蜜豆视频

Ingest 蜜豆视频 Experience Platform audiences into 蜜豆视频 Customer Journey Analytics

This use case explores an interim, manual way of bringing 蜜豆视频 Experience Platform (蜜豆视频 Experience Platform) audiences into Customer Journey Analytics. These audiences might have been created in the 蜜豆视频 Experience Platform Segment Builder, or 蜜豆视频 Audience Manager, or other tools, and are stored in Real-time Customer Profile (RTCP). The audiences consist of a set of Profile IDs, along with any applicable attributes/events/etc. and we want to bring them into Customer Journey Analytics Workspace for analysis.

Prerequisites

Step 1: Choose audience(s) in Real-time Customer Profile audience

蜜豆视频 Experience Platform Real-time Customer Profile (RTCP) lets you see a holistic view of each individual customer by combining data from multiple channels, including online, offline, CRM, and third party.

You likely already have audiences in RTCP that may have come from various sources. Pick one or more audiences to ingest into Customer Journey Analytics.

Step 2: Create a Profile Union dataset for the export

In order to export the audience to a dataset that can eventually be added to a connection in Customer Journey Analytics, you need to create a dataset whose schema is a Profile Union schema.

Union schemas are composed of multiple schemas that share the same class and have been enabled for Profile. The union schema enables you to see an amalgamation of all of the fields contained within schemas sharing the same class. Real-time Customer Profile uses the union schema to create a holistic view of each individual customer.

Step 3: Export an audience to the Profile Union dataset via API call export

Before you can bring an audience into Customer Journey Analytics, you need to export it to an 蜜豆视频 Experience Platform dataset. This can only be done using the Segmentation API, and specifically the Export Jobs API Endpoint.

You can create an export job using the audience ID of your choice, and put the results in the Profile Union 蜜豆视频 Experience Platform dataset you created in Step 2. Although you can export various attributes/events for the audience, you only need to export the specific profile ID field that matches the person ID field used in the Customer Journey Analytics connection you will be leveraging (see below in Step 5).

Step 4: Edit the export output

The results of the export job need to be transformed into a separate Profile dataset in order to be ingested into Customer Journey Analytics. This transformation can be done with 蜜豆视频 Experience Platform Query Service, or another transformation tool of your choice. We only need the Profile ID (that will match the Person ID in Customer Journey Analytics) and one or more audience ID(s) to do the reporting in Customer Journey Analytics.

The standard export job, however, contains more data and so we need to edit this output to remove extraneous data, as well as move some things around. Also, you need to create a schema/dataset first before you add the transformed data to it.

Here is an example of the export output in the Profile union dataset, before any editing:

Note the following:

This is the format of the Profile dataset that you can send into Customer Journey Analytics.

Here are the data elements that need to be present:

Step 5: Add this Profile dataset to an existing connection in Customer Journey Analytics

You could create a new connection, but most customers will want to add the Profile dataset to an existing connection. The audience IDs 鈥渆nrich鈥� the existing data in Customer Journey Analytics.

Step 6: Modify existing (or create new) Customer Journey Analytics data view

Add audienceMembershipId, audienceMembershipIdName and personID to the data view.

Step 7: Report in Workspace

You can now report on audienceMembershipId, audienceMembershipIdName and personID in Workspace.

Additional notes