You can use a Flow visualization with the Dataset ID dimension.
If you would like to rename dataset ID dimension items, you can use a lookup dataset.
The lookback window for rekeying depends on your desired frequency of data replay. For example, if you set up CCA to replay data once every week, the lookback window for rekeying is 7 days. If you set up CCA to replay data every day, the lookback window for rekeying is 1 day.
In some situations it is possible that multiple people log in from the same device. Examples include a shared device at home, shared PCs in a library, or a kiosk in a retail outlet.
The transient ID overrides the persistent ID, so shared devices are considered separate people (even if they originate from the same device).
In some situations, an individual user can associate with a large number of persistent IDs. Examples include an individual frequently clearing browser cookies, or using the browser’s private/incognito mode.
The number of persistent IDs is irrelevant in favor of the transient ID. A single user can belong to any number of devices without impacting CCA’s ability to stitch across devices.
Live stitching is available approximately 1 week after Adobe enables Cross-Channel Analytics. Backfill availability depends on the amount of existing data. Small datasets (less than 1 million events per day) typically take a couple days, while large data sets (1 billion events per day) can take a week or more.
Adobe handles GDPR and CCPA requests in accordance to local and international laws. Adobe offers the Adobe Experience Platform Privacy Service to submit data access and deletion requests. The requests apply to both the original and rekeyed datasets.
Persistent ID field is blank on an event in a dataset being stitched with field-base stitching, CCA fills in the
Stitched ID for that event in one of two ways:
Transient IDfield is not blank, CCA uses the value in
Transient IDas the
Transient IDfield is blank, CCA also leaves the
Stitched IDblank. In this case,
Transient ID, and
Stitched IDwill all be blank on the event. Events such as this are dropped from CJA in any CJA connection using the dataset being stitched where
Stitched IDwas chosen as the
Certain metrics in CJA are similar to metrics in traditional Analytics, but others are quite different, depending on what you are comparing. The table below compares several common metrics:
|CJA stitched data||CJA unstitched data||Traditional Adobe Analytics||Analytics Ultimate with CDA|
|People = Count of distinct
||People = Count of distinct
||Unique Visitors = Count of distinct visitor IDs. Note that Unique Visitors may not be the same as the count of distinct ECIDs.||See People.|
|Sessions: Is defined based on the sessionization settings specified in the CJA data view. The stitching process may combine individual sessions from multiple devices into a single session.||Sessions: Is defined based on the sessionization settings specified in the CJA data view.||Visits: See Visits.||Visits: Is defined based on the sessionization settings specified in the CDA virtual report suite.|
|Events = count of rows in the stitched data in CJA. Generally this should be close to Occurrences in traditional Adobe Analytics. Note, however, the FAQ above regarding rows with a blank
||Events = count of rows in the unstitched data in CJA. Generally this should be close to Occurrences in traditional Adobe Analytics. Note, however, that if any events have a blank
||Occurrences: See Occurrences.||Occurrences: See Occurrences.|
Other metrics may be similar in CJA and traditional Adobe Analytics. For example, the total count for Adobe Analytics custom events (events 1-100) should generally be very close in traditional Adobe Analytics and CJA (whether stitched or unstitched). Note, however, this may not always be true due to differences in capabilities such as event de-duplication between CJA vs. traditional Adobe Analytics.