Discover how Criteo’s AI Engine improves ad personalization through commerce data, predictive bidding, product recommendations, dynamic creative optimization, and shopper intent.
Digital advertising has moved far beyond showing the same advertisement to every member of an audience. Consumers now expect online experiences to reflect their interests, shopping behavior, context, and current needs. For advertisers, this creates a major challenge: how can they deliver relevant messages at scale while responding to rapidly changing consumer behavior?
Artificial intelligence is becoming an important part of the answer. Criteo’s AI Engine combines machine learning with commerce data to help advertisers predict shopper intent, select relevant products, optimize bids, and personalize creative elements.
Criteo describes its Commerce AI as being trained on real shopping behaviors and large-scale commerce data. Its current technology page says its AI works with data involving more than 5 billion products and 740 million daily shoppers, while its AI Engine documentation describes predictive bidding, product recommendations, and Dynamic Creative Optimization+ as core components.
Understanding how Criteo’s AI Engine is improving ad personalization reveals how artificial intelligence is changing performance marketing and the way brands engage consumers.
What Is Criteo’s AI Engine?
Criteo’s AI Engine is a collection of artificial intelligence and machine-learning technologies used to optimize digital advertising and commerce experiences.
Rather than treating every impression equally, the system can analyze signals related to products, shoppers, context, and campaign objectives. Criteo says its AI continuously learns from commerce activity and uses those insights to improve audience quality, creative performance, bidding efficiency, and product discovery.
The broader goal is to help advertisers make more informed decisions at the individual impression level.
This means personalization is not limited to choosing an audience. It can also involve selecting which product should appear, what creative combination should be displayed, and how much an advertiser should bid for an opportunity.
Using Commerce Data to Understand Shopper Intent
One of the most important elements behind Criteo’s approach is commerce data.
Criteo says its Universal Catalog includes billions of product SKUs, live prices, and transaction information obtained through direct retailer integrations. Its Commerce AI technology uses product information, images, attributes, and behavioral signals to create representations that help models understand product relevance and shopper intent.
This creates a different approach to personalization from simply relying on a visitor’s most recent page view.
For example, a shopper who previously viewed a pair of running shoes may not necessarily want that exact product again. Their broader behavior could suggest an interest in running equipment, apparel, accessories, or another related category.
Using broader commerce signals can help advertisers identify these connections.
Predictive Bidding Improves Ad Decisions
Advertising auctions take place extremely quickly. An advertiser must decide how valuable a particular impression is before the opportunity disappears.
Criteo’s AI Engine includes predictive bidding technology designed to estimate the value of individual impressions. According to Criteo, Predictive Bidding uses shopper data and campaign objectives to determine appropriate bids and allocate budget toward opportunities expected to produce better outcomes.
AI can evaluate large numbers of signals far faster than manual campaign management.
This allows advertising decisions to become more dynamic. Instead of applying a single bidding strategy broadly, machine-learning systems can continuously adjust based on changing patterns.
The result is a closer connection between personalization and media buying: the system helps determine not only who should see an advertisement but also how valuable that particular opportunity may be.
Product Recommendations Make Ads More Relevant
Personalization becomes more powerful when the advertisement contains products that actually match the shopper’s interests.
Criteo’s Product Recommendations technology uses behavioral insights to select products that may be more relevant to an individual shopper. Criteo says the system goes beyond simply displaying the most recently viewed product or general bestsellers, using broader browsing habits and shopping intent to determine recommendations.
This can help advertisers create more useful shopping experiences.
For instance, someone purchasing a camera may also be interested in lenses, memory cards, bags, or tripods. Recommending related products can provide additional value while supporting cross-selling and product discovery.
Dynamic Creative Optimization Personalizes the Advertisement
Choosing the right product is only one part of personalization. The advertisement itself also matters.
Criteo’s Dynamic Creative Optimization+ technology is designed to assemble personalized ads using brand assets, products, design elements, and other creative components. Criteo says DCO+ can select these elements in real time according to shopper intent signals and advertising objectives.
This allows two consumers to potentially see different versions of an advertisement from the same campaign.
One shopper might see a product-focused creative, while another might receive an advertisement emphasizing a different product or visual combination.
Criteo states that DCO+ uses more than 120 shopper intent signals and can improve click-through engagement by up to 31%. This figure is a Criteo-reported product claim rather than an independent industry benchmark.
Personalization Goes Beyond Retargeting
Retargeting has traditionally been associated with showing users advertisements for products they previously viewed.
AI is expanding this concept.
Criteo says its Dynamic Retargeting technology combines advertiser data with commerce AI to move beyond the last product viewed and recommend products a shopper may be more likely to purchase next.
This distinction is important because consumer interests change.
A person who browsed a laptop yesterday may now be searching for accessories or comparing alternatives. Personalization based on broader intent can potentially produce a more relevant experience than simply repeating the original advertisement.
Context Matters in Modern Ad Personalization
Personalization does not always require a detailed historical profile.
Context can also provide meaningful information.
