.
Only AI-native systems can efficiently match the right products to the right shoppers at scale. This is precisely the challenge Mirakl Ads was built to solve.
Where legacy systems fall short
Traditional retail media systems rely primarily on keyword targeting: advertisers bid on specific terms, and ads appear when those terms are searched. This approach has fundamental limitations:
Semantic blindness: A shopper searching for "running gear" won't see relevant ads for "athletic shoes" or "moisture-wicking shirts" unless advertisers explicitly bid on every synonym.
Context ignorance: A search for "dress" means something entirely different to a user who just viewed formal evening wear, versus someone browsing children's clothing. Static keyword matching can't adapt.
Scale constraints: As catalogs grow to millions of products, traditional methods become too slow and costly for real-time ad serving.
Catalog complexity: Marketplaces aggregate products from thousands of sellers, each with different naming conventions, categories, and attributes. Keyword systems can't bridge these inconsistencies.
"The sheer scale of modern marketplace catalogs and the expectation of instantaneous, relevant results have pushed traditional search systems to their limits," explains Sang-Hoon Yoon, Mirakl Ads' Data Science Lead. "To solve this, we need to think in terms of semantic understanding—truly comprehending the context behind a search and delivering hyper-personalized results."
Our approach: AI-native architecture
Rather than retrofitting legacy systems with AI capabilities, Mirakl Ads was architected from the ground up to use machine learning for every aspect of ad delivery and optimization.
Every ad request flows through a multi-stage pipeline — from capturing shopper context and filtering eligible products, through semantic retrieval, image recognition and performance scoring, to a relevance-first auction that determines winners.
Each stage leverages purpose-built machine learning models trained on retailer-specific signals, ensuring the system adapts to each retailer's unique catalog, taxonomy and shopper behavior.
Three capabilities set this architecture apart:
Semantic understanding at scale: Our system uses vector search to convert product information, search queries and user behaviors into mathematical representations that capture meaning and context — not just keywords. When a shopper searches for "elegant dining table," the system retrieves products that look elegant, even if their titles don't contain that word.
Relevance-first auction design: A naive bid-only auction would let high bidders dominate regardless of product quality—destroying user experience for everyone. Our mechanism ensures products compete within their relevance tier, not against the entire catalog. Shoppers see relevant products; advertisers compete fairly; retailers maximize revenue without compromising trust.
Continuous self-optimization: Static algorithms can't keep pace with evolving shopper behavior, seasonal trends, and catalog changes. Our self-learning engine uses Multi-Armed Bandit methodology to continuously test algorithm configurations, automatically shifting toward winners while exploring new possibilities.
Go deeper: Our white paper details the full technical architecture — from multimodal embeddings and vector search to performance scoring and auction mechanics. [Download: AI-Powered Retail Media: How It Works and Why It Wins]
The business impact of AI-native retail media
Mirakl Ads' AI-native architecture delivers measurable improvements across the metrics that matter most.
For retailers
Higher ad relevance drives better click-through rate (CTR) and conversion rates through semantic matching that truly understands shopper intent.
User experience stays protected — our relevance-first ranking preserves organic conversion rates rather than disrupting them with irrelevant ads.
Operational simplicity means no in-house AI team required; the platform handles the complexity.
Fill rates improve because our deeper understanding of shopper needs means more ad requests get quality responses. And ultimately, higher relevance means more clicks—a direct path to increased revenue.
For advertisers
Return on ad spend (ROAS) improves through automated bid optimization, ensuring every dollar works harder.
Product discovery accelerates thanks to boost mechanisms that give new products visibility, helping advertisers launch and scale faster.
For shoppers
Relevance becomes the norm, with sponsored products that actually match what shoppers are looking for.
Discovery expands as shoppers encounter products they wouldn't have found organically but genuinely want. Trust builds through a seamless experience where ads feel like helpful recommendations, not interruptions.
What's next: Leveraging agentic technology
Our roadmap pushes beyond today’s hyper-contextual signals toward a future defined by agentic solutions. We are moving from a world where AI simply suggests, to one where specialized AI agents autonomously optimize every facet of the retail media ecosystem to drive sustainable, long-term business value.
Generative Engine Optimization (GEO): As shoppers increasingly shift from traditional search bars to AI-powered discovery and LLMs, we are pioneering the "GEO Nexus" story. This ensures your catalog isn’t just searchable—it’s optimized for visibility within the generative engines and AI agents that now define the modern shopper’s journey.
The retailer business agent: We are building an autonomous strategic partner for retailers. This agent doesn't just display data; it answers critical monetization questions—"Should I open new ad placements in this category?" or "Which advertiser is at risk of churning?"—and then provides the recommendations and takes the necessary actions to protect and grow your revenue.
The advertiser business agent: For brands and sellers, our agentic framework acts as a 24/7 performance lead. It proactively identifies campaign bottlenecks, answers complex queries like "Which creative assets will resonate most for this specific product launch?", and autonomously reallocates budget or adjusts tactics to maximize ROAS without requiring manual intervention.
The guiding principle remains unchanged: personalization. What evolves is our ability to achieve it through autonomous agents that think, act, and scale at the speed of the modern marketplace.
AI-native retail media is the new standard
Building AI-native advertising infrastructure independently is prohibitively expensive and complex for most retailers.
Mirakl Ads solves this challenge with a purpose-built platform that delivers cutting-edge AI capabilities, out of the box, allowing retailers to focus on what matters most: customer experience and business growth.
The technology gap between AI-native and legacy systems is expanding. The winners in retail media will be those who successfully transform first-party data into hyper-relevant, high-converting advertising experiences.
[Download our white paper] to explore the full technical architecture behind Mirakl Ads—from semantic embeddings and hybrid search to our relevance-first auction design.
