Commerce Data

Commerce data is what separates advertising that understands shoppers from advertising that merely reaches them. It’s the foundation of commerce intelligence, and the reason intent-based targeting works. 

What is commerce data? 

Short definition: Commerce data is a combination of product knowledge and shopper behavior signals that captures consumer intent as it forms. It includes searches, product views, comparisons, cart additions and removals, purchases, and other interactions that reveal where a shopper is in their buying journey and where they’re likely to go next. 

Most platforms use “commerce data” to mean transaction data: a record of what was bought, when, and where. That’s useful for reporting, but it’s descriptive—a log of outcomes, not a window into them. What makes commerce data genuinely actionable is the upstream layer: the discovery, comparison, and consideration behavior that happens before a purchase. A model trained on the full shopper journey can be predictive in ways that transaction-only models can’t. 

That upstream layer is precisely what most audience providers don’t have. 

The two sides of commerce data 

Commerce data has two interconnected dimensions. The real value comes from connecting them: 

• Product understanding: What a product is, how it’s described, how demand for it shifts over time, and how shoppers move between products and categories.
• Shopper understanding: How people discover and engage with products, how their behavior patterns change over time, and what those signals suggest about what they’re likely to do next.

Taken in isolation, either side is incomplete. But, when brought together and organized with AI, they form a detailed, real-time picture of shopper intent. 

From commerce data to commerce intelligence 

Commerce intelligence is what happens when commerce data is processed by AI decisioning to surface patterns that aren’t immediately obvious. 

The result is advertising that can answer questions broad audience data simply can’t: 

• What might a shopper need that they haven’t searched for yet?
• What are they most likely to buy next?
• What products did they leave in their cart?

These questions become more powerful when one shopper’s journey can be compared against millions of similar ones, and when the AI behind it is purpose-built for commerce, not just general reasoning. 

What makes Criteo’s commerce data different? 

Volume matters, but it’s not the whole story. Criteo’s commerce data is distinct for four reasons: 

1. Direct connections. Signals come from direct integrations with brands, retailers, merchants, and publishers—not modeled estimates. Every interaction is observed, not inferred. 
2. Scale with structure. Criteo’s network covers 4.5 billion specific products (SKUs) and captures more than 120 real shopper interaction signals, from browsing and basket activity through to completed transactions. 
3. SKU-level granularity. Shopper behavior is tied back to specific products, making personalization precise rather than approximate. 
4. Normalized product knowledge. Criteo’s Universal Catalog organizes product data consistently across retailers and categories, with more than 250 attributes per product, which turns isolated signals into connected datapoints you can draw real conclusions from. 

At network scale, Criteo observes the commerce activity of 740 million daily active shoppers across more than 17,000 advertisers and over $1 trillion in annual transactions. 

Privacy is never an afterthought

Criteo operates on a privacy-by-design ethos. Privacy considerations are embedded into product architecture from the start, not added after the fact. Data relies on pseudonymized identifiers rather than personally identifiable information, and data minimization is a core practice. Criteo has maintained a dedicated privacy office and a Group Data Protection Officer for years, and participates in major industry transparency frameworks including IAB Europe’s Transparency and Consent Framework, the DAA, and the EDAA.