Combined event datasets

Last update: 2024-01-03
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When you create a connection, Customer Journey Analytics combines all schemas and datasets into a single dataset. This ‘combined event dataset’ is what Customer Journey Analytics uses for reporting. When you include multiple schemas or datasets in a connection:

  • Schemas are combined. Duplicate schema fields are merged.
  • The ‘Person ID’ column of each dataset are merged into a single column, regardless of their name. This column is the foundation of identifying unique persons in Customer Journey Analytics.
  • Rows are processed based on timestamp.
  • Events are resolved down to the millisecond level.

Example

Consider the following example. You have two event datasets, each with different fields containing different data.

NOTE

Adobe Experience Platform typically stores timestamp in Unix milliseconds. For readability in this example, date and time is used.

example_id timestamp string_color string_animal metric_a
user_310 1 Jan 7:02 AM Red Fox
user_310 1 Jan 7:04 AM 2
user_310 1 Jan 7:08 AM Blue 3
user_847 2 Jan 12:31 PM Turtle 4
user_847 2 Jan 12:44 PM 2
different_id timestamp string_color string_shape metric_b
user_847 2 Jan 12:26 PM Yellow Circle 8.5
user_847 2 Jan 1:01 PM Red
alternateid_656 2 Jan 8:58 PM Red Square 4.2
alternateid_656 2 Jan 9:03 PM Triangle 3.1

When you create a connection using these two event datasets, the following table is used for reporting.

id timestamp string_color string_animal string_shape metric_a metric_b
user_310 1 Jan 7:02 AM Red Fox
user_310 1 Jan 7:04 AM 2
user_310 1 Jan 7:08 AM Blue 3
user_847 2 Jan 12:26 PM Yellow Circle 8.5
user_847 2 Jan 12:31 PM Turtle 4
user_847 2 Jan 12:44 PM 2
user_847 2 Jan 1:01 PM Red
alternateid_656 2 Jan 8:58 PM Red Square 4.2
alternateid_656 2 Jan 9:03 PM Triangle 3.1

This combined event dataset is what is used in reporting. It does not matter which dataset a row comes from; Customer Journey Analytics treats all data as if it is in the same dataset. If a matching Person ID appears in both datasets, they are considered the same unique person. If a matching Person ID appears in both datasets with a timestamp within 30 minutes, they are considered part of the same session.

This concept also applies to attribution. It does not matter which dataset a row comes from; attribution works exactly as if all events came from a single dataset. Using the above tables as an example:

If your connection only included the first table and not the second, pulling a report using the string_color dimension and metric_a metric using last touch attribution would show:

string_color metric_a
Unspecified 6
Blue 3
Red 2

However, if you included both tables in your connection, attribution changes since user_847 is in both datasets. A row from the second dataset attributes metric_a to ‘Yellow’ where they were previously unspecified:

string_color metric_a
Yellow 6
Blue 3
Red 2

Cross-channel analysis

The next level of combining datasets is cross-channel analysis, where datasets from different channels are combined, based on a common identifier (person ID). Cross-channel analysis can benefit from stitching functionality, allowing you to rekey a dataset’s person ID so the dataset is properly updated to enable a seamless combination of multiple datasets. Stitching looks at user data from both authenticated and unauthenticated sessions to generate a stitched ID.

Cross-channel analysis allows you to answer questions such as:

  • How many people begin their experience in one channel, then finish it in another?
  • How many people interact with my brand? How many and what types of devices do they use? How do they overlap?
  • How often do people begin a task on a mobile device and then later move to a desktop PC to complete the task? Do campaign click-throughs that land on one device, lead to conversion somewhere else?
  • How does my understanding of campaign effectiveness change if I consider cross-device journeys? How does my funnel analysis change?
  • What are the most common paths users take from one device to another? Where do they drop out? Where do they succeed?
  • How does the behavior of users with multiple devices differ from the users with a single device?

For a more information on cross-channel analysis, refer to the specific use case:

For a more in-depth discussion stitching functionality, go to:

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