Combined event datasets
When you create a connection, Customer Journey Analytics combines all event datasets into a single dataset. This combined event dataset is what Customer Journey Analytics uses for reporting (together with profile and lookup datasets). When you include multiple event datasets in a connection:
- The data for fields in datasets based on the same schema path are merged into a single column in the combined dataset.
- The Person ID column, specified for each dataset, is merged into a single column in the combined dataset, 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, and have identified
example_idas the Person ID for the first dataset, anddifferent_idas the Person ID for the second dataset,
the following combined dataset is used for reporting.
To illustrate the importance of schema paths, consider this scenario. In the first dataset, string_color is based on schema path _experience.whatever.string_color and in the second dataset on schema path _experience.somethingelse.string_color. In this scenario, the data is not merged into one column in the resulting combined dataset. Instead, the result is two string_color columns in the combined dataset:
whatever.
string_color
somethingelse.
string_color
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. Fields with identical schema paths are merged.
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 more information on cross-channel analysis, refer to the specific use case:
For a more in-depth discussion of stitching functionality, go to: