Introducing Claravine’s CJA Integration: Clean Campaign Data, From the Start

In part one of this series, we walked through what Adobe Customer Journey Analytics is and how it works; it’s the shift from session-based to person-based analysis, the role of Adobe Experience Platform as the data foundation, and the AI capabilities now built into the product.

We also ended on a specific problem: CJA’s power is only as useful as the data going into it. If campaign metadata is inconsistent upstream (different naming conventions across channels, UTM parameters that vary by region or agency, tracking codes that follow five different taxonomies depending on who built the campaign) all of that noise flows directly into your CJA analysis. The platform will stitch it together faithfully. It just won’t be right.

That is the problem Claravine’s new CJA integration is built to solve. Here is how it works.

Where the data quality problem actually lives

Most CJA implementations focus (reasonably) on the technical architecture. Schema design, identity stitching, data view configuration, and source connectors. These are real and meaningful decisions. But there is another layer that tends to get less attention: the campaign metadata created before any of it.

Campaign tracking data (UTM parameters, tracking codes, placement IDs, campaign names, channel classifications) is created by marketers, not data engineers. It gets built in spreadsheets, briefing documents, and activation platforms. It travels across agencies, teams, regions, and tools. By the time it reaches AEP and eventually CJA, it has passed through enough hands that inconsistency is the rule, not the exception.

When CJA joins that data into a cross-channel journey view, the analysis reflects the classification logic used (or not used) at the point of campaign creation. Attribution models break down when the same channel is labeled three different ways. Journey analysis loses coherence when campaigns from different teams use incompatible naming structures. The Data Insights Agent will still generate an answer to your question. That answer will just be built on a fragmented foundation.

The data quality problem in CJA is not a CJA problem. It is a campaign creation problem. Fixing it at the analytics layer is too late.

What the Claravine CJA integration does

The Claravine platform governs the metadata and taxonomy decisions that happen at the start of a campaign, before anything is activated, before a pixel fires, before data enters any reporting system.

The CJA integration connects that governance layer directly to Adobe Experience Platform. When a dataset is submitted in Claravine, the integration automatically sends the campaign metadata (tracking codes, classification fields, placement attributes, and any fields mapped) into an AEP dataset built to the XDM schema your team has defined. That data then flows into CJA through your existing Connection and Data View, where it is available as dimensions and metrics for analysis.

The result is that every campaign that goes through Claravine arrives in CJA with clean, consistently structured metadata — governed by the same taxonomy rules, validated against the same standards, and organized around the same naming conventions — regardless of which team, agency, or region created it.

How the setup works

The integration is configured once, in two places.

In Adobe Experience Platform, you set up a schema that tells AEP what data fields to expect from Claravine — things like tracking codes, campaign classifications, and any custom attributes your team governs. That schema connects to a dataset, which flows into CJA.

In Claravine, you connect to AEP using your Adobe credentials, then tell a Field Set which dataset to send data to. Claravine reads the schema automatically, so you’re not manually mapping fields — you just pick what you need.

After that, it runs on its own. Every time someone submits a dataset in Claravine, the data flows to AEP without any extra steps. If your organization uses multiple AEP environments (separate sandboxes, regional instances, or parallel CJA setups), one Field Set can feed all of them.

What this looks like in practice

Consider a global brand running campaigns across paid search, display, email, and social — managed across three agencies and two internal teams. Each has its own campaign naming habits. Some use full channel names, some use abbreviations. Medium values vary. Tracking code structures differ by region.

Before the Claravine CJA integration, that inconsistency propagated into AEP and surfaced in CJA as fragmented attribution data. Campaign-level journey analysis is unreliable. Reporting requires manual cleanup. Cross-channel comparisons take longer than they should because analysts spend time reconciling terminology before they can get to actual insight.

With the integration in place, campaign metadata is validated and standardized at the point of creation in Claravine. Every field is governed. Every value is validated against the organization’s taxonomy. When the dataset is submitted, that clean metadata syncs automatically to AEP — and from there into CJA. The previously fragmented analysis is now built on a consistent, governed foundation.

The CJA integration card on each submitted dataset shows the sync status and which AEP dataset received the data, so teams have visibility into exactly what went where.

Why this matters now

Adobe is investing heavily in CJA as the forward analytics layer across the Experience Cloud. New capabilities such as the Data Insights Agent, Guided Analysis, Intelligent Captions, the Microsoft Copilot integration are all CJA-native. Organizations migrating from Adobe Analytics or expanding their CJA implementation are now making decisions about the data infrastructure on which those capabilities will run.

Those decisions tend to focus on the technical architecture. Schema design, identity resolution, and data view modeling. These matter. But the upstream data quality question, who governs the campaign metadata before it enters the system, and how, tends to get deferred until the inconsistency becomes visible in reporting. By then, it has already compromised the analysis.

Claravine’s CJA integration is designed to close that gap at the source, not after the fact. Campaign metadata that is governed before it enters AEP does not need to be cleaned up in CJA. The analysis is reliable from the first submission.

Ready to see it in action?

The Claravine CJA integration is currently available in Early Access. If your organization is running or migrating to Adobe Customer Journey Analytics and wants to address campaign data quality before it reaches your analytics layer, we would like to show you how it works. Click the image below to get started on your own journey into marketing metadata, governed right from the jump.