Understand the User Journey chart
- 26 Jun 2024
- 3 Minutes To Read
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Understand the User Journey chart
- Updated On 26 Jun 2024
- 3 Minutes To Read
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A User Journey is depicted in the form of Sankey charts using which you can visualize the path your end user takes in an application.
A Sankey chart is a visual depiction of a flow from one set of values to another. For example, map the paths your end users take from one User Action to another. In the following example, the chart shows the path from the Analytics-Cohorts User Action to Analytics-Create Cohort User Action.
View the different paths your end users have traversed in the application from the starting or ending page you have set for a particular period.
If you click the node or path in the chart, you can view the following information:
Metric | Definition |
|---|---|
| Total Session | It refers to the overall number of user sessions that have occurred within the application. A single session can encompass multiple user interactions, such as visiting several pages or completing multiple steps within the same journey path. A session usually starts when a user accesses the site and ends when the user leaves or is inactive for more than 30 minutes. In other words, this metric reflects the total number of distinct user sessions, regardless of the number of interactions that occur within each session. |
| Event Percentage | It refers to the percentage of sessions that followed a specific path or sequence of events starting from the initial point. It indicates the proportion of sessions that progressed through the User Journey and reached a particular event or milestone. For example, if 50% of the sessions started from the landing page and eventually reached the final page, the Event Percentage for the final page event would be 50%. It signifies that half of the sessions that began from the landing page completed the entire User Journey. This metric enables you to understand the most common paths taken by end users or where the majority of end users are navigating to and Note: A single user can have multiple sessions. |
| Min time (in s) | It refers to the shortest amount of time that a session took to complete, measured in seconds. |
| Max time (in s) | It refers to the longest amount of time that a session took to complete, measured in seconds. |
| Average time (in s) | It is the average amount of time that the users spend across all sessions on your application during a User Journey, measured in seconds. It is the average of all the sessions which constitutes the session count between steps. This enables you to get insights into overall user engagement. |
Example
The following image shows the paths end users take after clicking Analytics-Cohorts User Action on the Whatfix Dashboard. Analyze if they're completing the process of creating and saving a Cohort. See that the majority of the users have navigated to the Classic Analytics Tab. See that a few users have created a Cohort but dropped off before saving the Cohort or navigating to other pages.
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Drop-off Path:
Drop-off path displays the number of users who exit your application or abandon a process during a specific stage of the User Journey.
Others Path:
Others path displays the grouping of sessions that do not fall into the top 10 or top 4 most frequent paths, depending on the scenario. For example, if the range of number of sessions in the top 10 most frequent paths is large enough to provide a clear distinction between them, then these top 10 paths are shown. The remaining paths are grouped together as Others. If the range is too small to provide a clear distinction in the number of sessions for each path, then only the top 4 paths are shown and the remaining are grouped as Others.
This grouping enables for a simpler visualization of User Journeys while acknowledging the existence of less common or unique paths.
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- Export a list of the users who have navigated from one page to the other and the time taken by them in each step. For more information, see Export User Journey data.
- Filter your User Journey using the default and the custom date filters.
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