Validate stitching

The goal of identity stitching (or simply, stitching) is to elevate the suitability of an event dataset for cross-channel analysis. This elevation is achieved when all rows of data in the dataset contain the desired highest-order identity that is available. This elevation allows you to:

This article outlines analysis methods to measure the elevation of one or more newly created stitched datasets and to provide confidence that stitching is delivering these benefits.

The analysis methods involve Data view component settings that are typically accessible to admins. The methods also require analysts, who work in an Analysis Workspace project, to create calculated metrics and visualizations.

While these analysis methods can be used for both field-based stitching and graph-based stitching, some elements may not be present in the dataset, especially in a graph-based stitching scenario. These missing elements can make it difficult to calculate lift directly in Analysis Workspace.

See Stitching enablement and validation for a demo video.

Stitching in Connections validation

This section details how to validate stitching you have enabled in the Connections interface.

Connection recommendations

To validate stitching that you enabled in the Connections interface, select a short and representative period for Dataset backfill. For example, one week.

In the example below, you want to stitch the event dataset. You have set up a test connection in which you add the event dataset. For that dataset, you define the ECID Namespace as the Persistent ID and Visitor Hashed Email Address (directMarketing.hashedEmail) as the Person ID. To validate this stitching, you define a Dataset backfill for a small time window (January 24, 2026 - February 10, 2026). You use that small window to validate whether stitching does work as intended.

Data view prerequisites

For the stitching validation, you need to ensure you have all the required dimensions and metrics from your stitched dataset defined in a data view. Create a data view based on the connection you defined earlier. In the Components step of the data view configuration, you need to:

Ensure you save the data view.

Create Workspace project

In Workspace, create a new project and use a freeform table to show the Email set metric for the date range you have defined to test your stitching configuration. This freeform table shows the events that do have an email address before stitching.

To see the events that have an email address set after the stitching process, define a calculated metric Email stitched namespace. That calculated metric looks at Events that have an Identity Namespace equal to the hashed email namespace email_lc_sha256.

If you add the new calculated metric Email stitched namespace to the freeform table, you see the increase in the number of events that now do have an email address after the stitching process.

You can get further insights when you define two additional calculated metrics.

Add these two additional calculated metrics to your freeform table, and you can see the increase in stitched events.

For more insights, you can add two more calculated metrics (Percent increase and Lift) to your freeform table to see the impact of your stitching configuration.

Request stitching validation

This section details how to validate stitching that you have requested from Adobe. This method is deprecated, but you might still have datasets that were stitched using this method.

Data view prerequisites

For the stitching validation measurement plan, you need to ensure you have all the required dimensions and metrics from your stitched dataset defined in a data view. Verify that both stitchedID.id and stitchedID.namespace.code fields are added as dimensions. While the stitched dataset is an exact copy of the original dataset, the stitching process adds these two new columns to the dataset:

Furthermore, you need to add two stitching metrics that are based on the presence of values in a dimension.

Stitched dimensions

With both of these dimensions added to the data view, use Freeform tables in Analysis Workspace to check the data that each dimension has.

In the Stitched Namespace dimension table, you typically see two rows for each dataset. One row represents when the stitching process had to use the fallback method (ECID). The other row shows events associated with the desired identity namespace (email).

For the Stitched ID value dimension table, you see the raw values that are coming from the events. In this table, you see that values oscillate between the persistent ID and the desired person ID.

Device-centric or person-centric reporting

When you create a connection, you have to define what field or identity is used for the person ID. For instance, on a web dataset, if you choose a device ID as the person ID, then you create device-centric reports and lose the ability to join this data with other offline channels. If you select a cross-channel field or identity, for example email, you lose out on any unauthenticated events. To understand this impact, you need to figure out how much of the traffic is unauthenticated and how much of the traffic is authenticated.

Stitching identification rates

You want to measure the identification performance before and after stitching. To do so, create three additional calculated metrics:

Conclusion

If you combine all data in an Analysis Workspace Freeform table, you can start to see the impact and value that stitching provides, inclusive of:

Key takeaways

The key takeaways from this article are that stitching validation and analysis helps you:

These metrics together give stakeholders a complete picture of how Customer Journey Analytics stitching affects authentication success rates and overall person identification performance.