How to get your Criteo data ready for agentic AI

Criteo holds some of your most valuable retargeting data — campaign and ad performance, audience segments, creative results, and geographic breakdowns across every retargeting campaign you run. Getting it ready for agentic AI means giving AI agents access to a centralized, cleansed, and governed version of that data, so they can answer questions like "which creatives are driving the most conversions across our audiences this week" in seconds rather than days. Fivetran + dbt Labs delivers the complete data foundation agents need — Fivetran moves Criteo data reliably into your warehouse or data lake, and dbt transforms it into trusted, AI-ready tables.

Why Criteo data is critical for agentic AI

Retargeting works because it reacts fast — showing the right ad, to the right audience, before the moment to convert passes. But the data that shows whether it's actually working — campaign performance, audience response, creative results, spend by geography — often sits in a reporting interface that a media buyer has to log into and manually export before anyone can act on it. That means underperforming creatives keep running longer than they should, budget stays allocated to audiences that stopped converting days ago, and the person managing the account spends more time pulling numbers than acting on them. As retargeting programs scale across more campaigns, audiences, and creatives, that manual review process doesn't scale with it. Performance marketing needs infrastructure built for agents, not just analytics reviewed once a week, so decisions happen at the speed retargeting actually requires.

What agentic AI can do with Criteo data

Once Criteo's campaign and performance data lives in a governed warehouse, AI agents can act on it directly.

• A performance marketing manager can ask "which audiences had the lowest cost per conversion last week" and get a ranked answer immediately, instead of exporting a report first.
• A retail media lead can have an agent flag every creative whose performance has dropped over the past 7 days, so the team swaps it out before more spend goes to something that stopped working.
• A media buyer can ask an agent to summarize performance by geography, surfacing which regions are underperforming without building a custom breakdown manually.
• A marketing director can ask for a plain-language weekly summary of campaign, audience, and creative performance, without waiting on an analyst to compile it.

Each of these works because the agent has a governed, unified view of campaign, audience, and creative performance data — the same data Criteo captures for every retargeting campaign, just centralized and modeled so it can be queried with confidence instead of pulled manually.

How Fivetran gets your Criteo data ready for agentic AI

Raw Criteo data spans campaigns, ad sets, audiences, creatives, and geography-level statistics, each on its own refresh cadence and scattered across whatever reporting views a media buyer happens to check. Left that way, it stays too fragmented and too manual for an AI agent to use reliably. Fivetran moves Criteo data reliably into a central warehouse or data lake, keeping it fresh and complete, including rollback syncs that capture changes outside the normal update window and consistent history retained over time so agents have real context, not just this week's snapshot. Fivetran + dbt Labs are one company delivering the full stack from movement to transformation: dbt turns that raw data into clean, trusted, AI-ready tables, using its full modeling, testing, and documentation capabilities to make the data genuinely governed enough for an agent to rely on. For teams working from a data lake, the Fivetran Managed Data Lake Service keeps that data centralized too.

What your Criteo data unlocks for your team

With Criteo data in a central warehouse, AI agents can unlock capabilities your team couldn't access before.

• Faster creative decisions: agents flag underperforming creatives days sooner than a weekly manual review would catch them.
• Audience-level clarity: compare audience performance directly instead of piecing together numbers from separate reports.
• Geographic performance visibility: see which regions are driving results without building a custom breakdown each time.
• Governed, trustworthy answers: dbt's modeling and testing mean agents work from clean data, not raw exports.
• An open, interoperable foundation: the same data feeds agents, dashboards, and future tools without rebuilding pipelines.

FAQ

What does it mean for Criteo data to be AI agent-ready?

It means your campaign, audience, creative, and geographic performance data is centralized in a warehouse or data lake, modeled into clean and consistent tables, and governed so an AI agent can query it directly and trust the result, instead of working from a manual export.

What can my team actually do with AI agents and Criteo data?

Your team can ask direct questions about campaign, audience, or creative performance and get immediate answers instead of waiting on a report. Agents can also monitor performance continuously and flag underperforming creatives or audiences before more budget goes to something that stopped working.

Is Criteo data ready for AI agents out of the box?

Not without preparation. Fivetran + dbt Labs must centralize, model, and govern Criteo data before an agent can query it reliably.

Do we need a data engineering team to set this up?

No. Fivetran automates the data movement, and dbt provides the modeling framework to transform it, so performance marketing teams can get a governed data foundation running without building custom pipelines.

How does Fivetran get Criteo data ready for AI agents?

Fivetran moves your Criteo data reliably into your warehouse or data lake, keeping every campaign, audience, and creative current. dbt Labs, part of the same company, transforms that raw data into clean, tested, documented tables using its full modeling capabilities. Together, they deliver the complete stack an AI agent needs to answer with confidence.

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