These use cases show the flexibility and power of data views in Customer Journey Analytics.
For example, when creating a data view, you could create an Orders metric from a pageTitle schema field that is a string. Here are the steps:
On the Components tab, drag the pageTitle into the Metrics section under Included Components.
Now highlight the metric you just dragged in and rename it under Component Settings on the right:
Open the Include/Exclude Values dialog on the right and specify the following:
The “confirmation” phrase indicates that this is an order. After reviewing all the page titles where those criteria are met, a “1” will be counted for each instance. The result is a new metric (not a calculated metric.) A metric that has included/excluded values can be used everywhere any other metric can be used. It works with Attribution IQ, filters, and everywhere else you can use standard metrics.
You can further specify an attribution model for this metric, such as Last Touch, with a Lookback window of Session.
You can also create another Orders metric from the same field and specify a different attribution model for it, such as First Touch, and a different Lookback window, such as 30 days.
Another example would be to use the Visitor ID, a dimension, as a metric to determine how many Visitor IDs your company has.
Previously, integers would automatically be treated as metrics in CJA. Now, numerics (including custom events from Adobe Analytics) can be treated as dimensions. Here is an example:
Drag the call_length_min integer into the Dimensions section under Included Components:
You can now add Value Bucketing to present this dimension in a bucketed fashion in reporting. (Without bucketing, each instance of this dimension would appear as a line item in Workspace reporting.)
You can use a numeric dimension to get “metrics” into your Flow visualization.
This capability is specifically applicable to array-based fields. The include/exclude functionality lets you do filtering at the sub-event level, whereas filters (segments) built in the filter builder only give you filtering at the event level. So you can do sub-event filtering by using include/exclude in Data Views, and then reference that new metric/dimension in a filter at the event level.
For example, use the include/exclude functionality in Data Views to focus only on products that generated sales of more than 50 Dollars. So if you have an order that includes a 50 Dollar product purchase and a 25 Dollar product purchase, we would remove only the 25 Dollar product purchase, not the entire order.
These new settings allow you to view only high-value revenue and filter out anything below $50.
Your company may have spent time training your users to expect “Unspecified” in reports. The default in Data Views is “No Value”. You can now rename “No Value” to “Unspecified” in the Data Views UI.
Another example would be a dimension for a membership program registration. In this case, you could rename “No Value” to “No Membership Program Registration.”
Using the Duplicate feature at the top right, create a number of Revenue metrics with different attribution settings like First Touch, Last Touch, and Algorithmic.
Don’t forget to rename each metric to reflect the differences, such as “Algorithmic Revenue”: