Combined event datasets

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.

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

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:

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:

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:

• Cross-channel analysis

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

• Stitching overview
• How stitching works
• Frequently Asked Questions