Criteo’s Product Recommendations technology states that contextual signals and the content being consumed can be used to engage new shoppers, including people without extensive prior interactions with a brand.
This is particularly important for attracting new customers.
Advertisers cannot rely exclusively on historical behavior when communicating with first-time visitors. Contextual information can provide additional clues about what products or messages might be relevant at the moment.
AI Helps Advertisers Scale Personalization
One of the biggest problems with personalized advertising is scale.
Creating unique campaigns manually for thousands of audience-product combinations would be expensive and inefficient.
AI changes this equation by automating many decisions.
Criteo’s systems can combine large-scale commerce information with machine learning to determine audiences, products, bids, and creative combinations. Its Commerce AI platform describes continuous model training across real-world commerce activity as a way to refine audience quality and advertising performance.
Automation allows marketers to focus more heavily on strategy while machine-learning systems handle repetitive optimization tasks.
Personalization and the Rise of AI Shopping
The role of personalization is also expanding beyond traditional websites and applications.
Consumers increasingly use conversational AI to discover products, compare options, and research purchases. Criteo introduced an Agentic Commerce Recommendation Service in February 2026 designed to provide commerce-grade product recommendations to AI shopping assistants.
In March 2026, Criteo also announced an integration with OpenAI’s advertising pilot in ChatGPT. Criteo said aggregated insights from its U.S. clients showed that users referred from LLM platforms such as ChatGPT converted at approximately 1.5 times the rate of other referral channels. That figure was based on Criteo’s own aggregated client data and should not be treated as a universal conversion benchmark.
These developments show how AI-powered personalization is moving into new discovery environments.
Prompt-Level Personalization
Conversational advertising creates a new personalization challenge.
Traditional advertising can respond to a webpage, audience segment, or shopping event. Conversational AI can provide much more immediate information about what a person is asking.
Criteo’s Prompt Smart Ads capability uses catalog data, commerce intelligence, and prompt-level insights to generate optimized ad creatives and product descriptions based on the context of a consumer query.
This creates the possibility of advertisements that respond more closely to the shopper’s immediate intent.
For example, the difference between someone asking for “affordable running shoes” and someone asking for “premium trail running shoes for wet conditions” contains valuable contextual information.
AI can use these distinctions to make advertising more relevant.
Personalization Across Multiple Channels
Consumers rarely interact with brands through a single channel.
They may discover a product through search, research it on a retailer website, interact with social media, watch a video, and eventually purchase through another device.
Criteo’s advertising platform is designed for cross-channel campaign management, while its commerce technology seeks to use shopper signals across the purchase journey.
Cross-channel personalization can help advertisers maintain more consistent and relevant messaging.
Instead of treating every channel as a separate environment, AI can contribute to a broader understanding of campaign performance and shopper intent.
The Importance of Privacy and Responsible Personalization
More personalization also creates greater responsibility.
Consumers expect relevant experiences, but they also expect their information to be handled responsibly.
Criteo’s Commerce Grid describes its Commerce Audiences as being built on consented data and says its approach incorporates privacy considerations while using signals such as searches, views, purchases, ad interactions, and content consumption.
Advertisers must also consider applicable privacy laws, consent requirements, data governance, cybersecurity, and transparency.
Effective AI personalization should therefore balance relevance with appropriate data practices.
AI Can Reduce Advertising Waste
Irrelevant advertising can waste budget and create poor customer experiences.
When AI improves the likelihood that an advertisement matches a shopper’s interests, advertisers can potentially reduce inefficient impressions.
Criteo’s AI Engine combines predictive bidding, product recommendations, and creative optimization specifically to improve the relevance and performance of advertising opportunities.
The underlying principle is simple: better decisions can make advertising spending more efficient.
However, results will vary depending on campaign objectives, available data, creative quality, product catalogs, and market conditions.
The Future of AI-Powered Ad Personalization
The future of advertising personalization is likely to become increasingly predictive and conversational.
Rather than asking only what a consumer previously viewed, AI systems can increasingly attempt to understand what the consumer is trying to accomplish.
This shift could influence product recommendations, creative generation, bidding, audience development, and advertising placement.
Criteo’s continued development of Commerce AI, AI shopping recommendations, and advertising within conversational AI environments illustrates this broader direction.
The most important transformation may be the movement from reactive personalization toward intent-based personalization.
Criteo’s AI Engine demonstrates how artificial intelligence is changing digital advertising from broad audience targeting toward more individualized commerce experiences.
By combining predictive bidding, product recommendations, commerce data, contextual signals, and Dynamic Creative Optimization+, Criteo aims to make advertisements more relevant to individual shoppers and more responsive to changing intent.
The technology is also expanding into conversational AI, where product discovery can happen through natural-language interactions rather than traditional search and browsing. Criteo’s 2026 initiatives in AI shopping recommendations and ChatGPT advertising demonstrate how personalization is moving into these emerging environments.
As digital commerce becomes more intelligent and personalized, advertisers will increasingly depend on AI to connect the right product, message, creative, and moment. The challenge will be maintaining that relevance while respecting privacy, transparency, and consumer trust.
